Helping commercial teams see the value they create.

Problem

SoPost wanted to move beyond campaign-level metrics. Existing reporting focused on delivery and short-term results, which were useful but incomplete. It didn’t show what happens after the first purchase, or how trust and intent turn into future demand.

• · Brands judged campaigns on immediate results, without seeing how they build future demand
• · The Commercial Team lacked a clear, consistent story for uplift
• · They were spending time defending value instead of demonstrating it

A model with two pillars

Working from a methodology developed by our Head of Product, I defined the product interpretation of the model and designed the first interactive version. It rests on two complementary pillars of value:

• · Incremental revenue (modelled)
• · Purchase intent and trust uplift (measured)

Each is benchmarked, shown with confidence ranges, and positioned as an early indicator of lifetime value.

Building the pilot

Our Product Manager helped me understand how some of the underlying data was put together. I designed the pilot in Looker and tested it with the Commercial Team. Looker let us:

• · Prototype live with production data
• · Iterate quickly as the calculations evolved
• · Test real interactions and filters with the Commercial Team
• · Reuse existing data models for future integration into Campaign Manager reporting

That took us from concept to a validated prototype in weeks, with real user feedback captured before any engineering investment.

What the pilot measured

• · Revenue the campaign actually caused, compared against a control group, for one campaign or across all of them
• · Whether people came away more likely to buy, and more trusting of the brand, with a range showing how confident we were in each number
• · What normal looked like for that product category and across the network, so a number meant something on sight

What made it usable

• · A summary to open with, the detail underneath, and something the team could export straight into a deck
• · Agreed rules on what counted, what was excluded, and what each term meant

The pilot was there to find out whether people outside the team could read the numbers and believe them.

Testing and validation

The dashboard went live with the Commercial Team, where we validated:

• · How useful the thresholds and ranges felt in real campaigns
• · Whether the visuals made uplift easier to explain
• · Where terminology and labelling needed refinement

Impact

Incremental revenue and trust uplift became figures the Commercial Team could put in front of a brand and defend.

It changed the conversation internally. We stopped asking “did it work?” and started asking what value it would create.

Problem

Comparison reporting, the large summary and benchmark tables, lived across Looker dashboards, slides and ad-hoc exports. That fragmentation made it hard to spot performance patterns across campaigns or markets, and The Commercial Team had to build these tables by hand, collating data from each source.

Why it mattered

The Commercial Team asked for this. They were rebuilding the same story for every campaign, and needed a quick, standard way to answer questions that kept coming up:

• · How did this campaign perform compared to others in the same market?
• · Which product or activation delivered the strongest intent to purchase?
• · How does this brand compare against its own benchmarks over time?

Design approach

Working with our Product Manager, I defined the structure and logic for a comparison view that could sit directly inside Campaign Manager, removing the need to export data or, for some brands, to maintain external decks. It supports flexible comparison across brands, territories and campaign types, using shared indicators such as opt-in rate, response rate, recommendation and purchase intent.

Every table follows one visual pattern that prioritises speed, legibility and clarity. Lightweight filters sit directly above the table, so people can explore without visual clutter:

• · Brand and territory, for regional context
• · Benchmarks, to show category or network averages
• · Campaign type, to compare like with like
• · Date range, to track performance over time

I designed these in high fidelity in Figma and reviewed them internally to shape hierarchy, grouping logic and interaction patterns.

Example use cases

• · Aveda: product and market benchmarks
• · Puig: year-to-date fragrance performance
• · e.l.f. Cosmetics: Meta versus TikTok comparison
• · Coty UK: brand-level campaign summaries

The focus was a scalable component, not a one-off visualisation. By aligning layout, filters and terminology early, the design showed how one consistent pattern could unify several data views, and cut the friction between having the data and getting to an insight.

Expected impact

The framework was designed to cut the time spent on comparison reporting sharply, by taking most of the manual work out of it. Tables the Commercial Team built by hand, collating data from Looker, slides and exports, would come from one view in one consistent format, and the same question could be answered for every campaign without rebuilding the story each time.

Status

The designs were complete and reviewed internally, with a clear path defined for building the framework into Campaign Manager. The next steps were to refine naming, grouping and thresholds based on internal feedback, and to extend coverage beyond beauty and fragrance.