Leveraging Your Next Logical Product to Create Repeat Customers

"If you’re a marketer, you’ve no doubt heard about the 80/20 rule. For those of you who haven’t, it’s simple: 80 percent of your revenue comes from 20 percent of your customer base. If that is indeed the rule, it means the vast majority of your business is coming from repeat customers. The question becomes: How do you create repeat customers?"

If you’re a marketer, you’ve no doubt heard about the 80/20 rule. For those of you who haven’t, it’s simple: 80 percent of your revenue comes from 20 percent of your customer base.

If that is indeed the rule, it means the vast majority of your business is coming from repeat customers. The question becomes: How do you create repeat customers?

The email marketing answer is a Next Logical Product program. And it down to this: When someone buys a product from you, what do they buy next? And it’s not necessarily what you might think.

Using laptop computers as an example, here’s how it works:

• Review your purchase data for “Product A” (being a laptop). Pull out all data associated with repeat buyers—specifically the repeat buyers who purchased other products within 90-120 days after the initial purchase.
• Determine the top “Product B’s” purchased in that timeframe. Categorize them if necessary. (In this example, it could bags/sleeves, printers, and other categories.)
• Choose the top-performing Product B’s and build an email message that targets those Product A purchasers with these “complementary” products.
• You may have multiple Product A’s. Your NLP program could vary wildly depending on the product. Start small with a Product A you know performs well to get repeat customers and work your way backward.
• Per my laptop example, your Product B could be another Product A (particularly in the B2B space).
• Per this example, your Product B could be your service plan. That’s not a bad thing, either.
• It’s important to limit the timeframe for your NLP program to 90-120 days after initial purchase, as complementary products are most likely to be purchased in that time.
• It’s also important to revisit this topic at least once per year, if not seasonally, as the Product B’s could change very quickly.

Once tested, schedule and automate this message to go out at a certain interval after the initial Product A purchase goes through. You can decide on this cadence based on what your data shows might be an optimal time for such a message to go out.

For my example, it could be 3-7 days after initial purchase when that thought process might play itself out—your data will show you what’s important.

Some additional things to think about as you build this program:

It may seem simple and obvious, but this “low-hanging fruit” program is something not a lot of retailers do well with (or do at all).