‘Too Much’ Is a Relative Term for Promotional Marketing
If a marketer sends you 20 promotional emails in a month, is that too much? You may say “yes” without even thinking about it. Then why did you not opt out of Amazon email programs when they send far more promotional stuff to you every month? Just because it’s a huge brand? I bet it’s because “some” of its promotions are indeed relevant to your needs.
Marketers are often obsessed with KPIs, such as email delivery, open, and clickthrough rates. Some companies reward their employees based on the sheer number of successful email campaign deployments and deliveries. Inevitably, such a practice leads to “over-promotions." But does every recipient see it that way?
If a customer responds (opens, clicks, or converts, where the conversion is king) multiple times to those 20 emails, maybe that particular customer is NOT over-promoted. Maybe it is okay for you to send more promotional stuff to that customer, granted that the offers are relevant and beneficial to her. But not if she doesn’t open a single email for some time, that’s the very definition of “over-promotion," leading to an opt-out.
As you can see, the sheer number of emails (or any other channel promotion) to a person should not be the sole barometer. Every customer is different, and recognition of such differences is the first step toward proper personalization. In other words, before worrying about customizing offers and products for a target individual, figure out her personal threshold for over-promotion. How much is too much for everyone?
Figuring out the magic number for each customer is a daunting task, so start with three basic tiers:
- Adequately promoted, and
To get to that, you must merge promotional history data (not just for emails, but for every channel) and response history data (which includes open, clickthrough, browse, and conversion data) on an individual level.
Sounds simple? But marketing organizations rarely get into such practices. Most attributions are done on a channel level, and many do not even have all required data in the same pool. Worse, many don’t have any proper match keys and rules that govern necessary matching steps (i.e., individual-level attribution).
The issue is further compounded by inconsistent rules and data availability among channels (e.g., totally different practices for online and offline channels). So much for the coveted “360-Degree Customer View." Most organizations fail at “hello" when it comes to marrying promotion and response history data, even for the most recent month.
But is it really that difficult of an operation? After all, any respectful direct marketers are accustomed to good old “match-back” routines, complete with resolutions for fractional allocations. For instance, if the target received multiple promotions in the given study period, which one should be attributed to the conversion? The last one? The first one? Or some credit distribution, based on allocation rules? This is where the rule book comes in.
Now, all online marketers are familiar with reporting tools provided by reputable players, like Google or Adobe. Yes, it is relatively simple to navigate through them. But if the goal is to determine who is over-promoted or adequately promoted, how would you go about it? The best way, of course, is to do the match-back on an individual level, like the old days of direct marketing. But thanks to the sheer volume of online activity data and complexity of match-back, due to the frequent nature of online promotions, you’d be lucky if you could just get past basic “last-click” attribution on an individual level for merely the last quarter.
I sympathize with all of the dilemmas associated with individual-level attributions, so allow me to introduce a simpler way (i.e., a cheat) to get to the individual-level statistics of over- and under-promotion.
Step 1: Count the Basic Elements
Set up the study period of one or two years, and make sure to include full calendar years (such as rolling 12 months, 24 months, etc.). You don’t want to skew the figures by introducing the seasonality factor. Then add up all of the conversions (or transactions) for each individual. While at it, count the opens and clicks, if you have extracted data from toolsets. On the promotional side, count the number of emails and direct mails to each individual. You only have to worry about the outbound channels, as the goal is to curb promotional frequency in the end.
Step 2: Once You Have These Basic Figures, Divide 'Number of Conversions' by 'Number of Promotions'
Perform separate calculations for each channel. For now, don’t worry about the overlaps among channels (i.e., double credit of conversions among channels). We are only looking for directional guidelines for each individual, not comprehensive channel attribution, at this point. For example, email responsiveness would be expressed as “Number of Conversions” divided by “Number of Email Promotions” for each individual in the given study period.
Step 3: Now That You Have Basic 'Response Rates'
These response rates are for each channel and you must group them into good, bad, and ugly categories.
Examine the distribution curve of response rates, and break them into three segments of one.
- Under-promoted (the top part, in terms of response rate),
- Adequately Promoted (middle part of the curve),
- Over-promote (the bottom part, in terms of response rate).
Consult with a statistician, but when in hurry, start with one standard deviation (or one Z-score) from the top and the bottom. If the distribution is in a classic bell-curve shape (in many cases, it may not be), that will give roughly 17% each for over- and under-promoted segments, and conservatively leave about 2/3 of the target population in the middle. But of course, you can be more aggressive with cutoff lines, and one size will not fit all cases.
In any case, if you keep updating these figures at least once a month, they will automatically be adjusted, based on new data. In other words, if a customer stops responding to your promotions, she will consequently move toward the lower segments (in terms of responsiveness) without any manual intervention.
Putting It All Together
Now you have at least three basic segments grouped by their responsiveness to channel promotions. So, how would you use it?
Start with the “Over-promoted” group, and please decrease the promotional volume for them immediately. You are basically training them to ignore your messages by pushing them too far.
For the “Adequately Promoted” segment, start doing some personalization, in terms of products and offers, to increase response and value. Status quo doesn’t mean that you just repeat what you have been doing all along.
For “Under-promoted” customers, show some care. That does NOT mean you just increase the mail volume to them. They look under-promoted because they are repeat customers. Treat them with special offers and exclusive invitations. Do not ever take them for granted just because they tolerated bombardments of promotions from you. Figure out what “they” are about, and constantly pamper them.
Find Your Strategy
Why do I bother to share this much detail? Because as a consumer, I am so sick of mindless over-promotions. I wouldn’t even ask for sophisticated personalization from every marketer. Let’s start with doing away with carpet bombing to all. That begins with figuring out who is being over-promoted.
And by the way, if you are sending two emails a day to everyone, don’t bother with any of this data work. “Everyone” in your database is pretty much over-promoted. So please curb your enthusiasm, and give them a break.
Sometimes less is more.
Stephen H. Yu is a world-class database marketer. He has a proven track record in comprehensive strategic planning and tactical execution, effectively bridging the gap between the marketing and technology world with a balanced view obtained from more than 30 years of experience in best practices of database marketing. Currently, Yu is president and chief consultant at Willow Data Strategy. Previously, he was the head of analytics and insights at eClerx, and VP, Data Strategy & Analytics at Infogroup. Prior to that, Yu was the founding CTO of I-Behavior Inc., which pioneered the use of SKU-level behavioral data. “As a long-time data player with plenty of battle experiences, I would like to share my thoughts and knowledge that I obtained from being a bridge person between the marketing world and the technology world. In the end, data and analytics are just tools for decision-makers; let’s think about what we should be (or shouldn’t be) doing with them first. And the tools must be wielded properly to meet the goals, so let me share some useful tricks in database design, data refinement process and analytics.” Reach him at firstname.lastname@example.org.