Before getting onboard a PhD in Data Mining or acquiring one, there is some level of magic a marketer can do with the behavioral database he has set up, without the need for advanced analytical Tools. These are quick and dirty methods but can boost conversions, reduce blast volume and in general power up your marketing efforts with much needed “customer centric” intelligence.  Some basic knowledge of SQL can help though it is not mandatory.

You can experiment with the following scenarios and make the lord and master look up and take notice. Of course, the basic assumption is that R, F and M scores are computed on a frequent basis and a history of these metrics over time are maintained in a database.

1.Using Latency to predict the next Purchase Date for the customer.

1. Use the current R (recency) value (in days), and add it to the last purchase date of the customer to predict the probable purchase date.
2. Use Current Category R Values (R computed at Product Category level) for a customer, add it to the last purchased date of the respective category to predict the probable purchase date for a particular category.
3. Use a running average of Recency Values for a particular category or customer to fine tune the computation.

All that is required is to schedule campaign launches for respective categories and customer combinations on these dates and viola…you are on your way to kick starting your  first predictive marketing campaign on its way.

2.Cross Sell: Using Product Affinity / Market Based Analysis to build simple Cross Sell Models

Consider your simple transactional database, a stock register of customers and items purchased.This contains customer ID, Transaction Date and items purchased.

Create a Simple Matrix like the one given below that indicates the no. of times Pa is purchased along with Pb, Pc, Pd. That divided by the total number of transactions i.e. 5 gives a ratio of mot preferred group of products. This is the most elementary of Market Basket Analysis

Well, there are additional aspects to the whole process like Lift, Support and Confidence that give more statistical insight but hey, the conclusions for a rookie aren’t so bad. We did find out that as a combination, (Pb and Pa) and (Pc and Pa) occur frequently enough. Get the guys who have purchased only Pa, try Pb or Pc as Cross Sell Options before moving on to more advanced and specific techniques using Lift and Confidence measures.

1. Use Purchase Dates: To ensure you don’t go back too long in time and use product combinations that aren’t really happening now, either disregard transactions older than a particular date or allocate a smaller weightage for older product combinations. You really have to decide how old really is “old”.
2. Use Lift & Confidence Measures.
3. Use Latency: Once you hit on a cross sell product to a customer using the Magic Matrix, use his Recency data described in section 1 to hit on the optimal timing of the campaign. A hybrid approach, using multifaceted data to hit on the relevant and timely messaging. Well, wasn’t that good!! We managed Relevancy – through the right product to cross sell and timing using the Recency Data of the customer.

3.Retention : Winning back Fading Customers:

In General, Engagement data, as in response to marketing communication is a much earlier indicator of customer disinterest than Purchase behavior itself.  Purchase scores or P-RFMs fall much slower than E-RFMs or Engagement RFM scores which track a customer’s open, click and delete scores. A quick trigger to capture a free fall in E-RFM say from 4.5 to 3 can quickly give the marketer an early indicator of disengagement giving him the additional time required to retain the customer vis a vis a reaction that happens when the anticipated “next purchase” does not kick in.

These three use cases are by no means “end-all” but significant business scenarios that can provide solid value to a marketer helping him in Retention & Sales UpLift.