Getting Started With Data-Driven and Database Marketing
By now, you're likely to be tiring of all “the talk” about data-driven database marketing media outlets and pundits who go on and on. It’s the “new, new thing” ― for most. This month’s "Data Athlete" column is intended to be the antidote to some of that. Let’s start with where I got the idea for this … from practicing marketing professionals, who were anxious to leverage data to improve performance but needed to understand it more before they could act. So I’ll get right into some basic examples, and we’ll build on it. Feel free to find them basic, boring or ― better yet ― question-enducing.
In prior columns, we’ve covered the purpose and value of the marketing database in growing profit and business performance. With that as a foundation, let’s talk about some examples. Let’s be clear; this isn’t as strategic as it is tactical. It’s for the marketer who’s considering approaches and is looking for examples so as to better understand how mountains of data become actionable and usable.
A Good Place to Start: How Many?
The first question you might be surprised to know how few marketers have an accurate answer to is “how many customers do I have?” Many marketers, especially multi-channel retailers, are focused on units sold, revenue and store attribution ― and for good reason. These are important metrics. For a brand looking to build a relationship with a customer, and maximize the Customer’s relationship with the brand --you’ll need to do more. Getting an accurate customer count is a challenge for brands that do not have a significant history of marketing directly to the end customer. If you’ve sold through distribution/wholesale or you sell through retail stores that do not have personally identifiable information (PII), it may be a challenge for your organization.
If you’ve covered this base, then you’d be surprised to know how few brands have the data in place and organized. The biggest reason for this is the data is often tied up in an “IT” database, where it’s logged by the POS, website CMS or another system.
Moreover if you examined a transaction file, you would likely see many tens of thousands of rows of transactions, many of which are anonymous given the process in which they were captured. This creates some challenges. Without PII, we can’t associate the transactions to the customer. Even when we have the customer PII, the data is virtually never “clean” enough out of the box to just match on the name ― “John,” “Jon,” “Jack,” and “J.” are not the same to a database or POS system ― but they have to be in a comprehensive marketing database.
Data processing matching programs repair, cleanse and transform transaction data from a set of “raw transaction data” into a comprehensive buying history by customer.
For now, let’s assume we’ve completed this data transformation phase and have repaired various missing fields, and solved for the typical data capture problems that we should expect. We’re on to doing something with our newly minted customer data file. First, we can get a clean customer count! We now know we have 100,000 or 10 million customers.
Now we can begin to leverage the timing of those transactions. We’ll know not just how many units we sold, but who we’ve sold them to, and what else and when those customers bought. We can derive the timing of those purchases and begin to mine for statistical significance and opportunity. Before we go too far with leveraging timing data, let’s take a step back and first understand the breakout of new, repeat and loyal customers, too. Our first big question of course was “How many?”
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.”
Put another way, we’re quantifying customer value. This is strategically very important and for good reason, because:
“That which can be measured, can be maximized.”
This brings us two the last big focuses that your database marketing solution can help you answer, “Who is the Customer?”
Completing The Picture: Who? (Is The Customer?)
Continuing to build from the bedrock we started with in a raw transaction file, we can move on to learning about who the customer is who bought, repeat purchased, established loyalty and possibly went dormant. Now we match those customer records to data about the customer and the lifestyle.
This requires more matching logic, spinning through your database and matching individual customers based on unique identifiers or combinations of fields you may already have. Even then, you or a service provider will then need to perform iterative matching to maximize your coverage of these data fields.
Some of the more valuable and important categories to focus on of data enhancement include:
When this data is identified and completed, we “extend” the customer record so we can now answer questions that inform messaging, creative and even product selection/assortment.
Bonus Question: How Did They Do That? (Math and Models)
The element of database or data-driven marketing that generates the most interest is utilizing statistical methods to forecast or predict behavior and customer value. These are the least accessible methods but may offer the most sustainable competitive advantage.
With a well-organized database, we can now begin working with statistical methods and running calculations called models or model scoring.
These methods answer questions like: Who is likely to try an aggressive new design on the product? Who is most likely to drink brown liquor at dinner? What customers have the highest probability of attrition?
While these methods may be considered the most “glamorous” of the data-driven marketing sciences, it is perhaps most important to realize that they are only possible or cost-effective when they are built on a robust foundation. That is, a modeler will spend 90 percent of the time and effort on getting data into a format to be able to develop sensible and useful statistical outcomes ― which drives up cost and time to value if the fundamental database design doesn’t support this use case for the data.
In sum, consider how many customers you have, how much they bought, who they are and how they did what they did when you execute your data-driven marketing program. Answers to these questions will enable you to communicate better with your customers and unlock the value from your existing customer database.