Case Study

Providing the customer a way to capture their data into a single platform dashboard to forecast demand and capacity

The client is a premium restaurant business with four different brands and 30 restaurants. The brand is predominantly in Northern England with several locations in London and restaurants are situated across diverse settings including the city centre, suburbs and out of town shopping centres.


The client had significant performance differences across their sites, but very little visibility of data that would enable them to forecast demand or streamline spend. This meant there was considerable wastage across staffing and marketing.


By capturing their data in a single platform with dashboard to forecast demand and capacity, the company were able to adapt their marketing campaigns, balance their staffing needs with occupancy levels.


Increase in cash profit


When the client approached us, they were experiencing significant differences in performance across their sites. Diverse drivers of footfall had led to inconsistent demand day to day, and the disruption caused by the pandemic meant they didn’t have representative historical performance for valuable measurement metrics. Inflation and economic uncertainty had been impacting consumer spending, and all of this was contributing to increased levels of unpredictability in the business.

The client’s objective was to improve the control of operating costs by having a more accurate understanding of future demand. They hoped to use this understanding to adapt their operations and marketing site by site.

The Solution

Improving control of operating costs

We worked with the client to create a dashboard that forecast demand and capacity for every restaurant at any point of the day. Users could review this data at any level they choose.

As a result

  • The marketing team were able to adapt campaign, activity, and individual site promotions to drive demand in the places it was most required. This reduced spend on marketing and promotions where demand and capacity were sufficient.
  • The operations team were able to balance staffing levels between customer service and cost by having a more accurate view of demand.
  • The restaurants were able to optimise capacity for the first time by understanding occupancy trends and adjusting layouts at key points so that all tables were occupied and speed of service increased.
  • With so much more visibility over site performance, the management team had the confidence to make investment and divestment decisions, giving them greater certainty and stability for the future.

Part 2:
Building differentiated customer journeys that drive revenue

The solution has a variety of technical assets.

  1. It integrated data from different internal systems:
    • Booking
    • Sales
    • Email system
    • Loyalty databases
    • Web
    • App
    • Operational guidelines
    • Property data

We worked with structured data (e.g. booking times) and unstructured data (e.g. guest requests), processing unstructured data using Natural Language Processing (NLP). This meant that data points could be combined for more accuracy. For example, particular guest requests could ensure additional seating time was allocated to tables.

  1. The solution we built captured, processed and integrated external data at a site level, including weather and local events, using NLP to codify unstructured data.
  2. These different data points (Integrated data, internal and external) were then processed using machine learning (ML) to create accurate demand and capacity forecasts.
  3. From this, we developed dashboards with user interfaces designed with a variety of different roles and use cases in mind.
  4. We continually refined the algorithms we used for the demand and capacity forecasting, by monitoring actual restaurant performance and using the data in our machine learning models.

The Result

As a result of our work, the restaurant saw efficiency increases across its operations. This led to an increase in cash profit of 4.7% with no negative impact on customer satisfaction.

“This information is critical to how we manage
and improve the business”
CEO, November 2022


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