Predicting If Your Customers Will Make You Money, Or Lose Your Money

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What if DTCs could predict today which of their customers will have the highest value in 12 months? What if they could identify the customers that they could potentially lose money on? What if they could avoid acquiring the latter all together?

We believe that the evolution of data analytics will feature 3 key attributes: Customer-centricity, Predictive, Actionable

In a previous article I discussed the importance for DTCs to track CLV:CAC ratios to evaluate the performance of their company and marketing.  The example I gave in the article used a fictitious brand, Ice Cream Trux, to demonstrate the value of looking at your customers through this lense.

“…Ice Cream Trux might find that customers who live in one region and tend to place larger family sized orders, have an LTV of $1400 with a $300 CAC. While they are more expensive to acquire, these customers have a better 4.7:1 CLV to CAC ratio than the average.”

Zeenk launched to provide these types of customer centric analytics to help DTCs effectively analyze the performance of their business at the customer-level.  When developing our unique CLV:CAC ratio analysis, specifically, we sought to addresses what we (in collaboration with our customers) saw as 3 big product gaps within other solutions that provided this analysis:

  1. CLV:CAC analysis was provided in aggregate for broad audience cohorts and not on a customer-level.  While this was not true for every solution, a majority of them did lack customer level data. 
  2. The CAC is higher than the channel CPA.  CAC must account for all spend in a period.  This includes spending for customers who buy and those who don’t.  Further, it must take into account multiple advertising touches across potentially different advertising channels.  Therefore the actual per customer CAC can be quite different from the CPA reported by an advertising channel.
  3. Forecasted CLV may be more valuable than actual CLV. What’s more valuable to a DTC- giving them a list of customers they have lost money on right now, or giving them that list 3, 6, 12 months or maybe even years earlier? The former is insightful, the latter empowers them to take actions to improve or prevent.

Zeenk customer centric analytics uses proprietary data science models paired with our analytics technology to accurately forecast the value of each of your customers and the imputed costs to acquire them.  This enables DTCs to take proactive actions to optimize their marketing activities to improve their future outlook.  See Figure A.

Figure A. Distribution of CLV:CAC ratios by customer. Note: CLV is projected out to 12 months.  CAC includes costs of impressions served to both acquired customers and non-acquired customers.

The scatter graph illustrates the distribution of this DTC’s customers by CLV:CAC ratio, where CLV is forecasted and CAC accounts for all costs.  The dotted line is the “breakeven” ratio. This will vary by customer and product.  Customers above the line are forecasted to be profitable in 12 months, below line unprofitable. 

Armed with this data, DTCs can proactively:

  • Use higher valued customers as seed audience to created models for acquisition campaigns
  • Push away or block customers that are likely to cost more to acquire than they are worth
  • Use CLV as a signal to train machine-drive optimization in ad channels. 

Zeenk supports all of the above actions.