What You’ll Learn:

  • Identify the optimal pricing for your product portfolio
  • Determine how high your price can climb without losing the consumer
  • Leverage the Price Predictor™ methodology to test variations of your portfolio pricing as well as competitive response

Webinar Hosts 🎙️

Jordan Henderson, VP of Client Solutions, Decision Insight

Shelley Fow, Director of Pre-Sales, Blacksmith Applications


Regardless of whether you are a Brand Manager, in an RGM function or you are just a regular consumer, you have seen that cost pressures are up. Inflation is growing faster today than in at least a decade and impacts a broad range of categories across the store. With increased cost of goods and production expenses, consumer goods companies are feeling the hit and the bottom line is taking the brunt of it.

Uncertainty over the severity and longevity of these challenges is making it hard for manufacturers to avoid increasing prices. We’re already seeing price increases on the shelf in categories ranging from cereal to personal care.

The question is not IF – but WHEN – your business will have to respond… and how far you can take price while maximizing sales for your brand and the category overall.

Benefits of ShopperIQ® – Price PredictorTM

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Historical data is not adequate in determining price elasticity because there is not enough variation, or you need to raise prices beyond what is currently in the marketplace. We really need a forward looking approach. Therefore, we have developed our ShopperIQ® – Price PredictorTM which is essentially a what-iffing tool that allows you to:

test price changes for you and for your competitors simultaneously

see what happens to other brands and private label in the category 

see what happens to the category overall

find the optimal pricing for your product portfolio

find out how high you can go up without losing your consumers


Our new approach to price elasticity analysis removes the effects of market shocks. We combine Virtual Shopping with Machine Learning to achieve the optimal price.

Virtual Shopping

Puts shoppers in the context of an actual purchasing decision
Tests realistic sets of competitive alternatives
Data collected for current market conditions

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You might be asking “can we really trust the results from virtual shopping”? And the answer is – absolutely, YES! We regularly take the data we get from virtual shopping and match it back to in-market data from IRI or Nielsen and we find that it correlates at 0.90 or higher. So we have a really high degree of confidence that what we are measuring with our virtual shopping is really what is going to happen in the marketplace.

Machine Learning

Explores scenarios for market conditions
✨ Efficiently evaluates multiple test scenario combinations
✨ Can be updated quickly and regularly


How Price Predictor Works

Salty snacks example:

1. Design

Shelf simulations independently vary price levels of different brands or price groups.

Three to five pricing levels are recommended for your brand, while your competitors may have fewer levels.

Price levels are varied within a max range of 10% under to 20% over current price.

2. Virtual Shopping

Sales data is collected from shoppers exposed to multiple shelf scenarios with varied prices and promotions.

price predictor

3. Analysis

Ultimately, enough shelf variations are tested to analyze thousands of configurations.

Machine Learning algorithms extract complex patterns in behavior from this consumer sales data.

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4. Delivery

Interactive Simulator

User controls the price changes of products
Competitor response is modeled in simulations

Results estimate the sales and profit impact of any scenario by product, brand and category.

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Pricing Simulator

The pricing simulator is a really powerful tool. Companies can change their pricing and find the optimal price but also understand what is going to happen if a competitor responds or if the competitor makes their change first know what the impact is going to be to their business and how to react.

price predictor


If you are a current TPO customer, another great use of the Price Predictor services, is the ability to take the regular price lift coefficients generated from it, to be uploaded into the backend of our TPO solution to provide the ability to apply baseline change drivers when projecting out future baselines.

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If you don’t currently have a TPO solution and are looking for an opportunity to expand your account planning capabilities, and utilize data coming from Price Predictor, we would love to tell you more about it.

Check out more content on TPO here.