In Revenue Growth Management, price elasticity measures how much demand for a product changes when its price changes. It is estimated from historical sales data and usually expressed as a single figure per product, which is exactly what makes it attractive: it turns a commercial judgement into a number that can be compared, ranked and put in a business case.

The difficulty is not that elasticity is wrong. It is that consumer behaviour is more dynamic than the models used to estimate it. An elasticity value describes how shoppers responded under one set of market conditions. It does not promise they will respond the same way under the next set.

Elasticity is often described as the holy grail of RGM. If a business knew exactly how consumers would respond to a price increase, pricing decisions would become simple: raise prices where demand is inelastic, protect them where it is elastic, and maximise revenue and margin. Despite decades of progress in analytics, machine learning and consumer data, reality has not cooperated. Most of the difficulty comes from treating a historical observation as a forecast.

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The short answer

To use price elasticity well in CPG:

• Treat elasticity as contextual, not fixed. It varies by retailer, channel, geography, season, shopper mission, promotional intensity, competitive activity, brand strength and pack size.

• Account for promotional distortion. Historical data often records learned promotional expectations rather than genuine willingness to pay.

• Assume the market moved too. Models hold other factors constant; competitors, retailers and private label do not.

• Remember that consumers evaluate value, not price. The same percentage increase produces different outcomes depending on perceived brand value.

• Read elasticity alongside Price Pack Architecture. When shoppers can trade up, trade down or switch format, SKU-level elasticity becomes harder to interpret.

• Use elasticity as an input, never as the decision. It is a quantitative reference point, not a commercial verdict.

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What an elasticity estimate does and does not tell you

Most misuse of elasticity comes from asking it a question it was never built to answer. What an elasticity estimate can and cannot tell you differs on seven dimensions:

• Time frame. An elasticity estimate describes how demand responded during the observed period. It says nothing reliable about the next one.

• Scope. The figure holds for the retailers, channels and periods in the data, and cannot simply be extended to others.

• Driver. Elasticity captures how price and volume moved together without revealing whether base price, promotion or a competitor caused the change.

• Price observed. The model works from the shelf price actually recorded, which may differ from the price the manufacturer intended.

• Consumer logic. What comes out is the aggregate response; the perceived value, trust and habit behind it stay invisible.

• Portfolio effect. A SKU-level coefficient shows that volume moved, not where inside the brand portfolio it went.

• Decision use. The result is a quantitative reference point. The commercial decision remains a separate call.

In short, an elasticity estimate informs a pricing decision; it does not make it.

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Price elasticity is not a fixed number

There is no universal elasticity. There are thousands of contextual elasticities.

One of the biggest misconceptions in CPG is that every product has a single elasticity value. A business concludes that a SKU has an elasticity of -1.3 and then treats that figure as a permanent characteristic of the product.

In reality it changes constantly. It varies by retailer, channel, geography, season, shopper mission, promotional intensity, competitive activity, economic conditions, brand strength and pack size.

A price increase of 5% may have almost no effect in a convenience store where shoppers value speed over price, and trigger significant volume loss in a discount retailer where price comparison is central to the shopping trip. Same SKU, same increase, different outcome.

Holding those contextual differences in one place, rather than averaging them away, is part of what Sunstice Market Data is built for: connecting internal performance data with external market signals across categories, customers, channels and competitors.

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Promotions distort what the data records

Historical sales data records what consumers paid, not what they were willing to pay.

Few CPG categories sell consistently at full price. Many spend between 20% and 50% of the year on promotion. Over time, consumers develop expectations about what a product should cost.

When analysts estimate elasticity from that history, they are frequently modelling behaviour shaped by years of promotional activity rather than genuine willingness to pay.

Was volume lost because the base price increased, or because consumers were waiting for the next promotion? The data rarely separates the two cleanly. Isolating incremental effect from promotional habit is the work Sunstice Trade Promotion Optimization does when it measures incrementality rather than uplift.

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Competitors never stand still

Elasticity models assume other factors remain constant. In CPG, they almost never do.

A competitor launches a new product. Another brand increases promotional investment. Private label improves quality. Retailers change shelf positioning.

Any one of these can invalidate an estimate made six months earlier, because the estimate described a market that no longer exists.

Elasticity is not only about a company's own pricing decisions. It is also about how the market responds to them, which is a different and larger question.

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Retailers control the final shelf price

The price consumers see is often not the price the manufacturer intended.

Manufacturers rarely have complete control over what shoppers actually pay. A brand owner may implement a list price increase, only for retailers to absorb part of it, increase their own margin, or offset it with promotions. Retailers may also raise shelf prices beyond the recommendation.

Every one of those responses changes the price that consumers respond to, while the manufacturer's own records show a single list price decision.

This disconnect makes true consumer price sensitivity difficult to isolate, and it is one reason price realization, rather than list price, is the measure that matters. Sunstice Pricing Strategy works on that gap between intended and realized price across products, customers and channels.

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Consumers do not buy prices, they buy value

The same percentage increase can produce completely different outcomes depending on perceived value.

One of the biggest limitations of traditional elasticity models is that they focus almost exclusively on price. Consumers evaluate value.

A premium coffee brand may raise prices successfully because consumers perceive superior taste. A trusted household cleaner may outperform cheaper alternatives because shoppers believe it works better. Conversely, a small increase on a highly commoditised product can drive rapid switching.

Price is one component of the purchase decision, and often not the decisive one. An elasticity model that sees only price will attribute to price what actually belongs to brand equity.

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Price Pack Architecture changes the question

When shoppers can move inside the portfolio, SKU-level elasticity stops describing the business.

Suppose a manufacturer increases the price of its standard pack while simultaneously introducing a smaller entry pack, a premium larger format and a value multipack.

Consumers now have several ways to respond. Some trade down. Some trade up. Some switch pack size. Some remain loyal. Others leave the category altogether.

Did demand become more elastic, or did consumers simply choose a different option within the same brand portfolio? The SKU-level number cannot tell the difference. Price Pack Architecture fundamentally changes purchasing behaviour, which makes SKU-level elasticity increasingly difficult to interpret in isolation, and makes portfolio-level reading through Sunstice Assortment Planning more informative than a single coefficient.

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Elasticity is a snapshot, not a forecast

An elasticity model reports how consumers responded. It does not commit them to responding that way again.

Many organisations treat elasticity estimates as forecasts. They are better viewed as historical observations of a specific market under a specific set of conditions.

Inflation, wage growth, retailer strategies, innovation, consumer confidence and competitive actions continuously reshape price sensitivity.

What worked last year may not work this year. The estimate has not become unreliable; the conditions that produced it have changed.

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The human element still matters

Machine learning can estimate thousands of elasticities at once. It still cannot fully capture how people shop.

Advanced analytics have transformed pricing decisions, and the ability to model at scale is a genuine advance.

But consumers do not wake up calculating percentage price changes. They notice affordability. They compare alternatives. They react emotionally to trusted brands. They make impulse purchases. They forget previous prices. Sometimes they willingly pay more, and sometimes they refuse to buy at all.

Reading those behaviours requires commercial judgement alongside statistical analysis. The model narrows the range of sensible answers; it does not choose between them.

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How to use elasticity well

Rather than treating elasticity as a definitive answer, the strongest CPG organisations use it as one input among several. Robust pricing decisions combine:

• Price elasticity modelling, read as a range and by context rather than as a single figure.

• Consumer research, to explain the behaviour the model can only measure.

• Competitive intelligence, because the estimate assumed a market that keeps moving.

• Retailer insight, because the shelf price is where the consumer response actually happens.

• Price Pack Architecture, to see where volume moves inside the portfolio.

• Promotion analytics, to separate base price response from promotional habit.

• Brand positioning, because perceived value changes the outcome of an identical increase.

• Commercial experience, to weigh the evidence and own the decision.

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A practical check

Before acting on an elasticity estimate, several questions are worth asking:

• Which retailers, channels and periods does this estimate actually cover?

• How much of the observed volume was sold on promotion during that period?

• What changed in the competitive set between the estimation window and today?

• Did the shelf price move as the list price intended, or did the retailer absorb or amplify it?

• Is the response being attributed to price when it may belong to perceived value?

• Where would the volume go inside the portfolio if this price moved?

• Is the number being used as a reference point or as the decision?

• Who owns the commercial decision once the model has produced its output?

These questions do not test the quality of the model. They test whether the conditions it measured still describe the market the decision will land in.

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Explore pricing decisions further

Price elasticity remains one of the most valuable tools in Revenue Growth Management. It helps businesses understand how consumers may respond to price changes and gives a quantitative foundation for commercial decisions. The challenge is not that elasticity is unreliable. The challenge is expecting a single number to explain millions of individual purchasing decisions.

Within Sunstice Revenue Growth Management, pricing architecture and price realization sit in Sunstice Pricing Strategy, promotional incrementality in Sunstice Trade Promotion Optimization, internal and external market signals in Sunstice Market Data, and portfolio effects in Sunstice Assortment Planning.

Explore Sunstice Revenue Growth Management to see how the six capabilities work on one decision foundation, and Structured Agility™ for how Sunstice combines structure with responsiveness in commercial planning.

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