What levers do quick-serve restaurants pull in order to retain more customers?
Is it hot and tasty food? Cleaner dining areas? Or friendly, accurate service?
Is one of these attributes more effective in converting a one-time visitor into a loyal customer, or are they equal?
We frequently answer these types of questions for our quick service restaurant (QSR) customers — in particular those who may capture customer satisfaction data through in-store or online surveys. Typically, the results collected are a good measure of customer satisfaction for individual dining experiences. Ultimately, every restaurant brand wants to understand how their survey results impacts key loyalty metrics. Will negative or positive responses show a relationship to return visits? What about the frequency of return visits?
To understand this relationship, QSR chains are turning to Euclid’s location analytics through their existing in-restaurant Wi-Fi systems. Euclid utilizes a QSR chain’s existing Wi-Fi hardware to detect visitors at each restaurant location. In addition to providing visitor behavior insights like storefront conversion and average duration, one of the advantages to Euclid’s technology is the ability to understand new and repeat visits over time, across the entire chain of restaurants (even across locations!).
Here’s Euclid’s data-driven recipe for QSRs as they seek to understand the results behind their customer surveys.
Step 1: Measure the key metrics within your survey results
Most quick service restaurants solicit customer feedback on a number of different attributes spanning cleanliness, wait times, food quality, friendliness and overall service quality. Yet knowing these survey results is only half of the battle. Which attributes are affecting loyalty in the restaurants? For example, some customers remain loyal because of the overall experience and tidiness of a restaurant and place a secondary consideration on the quality of the food (strange but true).
Step 2: Measure frequency and loyalty
Through location analytics, understand the “return rate” of customers seen at each restaurant location over a 1-2 month period. For example, what is the 1 and 4 week return rate? These are the percentage of customers that are seen on a particular day who returned after 1 and 4 weeks, respectively.
Step 3: Correlate the survey and location data
By differentiating the duration visit types, tracking the frequency of repeat visits, and correlating with survey data we can begin to understand the drivers of customer loyalty for restaurants. A QSR chain’s operations team can then focus on locations with lackluster survey results to boost the rate of loyal customers. Best practices from higher performing locations could be shared across the chain, knowing that better survey results in certain dimensions (food quality, order accuracy, staff friendliness, etc) will lead to more frequent visits.
Euclid’s location analytics helps QSRs understand the true behavior of their restaurant guests. Combined with existing survey data, our technology unlocks key drivers of customer loyalty and increasing visit frequency.
To hear a case study example of what survey data told one of our QSR customers about loyalty and frequency, sign-up for our webinar next week: What’s loyalty got to do with it? How to decode loyalty in your restaurant and converting it to profit.