How your operations and marketing efforts can benefit from statistical analysis and modeling.
Forgive me if I generalize for a minute. There are two approaches to marketing analysis: the arithmetic and the statistical.
The Arithmetic Approach
Sometimes called “descriptive analytics,” this is relatively straightforward and inexpensive, depending on a spreadsheet and the sweat of your brow. Extracting a season’s sales from your transaction system to your spreadsheet, you can determine the following:
- percent response, by dividing your number of orders by your mail quantity per segment;
- average order value, by dividing your gross sales by your number of orders per segment;
- contribution per segment, by subtracting your cost of goods, promotional costs, and offer costs from your gross sales;
- contribution per order, by dividing the contribution per segment by its number of orders; and
- dollars per book, by dividing gross sales by the number of pieces mailed to each customer segment.
Of course, you have to use a best-guess formula to account for unsourced Web orders, but such formulas can be more than adequate to decide on next season’s mailings and promotions. Moreover, the arithmetic method also can yield reasonably good segmentation analysis, based on pre-season and post-season analysis of recency, frequency and monetary (RFM) value.
Why, then, would you want to invest in the other approach — statistical (sometimes called a “predictive” approach) — to marketing analysis? The benefit here is two-fold:
1. Because the first step in statistical analysis is creating a normalized data mart or data warehouse, your data will be cleansed of anomalies, duplicates and missing pieces.
2. And because the statistical approach is predictive, you can make more precise or more granular decisions about likely future customer behavior.
The statistical approach also will help you to:
- determine the likelihood a customer will become a repeat buyer and that a repeat buyer will evolve into a high lifetime value customer;
- improve promotion response rates by differentiating responders from non-responders;
- identify product cross-sell and upsell buying propensities.