Getting Started With Data-Driven and Database Marketing
To do this, we need the accurate customer count we started with, and now can use timestamps on transaction history to organize purchases by frequency.
We can see the difference between someone who purchases two times in three months and two times in a lifetime. Getting interesting yet?
But what else can we and should we know? Another imperative we’d urge you to “start with” is what percentage of the marketing database are new buyers vs. repeat and how many were new in a given period ― this helps you see if you are growing, and how fast or slow. Then move on to “how many” transactions, how many sales per customer? Per period? Calculate the average? How about a distribution of purchase frequency … oops … we can now see you have a one-time buyer problem … (or hopefully not).
Hopefully, I’ve illustrated in just a few paragraphs how much can be gleaned from transforming and organizing your raw transactions into a rather basic customer database. Let’s move on from “How many?” to … “How much?”
The Logical Next Step: 'How Much?'
If you’re a mature and sophisticated database marketer, again, most likely, “you’ve got this.” But still, today the majority of midsize retailers and on down do not. So how much are your customers worth to your brand? (Yes, they are "priceless," but really now, how much have they bought from your brand?).
Let’s move on to "How much do new customers spend, on average?" Two-time buyers? All buyers? How much by store, by geography? By salesperson? You can answer all these questions once you’ve worked through the data transformation we’ve discussed herein. How much were they worth by quarter (ie, how does seasonality impact customer value?)
How Much Promotion?
If your POS or e-commerce raw transaction files contain "discount applied" (most do) then we can go on to ascertain “How much promotional value did we trade for incremental sales?” How much did we use promotion to acquire new buyers? This of course requires that the raw transaction file contains promotion credits on the receipt record. Like the other data quality issues we spoke about, returns and credits can create serious challenges to making your numbers line up, but those can be handled through more advanced matching logic, as well. The trick is to take them into consideration from the start ― don’t “worry about them later.”