Retail case study · Betfred

Betfred x Optimal Monitoring

Emma AI turns consumption data into measurable savings at scale.

Illustration of an Emma AI robot connecting retail locations across a digital map of the UK.

1,400+ retail locations

Automated energy analysis and store-level action.

Existing data. No hardware. No added analysts.

East region: 10% savings

Other regions: 3%–7%

Ongoing action sustained results.

Overview

Betfred is one of the UK’s largest betting-shop operators. Although each location used relatively little energy, the combined estate created a significant and growing cost. Betfred needed a scalable way to identify waste, involve store teams and support its Net Zero strategy.

The challenge

Traditional energy-management approaches were difficult to apply across a large estate of small sites. Betfred held substantial consumption data but needed to turn it into clear actions that shops could understand and complete, without increasing headcount or disrupting operations.

The approach

  1. Analyse existing data

    Emma AI identified abnormal use and recurring waste.

  2. Turn findings into actions

    Daily work tickets gave shops clear, practical recommendations. Store feedback refined future recommendations.

  3. Validate the impact

    The East region received Emma AI instructions for comparison.

“Emma gave us the tool and the knowledge to make best practice in one store the new target for all stores.”

Estates Manager, Betfred

Results & learning

The case-study graph illustrates excess consumption followed by a period of improvement.

Betfred case-study electricity graph showing approximately £55 of excess consumption in the highlighted earlier period and approximately £40 of improvement in the later period.

What the analysis proved


  • Existing data contained value

    Emma AI converted underused data into practical instructions.


  • Local teams could act

    Simple recommendations fitted into normal store operations.


  • Savings could be sustained

    Continuous reporting maintained attention as staff changed.


  • Scale changed the economics

    Automation made analysis viable across many small locations.

Why this mattered

  • Scalable operating model

    Automated analysis replaced impractical site-by-site review.

  • Local teams could act

    Simple recommendations fitted into normal store operations.

  • Board-level visibility

    Energy management became a sponsored estate initiative.

  • Measurable impact

    Savings, emissions and engagement could be tracked.

Verified impact

6.3%
annualised reduction
£234k
annualised saving
131 tonnes
CO₂e avoided
Within 1 year
payback

What Betfred learned

  • Data creates value only when translated into clear actions.
  • Relevant recommendations improve store engagement.
  • General switch-off campaigns need continuous follow-up.
  • Board sponsorship helps embed estate-wide change.
  • AI can connect recurring patterns across many small sites.

Rollout status: Emma AI is rolled out nationwide.

Turn energy data into action across your estate.

Discover how Emma AI helps teams identify waste and take practical steps to reduce energy costs and carbon emissions.

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