Online food ordering has been growing extremely fast in the last years with many players entering the market and some others establishing themselves as market leaders in the respective regions. Some of bigger companies/holdings are Grubhub, DeliveryHero and JustEat. 
Most of them operate a marketplace model, meaning they don’t have to deal with the last mile delivery of food. Marketplace sounds like an easy way to have a scalable business without the complexity inventory can cause, but that comes with its own challenges.
- How do you manage over 150,000 restaurants across 40 countries when you are not directly responsible for their quality or delivery time but you are directly penalized by the customer for recommending them a bad restaurant?
- How do you decide which restaurant should the sales people go after and in which areas?
- How do you know which restaurants have the biggest potential to grow so you can support them with a dedicated account manager?
- How do you identify restaurants that are just churning your customers and therefore you would rather no longer list them on the platform?
The restaurant acquisition challenge:
There are multiple factors to consider when creating a lead list for the sales team to go after such as: How many and what type of restaurants are available on a specific area, what is the rating of the restaurant or whether the restaurant is available for delivery at a competitor’s website. This process is complex and can have a big impact on costs (sales bonuses) and customer experience.
To improve this process, Deliveryhero is using Machine learning that helps combine data from a variety of sources and create a lead list for the sales team with target restaurants to sign. Leads are graded based on the potential they have to generate incremental orders. To grade leads, some of the sources used are: previous sales history, google reviews, social media, competitor listing pages and performance of a specific cuisine and restaurant type that specific area. The algorithm runs every two months and updates the lead list with new leads as well as updates the grade for the existing leads as conditions may have changed and therefore a lead graded as “Α” quality before might not be not as relevant now.
Thoughts non addressed points:
Time needed to sign a new restaurant varies depending on how big a restaurant is (ie. a big chain requires significantly more time compared to a family owned restaurant). There are also a lot of synergies with regards to to the signing process that can occur (ie. a sales representative has signed the same restaurant in another country or area, therefore combining this effort can have higher chances of success). This information can be found online and also in the internal sales management software. Adding this info in the grade algorithm can increase significantly the sign up success rate and also help identify synergies in a global organisation that are often difficult to spot.
The restaurant management challenge:
Once a restaurant is online, it is important to ensure it is performing well and drives customer satisfaction. For a company with over 150,000 restaurants, assigning an account manager to each restaurant is not economically sustainable and therefore it is important to identify which restaurants should get one and which should be managed in a more “automated” way. Also actions within these groups can vary. For example restaurants that have high order volume but low operational performance should have a different recommended action plan compared to those that have great operational performance but have not yet reached their full order potential or restaurants that are doing great in both areas but have a low margin for the company.
Machine Learning algorithms are used in this areas of the business too to understand what drives restaurant performance and customer satisfaction and produce action plans for each restaurant. Some of the recommendation can be about improve operational performance, increase commission, run a free delivery deal or improve menu assortment.
Thoughts non addressed points:
The biggest challenge when implementing this algorithm is identifying business impact. Increasing the commission of a restaurant has a clear impact on Revenues but how about improving the order rejection rate? Though rejection rate is highly correlated to customer satisfaction it is challenging to understand what is the direct business impact. Usage of data mining to understand this better would be very helpful for business leaders to focus on the right priorities for their teams.
Machine learning algorithms are essential in identifying important tasks for the sales team but how happy would sales employees be to work for a company that doesn’t take into consideration their personal judgment regarding their area of expertise?
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