In my doctoral thesis I’ve made an interesting demonstration by running a simple experiment: I’ve measured the screen real estate (or pixel area) of an Amazon product page, and classified its different types of content. Three types of content appeared: basic information of product, content created by users, and content created by Amazon. It turns out that over 80% of the product page was dedicated recommendations generated by a mix of the user’s past behaviour, other users’ behaviour and Amazon’s own algorithms.

Therefore, when it comes to approaching customers online, it’s all personalized, but that’s not all. Even with personalized marketing, segmentation is a must -- and it’s great when segmentation is done from the business angle. The special alchemy consists in the optimal blend of data: past behaviour, group behaviour, demographics, best practices and experiments, but also trial and error. Even in it’s infancy, we have the beginning of a consumer science, here.

Furthermore, this so-called consumer science needs to be an agile one, and facilitate smart decisions. To achieve that, a lean approach is necessary, as well as sticking to the data that can effectively be converted into actions.

It is however easy to get drained in abstractions or entangled in the numbers that come with data. That’s why the origin of a consumer science must be divided in different known streams of action: identifying and verifying our client’s target audience, listening to the psychographic profile of that audience, and separating the audience into customers and into meaningful segments -- recent purchases, big spenders, early birds, frequent shoppers and so forth.

Meaningful segmentation comes from understanding how consumers are grouped by behaviour inside the business -- and attaching value and growth strategies around these groups is how you effectively drive data-driven business transformation.

A consumer science should be able to do more. Drilling down deeper into customer segments is what provides us with insights for marketing and for taking customers further into the individual customer journey. And for that, comes into the mix loyalty strategies, reward calibration, game theory and metrics attributing value to each touchpoint and interaction.

It gets better than that. For instance, demographics, in a data-driven world, is something you are able to quickly expand into psychographic informations.We have developed methods to gain qualitative insight out of quantitative samples. That brings a new perspective to the idea of “looking our customers in their eyes”. I would argue that the best way to do that is for instance an effort to analyze and measure points across tons of Instagram accounts of people with similar demographics, shopping behaviours or advocacy habits -- giving special attention to the most influential accounts.

You can take it further by estimating with the help of open data how many of these consumers exist in your market, as well as  the potential revenue they may bring -- for now, and over a period of a longer time.

I believe the difference and strength of a consumer science is the multi-strategy approach that a customer-centric culture demands. It’s looking at the customer from different points of view, and keeping the metrics reliable, up-to-date and always on.

Let’s say we use your Analytics to create a consumer group who only buy one product from your company. Based the buyers’ demographics of this product, we expand their demographical profile, find them on social media, check the short tail of influencers, observe what else these influencers do, and experiment further with these targets -- testing sales pitches, product offers and more. That’s how you can understand who is really your target, and what they actually respond to.

With enough information collected, a consumer science is able to measure value and manage customers further on the customer journey. That means moving customers from one segment to another: from yearly to monthly purchases, from one-product buyers to all-product-buyers -- measured and tracked from from the first kiss to the desired long-lasting relationship.

 

Posted by Sergio Tavares

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