Geographic performance analysis

Geographic performance analysis is the structured comparison of how different regions perform across revenue, cost, growth and retention, ranked so the strongest and weakest markets are obvious. It is the analytical step that turns a regional revenue breakdown into a decision. Reporting tells you the US made 600,000 pounds; analysis tells you the US made 600,000 pounds on a cost base that should have produced 800,000, and explains the gap.

The difference from a simple regional report is the why. Analysis does not stop at ranking markets by size. It decomposes each market into the levers underneath it, conversion, acquisition cost, deal size, retention, and asks which lever is holding a region back. A region with low revenue and a strong conversion rate has a traffic problem. The same low revenue with weak conversion has a localisation or product-fit problem. Those two regions need opposite interventions, and only analysis tells them apart.

Done well, geographic performance analysis also controls for the things that distort comparison. It adjusts for currency, accounts for market maturity, and holds revenue and cost definitions constant across regions. Read alongside customer acquisition cost by region, it shows not just which markets are big, but which are worth building.

The analysis is built from a per-region efficiency score that rewards regions for producing revenue cheaply and for growing. A simple form divides region revenue by region total cost, then scales by growth so a fast-growing region is not penalised for early investment.

For example, a region producing 300,000 pounds on 100,000 pounds of cost has a base efficiency of 3.0. A region producing 300,000 pounds on 200,000 pounds of cost sits at 1.5. Scale each by its growth rate and you get a score that ranks the efficient grower above the expensive flat market. The score is the starting point; the analysis is reading the inputs beneath each score to see which lever explains the rank.

1. 1

   Attribute revenue to regions

   Assign revenue to a region on a consistent rule, usually billing country or account headquarters. Pick one rule and apply it everywhere so the comparison is clean.

2. 2

   Roll up region cost

   Sum acquisition spend and serving cost per region. Include local marketing, local support and any partner fees, because leaving them out flatters high-cost markets.

3. 3

   Compute the efficiency score

   Divide region revenue by region total cost, then scale by one plus the region growth rate. This single number ranks markets while still rewarding early-stage growth.

4. 4

   Decompose the outliers

   For the best and worst scoring regions, break the score into conversion, acquisition cost, deal size and retention to find the lever that explains the rank.

Geographic performance analysis is a decomposition exercise, which makes a metric tree its natural home. The work is exactly the tree shape: split the company number by region, then split each region into the levers that produced its score. A spreadsheet can hold the ranking, but it cannot show the causal path from a soft global number down to the specific regional lever that moved.

When the analysis lives in a metric tree, the comparison stays connected to cause. A region that scores poorly is not just flagged red; you can follow its branch to see that conversion held but acquisition cost doubled. That is the difference between a dashboard that reports a problem and a structure that explains it well enough to act.

Benchmarks for this analysis are about the gaps between regions rather than a single target number. A healthy analysis surfaces a manageable spread in efficiency, a home market that is not overwhelmingly dominant, and new regions that are visibly ramping. The ranges below are practical reference points for a cross-border subscription business.

Improving the analysis itself, before improving any single region, is what makes the regional decisions trustworthy. The work is about cleaner attribution, fairer comparison, and tying each finding to an owner and an action rather than leaving it as a chart.

1. 1

   Comparing regions of different ages

   A market entered last quarter cannot be judged against a five-year-old market on efficiency. Without a maturity adjustment the analysis systematically punishes new bets.

2. 2

   Stopping at the ranking

   A ranked list of regions is the start of analysis, not the end. If the work does not decompose the top and bottom regions into levers, it has not explained anything.

3. 3

   Leaving local costs out

   Excluding local marketing, support or partner fees makes high-touch regions look cheap. Roll up the full cost of serving each region before scoring it.

4. 4

   Ignoring currency distortion

   A region can climb the ranking purely on a favourable exchange rate. Restate in constant currency so the analysis reflects real performance, not foreign exchange noise.