2016 EDITION

Web Guide How to build every aspect of ’s latest commodity

Brought to you by 2 Contents

Foreword 3 Chapter one: 4 Web analytics in 2016 Chapter two: 11 How to build a web analytics strategy

Chapter three: 18 How to select the right web analytics solution

Chapter four: 23 How to build a web analytics team

Chapter five: 28 Tips to take on board and troubles to avoid

Chapter six: 33 How to measure the ROI of web analytics References 39 About Adobe 40 About MyCustomer 41 3

Foreword:

In today’s digital world, consumers decide for themselves which channels they want to use when having interactions with us as marketers. Understanding their actions – or equally – the actions they don´t do – is essential for the success of every single campaign marketers are planning and executing.

Where it was easy to just measure the success of a campaign via the visitors or sales on your company a couple of years ago, today´s reality is different. One still needs to measure the website as a central piece of conversion for many of your campaigns, however, other channels such as mobile, social or video are key to getting the full picture.

Creating this holistic view of your customer and therewith, connecting the dots to show the impact of your marketing activities to bottom line business results, is another major trend that we can see emerging strongly.

Further, once identified, the most valuable segments should be leveraged to replicate the success with audiences in external databases, in order to drive profitable, additional new customers to the conversion points we were able to identify by our analysis.

We are seeing a need to automatically detect anomalies and provide an analysis on which channels have been contributing mostly to a conversion – in order to free up the precious time of the experienced data scientists for tasks as described before, rather than manually going through reports or delivering one-off customised analysis.

Analysing your website – and your mobile outlets – is more than ever, key to executing successfully on digital marketing activities and as a next step – to provide a seamless, personalised customer experience. Analytics data can unfold its real power, once it can be leveraged for personalisation, testing, content optimisation, campaign management and audience management. That will be the big task for the analysts of tomorrow.

Axel Schaefer Product & Industry Marketing EMEA Adobe Chapter 1: Web analytics in 2016 5

Chapter 1: Web analytics in 2016

As businesses become all the more led by the technology they use, web analytics tools have enjoyed a transition into ‘must-have’ territory, operating as the first port-of-call for the monitoring of a company’s online portfolio.

Today, web analytics has become “a commodity,” states Axel Schaefer, senior manager, product and industry marketing EMEA at Adobe Systems. Simply put, web analytics is a table stake these days – you have to use it merely to be able to compete. It’s the HOW you use it that can give you an advantage.

Modern-day tools can be used to pinpoint how people are coming into contact with a business, what they’re doing when the first and last interaction is made, and, if they didn’t go on to convert, the reasons they might have for doing so.

With so many business decisions now being underpinned by data, there is a greater need for CMOs and their employers to learn more about the people they sell to, which makes the insights produced by analytics all the more useful. This information can also be used to interpret where and how people interact with a business, with the end goal of improving the experience and service on offer.

Unsurprisingly, therefore, web analytics is the foundation of digital marketing strategies, enabling users to establish who is visiting your site, how they found it, what they’re doing when they get there and provide insight to support the improvement of the user experience. 6

When digital marketing strategies are scratching around to identify the metrics that will drive improvements in the overall customer experience to ensure that the organisation will drive more business, it is web analytics that provides the guidance.

Statistics underline its importance – with a survey of 300 digital marketers by MillwardBrown revealing that 46% of respondents said they planned to investment more in web analytics in the coming year. Furthermore, the same study found that web/mobile analytics software was the second biggest priority for investment in the next two years (customer experience management software being the first).

And it is not only business performance concerns that are driving this investment. Big Data is no longer a fad, or something for businesses to be afraid of. Now it is something organisations understand is there to be harnessed. MillwardBrown’s study indicates that over half (54%) of marketers rank Big Data as an important trend. Analytics, of course, is the key to capitalising on Big Data.

The ability of web analytics to influence so many aspects of online improvement has been one of the driving forces behind data’s launch into the modern-day marketer’s vocabulary, and with analytics users ranging from businesses at the top of their game, right across to those just starting out, the real challenge is to make the most of the information at hand.

Analytics the commodity

One of the most basic uses of tools for monitoring web-based activity is in assessing where users are first introduced to a business, with this helping determine the areas which need the most attention. Delving deeper, the technology can lift valuable insights from the crowds that arrive, giving businesses a bank full of data on their potential customers and a reason to cater for certain audience demographics.

Marketers have certainly benefited from this practice becoming more accepted as their ability to track returns from channels like email, social and display is made easier. But the applications of analytics continues to evolve.

Perhaps one of the biggest changes in the use of analytics has come in what it is applied to, and from the realisation that what it produces can be used to monitor and improve a customer’s journey around the web. The ‘single customer view’, for instance, has become The Holy Grail for enterprises that urgently want to provide a joined-up experience to consumers, and of course analytics is at the core of this conversation. 7

Schaefer notes: “Today, brands big and small are trying to follow their visitors across as many digital channels as possible. The goal is to understand a behavioural pattern and the journey of a visitor, or future customer, across all channels in order to improve and optimise their experience.”

Another change in analytics usage relates to the 5.2 billion mobile users, as estimated by Mary Meeker, who have seen their interest from web analysts grow exponentially in recent years. This trend coincides with the spike in usage of smartphones and tablets for initial product research, and with Google claiming to fulfil more searches on mobile devices than desktop, analysts are being urged to act on the findings.

In the grand scheme of things, the growth in mobile traffic along with a greater use of first-and third-party data means there is a huge demand for systems that join and contextualise everything being gathered.

“The ability to join up data across channels to assess overall performance metrics is key to driving growth for any business, and there is less focus on ‘this channel versus that channel’ and more attention on where to drive incremental growth at a cost-effective level,” highlights Colin Smith, a data scientist at UK digital agency agenda21.

With so much of the purchase journey now taking place online, a panoramic view of the customer is one of the best things that analytics can deliver at the company’s door.

Key considerations

The sheer breadth of information gleaned by analytics tools and the capabilities in 2016 being so much greater than they were in 2010, or any year before, has only amplified the need to have a clear plan for their use - a quality advocated by those behind the controls.

“The worse brief in the world is ‘we need to understand our customers’,” states Smith. “You need to work out what you can do, what changes you can make, what decisions you need to take, and then define the data you need to inform those decisions. At that point you should go and collect it and analyse with a clear goal in mind.” 8

Web analytics is also being pressed harder for a return than it may have done some five or ten years ago. The testing phase is well and truly over, with businesses now wanting to see the ROI to justify their investments of time and money.

“If you are not measuring your return then you cannot attribute a true value to your web analytics tools,” outlines Don Skinner, director of digital at UK marketing agency Net Natives.

“The challenge is that there will be a wide range of factors that can affect your ROI and it is likely that it is the combination of data from multiple analytical and technological platforms that give you this figure.”

Skinner sees plenty of value in companies having a champion that can explain in layman’s terms the need for the measurement of a tool’s performance and return, but finding qualified and capable specialists creates another issue.

The shortage of web analytics talent has been a chief concern for a number of years, threatening to undermine business efforts to harness Big Data.

Figures from the Office for National Statistics has estimated that half of UK businesses were planning to expand their ecommerce departments in 2015, meaning web analysts were further in demand.

“Businesses have been faced with an IT and ecommerce skill shortage for a while, specifically when it comes to finding specialist web analysts,” Lisa Holmes, director at recruitment company Uniting Ambition, has noted.

“There are a lot of people out there that are and Tealeaf qualified; but a lot of the time, this isn’t enough. Businesses are desperate for specialists with great mathematical skills, strong business acumen and a real understanding of their customer.”

Little surprise then, that the Online Marketing Institute has estimated that 37% of companies “desperately need” staff with analytics skills. The digital skills gap is of course alive and well in a range of countries, affecting a number of industries, and bringing forward the next generation of talent will be crucial to realising so many of the opportunities that technology can and will create.

But analytics success also rests heavily on leadership support from within the organisation.

Research from analysts BARC reveals the extent to which data analytics can thrive or die according to how much impetus is being driven directly from senior leaders. 9

The survey of 500+ enterprise professionals found that 61% of successful “data use cases” are attributed to management teams having a direct role in integrating data and analytics tools into business-wide process.

The importance of management support is two-fold: not only is it key to securing budget and resources, but it also helps the organisation enshrine the discipline in a more strategic model.

While there is a place for tactical ad hoc reporting, this needs to be part of a wider strategic approach to optimise analytics. Unfortunately, the BARC study reveals that analytics strategies are few and far between.

The results state that at present, 25% of businesses have a data analysis strategy for their marketing teams, and 23% for sales. However, 54% of companies plan to introduce further data analysis into their marketing and sales strategies in the future, though as it becomes a more integral part of business.

Having senior management on board is vital to a more strategic approach, which in turn is vital to having clear and focused goals for analytics, and what they can deliver to the business.

Looking forward

With analytics now a luxury afforded to businesses big and small, the overarching challenge for these outfits will be to make good use of the technology in front of them.

Part of this involves making strides with metrics which are likely to result in more long-term benefits than simply focusing on an increase in conversions in September, followed by a lift in traffic for October.

Any business can incentivise a customer in order to drive a specific outcome; the real challenge is in getting them to convert, check in, engage and share messages without being prompted.

Using analytics to uncover valuable information about a customer’s experience, their route to acquisition, their reasons for abandoning purchases and other matters can lay the foundations for improvement in these areas, which can equate to loyalty in some circumstances. With more and more emphasis being placed on learning more about the customer and what makes them tick, Schaefer sees web analytics as the basis for data-driven marketing initiatives, arguing that companies “will not be able to market to their customer efficiently” without a solid understand of how these tools work.

Richard Towey MyCustomer contributor Chapter 2: How to build a web analytics strategy 12

Chapter 2: How to build a web analytics strategy

While the power of web analytics is widely acknowledged by all and sundry, the sad fact is that in many cases this still hasn’t manifested itself as a robust strategy to tackle it. Businesses that invest the time and resources into building a strategy are able to tie website performance back to revenue – something that is vitally important when justifying budgets for the likes of new site features, content development and SEO.

Yet there are still many organisations that are winging it. Indeed, it is not uncommon to find organisations forced to take a reactive approach to web analytics, setting up relevant goals and custom metrics well after a campaign has gone live, due to the absence of an over-arching strategy. And the resulting data gap can have damaging repercussions for campaign evaluation and optimisation down the line.

Web analytics strategy is the art of combining statistical analysis of your website performance with measurable methods for improving it. But while most companies use statistical measurement tools for their website (most commonly Google Analytics), how many actually focus on using this data to develop a strategy – and how many of those who do, do it well? Not many, according to Victoria Petkovic-Short, marketing account manager at agency APT.

She believes companies commonly do one of three things:

1. Some will ignore the statistics 99% of the time, checking on an ad hoc basis for obvious changes in statistics, e.g. downward visitor trends, upward spikes, etc.

2. Many companies act first, then plan later; they decide on their marketing strategy, 13

approach and execution external to the statistics, then evaluate the effectiveness after the action. What they don’t do is analyse the statistics first, evaluate what is needed from their website and then develop a campaign that accounts for these needs as well as the strategic business developments

3. Most will focus on the wrong statistics, e.g. visitor numbers to develop their strategy. High visitor numbers do not signify a good website performance, they are simply an indication that your online and offline campaigns are working, or that your SEO is good. It does not mean that users are having a good website experience.

“Creating a good web analytics strategy comes down to looking at the right statistics; website performance extends beyond the number of hits or unique visitors, to a more in-depth study of trends, number of returning visitors, bounce rates, user navigation analysis, traffic sources, active user locations and platform,” says Petkovic-Short. “It might sound daunting, but done properly, these statistics should be the basis for all your web-related activity.”

So where do you start?

The first step is to consider your overall business objectives and identify clear goals for each of these objectives. Before you start your strategy, work without the data and work out what you want from your website, what you need from it and how you are going to measure it.

“Each goal should be aligned with at least one KPI (metric),” says Axelle Ros, senior digital analyst at POSSIBLE. “Finally, parameters need to be set to put each metric in context. One of the most common mistakes when looking at analytics data is not putting the metrics into context. A 60% site-wide bounce rate does not give any real insight into website performance. Bounce rate at a /keyword/traffic channel level, however, will help you understand visitor expectations and whether your site is meeting or failing them.”

Petkovic-Short provides some examples of how to work with the raw figures within the context of your business once you have outlined your objectives, goals and measures.

“If your company objective is profile exposure, your target is to ensure that organic visits (referrals and searches) are high and that the bounce rates are low; if your company objective is online sales not only will these statistics be important, but so will the way a consumer navigates through the site – do they consistently exit from the basket, do they leave after a search, etc.?” she says. “Each individual measure and statistic tells a story and each one can be tweaked, tested and enhanced to offer both a better user experience AND a better company asset.” 14

A recommended approach is to define an actionable ‘KPI Framework’ in the early stages of any project and ensure that all measures can be tracked effectively. This framework can take the format of a simple spreadsheet or a more advanced online dashboard. It’s important to avoid information overload and focus on which metrics matter most (and are actionable). There is no sense creating dashboards that focus on metrics that cannot inform optimisation strategy.

Ultimately, you want alignment between your overall business objectives and the website KPIs.

After defining business objectives / a high level KPI framework, you need to address the following points: cc Define who owns web analytics within the business. They will be the champion of web analytics and are responsible for the successful deployment of analytics technology as well as the KPI framework. cc Establish what technology is required. Different businesses have different requirements, while some technology is more appropriate for certain organisations and industries. Ensure that you comprehensively outline your requirements to identify the most appropriate solutions. cc Detail which stakeholders will be involved with the web analytics strategy from other teams. Sometimes organisations can have different teams managing the media budgets and handling the web analytics, which can lead to analytics tools not being set up to track certain campaigns due to a lack of communication. Address this upfront in the strategy by outlining who is involved.

With this established, what is the next stage of creating a good actionable web analytics strategy?

Petkovic-Short advises that organisations acclimatise themselves with the tracking tool, so that navigation becomes quick and easy – there are plenty of online tutorials to use – and then begin looking at the general trends associated with these statistics. She provides the following pieces of advice:

Know your trends

All statistics tools give you the opportunity to change your date range and compare two dates or two statistics. Start by looking at recent stats (i.e. the last three months), identify the trends (e.g. peak in the middle of the month, near payday 15

etc.), then change the date to see if this is consistent across the year. Once you’ve confirmed this trend annually, or identified variable trends according to the season for example, compare these with the previous 12 months to see if they are the same year on year. The more / longer the statistics are available for, the better your chances of developing a strong strategy and the more likely you are to be accurate. That said, companies without access to this length of statistics should not fret, because trends will still be noticeable. A word of caution though - the less data you have available, the more chance there is of identifying a short-term trend and therefore, the less emphasis should be placed on each. Instead, ensure that you revisit your strategy once a month to check it is on track and tweak it for any new emerging trends.

Measure your campaigns

The problem with website statistics is a lack of purity; you can’t put all your marketing and business activity on hold in order to ascertain what statistics are website-driven and which come from your campaigns – this is impractical and unadvisable. Instead, you have to work the other way by manipulating the campaigns to suit being measured. Think about your activity - is there the option to include a measurable code (e.g. promotional offer, or a bespoke URL specific to that campaign)? Adding these measures will enable you to measure the effect of specific campaigns on your website stats, giving you an indication of why users are visiting your site, as well as enabling you to measure the ‘pure’ website statistics that have no obvious origin. As a little extra tip, most analytics tools also allow you to ‘annotate’ your statistics, adding relevant notes directly to the graph. Use this to add updates about your campaigns - e.g. ‘sent monthly newsletter’, ‘advertised in XX publication’, ‘received 20 new business calls this week’ - which will all help to generate an anecdotal picture of your activity too.

Develop your recommendations

Just because there is an obvious trend, e.g. a consistently top exit page, it does not mean it needs fixing. I’ve had clients in the past tell me they ‘don’t want exit pages’, but like it or not, people have to exit your site. Your recommendations should always be based on context and you should review the statistics in line with the pages. Taking your top exit page as an example, the question isn’t where they are exiting, it is ‘are they exiting in the right place?’ If you are an online retailer, you expect your customers to exit after the checkout; they have done their shopping and are ready to leave. If however, they are exiting from the basket, this indicates that there is a problem with the customer journey. For instance, is your checkout simple enough? 16

The same applies to any other exit page; if they are exiting from a page that offers a link to an external site for example, test that link and make sure it is opening in a new window. Putting each statistic in perspective provides an opportunity to make a small tweak and see what happens. If the change is a large one and requires some additional programming, ask your developers to retain the previous system / process / pages, make the amends and then measure the effect. If the performance is better, go with the new one, if it is worse, switch it back and work out how else to tweak it. If in doubt, talk to a third party; ask them to test your website and identify any problems they feel, talk to them about your suggested changes and see what they think. This isn’t an exact science because each person is different, but sometimes it helps to get an outside perspective.

Execute your changes

Don’t make all your changes at once; chances are if you are at the stage where you can develop a strategy like this which makes small changes, you have the time to amend things gradually. Make each step individually, measure the effects and check the relevance of your strategy before you make the next one. Sometimes changes will have unexpected effects that negate your existing strategy and probably create new points.

Remember to keep updating

It might seem like a slog, but the best website analytics strategy is one that evolves with the company and with the current trends. Try to revisit your strategy at least once a quarter to check it is still relevant and to ensure any changes you have made to the website are still positive ones.

Compare it across platforms

In the current digital age, no website works in isolation and companies can and should be using multiple digital platforms to deliver their content to the market. Whilst this is great for driving , many clients often forget to compare statistics across their activity. For example, identify trends in your newsletter schedule; does sending day or time affect the open rate and if so, when is it highest? Combine this knowledge with your web statistics analysis to ensure that all your campaigns are maximising your website potential, as well as ensuring your website is maximising your campaigns. This kind of cross-platform analysis is more advanced, but it can have measurable results for your ROI. Some have to take this one step further and developed an additional tracking tool. Used to anonymously record user activity beyond your site, the tool legally tracks your visitors from and too their immediate destinations. This enables clients to identify how many of their customers are shopping around and which of their competitors have the biggest market share. Tools like these are great for giving you the edge and it’s worth shopping around to see what other statistics can be at your fingertips.

In addition to this, your strategy also needs to ensure that there is enough flexible resource factored into your plans to be able to deal with ad hoc queries that can pop up. Web analytics should be able to be used reactively to be able to answer questions that will inevitably arise over the course of a campaign.

And a further point to bake into the strategy concerns reporting. Reporting in and of itself is not a difficult task, but turning the dashboards and reports into actionable insight is a very common problem. For this reason, the strategy should establish a process that allows for continued analysis and provision of realistic actions.

Companies don’t necessarily need digital experience to execute these kinds of campaigns. By learning about the tools, applying common sense, and with trial and error, organisations can go a long way. Of course, if this doesn’t appeal, there is also the option to hire a consultant to develop a strategy that can be managed in-house and revisited by the consultant once a year to keep on track. “It’s an important tool in the toolkit and one that is so commonly overlooked,” notes Petkovic-Short.

So, building a web analytics strategy – certainly easier said than done. But definitely worth the effort in the long run.

“Web analytics should be seen as a major part of the information system by which you run your business. Unless your business changes fundamentally, a solid web analytics strategy will last a long time,” concludes Ben Gott, director of analytics at Periscopix, a Merkle company. “Of course it will need to be updated as new technologies crop up, but the core measurements you map out today are likely to still be correct this time next year and the one after. It makes sense to take the long view when thinking about web analytics, otherwise you will be forever chasing the latest fad or buzzword.”

Neil Davey MyCustomer Editor Chapter 3: How to select the right web analytics solution 19

Chapter 3: How to select the right web analytics solution

Whenever anyone mentions ‘web analytics’ there is always a gigantic elephant in the room: Google Analytics, which has virtual ubiquity in the market. Businesses in the market for a web analytics solution will always have in mind: “Why should we pay for your solution, when we can get Google Analytics for free?!”

I remember this very question was asked by an audience member at a web analytics vendor conference, to which the country manager replied: “Google Analytics is fine to get you started, but it soon runs out of steam. You will need something different to do more serious and difficult analytics, which you will want to in the fullness of time.” There has been some truth in this.

Also, some point to Google Analytics’ inability to track individual customers, the inaccuracy of its analytics results, and the storage of only the last 25 months of historical data. And there are bigger questions, such as how reliant do you want to be on Google?

Meanwhile, the analytics marketplace has experienced consolidation, with a growing number of vendors acquiring analytics vendors and including analytics tools as part of their all-encompassing digital marketing suites – for instance, Adobe acquired Omniture some years ago, and IBM Unica acquired Coremetrics. Only Webtrends of the major vendors is ‘sticking to the knitting’ of pure web analytics.

So how do you determine your web analytics needs? 20

Requirements gathering

Many different types of vendors have a web analytics offering, including enterprise marketing, online marketing, database and content, and specialist web analytics vendors. There are over 50 vendors within these broad categories vying for your business. Buyer requirements-gathering now needs to be deeper and more precise.

The key question when buying web analytics is ‘what do we do with the numbers once we have them?’ Core web analytics provides statistics and charts, but does not tell you why the numbers are the way they are. Analytics data needs to be actionable otherwise your investment in web analytics will be wasted.

More heavyweight web analytics products are required by larger companies, especially those with many web sites, and most companies in the retail, travel, financial services, media and online gaming industry sectors. In these sectors web site effectiveness and ecommerce are mission critical. Such buyers should seek industry sector-oriented product variants and case studies that demonstrate industry applications.

Look out for specific functionality and actionable analysis tools

Most web analytics vendors have seemingly similar products which can ‘tick all the boxes’. However, deeper analysis reveals their differentials. Selection should be guided by the platforms in use within your organisation, and the use cases you have in mind.

Buyers also need to extract more meaning from analytics data and take management action. Web analytics data can show where visitors leave or abandon shopping carts. A/B testing and Multivariate Testing (MVT) actions analytics data by helping to design homepages and check-out procedures, for example, that will improve conversion. Some solutions can even video record mouse movements, scrolling and clicks of actual web visitors to enable identification of ‘the moment of truth’ where visitor interest is lost so that design improvements can be pinpointed.

Finally, visitor segmentation and personalisation enables specific content (such as products viewed in last visit) to be displayed automatically to a customer segment or an individual for more customer continuity, relevancy and increased probability of conversion. Website optimisation can create dramatic uplifts (30%+) in visitor conversion to sale, and directly adds to the bottom line. 21

8 questions to ask web analytics vendors

These eight questions are designed to mitigate risk for buyers:

1. Do you have any new products or significant functions due for release shortly? What new functions are in beta testing? How often do you release new versions, upgrades, and bug fixes?

2. What data capture methods (e.g. Javascript and cookies) does your web analytics product use? How might these methods be compromised by future EU Privacy Directives?

3. What do you consider the main advantages of your company and your product over your competition?

4. Can you profile the usage of similar firms in our industry sector who use your product successfully?

5. What data sources can be used to populate the system? Can we integrate your product’s web analytics data with the data from other business intelligence systems?

6. What internal resources do we need to have in place to effectively use your product?

7. What are your commercial terms & conditions, and can you detail your local service and support offer? Can you describe your on-boarding process?

8. Do you guarantee customer satisfaction? Do you have returns policy should we not be completely satisfied?

10 questions to ask yourselves

These 10 questions involve managing web analytics within your organisation:

1. How much web analytics knowledge do we really have? Do we need external help?

2. Are there enough specialist product-specific skills available in the market, at a reasonable cost, for us to recruit if required?

3. Is there a clear product roadmap and commitment to long-term product development? Is the vendor likely to be acquired and for these commitments to be nullified? 4. Can we trust the vendor to work collaboratively with us to drive ever more advanced analytic insights over the longer term?

5. What support can we expect from our IT Department and what level of consulting support and training will we require from our vendor?

6. Should we have an on-premise or a hosted (ASP / SaaS) web analytics solution?

7. What internal buy-in do we have and need from our internal stakeholders? How often and to whom do we need to report our results?

8. Will we be able to analyse and evaluate the effectiveness of all our online and offline channels?

9. What change management is required and what new business processes are needed to ensure reports are acted upon?

10. Do we have a measurement framework and what key performance indicators will we use to measure business performance? How will we know we are being successful?

Summary and conclusions

It is important to ‘think big, start small and scale up fast’. By thinking about the big picture you will better direct your efforts towards a long term sustainable web analytics solution. Key considerations are to align with longer term business objectives, set goals for what you want web analytics to achieve, and to determine the data sources you want to access and integrate. Address your focus towards the future integration of the online/offline multichannel customer journey and experience. The overarching measures of web analytics success will be customer acquisition, conversion, retention and loyalty – and these goals should be the guiding light for your procurement process.

Gerry Brown Senior analyst for customer engagement and marketing technology, at Ovum Chapter 4: How to build a web analytics team 24 Chapter 4: How to build a web analytics team

While there is understandably a great deal of focus on the technology that enables web analytics, the valuable role played by the people using these tools should not be overlooked. In fact, web analytics guru Avinash Kaushik has long called for investment to be weighted heavily in favour of staff over technology, through the establishment of his 10/90 rule.

“The 10/90 rule says if you have $100 to invest in making good decisions on the internet, then invest $10 in tools and consultants to implement the tool, and invest $90 in the people who will analyse the data, because in the web that’s the point of failure,” says Kaushik.

“We don’t have enough money invested in smart people to analyse data and that’s why I’m confident that companies that embrace the 10/90 rule will succeed, because it gives them a robust set of tools to use and the people to go back and do some amazing things with that data.”

The validity of this rule has been called into question by some. For instance, Eric T Peterson, author of Web Analytics Demystified, has argued that the 10/90 rule is unrealistic and favours a more balanced ratio, what he calls the 50/50 rule, while others – such as Jim Sterne, founder of the eMetrics Summit and the Digital Analytics Association – recommend that the ratio should vary from company to company.

Nonetheless, the sentiment underpinning Kaushik’s rule is sound – people should not be eclipsed by technology.

“Web analytics technology is not a black box - it’s not magical, it doesn’t fix problems on its own,” emphasises Joe Stanhope, a former customer intelligence 25

expert at Forrester Research, who is now CMO at Hireology. “You need people to make sure that the tools are working properly and collect the right information and make sure the right people in your organisation can look at that information and interpret it and take action on it.”

Staffing your team

So what people and roles do you require to ensure that you have an appropriately resourced web analytics team?

Certainly, there is no blueprint for success that can be adopted, and different companies have different approaches to it, which can vary from industry to industry. There is also increasing specialisation and diversity, particularly amongst larger companies. Nonetheless, there are some commonalities in terms of skill sets required.

For instance, there is commonly a traditional analyst-type role – a digital analyst that looks at, interprets and develops insights to push into the business. But increasingly there is also a second level of analysts, often called ‘data scientists’, that complement the traditional analyst.

These data scientists take on more of an exploratory analytics approach to mine all of the data that’s available and start coming up with non-obvious, more ad hoc/ advanced analysis work, whereas the traditional analyst would cover more of the tactical and day-to-day analytics work that the business depends on.

Data scientists look at the likes of social data and mobile data, which can be arriving in high volumes, to analyse it and apply structure to it to establish what data matters and what doesn’t. This is quite a different skill set but can be valuable as it can provide a different context to the web data.

Then of course there is also the need for the technical skills to support all of this. Therefore, behind all of the tools and analysts there is a need for database architects, developers, application managers and administrators to keep the systems up and running to allow analysis to be performed.

For his part, Kaushik believes there are four distinct ‘families’ of analytics roles that exist within businesses: 26

cc Technical individual contributors – roles including senior product managers, senior architects, senior tech leads, that typically sit in the IT function and report up to a director or a VP. Responsibilities include setting policy, rules and regulation, and acting as the point of contact with the business team. cc Business individual contributor – roles including senior analyst, internal evangelist. Reporting to director or CMO, this person provides analysis and creates dashboards, as well as responsibility for rolling projects out across the organisation. cc Technical team leader – roles including manager for analytics, implementation senior manager, group manager for web operations reporting, and senior manager of website analytics. Reporting to senior manager or director, they are often in the business analytics team in the CIO/CTO function. cc Business team leader – roles including senior manager of web analytics, group manager for analytics and optimisation, director of web research and analytics. This role reports to senior directors, VPs and/or the CMO.

How big should the team be?

One challenge facing those building a web analytics team from scratch is to ascertain the size of the analytics team required.

Brent Dykes, the author of Web Analytics Action Hero, has provided some useful tips to successfully estimate the number of analysts that should sit in any given team. In an excellent blog post, he proposes that businesses should take the following factors into consideration when establishing the resources required: cc .How complex is your online business? cc .How many business units or groups require analytics support? cc .How many internal customers are there per business unit? cc .How many potential customers could you have within each business unit? cc .How data-needy are your customers? How frequently do they ask questions about the data and how complex are these questions? cc .Is your corporate culture data-intensive? cc .To what degree is the wider business able to self-serve in terms of data analysis? How well-trained are they at using analytics tools? Where should the team sit?

For those leading-edge businesses keen to evolve into analytics-driven organisation, Eric Peterson advises against spreading the web analytics talent across multiple departments. Instead, he advises that those firms that are serious about putting analytics at the centre of their operations should manage their web analytics talent by taking a centralised/decentralised approach.

This ‘hub and spoke’ model requires the creation of a centralised team (the hub), which takes responsibility for core analytics functions such as data collection, data vetting, data analysis and the education of the organisation about the system and data. This central analytics group feeds into marketing, operations, management, commerce and external agencies, and supports those analytics users (the spokes) within the organisation that have partial dependency on web-based data to do their jobs.

Peterson believes that an appropriate web analytics staffing model will be characterised the following attributes: cc You have a senior person who ‘owns’ analytics. cc You have dedicated resources for web analytics. cc You know who your ‘analytics amateurs’ are (those that have partial dependency on web-based data). cc Your analytics hub supports the whole company. cc Your analytics hub produces insights and recommendations, not just reports.

Ultimately, of course, the structure and staffing of web analytics teams may vary from business to business, and what is most appropriate may differ according to the specific needs and resources of the enterprise.

However, from examining the different models above, there are clearly common roles that could constitute the minimum resourcing required to drive analytics success in an enterprise: programme owner (who owns the function), technical staff and analyst staff. With these three bases covered, enterprises should veha the building blocks of a successful web analytics programme.

Neil Davey MyCustomer Editor Chapter 5: Tips to take on board and troubles to avoid 29

Chapter 5: Tips to take on board and troubles to avoid

The analytics market is set for staggering growth in the coming years, with IDC forecasting that attention on Big Data will drive 14.1% year-on-year expansion of worldwide revenues until the sector hits $50.7 billion in 2016.

Sadly, of course, not all of this spend will see the appropriate return. Analytics remains a complex field, and with expertise in such high demand – and at such high cost – mistakes are common.

So what are the most common pitfalls of a web analytics project? And how can organisations ensure that they give themselves the best chance of success?

MyCustomer.com spoke with a handful of experts, who nominated the most common web analytics mistakes that are made, and shared some of their most valuable tips.

Mistake #1: Focusing on the wrong metrics

“When developing web analytics strategies, businesses tend to focus on measuring what users do (how many pages they view, whether they convert or bounce, how long they stayed on the site...) rather than putting together a set of metrics that will help them to understand why users do what they do,” says Axelle Ros. “For example, do long sessions convert better than short sessions? Do these visitors come back? Every website’s main goal after getting users to convert is to get them to come back and convert again. This is why it is so important to understand what made them stay and buy on your site in the first place.” 30

Mistake #2: Expecting to get all the answers with one tool

“I grew up in the traditional business intelligence world, and in that world we were always constantly on a quest for the single source of the truth - one tool that could master and do everything for the business. That paradigm doesn’t work on the web,” says Avinash Kaushik, Google’s analytics evangelist and author of best-selling books, Web Analytics: An Hour a Day and Web Analytics 2.0.

“It is futile to think that you can get all the answers from one tool on the web, that’s just not how the world works because we live in an insanely complex world. The framework that I had outlined in Web Analytics 2.0 said that as you become more mature you need to think of five different sources of data in order to make comprehensive decisions: clickstream, outcomes, experimentation, worth of customer and competitive intelligence. These five sources of data answer four of the very important questions about your business - the what, how much, the why and the what else. Any company that is going to just pick one tool to measure success is not going to have spectacular success because they won’t be able to answer all these four questions.”

Mistake #3: Expecting insights to be obvious

“The biggest mistake is assuming that by just implementing analytics, the insights will come. They won’t,” suggests Alex Loveless, head of data services and insight at global marketing and technology agency, LBi. “Valuable insight takes effort, attention to detail and a sense of intuition. Analytics is like detective work – peeling back the layers of evidence to get to the fruit of the truth. Invest in a good analyst – someone with a sharp mind and an intensely curious nature – and give them valuable and interesting problems to solve.” 31

Mistake #4: Committing a lack of resources

While web analytics is now considered a vital component of digital strategy, there can be problems related to under-resourcing the discipline because there is no instant ROI from data analysis – action has to be taken based on the analysis. Many businesses want to focus on areas that can deliver ROI immediately, which often means exploring new digital media channels where instant sales can be delivered, as opposed to investing heavily in optimising existing channels.

Furthermore, even those businesses that commit more resources to web analytics can often decide to cut their budgets after only a few months as they haven’t observed a tangible gain in ROI in that period. But this approach of ‘dipping your toe in the water’ can never yield significant performance improvements, and businesses need to understand that web analytics is a long-term investment that will result in incremental gains over time.

Tips to take on board: cc “Don’t be seduced by the latest measurement tool or buzzword. For most of us a good solid implementation of Google Analytics will do the job.” Ben Gott. cc “Involve all the key stakeholders in the business in determining the performance metrics that you need to understand. What is important to your Board, to your sales team, to your customer service people and to your marketing department. Get active input and then determine how you will use this to inform your web analytics set up – you may find that you need a custom installation with custom filters or segments.” Axelle Ros. cc Focus on people over technology. Keep in mind the 90/10 rule (invest 90% of your budget in people vs. 10% in technology.) Avinash Kaushik cc “Test! There are so many free/cheap tools out there which allow you to test two or more variations of a page against each other in real time. There is no excuse for making decisions about your site based on what you ‘think’ is the right thing to do. Generate ideas and test them out. Let the data do the talking.” Ben Gott. cc “Make sure you resource the project properly – either internally or through an external expert. The fact that Google Analytics is provided free of charge leads many organisations to undervalue it, or to under resource the set-up.” Axelle Ros. 32

cc “Spend as little time as possible generating reports. Save your time for making improvements to your website and campaigns.” Ben Gott. cc “Don’t take the figures on face value. Your customers live in a complex digital environment in the real world. Their interactions with your brand and web and mobile properties are rarely simple, one-off events. To gain real insight you need to dig deeper than your basic usage metrics, to try and understand what is motivating your customers. For example, are you gauging the success of your marketing campaign on the last clicked on campaign? Are you sure your display ads didn’t play a part in this too? Are you sure that customers are bailing out of your checkout because they want to shop around, or is there a more complex reason? Dig deep, dig often, and never accept an answer because it’s convenient.” Alex Loveless.

Neil Davey MyCustomer Editor Chapter 6: How to measure the ROI of web analytics 34

Chapter 6: How to measure the ROI of web analytics

It’s safe to say that web analytics has moved far beyond its development phase, placing itself at the centre of an environment where companies are looking to squeeze the most out of their investments of time and money.

Analytics can be used to prize valuable customer insights from the depths of the web, providing the basis for strategies to tackle high bounce rates, issues with converting and even improvements in customer response times among several other applications.

But increasingly, questions are ringing out regarding exactly how many conversions were driven; how much time was saved, and the spend it took to get there.

Many will point to an air of irony in the fact that web analytics, used by businesses to asses the return of investments pledged to their digital platforms, is now having to prove ROI itself. However, its status as an ‘enabler’ is certainly something to bear in mind when doing so.

The need for return

Standing as a fixed-cost investment, analytics tools enter a business much like the personnel that control them - with a price tag on their head. Competition among service providers means the going rate of analytics platforms is on a very gradual decline, which goes a little way towards counter-balancing the rising cost of talent.

A little way, that is. Online commerce paved the way for small businesses to compete with the goliaths of their industry, and it’s with the same shrewd vision that these 35

enterprises will be looking to forge a clear link between their usage of analytics and the bottom line.

That practice of ROI trawling can even grow into a benefit in itself, as once it’s clear to see where the return comes from, the technology’s value is amplified - a vital consideration if the initial investment is minimal.

But before attempting to measure any return from a solution, those in charge of the tools need to know primarily what it’s being used for. Setting clear goals for results will ultimately make it easier to judge success and there are plenty of ideals to choose from.

“Return will be measured in reference to the key performance indicators (KPIs) which must be defined at the outset of any project or programme,” says Rob McLaughlin, analytics director at global marketing agency DigitasLBi.

“These KPIs will represent the drivers that have been commonly agreed to ‘move the needle’ for a business, coming across a range of areas such as, but not exclusively; reach, sales, service, lifetime value or customer satisfaction.”

McLaughlin’s process dictates that if actions based on analytics insight are being delivered against a KPI, then return is being generated. A similar approach to matching analytics data with the activity it inspired is observed even by the tool providers themselves.

Axel Schaefer, senior manager for product and industry marketing EMEA at Adobe Systems, highlights a rarity in analytics being able to generate additional revenue in a cut-and-dry manner.

“Usually, our customers are looking for the correlation of web analytics with other channels, so for example how did my segmentation of web visitors help us to create additional revenue in the next email campaign?”

There are of course exceptions to this role; instances where analytics will be able to deliver a direct return to the business.

The capability of modern-day platforms means that companies can drill down into every aspect of their site, and if they have goals for performance based on engagement or traffic, they can weed out the under-performers based on the findings. Once adding up the price of a costly image or piece of licensed content, it’s easy to see where money has been saved. 36

Drawing the line

Even the briefest of conversations with an analytics expert or tool provider about the return generated from their actions will come around to the same point: it’s holistic. Generally, ROI must take into account an ability to influence the performance of something which directly impacts the bottom line.

Perhaps the most salient point to come from the stages following goal creation is that it really is up to the business to design their own graphs from the result (the engagement, the conversion), back to the tool, which stems from a need for analytics to provide a lift to metrics like customer lifetime value (CLV).

One example could see a business using analytics to glean insights across their cost- per-click activity.

If that process unearthed areas where spending should be increased, the result of that change could be used to obtain ROI. For customer lifetime value (total revenue from one user’s purchases ÷ the number of months they came in) comparisons between a campaign, landing page or channel’s performance before analytics was used and how it fared afterwards, can highlight the return.

That line to ROI, unfortunately, comes with its fair share of barriers.

Challenges within

Relying on another party to prove value makes communication between tools and in real life all the more important.

For the former, the true challenge lies in collecting and combining data from several different platforms to determine success. On the latter - a consideration for the specialist - it’s all about communication with other departments, like sales and marketing, especially when it comes to measuring the impact of analytics on offline performance.

There are even things that cannot be measured for return, as Mclaughlin states.

“Equating an overall ROI to analytics is further complicated by the variety of ways that return can be understood. Whilst some insight can directly lead to actions which appear to drive sales, other insight may lead to understanding opportunity cost and may justify inaction, with returns impossible to calculate.” 37

Some businesses won’t require the granular analysis that a high-street merchant would as they go about rewinding a conversion in a brick-and-mortar store back to a landing page. Challenges at entry level come in the form of making the most of the data on hand; using this insight to inform business decisions and prove ROI in the aftermath.

The CMO survey from Adobe provides a progress report on this every year. In 2012, 37% of marketing heads claimed to have used analytics before making a decision within a project, but this went on to drop to 29% a year later. In 2014, a slight uptick saw 31% using analytics to inform their business decisions.

While analytics has done so much for improving results across digital properties, there is evidence of the ‘data deluge’ becoming too much for some. It only stresses the value of good talent behind the controls, and working to pre-set goals before moving onto something else.

Appreciation of the role

What certainly comes from learning more about the uses of web analytics is an understanding of how it works, and why it’s needed - with or without a balance sheet highlighting a return of X.

Schaefer insists that prospective clients “usually” won’t come to Adobe with return as one of their first thoughts, although experience of use can eventually make it easier to reach that goal.

“Our customers understand how important it is today to understand your customers and web visitors in order to gain deeper insight, which is most likely the most important element of the digital world.”

On a similar point, McLaughlin says: “Analytics is an enabler to other functions and a fixed cost which is at least one layer removed from return generation.

“Even in the case of optimisation there will always be a layer of user experience (UX) or media activity which will be key in generating returns. In fact, it is the integration of analytics teams and practices within these related disciplines which truly requires scrutiny to ensure that insight is successfully being delivered to the frontline of the business and can be effectively put into action.”

It’s for this reason why any attempt to obtain ROI from an analytics toolset - the “long and winding” process - should be tackled with a simple equation. As Schaefer states: “Success in web analytics is efficiently using what is available to you and knowing what you want to get out of web analytics in the first place.

“Once you can define those aspects clearly, then you can measure the return more easily.”

Richard Towey MyCustomer contributor 39 References

cc Group Company cc Gerry Brown, senior analyst for customer engagement and marketing technology, at Ovum. Follow Gerry here. cc Avinash Kaushik - The 10 / 90 Rule for Magnificent Web Analytics Success cc Avinash Kaushik - Web Analytics: An Hour a Day cc Avinash Kaushik - Web Analytics 2.0 cc Eric T Peterson - Web Analytics Demystified cc Brent Dykes - Web Analytics Action Hero cc Brent Dykes - How Big Should Your Digital Analytics Team Be? About Adobe

Adobe is changing the world though digital experiences. We help our customers develop and deliver high-impact experiences that differentiate brands, build loyalty and drive revenue across every screen, including smartphones, computers, tablets and TVs. Adobe content solutions are used daily by millions of companies worldwide-from publishers and broadcasters, to enterprises, marketing agencies and household-name brands. Building on our established design leadership, we enable customers not only to make great content, but to manage, measure and monetise it for maximum impact.

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