Automating Domino's local competitor pricing research

Domino's is the world's largest pizza company, with more than 22,500 stores across over 90 markets and a business built around delivery and carryout.

Within Domino's CMO office, local competitor pricing analysis is a recurring market intelligence workflow. The team needs to understand how competitor prices vary across markets, stores, ordering channels, delivery modes and discounts.

Brainbase benchmarked an agentic workflow to automate this process: collecting competitor prices, applying market-specific rules, filling the pricing workbook and routing exceptions for human review.

97%

Final benchmark accuracy

52%

Lower cost per source

41 min

Faster turnaround per run

#The problem

Competitor pricing research is highly manual because the “right” price depends on local context.

A human analyst has to check competitor websites, delivery platforms and app-like ordering flows; select the correct location; compare delivery and takeaway pricing; identify the cheapest qualifying item; account for promotions or unavailable products; and then transfer the result into a customer workbook.

The work is repetitive, but it is not simple. A wrong market, store, channel, promotion or menu assumption can make an otherwise completed source inaccurate.

The core challenge: Domino's needed a faster and more repeatable way to collect local competitor pricing without losing the judgment required for edge cases. The metrics that mattered were accuracy, cost per source, average runtime, and whether discount search could be automated at all.

#The agent workflow

Brainbase built an agent to automate the recurring collection process. The agent reads the target list, navigates competitor ordering surfaces, checks the correct local store or market, interprets delivery and takeaway pricing, identifies relevant promotions, fills the pricing workbook and flags exceptions for human review.

1. Target list
2. Local browsing
3. Price interpretation
4. Workbook fill
5. Human review

The agent converts a manual browsing workflow into a repeatable pricing collection process.

#The benchmark

The benchmark tracked multiple agent runs over time. Each run became a data point for measuring quality, cost and operational progress. For each run, we tracked:

This made the project measurable as an iteration loop, not just a one-time automation demo.

#Accuracy improved across versions

The agent improved as each run exposed new edge cases and failure modes. Accuracy increased from 41% to 97% across the benchmark period. The largest improvements came from making the playbook more specific: better location matching, clearer delivery and takeaway handling, stronger discount logic, and more explicit rules for unavailable stores or ambiguous menu items.

#Cost efficiency improved

Cost per source is the cleanest spend metric because run scope changed over time. Across the benchmark, cost per source decreased from $2.11 to $1.01, a reduction of approximately 52%.

This matters because competitor pricing is recurring work. Lower cost per source means the workflow can scale to more locations, markets and competitors without scaling manual effort at the same rate.

#Human work shifted from collection to review

The manual workflow took at least two hours per run. The agent averaged approximately one hour and nineteen minutes, reducing elapsed turnaround by about 41 minutes per run.

The largest change was the type of work being done. Before Brainbase, the human analyst had to manually collect every data point. After Brainbase, the agent handled the repetitive collection work while the human focused on reviewing exceptions, correcting edge cases and improving the next run.

#The improvement loop

Each run created feedback that improved the next version.

1. Agent run
2. Agent review
3. Corrections
4. Playbook update
5. Better next run

This loop was especially important because competitor pricing includes judgment-heavy edge cases: promotions, unavailable stores, local menu differences, channel-specific prices and source-specific quirks. The system improved because human corrections were converted into more specific playbook rules.

#Why it matters

Brainbase gave Domino's a more repeatable way to monitor competitor pricing. The value is not only that one workbook can be completed faster. The value is that the workflow becomes measurable, reviewable and improvable over time.

• Faster competitive visibility
• Lower cost per pricing source
• More consistent methodology
• Less repetitive manual analyst work
• Better handling of local pricing complexity
• A workflow that improves with each run

#Methodology notes

This benchmark reflects an iterative operational pilot. Accuracy was estimated through manual review. Some runs did not have corrected workbooks available. Run scope changed across versions, so cost per source is more useful than total cost alone.

Future versions should track manual review time, correction count, coverage rate and cost per completed pricing cell.

Brainbase converted Domino's competitor pricing research from a manual recurring task into a repeatable agent workflow. Across the benchmark, the agent improved accuracy, reduced cost per source, shortened turnaround time, and created a feedback loop where human review made each subsequent run stronger.