Client industry: dtc supplements e-commerce

Pain Points

The client, a sizeable competitor within the direct-to-consumer (DTC) supplements and vitamins space, was struggling to manage their customer outreach and sales prospecting efforts.

While the client had several years of rich and diverse data on customer purchases and web metrics, they were unable to translate that data into actionable business intelligence. This was ultimately costing them dearly in lost revenues and marketing overspending.

Beyond this, their database infrastructure, while fairly new, was still convoluted and improperly setup to handle analytics. To solve this problem, Market Theory tapped its best data engineers to automate a relational database management process, which only took 8 business days to implement.

Methods

Our first goal was to thoroughly understand and map-out the client’s original vision for a customer journey (i.e. the digital steps that a potential customer would take from their first interaction with the client’s brand to an eventual purchase and future purchases). To do this, we setup a short series of client-side, stakeholder interviews.

The client provided five years of daily data across their customer’s order histories and marketing channels (e.g. Google search, Facebook, etc.). This setup lent itself well to both time-decay modeling and panel data analyses.

Regarding the former model, we structured a non-parametric system of equations that would allow us to pin-point not just the estimated customer lifetime value (CLV) of each customer type but also the likely length of said “lifetime”.

And, with respect to the latter, we conducted a market basket analysis (a subset of “affinity” analyses) to help the client implement cross-selling and upselling tactics.

Results

From this analysis, we presented the client with several layers of work product across three categories:

1. A series of sales and revenue forecasts, broken-out by customer type, product set and geography;

2. An interactive map of product combinations and complementary marketing strategies; and,

3. A lean and automated financial and marketing measurement dashboard that only tracked and analyzed the metrics that mattered most to the client’s growth and business goals.

These analyses ultimately proved that:

• While there are always exceptions to every rule, >98% of all customers fit within the modeled trajectory, thereby proving that CLV was forecastable and predictable;

• If they were to re-optimize their spending habits to match the market’s natural demand cycles, they would save enough to introduce new, integrated marketing channels and new product promotions to further grow the business.

Working with the client thereafter and keeping tabs on their developments over the next three quarters, we noticed a >20% increase to their customer retention and a nearly 50% increase to the average purchase amount.