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Working with a restaurant chain they had heavily invested into digital, and they were struggling to quantify it’s impact as there was no visibility or trust in their analytics.


Undertaking an audit, the main insights:

  • Primary KPI was to get users to make a booking on the website: this was broken as the booking form was sitting within an Iframe and the tracking was not set-up for Iframe
  • Traffic numbers reported were in-correct due to the Iframe as it created a new session after the booking was complete
    • The new session was being reported as direct in Google Analytics and inflating the traffic numbers
  • The tracking set-up via Google Tag Manager for Google Analytics and other platforms was based on the click that was driven to the booking form, but it was being reported as booking confirmation. Reported numbers were in-correct

Recommendations from the audit: (there was going to be a new website build part of the process)

  • The booking form could not sit within the Iframe
    • This would allow Google Tag Manager to track the completion of the online bookings
    • It would ensure that true traffic numbers are reported in Google Analytics

With a solid technical set-up this would allow the business to understand which channels are driving traffic and online bookings.


When meeting with the developers 5 working days before the website was going live (this meeting was planned for a month prior live date) allowed me to understand, the technical set-up, the on-site user journey, and the data available when making an online table booking.

Using the API there was a rich set of data available when making an online booking, made the decision to turn the booking confirmation into an e-commerce event in Google Analytics using a data layer. When a booking is made the following data variables would be collected:

  • Date & Time of when booking was made
  • Restaurant location of booking
  • Restaurant ID
  • Date & Time of booking
  • Size of booking
  • Value of booking
  • Booking reference ID

The value of the booking was made on an average receipt value the business generated per customer.

The insights available (once the new website went live) was far greater than the business had planned for. With the data available it allowed the business to understand:

  • Which locations were getting booked
    • This could get segmented into date of booking (day of week, month), and time slot
  • The no of bookings that was coming from digital
  • Time lag of the booking: when the booking was made v the day and time of the actual booking

To better understand channel and campaign insights looked at improving the quality of data from campaign tagging. The channel mix was Paid Search (mainly branded search), Paid Social, Email and Programmatic. Paid Search had a good structure which was used as a template.

  • All the paid media budget is driving towards getting bookings for locations the campaign tagging was based around this set-up.
    • utm_source = platform i.e., Facebook
    • utm_medium = channel i.e., Paid Social, Organic Social, Email
    • utm_keyword = location of the restaurant or brand name (if it was more generic like for email or brand campaigns)
    • utm_content = which area is the campaign running i.e., Midlands
    • utm_campaign = campaign name i.e., Christmas, On-going, Easter

The location of the restaurant being the key variable to provide the insights:

  • Is the specific campaign driving an online booking
    • For the exact restaurant location or another restaurant location


The top line headlines:

  • With the new website and online booking process. 25% drop in traffic and 30% increase in bookings
  • Online bookings accounted for 40% of total restaurant bookings

Doing a deeper dive analysis using the booking reference ID as the key variable with data provided by the restaurant booking system:

  • 45% of online bookings did not visit the restaurant on the selected booking date
  • London restaurants were the biggest culprits and had the biggest dropout rate
    • London also had the highest % of walk-ins
  • Restaurants outside London had a 50% conversion rate from online booking to visiting
  • 15% of online bookings receipt value was higher than average

These insights impacted how campaigns were tactically set-up:

  • Campaign budgets were moved from London restaurants to outside London
  • Paid Social became the core channel to run the tactical campaigns

Next Steps

With more data collected it would allow the analytics to evolve further and provide more insight:

  • What is the baseline of bookings without any campaigns being run
  • Understand the receipt value per customer by location for walk-ins and digital bookings then update the data layer to better reflect so Google Analytics is providing more accurate data i.e., the avg receipt value per customer is 25% higher in London
  • With 1 years of data build out a location based forecasting model with the digital and booking system data

One of the core objectives is to get bums on seats, the insights from Google Analytics and the data approach have provided a way to use data smartly to deliver against the business objectives.