ENERGY & UTILITIES · PRICING
A European energy provider was preparing to announce a 12 % increase on its residential electricity offer, following changes in procurement costs and tensions on European wholesale markets. Several competitors were facing the same increase.
But the market is open: customers can switch to other providers, including more stable regulated-price offers. Industry studies projected attrition of 12 to 18 % over 18 months depending on the strategies adopted.
THE PROBLEM
The customer does not simply receive a new tariff. They simultaneously interpret why it is increasing, why now, by how much, what competitors are doing, what they receive in return, and whether the effort seems fairly distributed.
Their reaction therefore depends on price, but also on context, the alternatives available, accumulated trust and the perceived fairness of the decision. In a market where switching provider is possible, each of these dimensions can trigger comparison.
The commercial team was considering three approaches: technical communication justifying the increase through costs, explanatory communication about the European context, and communication combined with support for the most exposed customers. No internal data could determine between them before the announcement.
WHAT THE DECISION HAD TO SOLVE
A DISTINCTION: SAME PERCENTAGE, DIFFERENT CONSTRAINT
Two customers can face exactly the same percentage increase and experience very different consequences depending on their level of consumption, housing structure, heating energy, income level and actual ability to reduce consumption. The percentage is one decision; the constraint it creates is multiple.
A MECHANISM: JUSTIFICATION EXPLAINS, A LEVER CREATES ACCEPTANCE
The same increase announced without a bill-control program achieves 24 % acceptance. Combined with a credible program — free energy assessment, real-time monitoring tools, adaptive monthly billing — it achieves 62 %.
The customer does not only evaluate how much they will pay: they evaluate what they can still do about their bill. The price states what the service costs; what accompanies it signals the value placed on the relationship.
WHAT IS TESTED
The 5 strategies tested do not vary a wording. They combine justification, the associated program, differentiated treatment of the most exposed profiles and sequencing of the announcement.
3 800 synthetic customers were interviewed individually on the 5 communication and support strategies tested, with dynamic follow-ups at critical attrition moments.
The reconstructed population covers 2,4 million residential customers of the energy provider, calibrated on proprietary data — segments, consumption history, price sensitivity, previous exposure to provider switching — and on European energy industry studies.
It is structured into 16 relationship-to-provider profiles combining age, housing structure, heating type, income level and sensitivity to support programs.
HIGH BUDGET CONSTRAINT, ELECTRIC HEATING
modest incomes, poorly insulated homes: structurally the most exposed to the increase.
HIGH, HARD-TO-REDUCE CONSUMPTION
the bill depends more on the structure of the home than on everyday behaviors.
LONG-STANDING CUSTOMERS OF THE PROVIDER
long tenure, little or no previous exposure to switching providers.
CUSTOMERS WHO HAVE ALREADY SWITCHED BEFORE
previous experience of switching: changing provider is no longer an unknown.
HOUSEHOLDS RECEPTIVE TO SUPPORT PROGRAMS
receptive to assessments, consumption monitoring and adaptive monthly billing.
HOUSEHOLDS LESS RECEPTIVE TO SUPPORT PROGRAMS
the proposed support does not change how they interpret the increase.
CONTRASTING HOUSING AND HOUSEHOLD STRUCTURES
age, composition, housing structure: the same offer does not meet the same life circumstances.
HETEROGENEOUS PRICE SENSITIVITY
the perceived gap with alternatives does not carry the same weight across profiles.
These configurations reveal part of the population's heterogeneity. The simulation operates on synthetic individuals, not a handful of persona types.
REACTIONS
JUSTIFICATION EXPLAINS; IT DOES NOT CREATE ACCEPTANCE
The technical justification — procurement costs, European markets, inflation — explains the increase without making it acceptable. Announced on its own, the 12 % increase achieves 24 % acceptance. Combined with a visible bill-control program, the same increase achieves 62 %. What shifts acceptance is not the narrative, but what the customer can do about the bill.
ATTRITION DOES NOT HAPPEN AT THE ANNOUNCEMENT
Measurable attrition at the time of the price announcement is limited: less than 3 % of customers. The peak occurs when the first bill including the increase is received, with up to 12 % additional attrition over 60 days. The moment when the customer reacts is not when they learn about the increase, but when they pay it.
THE CRITICAL WINDOW CAN BE TREATED
Enhanced support over the 60 days following the first bill — proactive customer outreach, personalized optimization offers, adaptive monthly billing — divides attrition by 2,4 over that window. The same increase, the same bill: only the presence of support during the window changes.
THE MOST EXPOSED CUSTOMERS CAN BECOME THE MOST LOYAL
Vulnerable customers — modest incomes, electric heating, poorly insulated homes — are structurally the most exposed to attrition. Identified early and supported before the announcement, they instead become the most loyal at 18 months: their retention rate exceeds that of non-vulnerable customers, driven by recognition of the support received.
A MECHANISM: ATTRITION IS A TRAJECTORY
At the time of the announcement, fewer than 3 % of customers leave. Between the announcement and departure come a reaction, an interpretation — understanding or a sense of unfairness —, comparison with competing offers, and then the first bill including the increase.
That is when the peak occurs: up to 12 % additional attrition over 60 days. Reading the reaction on the day of the announcement means measuring the decision before it has been made.
A MECHANISM: TIMING IS A DECISION VARIABLE
Over the 60-day window following the first bill, enhanced support — proactive customer outreach, a personalized optimization offer, an adaptive monthly-billing arrangement — divides attrition by 2,4.
The amount of the increase did not change. The sequencing of support, however, is a parameter of the decision, not a communication constraint.
A DISTINCTION: THE MOMENT OF ANNOUNCEMENT, THE MOMENT OF PAYMENT
The system modeled the critical attrition moments over the 18 months following the announcement. Three moments stand out, and they do not call for the same measures. Focusing effort on the announcement means treating the moment when the fewest things are actually decided.
COMPARISON
Only dimensions for which the case documents measurements carry numerical values. “Not published” is retained rather than replaced by an estimate.
INCREASE + BILL-CONTROL PROGRAM
A lever on the bill acceptance: 62 %
INCREASE + JUSTIFICATION ONLY
Explanation without a lever acceptance: 24 %
THE STRATEGY SELECTED
The energy provider selected the strategy combining a price announcement with a bill-control program — free energy assessment, real-time monitoring tools, adaptive monthly billing —, proactive identification of vulnerable customers 60 days before the announcement with dedicated support, and explanatory communication about the European procurement-cost context.
The critical first-bill window was treated specifically: proactive outreach to affected customers and personalized optimization proposals over the 60 days following receipt. The amount of the increase itself was not modified by the simulation.
DECISION
PROJECTION, THEN OBSERVATION
At 18 months after rollout, measured attrition is 7 %, well below the 15 % industry projection. Vulnerable customers identified in advance show a retention rate of 96 %, above the portfolio average.
Measured NPS among customers who used the bill-control program is +34 points versus the portfolio average. The energy provider industrialized support for vulnerable customers across three other major pricing changes.
These values describe what was observed after the decision. They do not establish exclusive causality between the strategy selected and each of these movements.
A TEMPORAL MECHANISM: INERTIA HIDES RISK
Attrition observed at the time of the announcement is below 3 %. This apparent stability says nothing about the state of the relationship: between the moment the customer learns and the moment they pay, comparison has already begun. A portfolio can remain stable in volume while becoming more volatile in trajectory — precisely what an 18-month projection makes visible and a D+1 measure does not.
TAKEAWAY
The technical justification of the context explains the increase without making it acceptable. It is the associated support — bill-control program, optimization tools, monthly billing — that transforms acceptance: 24 % without a program, 62 % with one. The amount is the same.
And departure is not a reaction to the announcement: it occurs when the first bill is received, at the end of a trajectory of interpretation and comparison. Treating the 60 days following that bill divides attrition by 2,4. The customers structurally most exposed, identified in advance, become the most loyal at 18 months.
POSSIBLE FUTURES
A
EXPLAIN THE INCREASE
Technical justification through procurement costs, with no associated support program
B
COMBINE THE INCREASE WITH A PROGRAM
Free energy assessment, real-time monitoring, adaptive monthly billing
C
DIFFERENTIATE, SUPPORT, TREAT THE WINDOW
Early identification of vulnerable customers, bill-control program, explanation, enhanced support over 60 days
METHOD
2,4 million synthetic residential customers calibrated on the energy provider's proprietary data and European industry studies, 16 relationship-to-provider profiles, 5 communication and support strategies tested with 3 800 interviewed customers and dynamic follow-ups at critical moments, followed by an 18-month projection modeling within-segment word-of-mouth dynamics and competitive switching effects.
This case adds a mechanism to the library: departure is a delayed effect. Between the pricing decision and churn come an interpretation of the fairness of the increase, deterioration or preservation of trust, competitive comparison and a moment of payment. The system does not only answer “ how many will leave ? ”, but “ how does a pricing decision progressively shift the customer relationship ? ”.
This case is based on a simulation conducted for a European energy provider facing a major price increase across its residential customer portfolio. The client is not named, no personally identifiable data entered the system, and detailed results remain the client's property.
The simulation does not set the amount of the increase, the pricing policy or the scale of support. It concerns the consequences, across different populations, of several announcement, support and sequencing architectures.
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