So much of a restaurant brands marketing energy is dedicated on the heaviest users. Obviously, focusing on the smaller percentage of customers that drives the majority of sales makes sense in a mass media environment.
Good news, bad news. The mass media environment no longer applies to most brands. Enter the long-tail. By the end of the dot com era it was clear that anyone with an interest in an obscure subject could find the content to satisfy them. Apple’s iTunes store offered people the chance to find and explore tons of music they would have never heard about on FM radio. And dozens of publications like Pitchfork sprung up to feed interest in lesser known artists.
Top 40 pop radio still reaches the broad majority of music listeners. The obscure tracks occupy the long-tail following that the majority. Starting in the early 2000’s, marketers developed long-tail strategies aimed at capturing attention from those interested in long-tail subjects.
For example, a customer who always orders veggie items should be offered pork sliders.
When mass media budgets aren’t viable for a restaurant brand, the long-tail strategy can be flipped to apply to light users of the brand. A typical long-tail strategy aims to align the brand to content that reaches fewer people who are more engaged and passionate about what they’re reading. Digital media targeting makes this simple to execute.
For restaurant brands, the light users are the long-tail. The lower their visit frequency, the further out on the tail they are. By NPD estimates, light users make up just under half of all restaurant occasions. Media targeting still applies, but restaurant brands can target them more directly. Anime, non-GMO farming, Lars Von Trier films. These are examples of interest groups that might appear in a long-tail brief. Media would be targeted towards digital publications and content focused on these topics. In the case of restaurant brands, the interest groups are around specific menu items, LTOs or price points that interest them. Brands have the data to know what items individuals love, and not just the most frequent customers.
Loyalty programs and apps are geared towards the reward for many visits or purchases. But to attract light users, create incentives that are smaller but more specific to the individual. Instead of starting the program with a known reward, create a reward based on the initial purchase. A customer orders an Italian sub, offer them a discount on their next Italian sub.
Beyond that example, brands should respect customers enough to offer something relevant. If exact preferences aren’t known, start broad but custom. For example, a customer who always orders veggie items should be offered pork sliders. A customer that frequently buys kid’s meals might not want an alcohol based offer. Extend the length of redemption based on that customers frequency.
A brand can also offer a win-back reward based on the length of time from registered visit. A reasonable time frame might be 1.5x the average duration between visits of all customers. If your average customer visits twice monthly, offer a light user a discount at three weeks. Offer a richer reward at six weeks.
Simple technology can be used to quickly assess and create custom rewards based on a pre-set group of qualifiers. The user specific (but not personal) data consumers provide can be used to make them happier – and more frequent – customers.