Customer Analytics

The Customer Analytics Evolution: A Path to Cognitive

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Twenty years ago I was listening to Salt-N-Pepa and watching Seinfeld. Today I’m all about bluegrass and Game of Thrones. Just as my musical and TV preferences have evolved since those days, customer analytics has made a lot of progress as well.

Since 2006, I have watched the evolution from a front row seat. I entered the space when I joined Coremetrics and then moved to DemandTec where I learned how predictive analytics could be applied to answer specific business questions – in this case retail pricing. When DemandTec was acquired by IBM, I shifted my focus to a broader set of customer analytics capabilities including Coremetrics, also acquired by IBM and now called Digital Analytics. Here is what I’ve seen over the past 10 years and my expectations for the near future.


The evolution of customer analytics.

Web Analytics: From counting hits, to understanding visitors

A company in the 90’s with an online business was on the cutting edge and even more so if they used web analytics. These early adopters were forward thinkers who wanted to better understand the traffic coming to their website. It started with simply counting the number of hits made to a web server but quickly evolved to page views, sessions and answering questions like “What are people doing while they’re on my site?” This was the first step in moving past intuition to measurement and data-driven results when making digital decisions.

By the late 1990s, web analytics evolved to more sophisticated methods, tagging sites and cookie-ing visitors in order to gain more robust visitor insight. By the late 2000s, as we started to see digital business occur in places other than a traditional web site, we saw web analytics evolve to digital analytics to keep pace with the way consumers were interacting with brands.

Social Media Analytics: Understanding customer sentiment

By the late 2000s, Facebook, Twitter and all the other social channels changed the way people interacted with brands and each other. Businesses needed to have a presence on the major social sites to stay relevant and they, in turn, benefited from the offsite data generated. This data could be harnessed in several ways. It certainly added another dimension to understanding specific customers by adding information about their social interactions and their customer profiles. It also unlocked new, early insights into trends through sentiment analysis. In addition to seeing the number of people who follow and engage with you, you could also gauge the sentiment in the marketplace about your brand and products.

Customer Behavior Analytics: Reliving experiences

Then customer behavior analytics began to emerge—and we’re still seeing a lot of growth in this space now. Customer behavior analytics captures full session details – at a much more granular level than web analytics – and allows brands to get alerts when certain behaviors are exhibited, replay sessions, and view heat-maps to gain insight about usability. Whereas web analytics answered “what” was happening, customer behavior analytics started to answer the additional questions of “how” and “why” customers took particular actions. Why are customers converting or abandoning? Why is the click-to-conversion time so long?

With this advancement in analytics, brands were able to get better insight into qualitative data – the human side of analytics – to guide their decision-making and to deliver an outstanding customer experience.

Predictive Analytics: Improving decision making

Predictive analytics has been in the market and evolving for some time. It uses advanced econometric algorithms and statistical methods, like cluster analysis, to listen to data and use it to make even better decisions. As the quantity of customer data proliferates, predictive analytics is a natural progression. Applying predictive techniques to customer data allows brands to make better decisions about where to spend scarce marketing resources, decide what offers are likely to get the best response and identify which customers are most at risk for churn.

In the beginning phases of predictive analytics, analysts used tool sets to build models to answer questions and solve problems. In the past decade, I’ve seen a move towards vendors creating applications – designed for business users – that leverage predictive analytic technology and techniques to address specific business cases. DemandTec was a great example of this, where we incorporated predictive techniques and modelling services to ultimately deliver an analytic service that supported the retail pricing process. I expect to see this trend continue.

Journey Analytics: Visualizing the entire journey

One of the newest frontiers is journey analytics. With the rise of mobile as an additional buying channel and rapid innovation in the marketing technology space, marketers now need to follow an increasingly fragmented customer journey. Journey analytics is an innovation that ties together touch points across time and channels. By identifying the fastest and slowest paths to conversion and pinpointing where successes or problems occur, organizations can make corrections and help more people stay on the path to purchase.

Our newest solution, IBM Customer Experience Analytics, includes journey analytics as one of its key capabilities. Watch this video to see how brands can better understand customer interactions.

Unified Analytics: Best tools in one dashboard

Yet with all of these evolutions, years in the making, gaps remain in our understanding of the customer experience. Multiple departments are likely analyzing data, making it hard to put together a unified report. Web reporting and mobile reporting may be separate. Organizational effort goes into collating data instead of analyzing it. And managers may still find that too much time passes before they can get answers to their most important questions.

We recognized the need for one unified solution, presented in one dashboard, that brings together the best of digital, journey and customer behavior analytics to help brands better understand the customer experience holistically. Get a taste of how we’ve pulled these capabilities together with our interactive demo and learn more at

Cognitive: The future of analytics

What’s next in analytics? In one word, cognitive. Analytics powered by cognitive computing is already here with IBM Watson Analytics and we can expect more to come. At IBM Amplify, we shared a demo of how IBM Watson can harness data and analytics to help a marketer build an ad plan to support a campaign.

Cognitive marks a new era in analytics and an evolution of what we’ve seen since the days I was listening to Salt-N-Pepa. With cognitive, powerful systems are able to scan volumes of unstructured, natural language data to identify patterns, learn trends and predict future customer behavior. With cognitive, we make it easy for marketers and digital commerce professionals to access sophisticated analytics without relying on analysts. Cognitive democratizes data in a way we haven’t seen before.

Stay tuned. I for one can’t wait to see what’s next.

Program Leader, Product Marketing, Customer Analytics, IBM

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