How a Relational Database Makes Holiday Shopping Pay Off All Year
It’s Cyber Monday as I type this. As I wade through the deluge of Cyber Monday emails that has been hitting my inbox since last week, on top of all of the brouhaha about Black Friday over the Thanksgiving weekend, I am struck by the missed opportunity of so many retailers when it comes to the holiday season.
The annual profitability of many retailers will be determined this past weekend, as Black Friday officially begins the holiday shopping spree and Cyber Monday gives it a digital boost. These days or even this past week will be the make it or break it determining factor for 2015 sales for a lot of businesses.
But it could be the kickoff for a great 2016 instead, if marketers could only rethink this busiest time of year.
A boatload of buying is going on!
There’s a lot of buying going on this time of year. Even if you only consider the two busy days last week—Thanksgiving and Black Friday—you’re talking $4.45 billion in online sales. And today is expected to generate another $3 billion in sales in just one day. And the buying has just begun, with holiday sales expected to reach $885.70 billion over the two months of November and December.
That’s a lot of money, a lot of sales, and a lot of data in just a short time.
Here’s my thinking: Rather than look at these few weeks as a time to cram in all of the sales possible to close out the past year strong, what if marketers also looked at these few weeks as a time to collect all kinds of customer data to leverage in the year ahead?
Transforming the holiday buying spree into annual sales
For each email clicked on, website browsed, item searched for, cart abandoned and purchase made, data is being offered up by the consumer—data that could be put to use in the year ahead for smarter, more relevant and targeted marketing.
Maybe someone abandoned a shopping cart full of goodies for themselves, then remembered their budget only covered the gifts they were buying on another website, so the cart was abandoned but not the desire to buy. What if that consumer got a post-holiday email about the items in the shopping cart, with a message like, “Now it’s time to shop for you”?
Or perhaps someone will buy items prime for cross-selling, like a pedometer, which means running clothes and shoes might be appropriate add-ons to market via email post holiday.
Or consider the electronics and Legos being bought en masse this 2015 season: They all offer plenty of post-holiday potential for targeted email marketing!
Plus, beyond the actual browsing and purchasing, marketers could look at how much was spent and how frequently, to identify the most loyal customers.
There is a catch to this, of course: Truly leveraging all of that data in an effective way requires a relational database. Which brings me to my next rumination.
Relational database required
For a few years now, us industry folks have been espousing the virtues of the relational database in order to make this kind of advanced segmented, targeted email marketing possible. You simply can’t achieve the same degree of segmentation with a flat file database, because only a relational database lets you keep data in different tables and then relate them to one customer or group of customers for targeted marketing. Flat file databases can’t offer this kind of flexibility because they just aren’t designed to do so.
With a flat file database, you can collect all the data you want to on a particular customer, but you won’t be able to use it without some kind of manual and extremely time-consuming process. And for each additional bit of data you want to add, you must add that field for every customer. Flat file databases work great for simplistic needs only. If your goal is advanced segmentation and hyper targeted email marketing, a flat file database structure won’t work.
Here’s the catch: not every ESP supports relational databases
Now if using a relational database was as easy as flipping the “on” switch with your current email service provider (ESP), many more marketers might be utilizing the power of this kind of flexibility for segmentation. However, most ESPs don’t support relational databases. If yours does, then definitely consider putting that to work for your email marketing in 2016, using all of the data you’ll acquire during the next few weeks of frenzied shopping.
If, on the other hand, your current ESP does not support relational databases, it might be time for a good, hard look at your short- and long-term email marketing goals to determine if you can even achieve those goals with your current vendor. With the direction email marketing is moving toward ever-increasing personalization, and real-time cross-channel marketing, it’s hard to imagine how any marketer could keep up without a relational database making all that data usable.
Is 2016 the year for a new ESP?
If you’re taking my earlier advice to heart and want to consider all of the data you’ll gather during the next few weeks your goldmine of information for the year ahead, it could be that 2016 also becomes the year you seek out a new email service provider, one that supports relational databases and therefore your ROI goals.