Ecommerce: Not just Data Scientists but Data Creati-vists

As a part of the Consumer Behaviour course, we were asked to stand behind a shop counter for 2-3 days to observe – understand- analyse – predict shopping behaviours and come up with specific insights on shopper clusters and their behavioural Analytics.

Not so simple or linear when it comes to Ecommerce, today consumers research, compare, evaluate across multiple channels, at different time intervals with multiple influencers and triggers.

Amidst this chaotic shopping journey, the Ecommerce companies are interested in driving profits while increasing the customer base focusing on retention and reselling. Consumer’s expectations while shopping online can be summarized as: Best deals; Simplified shopping experience and customization (research journey, solutions and purchase).

Ecommerce has evolved over a period of time. Adoption of Data Analytics, Machine Learning and A.I Solutions have set the path for improved insights and learnings.

In my opinion, there is still a huge runaway to sharpening these insights to land at Up-Leveling Shopper journey experiences while driving business KPI’s simply put as:

  • Reasons for customer not buying
  • If Buying product X how can I make the customer buy not just X but also Y.
  • If buying a lower priced variant of X how I can increase the Average selling price by Z%.
  • How can I get customers who weren’t interested in my product X to consider it?

Data Analytics can help “Delight the Customers”

Ecommerce companies are using predictive algorithms, recommendation systems and big data to improve “User experience” and drive incremental sales. Some Hygiene Analytics cater to Fraud detection, Product Analysis, Seller Analysis, Recommender Engines but what’s of paramount importance is “Consumer Experience Analytics”.

As an Example, I want to Shop for a Fitness Tracker for myself. Here are some examples of treating “Basic” and Delight” triggers to explore:

Given that I would have done some research (Online /Peers), I may have some level of Information on the category. This can be deciphered basis the search query that I might put in or the Page that I visit first.

  • Let the information be categorized into Basic/ Advanced and Pro categories so that the information served can be mapped to the shopping journey and thus provide an incremental benefit and not duplicate information for the consumer.
  • Basis the first visit by a consumer- Category page or Product page which indicates the level of information and awareness, one can serve customized triggers, information to enable “Decision Making” or “Conversion” as the case maybe.

I have a certain Shopping history that determines my frequency of conversion, Average order value, interest in the category and propensity to buy more. Some other innovative methods to drive conversions and “Delight the customer” could be:

  • Incentivise with an offer on current/next purchase if one uploads a video on experiencing the product or gives a review.
  • Provide motivation by engaging an existing user/Influencer to combine this tracker with the right pair of Running shoes.
  • Urge the consumers to buy NOW with the incentive to participate “free” in an upcoming Marathon .

This discipline would be highly rewarding.  Why wouldn’t a Sports / Health tracker pay a premium to an Ecommerce player if the brand gets a sharply defined Target cluster based on interests, shopping patterns and propensity to buy leading to conversions /Upsell through Innovative Marketing levers while ensuring customer loyalty to the brand.

Shopping Journey:

The other parameter, that is, Shopping journey, is not linear and hence the AIDA (Attention-Interest- Decision-Action) model may be an assumption for a communication framework build up but not strictly followed by a consumer. Continuous studying and analyzing of Shopper journey patterns, overlaid with Triggers, Timing and Shopper Clusters will drive some science into it.

Taking the same example of a Fitness Tracker:

  • Based on my Social media consumption/ past shopping pattern one can predict that I might be interested in a Fitness tracker and show it upfront on my APP/ personalized page feed?
  • In case I put the search query, an A.I pop up then dictates my journey online interspersed with information and triggers thus speeding up my purchase decision while enhancing the overall experience.

Not just a Data Scientist, but a Creative one

We have established the criticality of a Data scientist but my narrative here is that today Ecommerce companies don’t just need a vanilla “data Scientist team” but creative, out of box thinkers who can create purchase situations and triggers basis the data.

These “Out of Box”interceptions will help Ecommerce platforms drive up customer engagement with their platform while growing their Cart values .

This practice would go a long way in differentiating the shopping platform, retaining the customer given that one of the biggest challenges that online retailers face today is “Cost of acquisition” of customers.

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