It’s not uncommon for direct mail response rates to be low. The good news is that even a 1% increase in response rate can have a significant impact on the bottom line. This is perfect for retailers who adopt a “think small, get big results” policy for their direct mail communications with customers.
Here are 3 tips retailers can use to create effective campaigns to increase direct mail response rate:
- Stay Relevant To Your Audience
- Make The Communications Personal
- Model To Leverage Greater Results
- Response modeling: identifies people who are most likely to shop within a set time frame based upon past shopping behavior.
- Lift modeling: identifies the people who will respond incrementally based upon a communication that is sent to them.
Blanket campaigns are easy to manage; they reduce the strain on the marketing team and simplify tracking. However, a one-size-fits-all approach to direct mail is a sure-fire way to ensure response rates remain flat. Customers won’t take action based on a mailing if the messages aren’t relevant to them.
Retailers must understand their client’s wants and lifestyles to assure that their messaging is always relevant, which, in turn, builds a customer’s confidence in the establishment being able to deliver what they want. For instance, empty nesters and retirees will have distinctly different priorities than young families or single professionals.
But segmenting the customer base alone won’t improve the results of direct mail; tailoring the images, messaging, and promotions so that they speak directly to an audience’s particular lifestyle is the key to an impactful campaign. Images of diapers, for example, are unlikely to spur retirees the way they would new parents.
Analytics can make direct mail more personal than ever before. For instance, Amazon has set the gold standard for personalized relevancy: when every customer logs in, they’re greeted with is a list of personalized recommendations based on their buying and browsing history.
With the right tools in place, direct mail can work similarly. Take a sports fan as a prime example: almost anyone who has purchased an item from the NFL shop, MLB shop, or NHL shop has received personalized, follow-up mailings that show their last name on their favorite team’s jersey. Images like this link the customer directly to the merchandise. Retailers can get in on this trend by including customers’ names on images of items and personalizing the salutation in the message itself. This creates an instant connection with the customer (reminiscent of a handwritten note rather than a machine-generated email), and increases the chances that the individual will read and act upon the message.
Leveraging statistical modeling can also help to identify those shoppers who are most likely to respond to a mailing. Modeling can take two forms:
Analytics provide retailers with the ability to determine who is the most likely to spend more based upon a relevant communication. If retailers can reach those customers consistently, and are able to incrementally increase their spending with each promotion, it instantly bolsters the effectiveness of direct mail communications.
Here’s how it works: Susie and Jane are likely to shop during the week of a promotion. However, Susie is more likely to shop if she receives a relevant communication in the days prior to the promotion. Jane, on the other hand, will spend a set amount regardless of whether or not she receives a communication based on her previous habits. Should retailers ignore Jane in a direct mailing? Of course not, but with the right communication Susie is a more profitable target – not only can you entice her to shop but you can motivate her to incrementally increase her spending: perhaps she was planning to spend $50, but the mailing may motivate her to spend $75.
By leveraging customer data, retailers can get targeted products with personalized messaging directly to segmented customers. And this highly specific, highly relevant mailing directly improves the response rate.