Valutus.

Case study · Customers

Valuing $100M a year of sustainability investment with Customer Science™.

A global telecommunications firm had been investing in sustainability and community programs for years. Inside the company, the belief was that customers cared. Nobody could prove it, and the people signing the checks wanted more than belief.

4x

The programs were worth more than four times what the company had been counting.3

Customer Science™ · 24,000+ customers, four streams · conservative floor against counted value · 2x the hoped-for case

01At a glance
Client
A $100B global telecommunications firm. Described by scale and sector rather than named.
CORE dimension
Customers
Challenge
Put a defensible business return on more than $100M a year of sustainability and community investment. The company had no model for it, so most of the value was being carried at zero.
Method
Customer Science™, run as four independent measurement streams.
Scope
More than 24,000 customers, including 4,000 in controlled tests, across multiple regions.
Outcome
Worth more than four times what the company had been counting, and twice what leadership had hoped for. It also returned more per dollar than investment in the core product.
02The challenge

Fact, not feeling.

The firm was spending over $100M a year on sustainability and community involvement. The internal argument had stalled in a familiar place. Everyone agreed customers cared. Nobody could say what that was worth.

This was not a model that undercounted the value. There was no model. Less than a quarter of what the programs were worth was showing up anywhere. The rest had never been resolved into a number, so it was carried at zero, which is the most expensive figure a real effect can hold.

A belief does not survive a budget review. The company needed data, and it needed the number to hold up in front of the people least inclined to accept it. Rigorous enough that a skeptic would concede the method. Specific enough that someone could act on the answer.

The measurement problem

Customers who choose you for a reason you never measured still show up in revenue. They just show up unexplained, which means the spending that produced them gets cut first when budgets tighten.

03What we did

Four streams. One number.

Any single measurement can be argued with. Four independent measurements that agree are much harder to dismiss, and that was the point of the design.

01
Before and after tests
4,000 customers in controlled tests measuring whether learning about the programs actually moved preference, rather than whether people said it would.
02
Matched store pilots
Experimental stores run against control stores, with more than 40 variables checked first to confirm the two groups genuinely matched.
03
Retrospective analysis
Historical data across diverse regions, tracking how awareness of the programs and Net Promoter Score1 moved together over time.
04
A city scale intervention
One major intervention in a single world famous city, with five control cities held alongside it for comparison.

Stream 01 · Individual customers, before and after

Before After +10points
Individual scores rose by 10 to 15 points once customers knew what the company was doing. The chart shows 10, the conservative end.2 The column base is truncated, marked by the break, because the absolute score is not disclosed.

Stream 04 · One test city against five controls

Intervention +5 Own avg. Test city Lift Five controls
Each city is plotted against its own average, so no absolute score is disclosed. The five controls establish what normal movement looks like. The test city moves with them until the intervention, then rises about five points and holds.
04What it showed

The value was real, and most of it had been invisible.

Four independent streams agreed, on an investment the company had never been able to model at all.

Combined across all four streams, the programs were worth more than four times what the company had been counting. Put the other way, less than a quarter of the value had been visible. Measured against what leadership had privately hoped for, it came in at twice that.

The analysis also showed where that value was coming from. Customers weigh several things when they choose a carrier, and network quality is one of them. Across the market it was also mostly equivalent. That dimension had already been competed away, so it could not separate this company from anyone else.

Sustainability had not been competed away. It was still open, which is why it carried so much incremental value, and why it out-returned investment in the core product. Not because the product mattered less.

From cost line item to competitive weapon.

The engagement, in one line

05The bottom line

Network quality was even. This was not.

A line that had been carried at zero turned out to be one of the few places left where the company could still pull ahead. Not a cost to justify each year, but a dimension its competitors had not yet closed.

How Customer Science and Demand Realization work →

From submerged value to banked value.

Let us talk about the value you create, and how you can demonstrate it. Credibly and concretely.

Notes on figures

  1. Net Promoter Score, a standard customer loyalty measure and, at this company, the primary customer metric leadership tracked.
  2. Observed lift was 10 to 15 points. The chart and the text state 10, the conservative end, per standing convention.
  3. Two different bases, so they are stated separately. 4x is against the value the company had been counting, which is the same comparison the 4x to 10x submerged value range uses; this engagement sat above 4x, and the hero states the conservative floor. 2x is against leadership’s own hoped-for case, which was an expectation rather than a model.
  4. All figures are the client’s own, produced during a Valutus engagement. The company is described by scale and sector rather than named, and both charts are drawn against each city’s own baseline so that no absolute score is disclosed.