Transforming the hospitality industry – the Analytics way

With the Food & Beverage industry at an all-time high, restaurants today are focusing on catering to an ever-growing and discerning audience. From increasing customer retention to menu improvement, Business Analytics is helping restaurants take their services to the next level.

A renowned restaurant chain approached Matrix & Vectors to improve same-store sales and increase revenue by optimizing their menu. Let’s take a look at how this was done.

Boosting Same-Store footfall

The factors that influence customer selection of a particular restaurant are highly unpredictable and numerous – making it difficult to identify motivations for revisit. Given the highly unpredictable nature of the industry, we used a BG/NBD model for analysis.

Customer visit data in the form of reservation and walk-in files was available to build the model. The data included:

  • Basic customer identification details
  • Day of visit
  • Details of purchases
  • Reservation details
  • For walk-ins, the data notably did not include motivations for the customer choosing the particular restaurant

As the first step, basic analysis was performed to identify the distribution of recency and frequency of footfall. The high number of one-time visitors in the raw data set along with the limited time frame covered, were constraints we had to live with while developing the model.

Boosting Same-Store footfall

As a result this model behavior needed tuning and validation. The initial version of the model was built on data of the first half of the time period covered. The resultant predictions were then compared to the actual data of the balance 48 days. Iterative tuning of the model was subsequently conducted to increase accuracy.

The results evident from this analysis highlighted that:

  • The reasons for lack of repeat visits was not identifiable from the available data
  • Predictions made by the model were directionally accurate – with the customer profile most likely to revisit clearly identified
  • The model was able to capture the relationship between recency and frequency of customer visits with accuracy and precision
  • Potential repeat customers could be identified and targeted using vehicles such as social media

Optimizing restaurant menu cards

There’s more to designing an effective menu for a profitable enterprise than just putting together a random selection of dishes. Apart from overriding considerations like the theme of a restaurant or genre of food, menu engineering must be responsive to the popularity and profitability of individual dishes.

For this client, data procured from kitchen and sales records was available for analysis. The data included:

  • Cost of raw materials
  • Quantities of raw material used to prepare specific dishes
  • Itemised quantum of dishes sold

Optimizing restaurant menu cards

The data indicated that munchies were by far the most popular in terms of frequency of ordering. In fact the breakup of the 50 top selling dishes showed a strong bias towards light munchies that could be paired with alcohol rather than main course servings. This was in line with the brand’s image as a lounge.

The analysis was extended to identify the profitability of individual items to help the enterprise retain items with maximum returns while doing away with those providing minimal value.

  • Several low volume and low profitability items were identified indicating that these could be considered for deletion from the menu
  • One particular item, the Bacon add-on, although not low volume, was identified as being sold below cost price. This would require price correction of this item or a corresponding increment to the dishes to which it was offered as an add-on
  • Since munchies were by far the biggest selling category, the restaurant could consider recommendation of specific snack / alcohol pairings based on compatibility
  • It was possible to identify popular dishes for recommendation to new customers
  • More details would however be required to enable targeted recommendation of new dishes / combos to repeat customers based on their past order history

Although the data provided by the restaurant was limited, Matrix & Vectors was able to harness it to help the chain make informed, data-driven decisions. With a more in-depth analysis, restaurants can further boost customer retention by identifying and targeting the right customer base.

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