Using artificial intelligence to enhance personalization of customer relationship management in the contact center space: Afiniti's technology case study By Jerme de la Croix de Castries
Bachelor in History Sorbonne University-Paris IV, France, 2013
SUBMITTED TO THE MIT SLOAN SCHOOL OF MANAGEMENT IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE IN MANAGEMENT STUDIES (MSMS) AT THE SLOAN SCHOOL OF MANAGEMENT MASSACHUSSETS INSTITUTE OF TECHNOLOGY
JUNE 2017
( 2017 J6r6me de la Croix de Castries. All Rights Reserved.
The author hereby grants to MIT permission to reproduce and to distribute publicly paper and electronic copies of this thesis document in whole or in part in any medium now known or hereafter created...... Signature of Author: Signature redacted MIT Sloan School of Management A May 12, 2017 Supervised By: oignature reaactea William K. Aulet Managing Director, Martin Trust Center for MIT Entrepreneurship Senior Lecturer SdT Sloan School of Management Accepted By: Rodrigo Verdi Associate Professor of Accounting MASA H ESI TITUTF OF TEQHN MGY Program Director, M.S. in Management Studies Program MIT Sloan School of Management
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2 Using artificial intelligence to enhance personalization of customer relationship management in the contact center space: Afiniti's technology case study By J6r6me de la Croix de Castries Submitted to MIT Sloan School of Management on May 12, 2017 in partial fulfillment of the requirements for the Degree of Master of Science in Management Studies.
ABSTRACT Increase in worldwide data generation and decrease in data storage costs associated with increase in computing power and decrease in computing costs that we are experiencing globally are opening a brand new world of opportunities. Yet, new technologies still fail to mimic humans inner ability to interact and influence each other, thus failing to efficiently replace human when comes the time for companies to talk with their customers. Yet some technologies in this field contribute to enhancing such interactions and may open the way for a sustainable Al revolution. Such a revolution is not one of human labor substitution by intelligent machines but one of collaboration through augmentation of human capabilities. One company, Afiniti, has developed such a technology and associated it with a very unusual business model and sales strategy that could very well be a game-changer in the space. Afiniti enhances human interactions by applying behavior-based personalization in the contact center space. Having developed a precise measurement system it only gets compensated on the precisely generated benefits it delivers. Selling such a business model has led the company to transform its sales engagement approach in order to tackle firms' organizational inefficiencies that hindered its ability to sell efficiently. Building the structure, levers and channels necessary to support this strategy, it has also strengthened its competitive position in its newly open market through its first mover advantage business model, its aggressive intellectual property building and its sales network. Finally, it appears that its approach to personalization is fitting in numerous academic fields and very relevant to the specific characteristic of the contact center space. Said to expect public offering in 2017 we still need to see what will be the long-term trajectory of a product that will probably not stay alone in its space.
Thesis Supervisor: William K. Aulet Title: Managing Director, Martin Trust Center for MIT Entrepreneurship Senior Lecturer - MIT Sloan School of Management
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4 Acknowledgements
To my Parents without whom I would not be where I am today. Thanks for giving me a great and structuring education, solid and coherent values, a happy life and being amazing role models. Thanks for trusting me, for helping me get up when I was down for making me understand that life is about doing Good not doing well and that effort and hard work is the only way to realize your dreams.
To Bill Aulet, for his amazing help throughout the production of this thesis, for his advice and for helping me understand that entrepreneurship is about discipline and people and not genius. I hope this is only the first of many collaborations and the start of a great friendship.
To Zia Chishti, for bringing me on such an unbelievable journey and believing in me, as well as for allowing me to go to MIT and live the Afiniti experience at the same time. I hope this is only the beginning of an amazing adventure.
To Tom Inskip and Rod Phillips for their support, friendship and trust during what has been the busiest year of a lifetime. Thanks as well for helping me grow my skills as well as become a better leader. Working with you was and still is a privilege.
To Bernard Ramanantsoa, Larry Joe Shrum and Frederic Genta, without their support and recommendations I would never have been able to realize this dream and live the experience of a lifetime in one of the most fascinating place on earth.
To Nicolas Gallot and Eric Le Flour for helping me throughout this year and achieving so much in my absence. It is an every day pleasure to work with you.
To my friends from MIT, P6n6lope, Charles, Andreas, Stephan, Alain, Marc, Thomas, Thilo, Kalman, Andr6 for making this year so special and fun, this is only the beginning of lasting friendships.
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6 Table of content
1. INTRODUCTION 11 11. MARKET OVERVIEW 13 A. WHAT MAKES Al AND BIG DATA RELEVANT NOW 13 B. WHAT MAKES CALL-CENTER MARKET RELEVANT TO Al AND BIG DATA 17 I1. GENESIS AND OVERVIEW OF AFINITI AND ITS TECHNOLOGY 19 A. FOUNDER'S VISION 19 B. TECHNOLOGY OVERVIEW 20 C. THE MAGIC BEHIND THE BLACK BOX: A CLOSER LOOK AT THE TECHNOLOGY 25 i. Data inputs 25 ii. Importance of choice in a matching algorithm 26 iii. Model Design and influencability analysis 27 IV. BUSINESS MODEL ANALYSIS 29 A. STRENGTHS OF THE MEASUREMENT METHOD 29 B. BIASING THE DECISION PROCESS: No COST / NO INVESTMENT APPROACH 30 C. GETTING THE PRICE RIGHT: AN ANALYSIS OF CHANNEL ECONOMICS AND PRICING METHODOLOGY 31 D. UNIQUENESS OF THE SALES APPROACH: THE CEO OBSESSION IN A B2B CONTEXT 33 i. Challenges to technology deployment: an analysis of organizational inefficiencies 33 ii. Redesigning the sales approach by understanding the role of a CEO in overcoming organizational inefficiencies and driving innovation 35 iii. Building the network of your ambitions 36 E. PROTECTING AN UNTAPPED MARKET BUILDING HIGH BARRIERS TO ENTRY 39 i. Intellectual Property 39 ii. First-mover advantage technology and opportunity cost 41 iii. Accelerated roll-out and Network effects 42 F. WEAKNESSES OF THE BUSINESS MODEL AND POTENTIAL THREAT TO THE BUSINESS 42 i. Lack of comfort of big firms for pay-for-performance models 42 ii. Difficulty to evangelize for a technology in the absence of competition 43 iii. Data security 44 iv. Regulatory framework for the usage of data 45 v. Sales strategy is making people extremely valuable to the company and business very sensitive to key employees churn 46 V. RELEVANCE OF PERSONALIZED AND BEHAVIOR BASED APPROACH TO CUSTOMER MANAGEMENT 47 A. DEFINING PERSONALIZATION IN THE CONTEXT OF AFINITI 47 i. General Concept 47 ii. Feature(s), target, agent & means of Afiniti's personalization 49 iii. Afiniti's personalization within Fan & Poole's ideal type framework 50 B. RELEVANCE AND BENEFITS OF PERSONALIZED CUSTOMER RELATIONSHIP MANAGEMENT IN A DATA ENHANCED WORLD 52 i. Favorable global trends 52
7 ii. Relevance of call-center channel generated information to data-driven commercial personalization 53 iii. Importance and impact of personalized human customer service 54 VI. CONCLUSION 56 VII. BIBLIOGRAPHY 57
8 Exhibits List
Exhibit 1: Annual data generation globally Exhibit 2: Microprocessor Cost per Transistor Cycle Exhibit 3: Raw compute performance of global supercomputers measured Exhibit 4: Price per unit of compute Exhibit 5: Comparison of companies' analytical capabilities and business performance Exhibit 6: Customer Service Satisfaction by Channel Exhibit 7: Afiniti intelligent routing solution Exhibit 8: Afiniti measurement cycles Exhibit 9: Data inputs into Al matching algorithm Exhibit 10: Choice influence on Afiniti's algorithms performance Exhibit 11: From historical data to real-time pairing optimization Exhibit 12: Influencability Matrix by Industry Exhibit 13: Afiniti Incremental Sales Value Creation - Telecom Industry Example Exhibit 14: Reporting function for call-centers within organization Exhibit 15: Traditional firm organizational structure vs. Afiniti impact and benefits Exhibit 16: Afiniti's sales effort focus Exhibit 17: Afiniti's Coverage Map of Global Telecoms & Pay-TV Industry Exhibit 18: Afiniti's Coverage Map of Global Financial Services Industry Exhibit 19: List of registered patents by Afiniti at the United States Patents and Trademark Office Exhibit 20: Data protection legislation in the EU from DPD (1990) to GDPR (2012) Exhibit 21: Personalization Ideal Types by Fan & Poole (2006)
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10 I. Introduction
"The sad thing about artificial intelligence is that it lacks artifice and therefore intelligence."
Jean Baudrillard
This quote by French philosopher Jean Baudrillard highlights one of the key challenges at stake when it comes to the development and usage of Artificial Intelligence (AI) technologies. This challenge is that computer enabled intelligence, despite its intrinsically strong computational power that matches and exceeds the one of humans in many fields, is nowhere near to match or even mimic humans inner ability to interact with or influence on one another.
In the information age, Al is the ultimate step that will and is already shaking and redesigning the world. We are living in a world where intelligent machines are starting to take over jobs and disrupt entire industries in what is our world's Third Industrial Revolution (Rifkin, 2011). After the failed attempts of the 1960's, 1970's, and 1980's and the Al winters (Crevier, 1993) that followed, we are living a new Artificial Intelligence spring (Reddy, 1988), that could very well be the inflection point, the finally successful ride. Yet, far away from most current opinions, which are stating that the rise of the Second Machine Age (McAfee & Brynjolfsson, 2014) will see the dawn of human labor and that machines alone will replace humans, and following A.Berwick thoughts, I have the strong belief that human intelligence and machine intelligence are not substitutable but complementary. In which instance mankind will achieve far greater results and benefits from the artificial intelligence revolution upon us through the enhancement of human intelligence by the use of complementary computational intelligence.
A key determinant to avoid another Al winter will be the ability of Al applications to generate sustainable income-stream derived from socially acceptable profitability enhancement. Thus, profitability derived solely from human to machine labor substitution is not a viable path on its own. There needs to be applications of Al where the combination of its computational power with human intelligence and capabilities delivers superior returns.
11 Besides, if new technologies have considerably improved one-to-information (via search engines and world wide web access), one-to-things (via the rise of e- commerce) and one-to-many (via social media) interactions, the ultimate one-to-one (interpersonal) interactions still remain lightly touched and could never be automated and dehumanized. When it comes to technologies usage in influencing and improving human interactions, the primary relevant business segment is customer relationship management.
The holy grail of customer relationship management is personalization. Long past is the time of Henry Ford's "you can choose any color as long as it is black", we are entering an era where mass marketing has lost traction and even segmentation is not sufficient anymore, firms are now trying to build a truly unique approach to each customer (known as one-to-one marketing (Peppers & Rogers, 1994)). In that field, the importance of human interactions combined with the explosion of data availability open a whole new space for technology enabled personalization that could contribute to prove that a combination of human and machine is able to achieve far superior results than either one alone. Beyond automation this well could be the space where we see that value creation between human and machine is not a zero sum game but rather a positive sum game, thus opening the way for a sustainable Al revolution.
In this thesis we will focus on one particular technology that is at the core of all the trends mentioned above. This technology is called enterprise behavioral pairing and has been developed by Afiniti International Holdings (formerly known as SATMAP). Leveraging on artificial intelligence technologies (notably Bayesian linear regression) and big data analytics, Afiniti has developed a technology that enhances personalization within the contact center space by applying behavioral matching in the allocation of calls to agents. By doing so it is opening a field for Al to be used to enhance human interactions and achieve greater returns by combining humans and machines rather than automating human labor. In order to do so, it has developed a very unusual business model in the software space with features that open paths for other Al enabled companies to get a fair share of the value they create. The company, its technology, its business model and the way it positions itself within the academic definition of personalization will be further addressed here below.
We will seek to answer the following questions: * Why is the contact center industry a relevant space for Big Data and Al usage?
12 - How is Afiniti innovating and disrupting the call-center industry and the telephony industry? > What are the key characteristics of Afiniti's technology? * How has Afiniti created a disruptive business model in the Al space by leveraging Al technologies to enhance customer care personalization? > What are the key characteristics of Afiniti's business model? > How does this business model protect the company from competitors? > How is Afiniti business model answering to the problem of value sharing between incumbents and Al-enablers? > How has the company adapted its sales approach to address difficulties in its sales process? - Is Afiniti's way of using Al to enhance customer service personalization a sustainable business model? e How is Afiniti's personalization fitting within the predefined academic definition of personalization? - What is the relevance and the impact of a behavior based customization approach to customer service?
II. Market overview
A. What makes Al and Big Data relevant now
Artificial intelligence and big data are nowadays at the top of most of corporate agendas around the world, from banking to telecoms to healthcare providers and in nearly every segment of the business world both these terms are coined to be signs of an ability to develop innovation strategies in front of financial analysts and financial markets.
We will not attempt here to retrace the whole history of these two words and what they encompass. Some reports and books are already covering extensively the subject. What we just need to remember is that we are witnessing what can be called the third technological platform change after cloud and mobile (Jeanneau, 2015). Data availability is finally liberating the power of machine learning.
In summary, at the core of the phenomenon are two key elements: data and computing.
13 Exhibit 1: Annual data generation globally (in ZB)
so 45 40 35 30 25 20
10 1S -- - -
2009 2010 2015 2020
0 Annual Data Generation
Source: EMC, IDC
On the one hand, the increase in data availability through the ever-expanding generation of data powered by mobile, cloud and loT coupled with the decreasing cost in data storage has made unparalleled amount of data available to mankind. As shown in Exhibit 1 above, the annual amount of data generated is increasing at growth rates that are over a hundred percent a year. Current estimations state that humanity should be generating in excess of 40 Zb annually by 2020, this number is to be compared to the 2,7 Zb estimated amount of data that existed in the world in 2012 according to IBM, i.e. we will generate annually by 2020 14x the equivalent amount of data that had been generated worldwide before 2012.
Exhibit 2: Microprocessor Cost per Transistor Cycle ($/Transistor/Hz, Logarithmic Plot)
10
10
1()
1975 1980 1985 1990 1995 2000 20015 2010 2015 H&Mnvg me I 1 yews Ya Source: NBER, Intel, ITRS
14 This increase in data availability is closely correlated with the decreasing cost of data storage. As shown in Exhibit 2 above, data storage costs have been drastically diminishing over the years, thus enabling companies and people to generate increasing amount of data that could be stored at diminishing costs. This has unleashed data availability as there were decreasing financial constraints to data generation.
Exhibit 3: Raw compute performance of global supercomputers measured (GFLOPs from 1993 to 2015)
100,000,000 10,000,000 1,000,000 200,0 10,000
100 100 10
-GOPS
Source: top500.org completed by Goldman Sachs Investment Research
Exhibit 4: Price per unit of compute ($ per GFLOPS in representative computer systems)
IE+12 IE+10- - - 1000000- 2000M0
10 ------1M
0.01 1961 1973 1962 1996 2003 2013 2016 per GFLPS (in 2016$)
Source: IBM, Cray, Sony, Nvidia, Press Reports, Goldman Sachs Investment Research
On the other hand, without computing all this newly and increasingly generated data would be worthless. The second side of the process is the increase in computing power following Moore's law (Brock D., 2006) and the decrease in computing costs. As
15 shown by Goldman Sachs in their November 2016 report Al, Machine Learning and Data Fuel the Future of Productivity, repurposing of GPUs, availability of lower cost compute power thanks to the development of cloud based services and improvement in neural networks modeling capabilities have increased general computing power on a global level and in particular the speed and accuracy of neural networks results. Besides, cost of computing unit has drastically decreased as shown in Exhibit 4 above.
These elements if not covering the entirety of the drivers of shifts occurring are key drivers of the data and Al revolution we experience. We are living a true artificial intelligence spring (Reddy, 1988) and the key elements to its prosperity will be the ability to demonstrate Al and big data technologies value addition and their ability to generate sustainable revenue stream for their providers.
On the first point, research already shows that using data is the key to achieving greater financial and operational performance as shown in Exhibit 5 below. Companies with culture and capabilities to use data to steer their business undisputedly outperform competition. More than a trend it has now become a necessity for companies to keep in the race.
Exhibit 5: Comparison of companies' analytical capabilities and business performance
liL. kooIIpquorh fiiaki pe~roc of 0 im d i..U' S 2.OX 1. 6X 5.3 15 1.4 16
10------9- - ---.. 2.6 05 . 2 1. K-40M Low Hc TOP Boftm Low M i g
0.6 2.
of ein )eect~ve'c exec, ,oeiko0 ung&-' Y4* .y 0 0 3OX2. 3.0 20OX1.0 .2n 1-
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Boloim LOW H T.p BOiOM LowTo Seedrs U U&.Iihood - Av.tag.
Sorc* Go n Wug Daol Dagno nJO9rvey. U Source: Bain Big Data Diagnostic Survey
16 As for Al's ability to generate Rol positive project, it is not fully established as most companies still struggle in finding direct measurable business applications. Yet, as we will see later in the thesis, Afiniti's technology present interesting potential on the matter.
B. What makes call-center market relevant to Al and Big Data
Afiniti's primary space of application is the contact-center space. A contact center as defined in the Gartner glossary "supports customer interactions across a range of channels, including phone calls, email, Web chat, Web collaboration, and the emerging adoption of social media interactions, and is distinct from telephony-only call centers. Although contact centers support more than one channel, they do not necessarily involve the use of universal queuing. Instead, they may support multiple channels but use separate systems and, in some cases, business processes to do so. Key underlying technologies include automatic call distribution, computer-telephony integration, interactive voice response and outbound dialers.". If Afiniti's technology can be applied to all channels mentioned above we will focus on the telephony part and hence the call-center aspect of it, which to date represents the vast majority of its installations.
Call-centers represent a customer service channel that is very prone to the use of big data analytics because of the nature of the data it generates and the fact that it is a space where data has been under-invested in. Besides, it is an industry critically exposed to automation and Al-technology disruption. Yet, it is also an industry where the human component of the equation seems paramount in the achievement of its core objective: customer satisfaction.
First, call centers are very relevant to the usage of big data because of the nature of the data they generate. Call-centers generate data of good quality in that sense that it can be standardized and analyzed in a unified manner and extremely important volumes of data given the number of calls they receive. As most of the agents in call-centers are incentivized on performance, you have records of calls' outcomes in 78.7% of cases (Dimension Data, 2015). Most call-centers for big companies in the world receive tens of thousands of calls every-day into their customer service, each of these calls generate multiple data points like C-Sat, sales, duration, geography, hour of the day, reason of the call, speech analytics elements, complaint etc. This equates to millions of data entry everyday with identified
17 customers via a single channel, where we have a rather accurate view of the reason why there was a customer interaction, what was the customer's intent and what has been the outcome of this interaction.
Such richness and quality in data should give birth to huge data analytics research and optimization. Yet, 40% of all call-centers have no data analytics tools (Dimension Data, 2015) while, analytics is voted top factor to change the shape of the industry within next 5 years, 75% of organizations view the contact center as a key differentiator in customer experience and 57% of companies can relate improving customer experience levels to revenue/profit growth (Dimension Data, 2015). There is a very large gap here in order to deliver the full potential of data analytics in the space.
Then, the call-centers space seems prone to disruption and automation. First the rise of digital channels (chat, email, web self-service) is shifting a lot of traffic away from the classic telephone channel. Then as a lot of interactions are routine and with low complexity and low value add (asking for your bill, changing a password, solving technical problems etc.), thus, this space is highly exposed to automation notably via bots. For instance, Gartner estimates that, by 2025, 85% of current work in call-centers could be automated. Yet as shown in the Exhibit 6 below phone channel is still yielding the best customer satisfaction results of all channels.
Exhibit 6: Customer Service Satisfaction by Channel Based on survey of 1,017 US Adults (18+) in June 2015 'Now ,.uiW you rate the custome- service fbr most companies on the folowing chnnels?" UM00111 Uxmta+-- Hoots N apedasionss * not Smot xpectns
Phone Online chat 33% 13% Email 71% 13% Web self-service 64% 23% Text message 61% 2 % Mobile app self-service 01% social media 111112%
Letter 54% -
Source: Marketing Charts, The Northridge Group
18 Despite the risk of automation it seems that human interactions are still central and crucial in customer service. In 2010, 67% of customers declared that they hung up the phone out of frustration they could not talk to a real person (American Express Survey, 2011). According to Harris Interactive 75% of customers believe it takes too long to reach a live agent, and 53% of customers are irritated if they don't speak to a real person right away (N.Brookes, 2014). One statistic is even more compelling 73% of consumers say friendly customer service representatives can make them fall in love with a brand. (RightNow, 2011).
Thus we can see that the human component of customer service is absolutely paramount. Interpersonal skills and the ability of humans to relate with one another, to express and understand feelings and emotions and to create trust is critical in the building of a good customer service. As explained later in this paper in the analysis of Afiniti's performance by industry, the influencability of calls and the ability of individuals to generate trust over the phone plays a key role in some industries. For instance, direct insurance businesses performance is highly reliant on the ability of its customer representatives to gain confidence of customers calling and to convince them that the company is a reliable partner that will cover for them when they are in need, this is the only condition under which the subscriber will be ready to pay the company now in exchange for a future service. Given that the human factor is still key in customer service interaction, this might be the space where a combination of machine capabilities and human abilities are able to achieve better results. As we will see Afiniti is centering its core technology on human interactions and human diversity to create value. It is by no mean an automation process it is a use of technology to enhance human characteristics and ability and thus represents a good embodiment of that overall trend.
Il1. Genesis and overview of Afiniti and its technology
A. Founder's vision
Zia Chishti, a Pakistani-American serial entrepreneur, founded Afiniti back in 2006. Prior to founding Afiniti, Zia Chishti had been involved in two notoriously successful entrepreneurial ventures. The first one, Align Technologies was a Medical Technology company that developed the product named Invisalign. Founded in 1997, Zia Chishti took it public in January 2001 for nearly $1 Bn. He left the company in 2002
19 and went on to create TRG Holdings a private equity venture specializing in business process outsourcing.
Finally in 2006, Zia Chishti founded Afiniti, which was at the time named SATMAP Inc. (a rebranding occurred in January 2016). The initial idea came from an analysis he made seeking to optimize performance of a call-center company TRG owns called IBEX. Going through the analysis, Zia Chishti identified that the one non- optimized part of a call center was the call distribution. Indeed the way it was generally done is FIFO allocation, which is basically random. From this analysis he sought to optimize the process and after spending the first few years working on research and technology development, the technology was successfully tested at IBEX.
After a first successful implementation, it was decided to turn it into a company with the purpose of helping companies have more successful interactions with their customers by optimizing workforce allocation. Zia Chishti designed his business model (that will be further described here below) so as to make it as straight forward and as rational as possible to decision maker in a company. After a 3-4 years selling to the wrong people Zia decided to shift his sales engagement process, as we will se below. This dramatically changed the business and helped the company flourish.
According to Zia Chishti, automation is not a near or even medium term risk to the business, because bots are in his view far away from replicating human capabilities and because companies will never trust computers to accomplish the tasks that require most empathy and human connection in their business. Social acceptance of Al technology being key, Afiniti by augmenting human potential through Al rather than replacing it seems well positioned to be socially accepted in the area.
B. Technology Overview
Afiniti's technology transforms the way human interact by enhancing pairing between people using matching algorithm powered by big data usage in order to discover, predict and affect patterns of interpersonal behaviors in order to improve interconnections between humans.
The first field of application of Afiniti's pairing algorithm is within the contact center world. Indeed, this environment provides high volumes of interpersonal
20 interactions generating results that are influenced by the quality of these interactions, in particular sales, retention and collections actions over the phone.
Exhibit 7: Afiniti intelligent routing solution
PX/ACD -+ IVR/SVR Coitact Que aeo AVAYA Languag Purpoe n n.I n-2 3 2 c Gsce Value Geo'raph A Cal Calls follow a triage path based on factors such as language and call purpose. After passing through skills based roudag or ineractiw vole response, calls are Call Asailpinaut allocated to a pool of apmts within a queue afi niti
A I
* ermus Archives With Aflutti, Instead of automatically assigning Z x Historical customers to agents in Y nteractions Optional time ord, we predict the survey likely behavior of both the caller and the agent from internal and external databases to Improve the ~ qalit at 11ntarastiefs ------
Source: Afiniti Interna/ Material
In the contact center world today, calls come in from the cloud. Calls are then processed by a PBX/ACD system that segments inbounds calls based on their purpose (sales, cancellation, technical problem) through the use of skills based routing using an Interactive Voice Responding System.
At the end of the qualification process of the call, it gets assigned to a queue. In this queue callers are ordered based on the time of their arrival. The first call in queue, is the longest waiting caller. A pool of agents trained to answer this very particular type of calls handles this queue. Whenever an agent becomes available he answers to the longest waiting caller in the queue. This is what is called First-In-First- Out (FIFO) call assignment. A vast majority of call center in the world operates following this scheme'.
1 Some call-center use performance based technologies. These technologies are overall rather inefficient.
21 Afiniti intervenes at the very end of this process. Indeed, the intuition behind Afiniti's technology is that FIFO call assignment is highly inefficient in a data rich environment. The fact that we have data on the callers and data on the agents and that this data is not leveraged upon to enable a better matching process represents a purely random and highly inefficient process.
What Afiniti does is that instead of randomly assigning calls on the basis of time allocation, it is matching callers and agents on the basis of expected behavior and personality in the call.
When an agents becomes available, Afiniti scans all the callers in the queue by identifying them through their phone numbers and leveraging on the available data on these callers it is identifying patterns of successful interpersonal interactions for each caller and for the available agent and finds a pairing believed to be more efficient than random to serve the accomplishment of a given KPI (conversion rate, problem resolution etc.)
To do so, Afiniti leverages on available data on both callers and agents and uses internal data, external data and context-based data (Agarwal, Xie, Vovsha, Rambow, & Passonneau, 2011 ; Gartner, 2015 ; Dey & Abowd, 1999 ; Van den Berg 2015). On the caller side Afiniti gets data from internal and external data sources. Internal data sources are the Customer relationship Management System (CRM), the billing system, the telephony system, historical records of information and collections files. External data sources are gathered in the form of prospect files coming from the likes of AllantTM, AcxiomTM, ExperianTM, TargusTM, FacebookTM, LinkedinTM. On the agent side Afiniti is looking at two main sources of data, historical interactions (10/100/1,000 last calls handled by the agent) and a personality questionnaire administered to the agent during technology implementation. Additionally it can use training information, workforce management statistics and inputs to improve performance. Based on these data sources, Afiniti operates a pairing that is expected to be more efficient than chance.
The results of this optimization are multiple. The main and most important ones that are driving all the others are an increase in customer satisfaction (CSAT) and an increase in agent satisfaction (ASAT). Indeed, thanks to an optimized pairing conversations on the phone are going better, thus the customer has a better
22 experience and the agent struggles less to handle client interactions and witnesses improvement in his performance and consequently in his compensation.
From this primary improvement other improvement are derived that have direct financial impact for the client: - On the revenue side some improvements include: > Increase in sales: customers routed by Afiniti when calling in to buy a product or a service display higher propensity to indeed buy a more expensive products or service > Increase in retention: customers routed by Afiniti when calling in to cancel a product or a service display higher propensity to stay as a customer S'Better revenue protection: customers routed by Afiniti when calling in to negotiate down the price of a service or product generally get less important discounts > Increase in up-sell: customers routed by Afiniti when calling in to upgrade a product or a service display higher propensity to upgrade to a more expensive product or service > Increase in cross-sell: customers routed by Afiniti display higher propensity to buy additional products or subscribe to additional services. - On the cost side: > Increase in First Call Resolution (FCR): Customers routed by Afiniti have a lower propensity to call back after their first call i.e. their problems are more often resolved during the first interaction > Decrease in Average Handle Time (AHT): Customers routed by Afiniti have on average a lower call duration with the agent i.e. their interaction is more efficient and leads to better client servicing > Decrease in truck-roll costs: Customers calls routed by Afiniti have a lower propensity to lead to a field agent intervention at the customer's home or office i.e. more problems are resolved over the phone leading to fewer on-site technical interventions > Increase in collections: Collection calls routed by Afiniti are often more successful and more money can be collected from customers in payment difficulty i.e. less money is lost and less money needs to be provisioned against impaired loans and bills
23 In the way Afiniti works, Afiniti teams and its partner teams decide on a primary optimization metric that the algorithm is going to optimize (for instance conversion rate) and then they determine control metrics that must not be affected (First Call Resolution).
When implemented Afiniti is able to precisely show daily improvement on the chosen KPI. Indeed, through its patented measurement process Afiniti offers a scientific measurement of the value it delivers. The measurement process operates as follows: for given short-time intervals (e.g. 30' minutes) Afiniti will route the call for 80% of the time and for the remaining 20% calls will be routed following the pre-existing routing mechanism in place (generally FIFO call assignment but not always).
Exhibit 8: Afiniti measurement cycles
Afiniti ON OFF Afiniti ON OFF Source: AfinitiWebsite
At the end of a given period of time (generally a month to ensure statistical significance of the gain) the weighted results for the chosen KPI are compared between ON sample and OFF and the delta represents the incremental value created by Afiniti on this given KPI. 2
(% conversion with Afiniti ON - % conversion with Afiniti OFF) x Number of calls ON
Incremental Value delivered by Afiniti
This patented measurement mechanism is at the heart of Afiniti's technology as it is core to its learning mechanism training.
2 There can be alternative benchmarking processes such as in-line benchmarking based on last digits of phone number instead of time stamp
24 On top of that, this measurement process is at the core of Afiniti's business model of pay-for-performance compensation. Indeed, as part of its partnership with the institutions it works with, Afiniti has established a business model under which its partners don't pay for Hardware, don't pay for software, don't pay for any professional services or license fees. Basically Afiniti covers all the fixed costs of a deployment. The way it gets compensated is by taking a share of the incremental value it has delivered and a share of the incremental cost savings it has generated. The exact pricing rationale and mechanism will be discussed later in this paper.
C. The magic behind the black box: a closer look at the technology
i. Data inputs
As a data driven tool, Afiniti's performance is heavily relying on the data it gets to fuel its performance. We have seen above that sources of data are both internal and external. We are now going to refine on these data sources to distinguish between static and dynamic data (Asif & Krogstie, 2012) as well as soft and hard data (Petersen, 2004)
Exhibit 9: Data inputs into Al matching algorithm
AGENT SURVEY PBX/DIALER
120 questions survey AVAYA, CISCO. Q"DI"yj enabling to get data on >1call information in real- agent personality tkme (ASA. hours. interpersonal phone number) skills
CUSTOMER CALL HISTORY PERSONALITY
Caller ID is linked back t ) Previous call outcome. databases (internal and historical call patterns, external) to retrieve revenue, value, duration psychographic and demographic data jLIs Source: Afiniti Intemal
The first distinction in the data sources used by Afiniti is the distinction of static and dynamic data. Afiniti leverages both data sources but not in the same timeframe. Dynamic information in the case of Afiniti is all data points associated directly with a
25 call at a given point in time. These information include for instance, call time, caller waiting time, IVR path, agent waiting time, agent availability, agent skills and the likes. These come mainly in real-time from the PBX/Dialer (as shown above). Then there is static information, these are all data points not generated live on the call but that are inputted in data models to optimize pairing by determining the likely behavior of agents and callers. These sources include CRM data, historical call data, agent survey etc. Then comes the difference between hard and soft data. Hard data is quantifiable information generated from a device or a system. In the case of Afiniti such data is any data points that is not subject to interpretation such as ARPU, tenure of a customer, agent id, caller id. Besides, Afiniti also uses soft data such as customer churn score, performance of agent against a given type of call, expected lifetime of a customer. Thus, by leveraging on both aspects of data Afiniti is able to operate a pairing guided at the same time by the real-time indication of a customer behavior and the historical prediction of its expected behavior.
ii. Importance of choice in a matching algorithm
A core intuition at the heart of Afiniti's technology is the idea that the amount of choice you have at the moment of pairing calls is highly correlated to the efficiency of the pairing.
Exhibit 10: Choice influence on Afiniti's algorithms performance
0 ~~OO23 5050 2 3200
Sa s so 21 10 1 a W Moy ess in Queue Many agents free t I of Agts in Poo Source: Afiniti Website
26 As shown in the above graph the more choice you have on either side of the pairing (agents or callers) the more performance you can achieve from your algorithm. The root cause of this is that, by having a large sample available for pairing you have more diversified profiles and thus different sets of interpersonal behavior capabilities on which you can leverage to achieve a more efficient pairing.
Given these elements we can see that, as a data driven technology, Afiniti's performance is strongly correlated to the call volumes it is handling and the number of agents in the pool. The graph above gives a good sense of how big a call-center queue should be in order to expect significant gain out of the optimization.
iii. Model Design and influencability analysis
In this part we will take a closer look at the way Afiniti designs its initial algorithms and update them and then we will look at the importance of the influencability of calls in Afiniti's optimization.
Exhibit 11: From historical data to real-time Dairina oDtimization
Historical 120 days Initial Model Design
60 .>
Real time pairing Initial Model Testing
20s. C) O aw 800 iterations
To operate the first design and test of its algorithm Afiniti relies on the exploitation of historical data. Indeed, to produce its model, Afiniti starts by gathering 120 days of historical information and data points. It gathers in particular call and calls outcome data and look at the match rate between these to. Then when matching is completed it analyzes the data in search of patterns of successful and unsuccessful interactions. Based on its first finding it does iterative testing on the data set to test
27 their ability to predict outcomes and the accuracy of their model. Once the initial model is designed Afiniti will then deploy it. From that point onwards, Afiniti receives daily overnight extract of the calls and calls outcomes. This data is then fed into the algorithm to update it and to try and discover which are the successful patterns persisting in ON sample that can be replicated and which are the ones that are not efficient anymore and need to be discarded.
Exhibit 12: Influencability Matrix by Industry
0% 1-2% 2-3%
3-5% 5-7% >7%
If data quantity and quality is an important component in the performance of Afiniti, one other component that is absolutely critical to the performance is the influencability of any given call.
Indeed, Afiniti can only deliver a significant increase in performance on the call if there are material differences and variability in call outcomes. As shown in Exhibit 12 above, Afiniti will have no impact on a 911 call. Indeed, whatever the pairing is an ambulance is going to get sent to the place of the event. If you look by industries you experience significant differences in performance. In the airline/transportation industry the performance is in the 1-2% range. Indeed, when a customer calls in, he generally has decided to go from a point A to a point B on a given date, and the only elements you can play on are options and add-ons such as insurance. In the hospitality industry, options offer a broader array of choice thus you can expect 2-3% gain. The telecom industry is one where the impact is very significant. Indeed, especially in retention cases a lot of what happens on the phone is influenced by the person you have at the other end of the phone, thus the pairing done over the phone heavily influences variability in call outcomes. Finally, insurance and bank mortgages/loans collections
28 are the two cases where the impact is the greatest. This is easily understandable when you understand the fact that the vast majority of the outcomes in these calls are coming down to the ability of a sales representative to generate trust during his conversation with the client. In this context, the human interaction is the primary determinant of the call outcome and these are the situations where you can expect to get the best gains often north of 5%.
IV. Business model analysis
A. Strengths of the measurement method
As explained before, Afiniti has developed a proprietary method of benchmarking that enables a precise measurement of the benefits it delivers.
This measurement method is a key attribute of its solution. Indeed, by using it Afiniti is able to solve one of the key challenges that many software vendors and artificial intelligence solutions are confronted with: the ability to prove in an undisputable way the value you deliver and thus show your true value creation outside of exogenous factors.
This is particularly relevant in the contact center industry, where returns on contact centers investments have been difficult to measure, as it was difficult to separate their impact from those of changes from exogenous factors such as training, offer change, economic or meteorological impacts.
Thanks to its measurement Afiniti is able on a day-to-day basis to prove to its partners the value it creates and gives full transparency on the impact it has on all metrics associated to the optimized KPI.
Its measurement process is fully transparent and its pairing follows a triple blind process whereby, neither the caller nor the agent knows when Afiniti is pairing, which ensures unbiased call behaviors. To this initial double blind process Afiniti adds an extra layer, which is that Afiniti does not know the result of the call before producing its gain report. Afiniti submits the calls log to its partner at the end of the day, it indicates if on a given call Afiniti was ON or OFF. The partner then reverts with the results of every call against the agreed KPI, which are then used to produce the gain estimate. In that process, Afiniti cannot cheat by giving the client a biased gain
29 estimate where it would say it was ON only on successful calls. It also forces transparency on the clients' side because, if the clients try to alter call results in order to reduce his bill, it will then affect Al gain as algorithms will be training on inaccurate data samples. That's why this process is called triple-blind measurement and ensures full transparency and trust between Afiniti and its clients.
B. Biasing the decision process: No cost / No investment approach
One of the key features of Afiniti's business model is its pay-for-performance approach. Despite being a software company Afiniti has chosen a very differentiating approach to pricing and selling its product than most software companies. It has decided to adopt a 100% pay-for-performance compensation scheme.
Indeed, when selling and deploying its solution Afiniti does not charge for any of the following items: hardware, software, professional services, license fee. There is basically no fixed costs incurred by its customers, it even reimburses the internal IT hours from their clients when asked to.
In doing so Afiniti manages to bias an organizational decision process in its favor. Indeed this gives a unique position when it comes to making a decision for the client, which is that there is no investment and no cost associated to the project internally on the client's side. This makes it a project with an infinite IRR, whereby the client does not invest and just gives back a share of the incremental value created to Afiniti. This is a very unusual approach in the software business where most companies work with license fees or professional services.
Thanks to its measurement technique, Afiniti is able to prove on an ongoing basis the incremental value it delivers to its client and thus is able to claim a share to compensate the value creation attributable to its Al enabled optimization. This is partly solving the dilemma faced by Al firm when it comes to sharing the value created through the use of Al. Given that the value Afiniti creates is purely incremental and comes at no costs a value share approach is more acceptable.
Then comes the question of how much value should be retained by Afiniti, and how to fit value delivered by Al and data within models that echo the standard way client view their business.
30 C. Getting the price right: an analysis of Channel Economics and pricing methodology
To deep dive on the point discussed above, one of the key features of Afiniti's technology to bear in mind is that it delivers incremental sales. What is meant by incremental is that the sales delivered in a precisely measurable manner by Afiniti are sales that would not happen otherwise.
Indeed, in a normal context for one of its client, all costs are already incurred, everything is optimized to try and deliver as much value as possible and the approach to the overall acquisition channels structure is fixed. The value of Afiniti is that it brings incremental sales, all other things being held equal. In that sense the value of the sales brought by Afiniti is far greater.
Exhibit 13: Afiniti Incremental Sales Value Creation - Telecom Industry Example
Normal Value Split for Variable Costs Fed Costs EBITDA 100 of Customer Value -206 ~5M -306
Aflniti Value Split for Variable Costs Afinkti Gwnerated Customer EBITDA C100 of Customer Value -206
In the example presented above we can see how the value creation works. Sales in a normal environment are loaded from both fixed and variable costs. Fixed costs are sunk and amortized over the number of sales made, they include amongst others: sales and marketing costs, IT costs, personnel costs, real estate and back- office. In an Afiniti world, Afiniti brings incremental sales out of an already optimized environment and thus, from an economics analysis standpoint, only the variable cost should be deducted from these sales. This means that the customers delivered by Afiniti are far more profitable than the normal customers.
An analogy that illustrates well this idea comes from the Airline Industry. When you sell ticket for a plane, if some seats remain empty right before the departure of the plane, and someone comes in to buy a ticket. The rational economic decision is to
31 sell him the ticket for the variable costs (in flight meal + airport taxes) + Icts as this sale is purely incremental and any cts generated on it goes straight to the bottom line.
As it comes clear to its client that Afiniti is a very strong driver of enterprise profitability and that it is delivering value comes the question of value sharing. As many Al technologies, Afiniti is confronted with the problem of value sharing with incumbent (Jeanneau, 2015). In a sense, it is creating out unique value through a proprietary technology, yet to do so it relies on the incumbent estate and business. Thus comes the question of how to split the precisely measurable value created.
To do this Afiniti has adopted a pricing scheme that is positioned to fit within the way clients typically look at their businesses. It has positioned itself as an acquisition channel for its customers, and it is asking to be compensated for incremental sales it delivers just a bit less than the price paid on the most expensive acquisition channel. Let's take an example from the telecom industry:
There are three main channels of acquisition in the telecom industry in the US: stores, call-centers and online. As a loose order of magnitude the average cost of acquisition is shown below.
Cost of acquisition per channel
$400 $250 $150
In that context US telecom companies are ready to pay up to 400( on average (marginal cost of the most expensive customers is far greater), to acquire customers via stores. Thus, Afiniti by pricing the customers it delivers at $399 (assuming for simplicity that all costs are fixed and not variable) represents a very rational option for its partner to add as acquisition channel. Afiniti will deliver incremental customers at a price competitive with the fully loaded (i.e. including all fixed costs associated with the sale) customer acquisition cost of the most expensive channel that the company is using.
32 Yet, even if this pricing approach seems rational, from discussions with Afiniti senior executives it seems that it does not always goes as simple in contract negotiation.
Indeed, it seems that in the B2B sales environment some misalignments of interests between the key stakeholders are making it more difficult to sell such a product and such a business model. That is the reason why Afiniti has developed a very unique sales approach that we are going to develop in the next part of this study.
D. Uniqueness of the Sales approach: the CEO obsession in a B2B context
i. Challenges to technology deployment: an analysis of organizational inefficiencies
Afiniti in the sale and deployment of its technology is often facing challenges that are highlighting organizational inefficiencies, intrinsic to big organizations.
There are several elements that are harming Afiniti's ability to deploy in organizations: > Organizational position of call-center operations > Evaluation methods of IT teams and Operations Team > P&L responsibility ownership
The first challenge faced by Afiniti in its deployment cycle is the way call-centers are positioned from an organization standpoint.
Exhibit 14: Reporting function for call-centers within organization
Which function(s) do contact centers report to within your organhation?
Operations 43%
Business Units 42%
Sales 31%
Finance 30% rr 29%
Marketng 27%
Don't knowNA 13%
Source: Deloitte, 2013 Global Contact Center Survey Results
33 Indeed, as shown in Exhibit 14 above the primary unit of reporting of call centers is operations (43% of cases), IT reporting is still as important as reporting to finance and sales and marketing comes last. This clearly shows how contact centers are viewed from an organizational standpoint: it is mainly a cost center.
Yet, when you look at what are its actual tasks it should be above all driven by marketing and sales. Indeed, call centers are a direct channel of communication with customers. Customer service as one of the main drivers of brand recognition and customer experience is vital in the way customers interact with the brand and in the brand ability to increase loyalty and brand awareness. For instance, according to White House's Office of Consumer Affairs, news of bad customer service reaches more than twice as many ears as praise for a good service experience. Besides, quality of customer service is directly related with capacity to up-sell/cross-sell/retain your customers. According to a 2011 American Express Survey, "3 in 5 Americans (59%) would try a new brand or company for a better service experience and 7 in 10 Americans said they were willing to spend more with companies they believe provide excellent customer service." Thus the predominant chain of reporting of call-centers should be into sales and marketing not into operations and even less into IT. The misperception of call-centers into cost centers is proving to be a key challenge to Afiniti's ability to successfully sell and deploy its technology.
Another element that comes in conflict when selling and deploying Afiniti is the structure of performance incentives and KPIs in organizations. Indeed, as we have seen above call-center are mainly considered as a cost center thus reporting into operations/COO. Besides Afiniti is an IT deployment project and therefore implementation falls under CIO/CTO responsibility. Afiniti's technology is a strong revenue and profitability driver, yet none of the people responsible for its implementation are generally incentivized on this. Indeed, incentives and target are generally set as follows: > CIO/CTO: Incentivized on up-time (i.e. the time on which IT systems are working efficiently) and works on budget allocation (Given that Afiniti reimburses IT costs, he faces a problem because he is spending hours that are not costing him anything and thus he can have problems spending his entire budget before the year ends and thus risks a budget cut)
34 COO: Primarily incentivized on costs reduction, his main objective is to find way to improve overall efficiency of the organization not to deliver incremental revenues or profitability
In Afiniti's technology the primary beneficiary is the CMO whereas for most software vendor it is directed to the COO and CIO/CTO as shown in Exhibit 15.
Exhibit 15: Traditional firm organizational structure vs. Afiniti impact and benefits
CEO I ~J-
CFO CMO COO CTO Legal Pays on a run - Gets the Gets affeed in the Has to Clears data rate basis benefits way operations work implement utilization
Tradftional buyers for IT/ 1 9 --- ,software vendiors afun t Primary benefiter-- fin iti
In the end, the primary hurdle to Afiniti's deployment is the absence of Global P&L ownership or incentives of IT and Operations people within organization.
ii. Redesigning the sales approach by understanding the role of a CEO in overcoming organizational inefficiencies and driving innovation
In order to overcome this organizational structure hurdle to the sale of its technology, Afiniti has developed an alternative approach to B2B Software sales. Indeed, after a few years not selling successfully enough to the above mentioned structures and not being able to overcome misalignments in organizations, Afiniti changed its sales strategy. It decided to focus its whole sales process around CEOs.
35 The rationale behind such an approach is that the CEO tends to be the most rational person in his organization. Indeed, he is not biased by being measured on the wrong incentives and is expected to maximize shareholders' value. As a rational individual, he should be able to get the full vision on benefits and make the right arbitrages to prioritize such a project. As shown by Exhibit 16 below, Afiniti has thus decided to shift its effort and focus on the sales strategy to CEO access and CEO relationship building.
Exhibit 16: Afiniti's sales effort focus
CEO
CFO CMO COO CTO Legal *s on a run- Gets the Gets aflected in the Has to Clears data rate basis benefits way operabtons work enplenent utilization
Taret for Al comnpanies afnt fftWerayt ytose
Yet, these individuals are not easy to access so one other key and core element of Afiniti's go-to-market strategy has been to build a network to support its ambitions.
iii. Building the network of your ambitions
Indeed, looking at Afiniti's board, advisory board and management team it clearly appears that the company has built a very strong network to support its sales strategy.
Indeed, in every geography and industry, Afiniti has onboarded key players to support its sales strategy for its key industries. As we will show in the coverage maps below Afiniti has visibly managed to cover its key target industries (Telecoms, Cable TV, Financial Services) across the globe.
36 Exhibit 17: Afiniti's Coverage Map of Global Telecoms & Pay-TV Industry
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Source: Afiniti Website
In the Telecom and Pay-Tv industry, Afiniti has a very broad coverage. Through Elisabeth Murdoch it has access to the NewsCorp Conglomerate (incl. Sky (Italy, Germany, UK, New Zealand), 21st Century Fox, Foxtel), it has access to the top US Telecom operators (notably Verizon through Larry Babbio and Ivan Seidenberg, AT&T through Laura Tyson and Sprint via Fred Singer (Soft Bank owns Sprint) and Jose-Maria Aznar (Sprint's CEO Marcelo Claure is from South America)).
The same set of relationships plays in Europe, with the likes of Sir Peter Bonfield (former CEO of BT) and Alexandre de Juniac (Board Member of Vivendi - Parent Company of Canal+ and Telecom Italia).
In Latin America, the influence of Jose Maria Aznar (former Spanish Prime Minister) ensures prime access to all decision makers and CEOs.
37 In Asia, a combination of Simon Murray (Former CEO of Hutchinson Whampoa), Elisabeth Murdoch and Fred Singer enables the company to cover China, Australia, New Zealand and Japan. Further to be noted, the company is based out of Washington DC but has a very large operation in Pakistan where its CEO Zia Chishti is a well knowned public figure and for India, Sunil Prashara, Global Head of Sales Operations has previously been extensively involved in sales operations at C-suite level in his roles as Global Head of Sales for Nokia and Managing Director for Accenture in APAC.
Exhibit 18: Afiniti's Coverage Map of Global Financial Services Industry
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38 very strong relationships at every level. From Sylvain H6f6s (former Chairman of Paris- Orl6ans - holding company for the Rothschild family) to Simon Lee (former CEO of RSA) and Federico Ghizzoni (former CEO of Unicredit).
Besides its core network of Board Members and Advisor, Afiniti has also a Global Partnership with McKinsey. This partnership enables Afiniti to be introduced to McKinsey's client by McKinsey and to be included in McKinsey's customer journey transformation and Big Data/Digital transformation programs for its clients. This again comes to reinforce an already strong preexisting set of relationships.
E. Protecting an untapped market building high barriers to entry
One of the key differentiating features of technology companies is their ability to create barriers to entry to protect the market they operate on. It enables them to get a reward for the innovation they have developed during at least a short period of time. This is something in which Afiniti has proven to be particularly successful and inventive. The three key features in the barrier to entry creation strategy of Afiniti are, a systematic approach to patenting and IP protection, a first mover advantage business model and finally an early global rollout supported by a strong sales network strategy.
i. Intellectual Property
The first strategy to protect its market position that Afiniti has developed is to build a strong intellectual property portfolio. The company is said to have 42 issued patents and a further 60 pending (see Exhibit 19 below for some of the patents registered at the United States Patents and Trademark Office).
Having a large portfolio of patents puts the company in a strong position when it comes to defending its market. Any new entrants developing a technology will have to be wary of not overlapping of Afiniti's IP, otherwise he faces a significant risk of being sued.
39 Exhibit 19: List of registered patents by Afiniti at the United States Patents and Trademark Office
PAT.NO. Tise I 2A.2 T Mgag"/a sensitiv to pRKMMM 2 9300 T Tachiq for behaviora pdrn in a contact center w 3 2,2M T Systenu and methods for muting a contact to an agent in a contact center 4 92325 T Systems and methods for Muting callers to an agent in a contact cenr 5 9.277M5 T CWu=ft "al=sfoms ad ethcds nsing rce algrthm and/or distbtion mpensan 6 92=.=23 T Selective mapping of callms in a call center routing system 7 9225.757 T CAUl MgVpp ng sytmand methd& bkynhain man represion (BhM 1 9220137 T M n sagft sendtivi to pub== 9 8.929.3 T Call tedpcag tim and md f vara algactlm (AM and/or dston gmprsse 10 .90379 T Rtgs call frm a s of or bo w callers an age c data 118.879.715 T Call Maystems and methodsung aMrs( and/or diribution nompacsttit 12 8.824.65 T Sepactwe mapingof cals in a caol Ceuta MssgotedM w 13 fr T Ue of absignifiat paten atha A is st u 14 m.78h1.1 T Ag natifabon da fcas routing bsed on patern maSPing al N.,38, S 15Pa .7N.1 T8,2b95,471y muipled p ei for call amter aimoe 16hs s T Predicted camtim as tie vantable Ina call t ar centert.e 17 in a ubse and to for tn r manneds idre r taotto an s in a con=client. Al l t .731.17 T suemnt methd for o dcates to an Aeihe in o Contrpreent 19 8nr797 T Eilmame amb e ina ca muwita center vln e a m 2 8.71.271 T C&II MOU med and MV based CM MMtp Vaisbie Mwndrized 21 struggl t e convning in te sae pr aocaesi ad to ag their case. So, it 22em9tt T pated rt-Af poi fti a ca c center Mon tee 23p8t 7054 T JuMplitrM caller held In go= for a call enterom 24 8S4A9 T Shadow qumw for callr In a pjrfamunwe/pm=r matn based call moutgys 25 8,634.542 T Sara go=er Ma=chng aigcOhm and =op=te modeks based on available calla dama 268SA72.611 T BaWacin mukiple oMapu= models JU a caLg= Mesrrugsse 27 8.25A7 T Select mappbg &fcalkn in a call-=Wte rmutgg systm bwsed on individual agentseting Source: United States Patents and Trademark Office
By far the most significant patent that Afiniti has registered is its measurement mechanism, it is notably encompassed in Patents U.S. Patent No. 9,413,894, U.S. Patent No. 8,295,471. Indeed, by being able to claim a patent on this element Afiniti has secured a key competitive advantage on its market. It has the exclusivity on proving in an unbiased and totally transparent manner what it delivers to its client. All alternative measurement methods to date seem to be either biased or presenting some flaws. According to Afiniti's executives "it seems highly unlikely that anybody in the near future will be able to come up with a viable alternative measurement method".
Without a viable way to prove to their clients the value they deliver, competitors should struggle to be convincing in the sales process and to argue their case. So, it seems that, via its patent portfolio, Afiniti has created a significant barrier to entry for potential competitors.
40 ii. First-mover advantage technology and opportunity cost
Afiniti's technology benefits form a first mover advantage. Indeed, let's proceed to an analysis based on one of the case studies presented on the company's website.
Example:
This example is based out of the T-Mobile case study presented on Afiniti's website:
Annual Benefits from Afiniti (in $M) $70 Annual Costs (in $M) Estimate - 20% $14 Time to implement 3 months Cost of implementation 0 Gross Monthly Benefits (in $M) $6
In this example, we assume that Afiniti retains 20% of the total value it generates for T-Mobile. In such a situation, T-Mobile gross monthly benefits from Afiniti are $6M thus they get $70m on annual basis.
Let's now assume that a competitor enters the market with a new technology that competes with Afiniti and decides to compete with Afiniti by cutting prices by 20%. Assuming its product is much more efficient and easy to implement and only takes 2 months.
The first year cost of switching from Afiniti to the competitor for T-Mobile would be as follows (assuming that the competitor delivers as good a performance as Afiniti):
Benefits from switching: $2,8M (in yearly cost reduction)
Cost of switching: $12M (two-months of implementation)
Breakeven from switching provider = > 4years
As we see Afiniti would be endangered if a competitor were to enter the market with a product: With similar or superior performance
41 At a cost that is significantly lower than Afiniti's - With a deployment cycle that is shorter than Afiniti's (2-3 months) That has a clear and unbiased measurement mechanism - That does not infringe Afiniti's IP
Even if such a possibility is not to be neglected, it seems unlikely that one given product would match all these characteristics. Even if it did, most companies are adverse to technological change, and will not probably directly switch away from Afiniti before a significant period of time. On top of that, Afiniti benefits from the credibility that its 10 years of experience, its client portfolio brings. Given the inherent hurdles in big corporations, a potential competitor would struggle to scale as rapidly as Afiniti is doing.
iii. Accelerated roll-out and Network effects
Finally, it appears that based on the above-mentioned facts Afiniti has decided to expand rapidly on a global basis. It has established offices or personnel in 11 countries in 2016 (Australia, Brazil, China, Dubai, France, Germany, Italy, Mexico, Singapore, Spain). By accelerating its global rollout Afiniti probably intends to leverage on the first mover advantage effect.
Besides, its sales strategy, which seems to be efficient, will probably be difficult to replicate as it has already gathered a very broad and powerful set of individuals to back its CEO-focused sales strategy. It thus benefits from positive effects of its network building strategy.
F. Weaknesses of the business model and potential threat to the business
After, highlighting the features and strengths of Afiniti's business above, we thought it is necessary to expose its weaknesses. From our point of view the business model suffers from several weakness and threats.
i. Lack of comfort of big firms for pay-for-performance models
One of the first challenge that seem to arise for Afiniti in the deployment of its technology is related to its very specific business model and the efforts engaged by
42 the company to overcome operational resistances. Indeed, its pay-for-performance model seems to be bringing some adverse effects and being not common in the business-to-business sales market is sometimes a bit difficult to deploy.
The first materialization of this challenge is in the fact that the company covers all deployments costs for its clients. It is reimbursing all software, hardware and license costs that its clients' incur while deploying and it even goes as far as covering for the internal IT-hours engaged to deploy the project. The challenge rising from such an engagement from Afiniti's part is quite surprising. Indeed, as the company covers all its client costs it is sometimes faced with the lack of commitment from its partners, which are less interested and involved in a project costing them nothing, even if the revenue potential is very significant. The absence of cost line associated to the project makes it more difficult to apprehend and to include in work plans.
The second challenge faced by Afiniti is due to its purely pay-for-performance business model and the fact that its product is a statistical product thus showing important variability in results from one month to the next. Indeed, given that the company determines a fixed price per increment product/customers delivered and that this total value to be paid by the client is linked to the Al gain, there is a very significant variability in costs every month to the client. For instance, if the Al gain is x% one month 0,5x% the next and 2x% after that, there is a fourfold variation in price from month 2 to month 3. Such variations are common events for Afiniti but the consequences are sometimes challenging to deal with. Indeed, its partners do not like such volatility in costs when they are trying to run quarterly or even yearly budget exercise. This variability, even if it is intrinsically beneficial to them because they only pay for what they get is proving to be a challenge in financial and operational planning and can sometimes cause conflicts and tensions internally.
ii. Difficulty to evangelize for a technology in the absence of competition
Another surprising challenge for the company seems to be the absence of a true global competitor. Indeed, the company seems to be operating on a yet untapped market where it is alone in its space. None of the switch providers are claiming to have fully functional and efficient behavioral pairing technologies yet and there are no competitors in the space claiming such breadth and depth of client portfolio. Zia Chishti is claiming that the main competitors to date are "the likes of
43 Accenture or McKinsey, competing for mindshare at the senior-most level in the organization".
Though being an undeniable competitive advantage in the company's pricing power and ability to seize the market, the absence of any competitors is proving to bring some challenges for two aspects: - Challenge in legitimizing market e Difficulty to run in Request for Proposals (RFPs) processes
First, it seems that the absence of significant competitors in the market is somehow a challenge at the same time as an advantage. Indeed, the absence of such competitors poses a problem in terms of legitimizing the market Afiniti is operating on. According to several company executives, it seems that there is some irrational component to this and working alongside a rational stating that if nobody else is doing it, there must be something off.
Then, the additional complexity coming from the absence of significant competitors is due to the fact that in the absence of competitors it can be difficult to enter into companies that are RFP driven. As a matter of fact, some companies, notably those that are still largely owned by governments, often have very standardized RFP processes to onboard new suppliers. The absence of such a competitive landscape in Afiniti's behavioral pairing market can make it more complex to formally enter in a contractual relationship with such companies.
iii. Data security
One important challenge to Afiniti's installation is the tighter requirement and fears surrounding data sharing within companies. These are driven by threats of data leaks and the importance of data in the construction of a competitive edge over competitors.
First, the key concern for client sharing their data is the fear of a data leak. Indeed, given recent hacks at TJX (Goodman, 2014), which had an estimated cost of $1 Bn and notoriously affected reputation and competitive position of the companies in its markets, and similar hacks in many similar firms such as Sony or Yahoo, most of major global firms are now imposing extremely tight regulations around the way they share data with their vendors. Afiniti's reply to that has been to adopt an on premises
44 deployment model, whereby Afiniti is getting the data on servers installed at client premises behind their firewalls and within a set up where no data can be extracted and Afiniti engineers are able to process the data by connecting securely via VPN.
Then, in a world where data is becoming a core competitive edge on competitors, many clients are extremely concerned and careful when sharing data with vendors that do not work exclusively with them. Indeed, it often is a concern for clients that their data could be used to other ends, and notably to increase performance with other clients. Afiniti has managed to avoid such problem by adopting tight internal policy prohibiting any data extraction as described before.
If Afiniti has set up a relevant framework to protect its client data it is still often confronted with such fears in its sales cycle and any incident on that front could prove massively disruptive and harmful to the company.
iv. Regulatory framework for the usage of data
One key element of concern for Afiniti is the rules surrounding the usage of customer data in the different geographies. Indeed, there has been rising focus and concerns from the public and from governments around the usage of customer data by private firms and to marketing and customization end.
For instance in Europe, as shown by Exhibit 20, from the DPD (Data Protection Directive) enacted in 1990 to the EU GDPR (General Data Protection Regulation) enacted in 2012, there has been a very steep increase in the legal frameworks surrounding data usage and data protection in Europe. This trend can be witnessed on a global basis, with government publicly showing concerns around data usage by private firms.
45 Exhibit 20: Data protection legislation in the EU from DPD (1990) to GDPR (2012)
33 a No. of articles DPD (1990) 24 No. of recitals 27 t No. of pages
DPD 1995 34 7 Note: no. of pages of DPDlegiative text are from English PDF versions - excluding explanatory text 91 GDPR (2012) 139 82
0 50 100 150 0 2015 Kuan Hon kuan .com.You may copy/usethis diagram under a CC BY 2.0 UK licence https:/Icreativecommons.orgAicenses/bv/2.0/uk/retainingthe attribution in this paragraph.
Besides government focus, there has been increasing focus from the general public around the usage of their private information to commercial ends. For instance the French data regulation body (CNIL) enforces direct access right which is the right for "Any person (to) see all the data that affects him/her in a file by making direct contact with those who are holding, and by obtaining a copy of it at a cost that is no greater than the cost of copying it." Any firm owning or using data on given individuals is required to share the entire record of data in its possession to any person claiming the right to view its own record.
In such a context, if the regulatory environment continues to get tighter with more demanding and restrictive regulations around data usage, Afiniti's could soon be faced with a challenge in the implementation of its technology. A rise in data regulation could increase the legal burden on the company due to tighter rules and increasing claims as well as limit the performance of the optimization by limiting the data fields that can be used.
v. Sales strategy is making people extremely valuable to the company and business very sensitive to key employees churn
As shown above, a large part Afiniti's sales strategy is based on building a network that opens the right level access to the key executives in its target clients. The company has massively invested in building such a network and sales force and seem to have succeeded there.
46 Yet, relying heavily on these networks makes the company vulnerable and exposed to churn of its key executives. As we have seen, most of the sales process is driven top down and in doing so the relationship coverage ensures the required level of prioritization over other projects as well as an escalation path to solve problems and senior leadership focus to force transformation within their companies. These relationships need to be maintained and reinforced on a day-to-day basis in order to ensure business continuity and progress. The company being still relatively small it only has one or two key individuals per market it opened, thus it is overly exposed to the churn of these key employees.
In order to mitigate this risk the company could probably encourage longer-term contract that will protect the active business in the long run and also develop a second-layer relationship network in order to ensure that in case of loss of a key executive it will still have the management depth required to carry on the relationships.
V. Relevance of personalized and behavior based approach to customer management
A. Defining personalization in the context of Afiniti
i. General Concept
Before talking about the relevance of implementing a personalized behavior based approach to customer management in the contact center-space it is important to come back to the academic definitions of personalization, and to determine how Afiniti's technology matches the different academic definition of the concept.
Personalization encompasses technologies and elements covering a vast array of academic fields. To try and cover these definitions, we will use the different definition elements laid out by Fan & Poole in their 2006 Paper based out of more than 142 references on the topic: What is personalization? Perspectives on the design and implementation of personalization in information systems (Fan & Poole, 2006).
In their analysis they distinguish 5 fields where personalization is applied: - Marketing / e-commerce - Computer Science / Cognitive sciences
47 * Architecture / Environmental Psychology - Information Science * Social Sciences
After a thorough analysis of the contents of definitions of personalization in these different fields, we have restricted to a subset of definitions in selected academic fields that seemed relevant to Afiniti's behavioral pairing technology.
The first relevant definition of Afiniti's personalization is the definitions generally retained in the marketing and e-commerce space. In this space personalization is generally defined as "building customer loyalty by building a meaningful one-to-one relationship; by understanding the needs of each individual and helping satisfy a goal that efficiently and knowledgeably addresses each individual's need in a given context" (Riecken, 2000). This definition is very appropriate in characterizing Afiniti's approach to personalization, which relies on the optimization and enhancement of one-to-one interactions in the contact center space in order to create a more efficient interaction but above all a better customer experience and to increase customer expectations fulfillment against a given goal.
Then, Afiniti's personalization is also relevant to its definition in the cognitive sciences' space, which is according to Larsen & Tutterow "the process of providing relevant content based on individual user preferences or behavior" (Larsen & Tutterow, 1999). As we can see, this definition is very relevant to Afiniti's usage of behavior based consumer segmentation. Afiniti's is enhancing customer care by leveraging on behavioral characteristics to maximize likelihood of positive and successful interaction.
Besides, Afiniti's personalization also correlates with social science qualification of personalization as a "Technology that reflects and enhances social relationships and social networks" (Wellman, 2002. Cummings, Butler & Kraut 2002).
Finally, another relevant and accurate definition of personalization operated by Afiniti is in the computer science field where personalization is defined as "machine- learning algorithms that are integrated into systems to accommodate individual user's unique patterns of interactions with the system" (Hirsch, Basu & Davison 2000). In the case of Afiniti, we can see that a broader understanding of the word system to encompass not only the technological component of it but also the overall customer
48 management experience, describes rather accurately the personalization operated by Afiniti on the basis of individual patterns of behaviors and personality to enhance customer overall experience.
As we have just seen, Afiniti's technology is at the crossroads of many academic fields from marketing to cognitive science. Its technology truly qualifies as a personalization mean under these multiple definition and we will now further explore what are its features and in what frameworks it fits before discussing the relevance of customer based behavior based personalization within customer management strategy.
ii. Feature(s), target, agent & means of Afiniti's personalization
Still following Fan & Poole analysis of personalization in information systems it is further needed to qualify Afiniti through the three main attributes that they lay out in their research (Fan & Poole, 2006). These attributes will enable us to define and analyze academically the way personalization is implemented by Afiniti. We will define its: " Features - Target
e Agent and Means
Fan & Poole distinguish between four features that can be personalized: content, interface, channel access, functionality. In Afiniti's case, the personalization that happens is a mix between content and interface, i.e. between the information presented itself and the way it is presented. Indeed, the technology enables Afiniti to better match agents and customers, so that a given agent will present a more relevant content in a more appropriate way.
Then, it is necessary to determine what is the target of the personalization. Always under Fan & Poole framework, Afiniti qualifies as operating individuated personalization by categorical analysis. Individuated personalization is qualified as "personalization (...) targeted to a specific individual, and its goal is to deliver goods, services, or information unique to each individual as an individual". (Fan & Poole, 2006). Within Afiniti's technology the individuation proceeds by using categorical analysis, whereby the unique behavioral patterns of an individual are determined
49 through the intersections between the different categories he belongs to using clustering mechanisms.
The final dimension that needs to be addressed is agents and means by which it occurs. In Afiniti's case, the personalization is qualified as implicit personalization, which is a system-initiated personalization where users have no choice. Afiniti's technology inputs adaptive mechanisms in the telephony routing that react to each individual set of data to provide the best-suited agent.
Another qualification that suits well Afiniti's behavioral pairing technology has been developed by Amoroso & Reinig. Their classification scheme distinguishes between user-behavior tracking technologies, personalization database technologies, personalized user interface technologies and customer support technologies (Amoroso & Reinig, 2003). In Afiniti's case the most appropriate concept is personalization database technology, which is described as "built on large database systems and including statistical analysis, data mining, web housing, intelligent agent, recommender systems, collaborative filtering and user profiling." (Fan & Poole, 2006).
iii. Afiniti's personalization within Fan & Poole's ideal type framework
Based on the different definition of personalization, Fan & Poole then continue to present and determine four ideal-types of personalization. Max Weber's analytical frameworks inspire the ideal-type approach, which defines by stating, "an ideal type is formed by the one-sided accentuation of one or more points of view" (Weber, 1903- 1917/1949). In their analysis they distinguish between architectural, instrumental, relational and commercial personalization. We will not go into lengthy details into each of these (illustrated in the Exhibit 21 below) but rather focus on the commercial personalization ideal-type to illustrate how behavior based personalization operated by Afiniti fits into this framework.
50 Exhibit 21: Personalization Ideal Types by Fan & Poole (2006)
1 Personallzation Ideal Types Architectural Instrumental Motive: To fulfill a human being's needs for Motive To fulfill a human being's needs for expressing himself/herself through the efficiency and productivity design of the built environment Goals: To create a functional and delightful Goals: To increase efficiency and productivity Web environment that is compatible with a of using the system sense of personal style Strategy: Individualization Strategy: Utilization Means: Building a delightful Web Means: Designing, enabling, and utilizing environment and immersive Web useful, usable, user-friendly tools experience User model: Cognitive, affective, and social- User mode: Situated needs of the user cultural aspects of the user Relational Commercial Motive: To fulfill a human being's needs for Motive: To fulfill a human's beings needs for socialization and a sense of belonging material and psychic welfare Goals: To create a common, convenient Goals: To increase sales and to enhance platform for social interaction that is customer loyalty compatible with the Individual's desired level of privacy Strategy: Mediation Strategy Segmentation Means: Building social interactions and Means: Differentiating product. service, and interpersonal relationships information User mode: Social context and relational User models: User preference or demographic aspects of the user profiling; user online behavior and user purchasing history I I Source: Fan H. & Poole M., What is personalization? Perspectives on the design and implementation of personalization in information systems, Journal of Organizational Computing & Electronic Commerce - 2006
In their paper, Fan & Poole define commercial personalization as the "differentiation of product, service, and information to increase sales and to enhance customer loyalty by segmenting customers in a way that efficiently and knowledgeably address each user or group of users' needs and goals in a given context. Commercial personalization is strongly technology driven" (Fan & Poole, 2006). This definition reflects rather accurately the process and technology that Afiniti has developed and that is described above.
Taking a closer look at the commercial personalization ideal-type framework we see that Afiniti's technology is relevant on every feature. In terms of commercial motive, Afiniti fulfills a "human's beings needs for material and psychic welfare" by improving C-SAT through the enablement of a better customer experience based on a more efficient and enjoyable behavior tailored human interaction. Afiniti's behavior- based personalization has successfully proven that it can increase sales and retention by increasing customer satisfaction, which are two of the primary metric optimized in its deployment. This is accomplished by differentiating product offering, but above all by tailoring service experience on the back of using individuals' information be it
51 demographic, psychographic, historical interaction, online behavior, or purchasing history.
B. Relevance and benefits of personalized customer relationship management in a data enhanced world
After refining our understanding of what behavior based personalization means and covers in the academic world, we will now seek to understand and establish the global trends favoring such personalization and its relevance within the contact center world.
i. Favorable global trends
One key element that needs underlining is that the principal enabler of new personalization strategy and especially behavior based personalization is the explosion of data availability that enables firms to adapt and customize their offerings and experience in a world where face-to-face interactions are not the norm of transactional exchanges anymore. In such a world, service and experience matter more than advertising. As Ledford & Lawler say in their 2002 paper: "In this era of technological innovations, the Internet, and new media, personalization is possible on a broader scale and can be done more quickly and effectively than ever before. As an important social phenomenon that carries great economic value" (Ledford & Lawler, 2002)
As we explained in 1I. A. the global increase in the volume of data coupled with decreasing cost of data storage opens the door to a more and more personalized approach of customer management thanks to increasing data relevance (Violante, 2011). Indeed, as people are increasingly active on social media, active on their mobile, acquiring data generating tools as part of the Internet of Things revolution, they start to generate more and more exploitable data feeds that give increasing insights into their day-to-day life and their behavior. New data sources give ever more accurate indications of each individual network, preferences, habits and these enable companies to determine with increasing accuracy patterns of behaviors down to the individual layer. Such trends and capabilities strongly favor new dynamics and incentives to customize and tailor service and products on behavioral attributes. We are nowadays in a situation where companies' survival rely on large database exploitation to relationship building ends (Culnan and Armstrong 1999)
The second trend that is increasing the relevance of personalized behavior
52 based approach personalization of non-physical purchasing channels is the diminishing importance of in-store purchase. Indeed, with the rising importance of e- business and the digitization of customer journey, in-store purchases are declining. Stores are now becoming a mere touch point in the customer experience process and are not anymore the ultimate end point of purchase. Yet, physical interface of sales assistant with customers in store have historically been one of the critical factors in the customer decision journey and were based on human interactions, with sales increasingly switching to digital and phone channels the ability of firms to leverage on their IT and databases to replicate personalized behavior based experience will be a key determinant of success in this new era (Weill & Vitale, 2001). A large share of customers now wants to be recognized and demand cross-channels and cross device personalization of services and experience (MyBuys, 2014). Besides the emphasis, is should be put on service rather than on advertising, because the latter is largely viewed as less beneficial (McLaughlin 2002). This explains abating concerns of consumers around data privacy related issues on the matter.
ii. Relevance of call-center channel generated information to data-driven commercial personalization
Afiniti's channel of personalization is centered on call-centers. This channel offers unique characteristics in terms of information about customers. Indeed it provides unique set of data points in terms of intent, customer identification, past interactions, psychographic and demographic characteristics, time, context, location of the call and the caller. This unified channel offers unique opportunity to have a unified, coherent and holistic strategy of behavior-based personalization.
Such capabilities are prerequisites to a successful commercial personalization as shown by Fan & Poole in their 2003 paper Perspectives on Personalization. Indeed, large and diverse data sets on the individual are a necessity for an efficient strategy in commercial personalization. Such data is gathered by continued accumulation of data points through customer journey and life and enable to seize their inners preferences and behavioral characteristics (Larsen & Tutterow, 1999). Besides as Fan & Poole mention "cognitive ability, motives, demographic or psycho-cultural profiles" (Rubini, 2001), "user behaviors" (VignetteCorp, 2002)", and specific contexts" information further increase personalization efficiency. Besides, call-center generated and exploited data are highly relevant as they are encompassed in the two types of contextual information that Fan & Poole identify as key to adaptive personalization which are "users' intent, preferences, and purchasing history, whereas the other
53 relates to environmental factors such as time and location of the user" (Bayler & Stoughton, 2001). Processing of such data is the key to anticipating customers expectations, needs and predicting their likely personality and behavior in order to increase their overall satisfaction (Fan & Poole, 2006). This will build a positive attitude from customers feeling that vendors care for them and are receptive to their personal needs in their customer service experience (Liang et al., 2012)
iii. /mportance and impact of personalized human customer service
As one of the trends in the industry of contact centers pushes for automation and computer automated customer service, I think it is important to highlight the benefits expected from human operated personalized customer service and the expected return of personalized customer service.
In the experience of customer service it appears that the human component is a very important one in the customers mind. The person-to-person relationship still is at the core of the success of customer service; in fact 73% of customers declare they still want contact with human operatives of the companies they deal with (Van Belleghem, 2015). This is driven by the fact that often people that call in into a customer service do so because they have a problem with a product or a service, thus there is often frustration or anger behind such calls. In such calls, the human ability to display empathy is critical in ensuring that the company addresses the customer needs and enables him to be satisfied and appeased in his interaction.
Yet computers are incapable of having or even mimicking emotions, they are purely rational entity whereas people can be rational and emotional (Van Belleghem, 2015). The most important part in customer service is the ability of the sales representative to turn negative emotions into positive emotions. Components necessary to generate positive emotions are creativity, empathy and passion. These are uniquely human attributes (Van Belleghem, 2015), thus making the automation of customer service a potentiality for the future but not at all a certainty. I am convinced that a lot of brands will want to retain human customer service especially for high value or highly emotion related tasks as to ensure that customers are feeling that their problems are understood. Nevertheless, this does not prevent from using many tools to guide humans in their interactions; such tools already exist and are gaining increasing momentum. For instance, speech analytics technologies are good tools to help sales representatives and customer service agent perform better. As shown by
54 Genesys in their "The Cost of Poor Customer Service" Global Survey from 2009 a happy customer experience comes from competent Sales representatives (78%) and personalization (38%).
Besides, it appears that behavior based personalization is delivering higher return per dollar spend than traditional marketing or FIFO customer service (Tsai and Chiu 2004). It is a repeatedly proven fact that service personalization improves loyalty (Ball, Dwayn, Coelho & Vilares, 2006). It does so through several channels. The first one is customer satisfaction. As explained previously, Afiniti primary driver is the increase in customer satisfaction. Customer satisfaction is the primary driver of loyalty. Simply said personalization by adjusting a service to customers' need will naturally deliver more satisfaction than a one-size-fits-all offer. Then experiencing personalized services can convince the customer that the firm is benevolent towards him or her, increasing trust, which is an other antecedent of loyalty (Ball, Dwayn, Coelho & Vilares, 2006). Finally, the third element of personalization that drives it benefits is the fact that a personalized service is often viewed as more difficult to replace than a standard one. The impression that the companies knows you and your habits often has an important weight when it comes to switching from providers of the given service because you have an opportunity cost of the next provider taking time to adapt to you needs and habits.
As seen above, having a personalized human customer service translates into direct benefits increasing customer satisfaction and loyalty. Such impact drives an increase in customer lifetime value by reducing churn and an increase in Net Promoter Score that contributes to improve a company's reputation. In a world where 78% of consumers bailed on a transaction or don't make an intended purchase because of a poor service experience. (American Express Survey, 2011) and 70% of buying experiences are based on how the customer feels they are being treated (McKinsey) it appears that is increasingly importance to adopt a systematic and strategic behavior based personalization approach to customer service.
55 VI. Conclusion
We have seen through this thesis that by following the global trends using data and Al to create technologies that enhance human performance and capabilities, Afiniti has tapped in mature market for Big Data and Al technology. It has done so by creating an innovative technology solving for a long-standing inefficiency. Besides, it created a business model that has managed to overcome hurdles to its technology spread and is able to commercialize it by supporting its sales strategy on a very strong sales strategy driven by redefining business to business industry sales practices. Through aggressive IP building, first mover advantage development and sales network strategy, the company created strong barriers to entry in a market that is particularly relevant for implementing personalization technology. Being covered by several academic fields Afiniti's technology is opening a new perspective in the behavior based personalization field that could well be a redefining component of the one-to- one marketing for customer relationship process.
Above all, the most interesting and valuable component of Afiniti, its technology and its business model, is that they are solving for a bigger problem. Indeed, by leveraging on human assets and Al capabilities to have better performance while enjoying a sustainable and profitable business model, it is proving that automation is not the necessary path. Indeed, its technology by playing on the inner differences between individuals is showing that it can improve human labor and not only replace it. Such a model could very well be a breakthrough in the quest for a socially acceptable Al revolution.
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62