Bath · Bristol · London
Case Study · Paid Media

Same spend, reshaped to demand

Same budget, better shape.

Rebuilding an annual media budget for a seasonal British manufacturer from the ground up, allocating the same total spend against four independent sources of evidence rather than precedent.

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The Engagement

Seasonal British Manufacturer

Client
Seasonal British Manufacturer
Sector
Manufacturing & Industrial
Discipline
Paid Media
Outcome
Same spend, reshaped to demand

Client name and figures withheld for confidentiality.

The Challenge

The problem on the table.

Most annual media budgets are built the same way: by rolling the previous year's monthly pattern forward. It feels safe, but it quietly carries last year's shape into the new year, whether or not that shape still matches where the demand actually sits.

For a seasonal British manufacturer, the brief was to rebuild the annual budget from evidence rather than precedent: to fund the genuine demand windows properly rather than the months that look like peaks but convert poorly. The total was never the question. The shape was where the return was hiding.

The Approach

What forensic looked like here.

The budget was rebuilt from the ground up against four deliberately layered inputs, so no single signal carried the whole decision: first-party analytics on paid performance, measured across volume, conversion quality and engagement depth; the same performance broken out by product line, to separate genuine seasonality from noise; independent external search-trend data as a second opinion; and the known trade-show intent spikes that demand data alone misses.

Every first-party input used a three-year average rather than any single year, to smooth out one-off distortions from launches, weather or supply. A clear efficiency ceiling in paid search was identified, the weekly spend beyond which cost-per-acquisition rose sharply, and budget was capped at that ceiling and redistributed to the months that could absorb it efficiently. A macro overlay across energy, rates, fuel and consumer confidence framed the risk around the plan.

  • A four-input evidence model, layered so no single signal dominates
  • Three-year first-party demand averages to remove single-year noise
  • A paid-search efficiency-ceiling analysis
  • A demand-weighted monthly allocation
  • A macro-risk overlay with quarterly review checkpoints
The Result

What changed.

The same annual budget and the same channel split, reallocated against four independent sources of evidence rather than habit. Spend was flexed down in the months that could not absorb it efficiently and concentrated into the genuine demand windows.

Just as important, the plan was built to be reviewed month by month, so spend follows return rather than the plan, pulling back quickly where the data turns instead of spending for the sake of it. Evidence, not inertia, now decides where the money goes.

  • 4independent evidence inputs
  • 3yrfirst-party demand averages
  • Same spendreshaped to real demand
  • Quarterlyreview checkpoints
More Work

Other engagements.

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