User centricity: the biggest challenge in digital analytics and marketing.

There is only one boss. The customer. And he can fire everybody in the company from the chairman on down, simply by spending his money somewhere else. Sam Walton.

The sentence is nothing but true. It’s simple and responds to common sense above everything. It was pronounced by Sam Walton, founder of Walmart. This represents by itself a change of the paradigm of performance and operations optimization from store-centric (or website-centric) to customer-centric or, in a more general perspective, user-centric, understanding user as a customer or a potential. The folks from Wharton University in Pennsylvania are on the lead of customer-centricity and customer analytics research. Many applications come out from this kind of analytics: customer lifetime value, in-store analytics, etc.

One thing, however, is also clear: a customer, before becoming a customer, is just an user, flowing through different stages of its cycle (attention, awareness, etc.). This distinction between customer and not-yet-a-customer is specially important when it comes to a website or a native app. In other words, how does this shift of paradigm apply in a web-based environment? We have been observing the transformation from session-based to user-centric tracking and optimization.

Over the last months we have been witnesses of how the digital analytics tools have been shifting their main reports focusing on the user. Tools like Google Analytics report users before sessions, while some months ago it was doing it in the opposite way. For instance, look at the order in which Google Analytics reports: first users then sessions (for the App views).

Here we already face the first challenge: how to define a user. We will come with this in other posts. Before that, I want to recall a situation we faced some time ago while doing consultancy for a e-commerce site in the European market. We were requested to understand how the sessions were browsing in terms of multi-category behavior. Concretely, the website had a header looking like this:

The problem was to understand the share of sessions that were browsing through only one category, through two categories, through three categories, etc. over different periods of time. We had a split looking like:

Even more, we also showed how the carts were looking like in terms of cross-category items:

The management reacted very worried, and they immediately initiated actions towards incrementing the share of sessions that were browsing through more than one category, and to increase the number of distinct categories on each basket. If you agree with this course of action we must say you are doing it probably wrong. Indeed, all intents to increase the cross-category sessions and carts were unsuccessful.

What could have been a better approach? A good advise: put the user in the center and understand the intention of each one of its sessions. You can’t simply pretend to have every user stepping at all your content on each session. Instead, you can get the most out of each session by understanding the intention of such session. If a user is visiting category A-related content on a given session, then make sure it performs a purchase over such category. The important is to prevent the user from spending its money somewhere else.

Is this everything we can take out of the user? On a session level, probably. But, what if we widen the time window? Instead of looking which category the user browses on a single session, we could check all the sessions that user performed over a week, month, quarter, etc. In the case we have being considering, the charts looked significantly different:


As we can see, the share of users that only browse over one category dropped from 60% to 40%, while the share of users that browse over 2 categories increased from 20% to 30%. And here is where we can induct some change. By incentivizing the user to reach other categories (again, over different sessions) we can improve the awareness of the user over such categories not browsed before. If the content is appealing enough, we might get a chance that the user will actually buy over it.

Another analysis that was interesting for this case was the one showing the distinct categories bought by every customer over a period of time, considering all the orders placed. We had a situation looking like:

which transformed into this:

after applying direct marketings action whose goals were precisely that: to increase the share of wallet of the user over different sessions and, probably, over different orders.

That is, be patient, and keep the user in your focus. Don’t overwhelm it, and take advantage from each session… one at a time.

There is, however, a new variable that comes as an input in the equation of user-centricity, which applies specially to digital environments: the device used to reach the site. Identifying the user when it browses over different devices is a big challenge. Universal Analytics enables us to identify at least one important bunch of such users, and it can be applied for sites that somehow identify them via login, newsletters, etc. The reports look very promising:


This information is extremely useful to understand the intention of a given user (or a set of users) when reaching the site with the different available devices. For sure is not the same intention when a user reaches the site with a smartphone in the morning or with a tablet at the afternoon. Placing the user in the center is, at the very end, about understanding its intention on each one of the sessions at each seasonal moment (in-day, in-week, etc.), with each one of the different devices. To make it a bit more complicated, we can also introduce the fact that some of these users might be also visiting your traditional store (in case you have one, of course). Many sites (fashion industry, mainly) allow you to buy over the web of App and pick it in-store. Lots of research is currently ongoing, in order to track in-store behavior. For instance, Estimote is doing some efforts towards this goal.

In following posts we will talk about the shift of paradigm on the reporting strategy (not necessarily about tools but techniques and contents) whose center is on Customer Lifetime Value (CLV).

In any case, if you have more questions or issues with your omnichannel approach, don’t hesitate to contact us here.

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