This week, the folks at Loyalty Builders shared four recommendations retailers can follow for a higher-ROI from data-driven marketing.
According to the company, which aims to lift revenue through more relevant marketing at every stage of the customer’s lifecycle, here re the 4 steps in question:
1. Evaluate the “Total” Cost of the Analytics
The Big Data imbalance:
- 80% — Of total time and cost spent on data gathering, cleansing, integration, normalization
- 20% — Of total time and cost spent on data exploration, analysis, modeling, testing.
The imbalance increases as more source data bloats the analysis, leading marketers on an ROI-crushing – and unnecessary — chase for more useful predictor variables and a “360° view of the customer.”
2. Get More Value from Less Data
To predict customers’ purchasing behavior, focus on their purchase history. It’s far more simple – and cost-effective. This doesn’t refer to the typical RFM approach (recency, frequency, monetary value). Instead, we can now make predictions that are either impossible with RFM alone or made much more accurate by deriving more predictive indicators, such as inter-order timing, products purchased, sequence, trends, range of categories purchased and others, from the basic purchasing inputs (customer ID, item purchased, amount spent and purchase date).
And — purchase data is easy to access, always complete, always accurate, always unambiguous, and, unlike most variables, always available for every customer. It’s also not considered “personal” or controversial data. Additional data on customers can be helpful, but can lead down a slippery slope, increasing time and cost for sourcing, integration, governance, etc., when, in fact, purchase records are what you have and all you need.
3. Simpler Can Be More Accurate
Using a few unambiguous, universally available, and very predictive variables usually delivers the best accuracy. That’s because too many variables can lead to errors of over-fitting the data with conflicting indicators. The real world is complex. It’s hard to model it correctly and adding poorly understood or ambiguous variables seldom improves accuracy. Instead, it can lead to unjustifiable complexity.
Fewer input variables reduce cost of data acquisition and computation time while yielding models that are usually easier to interpret and optimize. The approach that delivers the most revenue from better recommendations and minimizes the cost of data preparation and analysis wins. It’s hard to beat simple but effective.
4. Focusing on the Best Predictions from the Least Data Powers Automation
Where there is a limited, known, consistent set of data inputs and outputs, it’s possible to automate. Automation drives down cost, reduces errors, saves time, and affords consistency and repeatability to the marketing process. Scores and product recommendations can be calculated for each individual in one pass. With automated, affordable Analytics-as-a-Service, retail marketers can get ongoing, on-demand access to purchase predictions, loyalty and value scores, and lists for email, direct mail, catalog, web, mobile, and sales campaigns — all without the typical time, cost, and complexity of data preparation and data modeling.