4 - Bootstrappng for the Marketer - Quick and Dirty

Before getting onboard a Phd in Data Mining or maybe acquiring one, there is some level of magic a marketer can do with the behavioural 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.

    a.  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.
    b.    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.
   c.   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 it’s way.

2)Cross Sell: Using Product Affinity  / Market Basket Analysis :Consider your simple transactional database, a stock register of customers and items purchased.  This contains customerid, Transaction Date and items purchased.
Customer    Tran Date    Item1    Item2    Item3    Item4
C1                  XX              Pa         Pb      
C1                  YY              Pc         Pd      
C2                  XX              Pa         Pc     Pb  
C3                  YY              Pc         Pd     Pa  
C4                  XX              Pa         Pb     Pc            Pd
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 ie 5 gives a ratio of mot preferred group of products. This is the most elementary form of Market Basket Analysis
    Pa    Pb U Pa     Pc U Pa            Pd U Pa
Pa        3 / 5 =0.6    3 / 5 =0.6         2 / 5= 0.4
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 are’nt 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  concepts using Lift and Confidence measures. Some quick Tips...

• Use Purchase Dates: To ensure you don’t go back too long in time and use product combinations that are’nt 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”.!!
• Use Lift & Confidence Measures. (if you got your Phd)
• 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,  was’nt 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. A SMART 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 “nxt 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. Happy Mining !!!