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04Strategy and forecasting

EY-Parthenon

Role
Strategy Consultant
Dates
September 2023 – April 2024
Location
New York

I co-led this workstream. The subject was digital transitions — legacy companies moving into digital ventures — and the question underneath every engagement was which of those moves were likely to work.

Every answer to that is a forecast, which makes reliability the whole problem. A model that is confidently wrong is worse than no model at all, because it moves a decision it has not earned the right to move.

20–40%

Revenue uplift on client initiatives the forecasting models supported

  • 15+

    Business KPIs structured and monitored — ROI, conversion, churn, MRR

  • 68

    Companies evaluated, across four sectors

Forecasting the transitions

Excel and Python models on financial and growth data

Forecasts were being fitted to historical financial and growth data without first establishing what kind of growth the data described — and that diagnosis is what decides whether a model can be trusted at all.

25%

improvement in forecast reliability, from diagnosing the growth pattern before modelling it

We diagnosed the growth pattern first and chose the modelling approach to match it, then put every apparent relationship through t-tests and regression rather than accepting a fit because it looked convincing.

Forecast reliability improved 25%, and the models supported client initiatives driving 20–40% revenue uplift.

Read the approach

Problem

A forecast is only as good as the assumption about what shape its history has. Fit a straight line to something compounding, or a compounding curve to something that has already saturated, and the model will still produce a number — a precise one, with decimal places, carrying no warning that its premise was wrong.

That failure is invisible in the output. It only shows up later, in a decision that was made on it.

Approach

We built the models in Excel and Python on historical financial and growth data, and started each one by diagnosing the growth pattern rather than by choosing a technique. What the data was doing decided what was fitted to it, in that order.

Then we tested rather than trusted. Quantitative modelling backed by statistical hypothesis testing — t-tests and regression — so an apparent relationship had to survive being challenged before it was allowed into a forecast a client would act on.

What it produced

Forecast reliability improved 25%.

The models supported client initiatives driving 20–40% revenue uplift. That range is wide because it spans several engagements, and the models were one input into each of them rather than the cause of the result.

Measuring the decisions

KPI frameworks across ROI, conversion, churn and MRR

Stakeholders had no shortage of metrics and no agreed frame for reading them, so each decision restarted the argument about which number mattered.

30%

faster stakeholder decision-making, once every KPI carried a definition

My co-lead and I structured the measurement frameworks and then monitored them — the second half is the one usually dropped, and it is the half that turns a framework into something people keep using.

Stakeholder decision-making accelerated 30% across more than fifteen business KPIs.

Read the approach

Problem

The problem was never a shortage of measurement. It was that nothing said which metrics answered which question, so every meeting spent its first half re-deciding what to look at and its second half deciding on the basis of whichever number was raised most forcefully.

Approach

My co-lead and I structured measurement frameworks across more than fifteen business KPIs — ROI, conversion, churn, MRR — with each one tied to the decision it was there to inform.

Then we monitored them. A framework delivered and left alone decays quietly: definitions drift, one team starts counting something slightly differently, and within a quarter two dashboards disagree and nobody can say which is right. Monitoring is what keeps a framework worth consulting.

What it produced

Stakeholder decision-making accelerated 30%.

The speed came from removing an argument, not from adding a report. Once each KPI had a definition and a threshold attached, the meeting could start at the decision instead of arriving at it.

The evaluation rubric

Digital startups launched by legacy companies

Every company was scored on the same 16-factor rubric, graded weak, medium or strong, against 28 financial metrics drawn from PitchBook — so comparing two of them compared identical measurements rather than two separate opinions.

Whether a legacy company’s digital venture will work is exactly the kind of question that gets answered by whoever is most confident in the room.

74 discrete evaluations across 68 companies in four sectors, feeding a predictive model rather than a verdict.

74

discrete evaluations, across four sectors

Read the approach

Problem

Legacy companies launching digital ventures is a pattern common enough to have accumulated a great deal of received wisdom and very little comparable evidence. Each case gets assessed on its own terms, which makes every assessment unfalsifiable and none of them transferable.

Approach

We scored every company on the same 16-factor rubric, each factor graded weak, medium or strong, against 28 financial metrics drawn from PitchBook.

The constraint that made it work was refusing to vary the rubric by case. A factor that fitted one sector awkwardly stayed in anyway, because a rubric adjusted per company measures the analyst rather than the company, and the adjustments are always defensible individually and fatal collectively.

What it produced

74 discrete evaluations across 68 companies in four sectors, feeding a predictive model.

The output was a model rather than a verdict, and that distinction is the point. A verdict on 68 companies is 68 opinions. A model fitted to 74 consistent evaluations can be applied to the sixty-ninth.

Built with

  • Python
  • Excel financial modelling
  • Regression and hypothesis testing
  • KPI frameworks