The Food landscape

The food landscape in Sri Lanka is peppered with Quick Service Restaurants offering a variety of fast food options to entice the busy urban middle to high income earners. From Burgers, Pizzas and Sandwiches to Chinese and Mexican cuisine, QSRs are a popular option offering variety menus, family friendly atmospheres and convenience. In spite of quality and perhaps health related concerns, most international QSR brands have been warmly embraced by particularly the high-income earners. This is in sharp contrast to middle to low-income earners targeted by these chains in their countries of origin.

Nevertheless, with increasing number of Fast Food options, online ordering, delivery options, shifts towards healthier lifestyles, consumers tend to be less loyal to a particular chain. This is further intensified by the competing promotional offers marketed by various QSRs which drives more impulse based purchasing rather than loyalty-based purchasing decisions.

The Leaky Bucket

Most of the QSRs have at least some contact information of each customer and a wealth of historical purchase information. However, given the aforesaid questionable level of loyalty a QSR’s customer base is much like a “leaky bucket”.

Loss of customers irrespective of industry is a big business killer. Churn/Lapse is also one of the few metrics that can be directly correlated to revenue. If one reduces Lapse among paying customers, they’ll be rewarded with higher revenue, guaranteed. According to an article on Forbes, it is six times more cost-effective to retain a current customer than it is to acquire a new one. Existing customers are 50% more likely to try new products and spend 31% more money compared to new customers. And increasing customer retention rates by a mere 5% will increase profits by 25% to 95%.

But the problem is spotting where “the leaks in your bucket” will appear well in advance and knowing the best way to plug each hole.

The Problem with Bulk Promotions and Deals

While promotional offers can spur impulse purchases and some increase in sales, there is little to no relationship between the offers and its impact on customer retention in the long run. Often the offer is communicated to the entire base without much segmentation nor attention to timing and relevance. Further, with the number of promotional offers consumers are bombarded with each day by various QSRs via SMS and social media, there is a high chance of even some of the more relevant offers going unnoticed.

Promotions done with the intention of retaining customers are designed with a generalized definition of “lapse”(Eg. A customer who hasn’t made a repeat purchase for the past 30 days may be considered a lapsed customer). However, “lapse” is dependent on individual frequency of purchase and generalizing this is fundamentally inaccurate.

Segments of One

But what if there was a science to make sense of this madness? What if lapse could be defined based on the purchasing habits for each individual customer (Segments of One)? What if likely lapses could be identified in advance so that they could be enticed with tailor made offers based on their past preferences and bahavior? What if these offers could be communicated at the most optimum time for each individual? While all this sounds possible in theory, executing campaigns to segments of one would be a massive challenge due to the sheer complexity of the problem, and lack of algorithmic and technical expertise and resources inhouse to support.   

 

Data Science to the Rescue

This is where Linear Squared with our advanced data analytic skills, algorithmic knowledge and technological expertise comes into the picture.

Irrespective of the scale of data, the granularity of execution nor the number of constraints at play, the AI and ML based Retention2 product of Linear Squared would be able to provide an optimized and automated execution plan for QSRs to run promotional campaigns targeting segments of one with guaranteed revenue uplift.

Find out how in our next article.

Image Credits:
https://www.springwise.com/diners-give-free-burger-friend-sharing-codes-online/

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