3 Ways Data Analytics For DTCs Will Evolve

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Note: The following is an excerpt from an interview between Zeenk CEO, Brian Eberman, and the publisher of Sandhill.com, M.R. Rangaswami, that first appeared on Sandhill.com.

M.R. Rangaswami: What are some of the gaps you see in these current data analytics tech stacks and services that these companies rely on and how do you see these solutions evolving?

Brian: I think most of the e-commerce analytics stacks that we see DTCs investing in were initially designed to measure and optimize digital advertising.  So while many of them have robust ad reporting systems, they are pretty light on actual data analytics and even more so on data science.  Even the dashboards are prescribed to provide aggregate ad reports.  So the application is limited to marketing, even though there are several parts of the business that contribute to profits.Here’s how we see data analytics evolving:

1. Optimize the performance of the customer versus the media channel.

Instead of looking at the performance of broad based cohorts in ad campaigns, these systems will look at the performance of  individual customers as measured by CLV versus the cost to acquire them.

2. Develop accurate forecasting and prediction models.

These systems will include causal modeling technology that takes a set of customer behaviors and hundreds of other data attributes to accurately predict future behavior, e.g. projected CLV, Churn probability, etc.

3. Cross-departmental integration. Data analytics systems will be able to ingest data from all parts of the business, create integrated analytics, and publish reports that give operators much deeper insight to help them optimize the business.  Example, finance can share inventory data with marketing to optimize merchandising and promotions.  Marketing can project forward customer sales as a function of advertising spend which provides finance with gross cash projections.