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The Future of Product Development: Using computational to gain deeper insights

EXECUTIVE SUMMARY

In an increasingly competitive business landscape, enterprises that develop a deep understanding of their prospects’ and customers’ will win. However, traditional market research tools and product development processes fail to deliver deep and actionable business insights affordably and systematically. The result is inefficient R&D processes that cost the Fortune 1000 billions each year. This paper explores applications of the latest breakthrough in natural language processing, computational psychology, and how it can be used to help enterprise product and R&D leaders more efficiently create customer-centric experiences and products.

By using a rapidly deployable, computational psychology engine to analyze internal and external data, enterprises can:

1. become more agile and speed up product development and go-to-market 2. dramatically increase ROI through improved product-market-fit 3. distinguish outcomes and predict critical issues to cut churn

INTRODUCTION

We are living in an era of customer centricity, impacting product life-cycles, both pre- and post-sale. Today, users have more product choices and almost instant access to any competitor, via marketplaces like Amazon or traditional retail channels. A company’s ability to understand and apply customer insights to its product development process now dictates its competitiveness. In many companies, the true cost of product development is unknown, yet most executives agree that timely and efficient product ideation, development and iteration can make or break their business. There’s no activity that has a bigger impact on the bottom line than product development. In-fact, a recent Harvard Business Review study of corporate risk found that no company function, department, or activity creates more risk to corporate earnings than R&D and product development. The current state of play is almost unbelievable. Companies spend $68 billion on market research each year (Source: ESOMAR); yet lose an estimated $75 billion a year because they fail to create positive, emotional connections that drive customer loyalty. Simply put, customers want and expect more than ever and failing to meet these expectations results in a big downside. Improving product development by reducing R&D risk is not fast, easy or reliable. Two key problems face product leaders today: the inability to unlock “dark data” – masses of unused data that could help inform product planning – and lack of agility in decision making. Despite spending tens of billions on market research, companies aren’t getting the level of insight they actually need to reliably develop products people love. For example, traditional survey- based approaches are known to invoke biases and only measure what one decides ahead of time to ask about. On the other hand, established data-driven tools, like sentiment analysis, only yield one or a few dimensions of information (i.e. positive, negative, neutral) that do not yield digestible or actionable insights. Alternatively, custom solutions may be , require years of integration and cost millions of dollars. These problems can, however, become opportunities given the right technological solution. Now with new evolutions in Artificial Intelligence (AI), data science techniques can be integrated with psychological and human factors. This new subfield, which this paper will refer to as computational psychology, espouses the idea that AI can only get so far without considering the human context under which data is generated. Unlike previous solutions like sentiment analysis, techniques emerging from this new field are opening new possibilities for producing open-vocabulary insights.

By bringing data science and psychology together, brand leaders can find deeper, novel, and more actionable insights to increase the efficiency and ROI for their enterprises.

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This whitepaper explores breakthroughs in computational psychology that enable enterprise teams to better understand how their customers think and feel to help them innovate faster and with more precision.

Fig. 1 THE OLD TECH

Innovation without intuition

There is often no clear path to innovation. As Steve Jobs pointed out, “A lot of times people don’t know what they want until you show it to them.” Yet IBM’s Chief Innovation Officer, Bernard Meyerson, believes customers are an important part of the innovation process. "Our customers can’t tell us about a future that doesn’t exist yet,” he says. “But they can tell us about unresolved problems.”

The week that Apple launched Apple Retail, is the exact same week that the very last Compaq Computer Store shut down. This has become a very interesting dynamic and an important lesson in market research. Ron Jonson will tell you that every single analyst, every single market researcher and consultant Apple brought in told them, “people do not want to buy computers through brick and mortar (that’s why Compaq is shutting down)”. There was 100% universal consensus that the Apple Store would not work and was going in the exact opposite direction of consumer interest. But Steve Jobs had an undying belief that the way you sold high-end consumer electronics was by putting them into peoples’ hands. Steve Jobs relied on his intuition. If he hadn’t, Apple would have lost hundreds of billions in enterprise value.

Intuition is impossible to teach and difficult to recreate. Steve Jobs didn’t like traditional market research because the tools available to him failed to provide insights for true innovation. Today’s breakthroughs in data science and psychology shed new light on opportunities for innovation and machine-powered intuition. Imagine every company being able to access the intuition of Steve.

What if we lived in a world where companies could quickly analyze the emotions of a potential market from a sea of disorganized qualitative information and language. What if true innovators from all levels of workers could suddenly use data to convince others in their enterprise that product concept A is more likely to succeed than product concept B? It stands to reason that if this were possible, the path to innovation would become much faster, cleaner and more effective.

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THE NEW TECH

Use big data and psychology to understand your target market

Big data has the potential to be a game-changer for market research and customer feedback if used correctly by product teams to make both strategic and tactical decisions. The wealth of information about consumers’ likes and dislikes that is readily available online holds promise, but most enterprises lack the tools to leverage it. The issue with traditional channels for feedback, such as focus groups, ratings and NPS scores, is that they often scratch the surface, but fail to extract the true “why” behind complex consumer emotions.

In fact, extracting quantitative insights from the sea of qualitative online data is an exciting new frontier for data scientists. If product teams could better understand the emotional context behind an NPS score or a bad review, they could more efficiently prioritize bug fixes around experiential severity that may not be expressly said or readily evident. It might also help them find a future solution to a problem their current product doesn’t address at all, accelerating the path to new product innovation.

The paper “Using Big Data as a window into consumers’ psychology” explains:

"Whether it is their Spotify playlists, Facebook profile, Google search queries, or mobile location, the digital footprints consumers leave with every step they take in the digital environment create extensive records of their personal habits and preferences. By tapping into this rich pool of consumer data, businesses can enhance consumers’ experience by better matching the marketing offering to consumers’ preferences and do so at the appropriate moment." (Source: Data Science article by Matz & Netzer)

When you consider that most Fortune 1000 companies spend anywhere between 10% and 23% of their revenues on global R&D, deploying an automated solution to leverage untapped market and consumer data readily available online becomes a business imperative. When companies understand what makes their prospects and customers emotional, they are one step closer to avoiding unsatisfied customers and turning satisfied customers into their biggest promoters.

This next evolution in AI solutions combine Natural Language Processing (NLP), quantitative psychology, and machine learning. Techniques from this field can be used to determine the relative importance of each part of a customer’s experience - making it possible to generate more reliable quantitative information about qualitative customer insights.

Why is this important? There is endless and invaluable hidden data between the lines. Customers have much stronger feelings about certain parts of their experience than other parts. Their feedback likewise gives us insight into which parts they actually care more about. But there’s a huge difference between capturing what someone expressly tells you and understanding what they mean by what they tell you.

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This “implied feedback” has an obvious impact on how brands are perceived in the marketplace. But looking at this scale of data using old techniques or doing it manually (which a surprising amount of Fortune 1000s do), would be a herculean undertaking in-and-of itself and would still miss key insights that could dictate billions of dollars in value. Similarly, understanding your competitive differentiation from dozens of other competitors along these critical dimensions would be prohibitively expensive in terms of time and resources for even the largest organizations.

Go beyond public data and unlock hidden insights

While enterprises can gain insights about the emotions of their target market from external data, such as customer reviews and forums, there is another gold mine of information that often goes untapped - internal enterprise data, which may include a wide variety of structured and unstructured data.

Two important sources of internal data that, if harnessed, can deliver valuable insights are warranty claim and customer support data. Support teams get thousands of requests a day but struggle to sort through them efficiently. Working with qualitative data has traditionally been a difficult, manual process. Add to this the challenge of organizational silos and gaining value from the massive amount of customer information can seem an insurmountable challenge.

The latest evolutions in AI can help shine a light on the “dark data” (or untapped internal data) available to Fortune 1000 companies. Using a series of breakthroughs in natural language processing, data science leaders employing psychological analyses (such as the team at Stitched Insights) enable support teams to leverage their valuable qualitative data. By going beyond standard data analysis and employing the latest Robotic Process Automation techniques, teams can provide faster and more accurate problem resolutions, increase customer retention, and more easily identify meta trends in their and their competitor’s warranty cycles.

Fig. 2

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Find the fastest path to the deepest insights

For anyone that has spent time in product roadmap discussions at the executive level, it is all too common for the loudest and most passionate voice to sway the room whether or not there is any data to back up their position. Valuable time is then lost when organizations move ahead in the wrong direction and need to course correct later. The reason this problem continues to exist is that much of the data available to organizations is qualitative, and enterprises are not adequately resourced to manually pour through it and quantify the results. This allows every decision a company makes to be data-driven and on-demand.

Traditionally, quick and affordable ways to automate the quantification process of qualitative data have been lacking. Enterprises that find a turnkey way to automate the analysis of external and internal company data will enjoy increased profits as a result. For a typical Fortune 1000 company, just a 10% increase in data accessibility will result in more than $65 million in additional net income. (Source: Analytics Week, “Big Data Facts”)

The path to profitability relies on efficiency in the product planning, development and iteration cycles. Improved access to data provides deeper insights and makes product teams more agile. Making better decisions faster reduces waste in human cycles and the number of failed products or flawed products resulting in excessive support calls or warranty claims.

The ideal AI big data product solution will be able to analyze the emotional context from external reviews and forums and internal warranty/customer data. It will leverage natural language processing combined with psychological analysis. This is a tall order, and some custom solutions (such as IBM’s Watson) are so complex they require millions of dollars and years of integration to implement. Newer SaaS-based AI solutions promise to be turnkey, offering similar or comparable insights in days or weeks at a fraction of the cost.

“Every modern company is trying to get closer to their customers and accelerate innovation

Today, there is no one that can get you there faster and closer than Stitched Insights”

TOM KALINSKE Legendary CEO and Brand Marketer

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COMPUTATIONAL PSYCHOLOGY

Data science meets psychology

With all this talk about big data, AI and psychology, inquiring minds may be wondering, “How does it work?” and “How reliable is it?” Before tackling these questions, let’s discuss the fields at work:

Machine Learning - A field of computer science that designs programs, often based on and logic, which have the ability to "learn" (e.g., progressively improve performance on a specific task) with data, without being explicitly programmed.

Artificial Intelligence (AI) - Generally, the field concerned with designing computer programs which can solve problems that regarded as only solvable by humans. The grand challenge of AI is imitating intelligent human behavior, but much of the field is focused on steps toward such a general solution.

Natural Language Processing (NLP) – A branch of AI which is concerned with designing programs that can solve the problem of natural language, such as recognizing speech, translating between languages, processing the of natural language. The study of natural language processing has been around for more than 50 years and grew out of the field of linguistics with the rise of computers. (Source: Machine Learning Mastery).

Quantitative Psychology - The use of numeric and statistical techniques along with data to study the human mind and behavior. Techniques in the field can generally be broken down into those which “estimate” -- quantify the strength of associations (i.e. correlation) and those which “hypothesis test” -- roughly, check whether a numeric result is worth noting (i.e. the strength of an association between language and a psychological trait is stronger than chance and expected to generalize to similar situations). (Source: Medical News Today)

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While many companies are innovating in AI and its subset NLP, the truly interesting insights may very well lie in combining psychological analysis with NLP. To illustrate this point, let’s look at what a group of pioneering data scientists out of the University of Pennsylvania have created — The World Well Being project.

The World Well-Being Project (WWBP) is pioneering scientific techniques for measuring psychological well- being and physical health based on the analysis of language in social media.

As a collaboration between computer scientists, , and , we are shedding new light on the psychosocial processes that affect health and happiness and exploring the potential for our unobtrusive well-being measures to supplement – and in part replace – expensive survey methods. (Source: http://www.wwbp.org)

The WWB project’s co-founder, Johannes Eichstaedt further explains, “I use Facebook and Twitter to measure the psychological states of large populations and individuals, to determine the thoughts, emotions and behaviors that drive illness or support well-being. Machine- learning-based methods allow us to better understand these psychological phenomena, as well as measure their expression unobtrusively and at scale for large populations.”

The key here is that, unlike most techniques in NLP which focus on modeling language, this new field of computational psychology focuses on methods, like quantitative psychology, which pull out meaningful insights – novel but reliable pieces of information, often about people and now also about customers and products.

How might the same algorithmic approach used in the World Well Being project be applied to solve some of the most pressing problems in market research, product planning and development, and even marketing and sales today? By broadening the data beyond social media to reviews, forums, internal data lakes and even customer support narratives, AI can help automate customer insights to create more happy customers and minimize the risk of products that fail to satisfy customers.

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Matz and Netzer explain: “Turning customer data into meaningful psychological profiles offers tremendous opportunities for a more holistic Customer Relations Management [CRM] that bridges the gap between online and offline channels. For example, knowing that a consumer follows a cognitive style that is analytical rather than emotional, makes it possible for both computers online and salespeople in brick and mortar stores to adapt their communication to the preferences of the customer.” (Source: Using Big Data as a Window into Consumers’ Psychology, Sciencedirect). 8

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A variety of mathematical approaches and statistical analyses make their way into the unique computational psychology algorithms, such as those used by the team at Stitched Insights. As explained by data scientist Andy Schwartz, “While the full stack of algorithms together form a complex process of differential language analysis and theme modeling, accessible and well-known techniques like Pearson Correlation can be used to summarize findings. S.W.O.T. characteristics, product emotional profiles, and keys to successful all contribute to turning the previously impossible task of quantifying qualitative information into a reality.”

Example of insights delivered through computational psychology for a Fortune 100 POC

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When it comes to algorithms that can add this layer of analysis to NLP, there are only a handful of experts in the world. And enterprises that plan to win should pay very close attention. The future of product development may depend on it. 9

"Above all, understanding human emotions… [allows us] to gain a much better understanding of how humans make decisions.

If these emotions and desires are in-fact no more than biochemical algorithms, there is no reason computers cannot decipher these algorithms—and do so far better than any Homo sapiens”

(source: 21 Lessons for the 21st Century, Yuval Noah Harari)

CONCLUSION

Inefficiencies in product development processes — including product planning, development and iteration — present a massive risk to corporate earnings. The key to de-risking product development and jump-starting innovation lies in the access and utilization of external and internal data for rapid decision-making and agile processes. Traditional market research and consumer feedback tools are slow, expensive and resource intensive (e.g. focus groups) or offer only surface-level information devoid of emotional context.

Thanks to a series of breakthroughs in the nascent field of computational psychology, pioneering data scientists have been able to leverage otherwise invisible patterns in external consumer data (e.g. customer reviews) as well as internal data (e.g. customer support calls) to enable Fortune 1000s become dramatically more agile and innovative.

About Stitched Insights

Stitched Insights enables the world’s top brands to go beyond old-world market research and get inside their customers' minds at scale. Our pioneering machine learning platform leverages a series of computational psychology breakthroughs out of University of Pennsylvania to understand from text. Our core, patent-pending technology detects +100s of invisible emotions from customer feedback (like reviews and support tickets) and automatically prioritizes recommendations based on real-time market trends and how strongly customers feel about each part of their experience. Our goal is to empower the world's best teams to accelerate digital transformation initiatives and create products and services that people will love.

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