The most important job for any retailer is to keep customers coming back to the store. And while many factors play into the consumer’s choice on when to return, there are three strategies a business can practice to maximize its opportunity to retain customers. A data analytics company can help a retailer gather the information it needs to make these strategies work.
Identify which customers are most likely to come back
Most retailers consider a shopper as gone for good if they have not shopped at the store within a year. Yet shoppers on the verge of being lost often return if the retailer delivers a well-timed communication. Perhaps it’s an email notifying the customer of a special sale. A retailer can work with an analytics company to focus on those customers who returned, either as a result of an ad campaign or for other reasons, and see if they have any common characteristics. The company can also focus on customers who didn’t return and look for their common characteristic. The retailer might find that people from one socioeconomic demographic generally returned, while those from another socioeconomic demographic did not.
Mirror successful strategies used in the past
There’s no need for retailers to reinvent the wheel with every new marketing campaign. The best insights come from observing past behavior and successes. If a retailer can identify customers who have come back it can build a statistical model around this segment to find out what type of communication will influence their behavior. In addition, once the segment is identified, the company can overlay that data to find similar customers.
Out with the Old,
In with the Old?
Just because your customers haven’t shopped in a while doesn’t mean they are lost forever.
With this data a retailer will know who is likely to come back and what proven tactic the customer will respond to. For example, a retailer’s research might show that a significant percentage of customers who had left the store returned if they received three communications over a three-month period. With this knowledge, the retailer can mirror the previous campaign, target the identified customer segment and be assured of a high success rate.
Follow the ‘Test and Learn’ or ‘Champion verses Competitor’ concept
Not a lot of progress will be made if things never change. However, once you have found a great solution there is no point in changing it until you find something better. This is the rationale behind the age-old “Test and Learn” or “Champion versus Competitor” strategy often implemented in the world of consumer marketing. So if a retailer finds that red envelopes in a direct-mail campaign drive more results than blue or white envelopes, it should consider red envelopes its “champion” and keep using them until it proves through testing that another color of envelope — the “competitor” — drives more results.
Not surprisingly, research has shown that customized campaigns deliver more lift (assuming they are properly targeted). For example, let’s say Sally is inspired by beautiful images and ideas, and Bob is looking for the next great bargain. A one-size-fits all-campaign will have some impact, but two customized campaigns will garner a larger lift. A glossy magazine execution for Sally and a sale flyer for Bob will outperform the “vanilla” option.
People are as individual as snowflakes, so how can retailers customize their offers to maximize their effectiveness without blowing the budget and creating hundreds of different offers? In a word: Segmentation. Through data analysis, retailers can create a manageable number of meaningful segments that will allow more customized executions for the Sallys and Bobs of the world.
A successful campaign to win back customers is based on who the retailer should go after and how. Retailers who follow this cycle must also test results and learn along the way. It’s important to constantly gather data and quantify the results of marketing efforts.