According to data published by Caslon & Co. based on research by the DMA and PODi, personalized marketing content generates response rate 3x or more than static content for various marketing objectives, including ordering.
But what if you have thousands, tens of thousands, or even millions of customers, like many retailers, e-tailers, consumer products and services companies, and B2B distributors? If they could send the right product offer to every single one of their customers in every email campaign, the results would be fantastic. But conventional wisdom puts this beyond all but the largest companies.
There is so much attention to this area now that things are changing. What you assumed true a couple years ago may not be so today. Here is a list of the most common misconceptions I hear:
1. More types of data means more accurate predictions. Conventional wisdom is, you need to pull data together from as many sources as possible to discover the right customer attributes and behaviors that most accurately predict a customer’s loyalty or buying interests. That, by necessity, entails a huge data integration project before you can get started, but the basic premise is not true. Skip the massive big data project. With the right technology, you can get highly accurate purchase predictions from just a few data fields of past purchase history.
2. The best lift comes from finding the right customers for each product. By far, the most typical use of predictive analytics for email marketing is to determine which customers are likely to be interested in a given product and run a campaign offering that product to those customers. This is partly because the company has reasons to want to move a given product (high inventory, better margin, etc.), but it’s also because of limitations imposed by conventional wisdom. Data modeling is expected to figure out a way to group or “score” customers on their likely interest in the product. Many tools, algorithms, and techniques may be involved, and it could take days. Probably okay for a handful of products, but forget about finding the best customers for “all” your products, right?
Well, there are technologies out there that automatically score all products against all customers without anyone doing any modeling. Rather than product-centric campaigns, you can do customer-centric campaigns that make every customer a different, personalized product offer. In our experience, the best lift comes from these campaigns.
3. Executing individualized marketing campaigns is hard. Many marketers still assume it’s too hard to individualize emails to every customer. In fact, all but the most basic email automation systems allow templated emails that contain areas for variable content. That content can be automatically pulled from a control file: a customer list that contains or points to the variable content for each customer, which is inserted into the template. The hard part is building the control file. But that’s getting easier too. There are services and do-it-yourself tools for building these files from the analytics. So, a marketer could specify a list targeting fading customers where they will see four product offers, three they’ve purchased before and one they have not, based on purchase probability, then automatically output a control file in the format needed by their email system.
4. Finding new customers is most important. While this is an important job, it’s still more important to get the most value out of existing customers. There’s no quicker and easier way to move the company’s top and bottom line than increasing the lifetime value of each customer (not just on average, but of “each” customer). There is no guarantee your “loyal” customers will continue being loyal. Personalized marketing to your customers makes that difference.
So there you have it. It doesn’t have to be that costly or difficult to predict the products each customer is most likely to buy.