Location analysis is often used in a narrow way for the purpose. ; allocation. However, it can and should be used more broadly for audience and competition perspectives. Viant, a people-oriented advertising technology company, is a prime example of this phenomenon using location data, loyalty cards, transaction data, and machine learning to understand the affinities and behavior of fast food buyers and consumers. group into a separate sub-segment. More personalized targeting
Five million customer visits analyzed
In partnership with an unnamed intelligence company, Viant analyzed nearly 5 million visits from nearly 2 million people over a period of six months in 2018. The customer was "a national chain of medium-sized sandwich shops with more than 1,000 franchised restaurants".
Variables examined included, among other factors, frequency of visits, meals purchased, and expenses. The study also tracked the most-watched television shows, affinity with retailers, and competitor visits to the cohort sites. Using machine learning, Viant identified five key customer segments that sometimes overlapped, but had distinct preferences and behaviors:
Breakfast – Buyers – Loyalists BreakfastPrimetime PatronsWeekendersDevoted Diners
Although this type of character work (and alteration) is not new, the location of use data to build personas and combine them with a A host of other information derived from real-world activities and transactions offer a much richer and more accurate view of the customer base. It also offers a new variety of lenses to watch customers.
Generally Separate Audience Segments
There is a tendency to segment fast food audiences by age, ethnicity, or gender. However, this model is more complex and incorporates several levels of customer behavior.
Breakfast shoppers were "more enthusiastic than others" and more likely to drive an SUV. Papa Johns has turned out to be the most competing brand on the QSR list on a list of six. These people buy more from Nordstrom, Target, Walmart and Amazon compared to the national average. According to the data, their most watched show was that of NCAA football.
The Loyalists of Noon Time watch Fixer Upper, shop at Nordstrom and visit Chick-Fil-A when they are not eating at the customer's. restaurant. They also visit Starbucks and drink Coke far more than the national average. Primetime customers are more likely to drink beer, eat at the Olive Garden, watch ESPN and refuel at a Shell brand name station.
The so-called Weekenders were found to be loyal to one restaurant, "most likely near their place of residence." Panera is the most competitive chain. They spend less than the national average on Amazon and, like Breakfast Breakfast, NCAA football is their best show. They also spend about $ 56 per quarter at McDonald's.
Finally, Dedicated Diners are fans of The Voice and spend $ 67 a quarter at Papa John's. More importantly, they were the most frequent customers, "who visited more than any other group" and frequented several franchises in a given month. They also go on time or day of the week.
Creating More Personalized Campaigns
One of the most interesting parts of the study, which is not fully exposed, explains how different channels work. best for different audience segments. For example, office ads were 2 times more likely to influence breakfast buyers than mobile ads. Mid-day loyalists were also more sensitive to computer campaigns. Primetime customers have been the most responsive to CTV advertising: "They're 40% more likely to visit after seeing a CTV ad than a mobile ad." Desktop video worked better with Weekenders. However, mobile ads have been more effective with loyal ultra-loyalist devotees.
With this knowledge, Viant and its customer can target different segments through more personalized, specific ad campaigns and creatives. The company may want to address the group with the lowest loyalty or frequency to see if this behavior can be changed. It can also test a conquest campaign with a specific segment and then extend it to additional audiences if successful.
But the paramount value of all this, no doubt, is that location information can allow for a more complete understanding of the client. . And the distinction between location intelligence and survey data, it is that you get real behavior rather than opinions and memories, which can be inaccurate or otherwise erroneous. Indeed, this case demonstrates all the versatility and importance of location intelligence. in the modern customer journey, essentially online-to-line.
About the Author
Greg Sterling is a collaborative editor at Search Engine Land. He wrote a personal blog, Screenwerk, about the connection between digital media and consumer behavior in the real world. He is also Vice President of Strategy and Knowledge for the Local Search Association. Follow him on Twitter or find him on Google+.