Analytics vs Research. How to drive towards Pricing Strategy?

The boom in analytics leads us to think that exploring historical data can be a viable roadmap to structuring an efficient pricing strategy. On the other hand, market research suggests that, given the changes taking place, it is necessary to study the pricing strategy from the consumer's standpoint, since in historical data we do not have the same competitors, SKUs or settings in the present as in the future. Which path to choose?

In this blog, we want to share some experiences, pros and cons of both approaches and to do so the table below may help:

  Analytics Research
What’s the approach?
  • Exploit historical data to define future pricing strategies

  • Study the consumer's response to different price scenarios, understanding the trade-offs the consumer must make to purchase a product at a given price. Conjoint methods play a key role in price optimization
Pros
  • You monetize the historical investment of data purchase, such as Nielsen, Kantar, etc.
  • You take advantage of transactional data of your relationships with channels and retailers
  • This represents a clear overview of price and volume of one's portfolio
  • The business is in control of the analytical processes that revolve around pricing
  • Simulate prices without the need to carry out studies each time a re-analysis is required
  • Allows for testing pricing strategies in current contexts
  • Helps analyze the adoption of pricing strategies for products that do not yet exist in the market
  • Use of statistical and econometric models that are highly accepted and understood by financial and product management 
  • Simulation based on empirical methods, centered on the data collected in the study
  • Enables analysis of the trade-offs that consumers are willing to make in order to purchase a specific product at a specific price
Contra
  • To gain insight into the competitor, you will need to purchase exogenous data
  • If you only have information from your own portfolio, you will only be able to estimate point-slope elasticities but not migrations
  • Use of machine learning methods, whose results are difficult to understand by the end business user
  • Monte Carlo simulation, based on data "created" based on variables that explain the demand
  • Trained teams are required to prevent errors, false positives or erratic predictions
  • Does not allow analysis of the consumer's "trade-off"
  • Typically, the process of design, data collection and analysis can take a minimum of 5 weeks 
  • The number of SKUs, product attributes, etc., are often limited
  • The main challenge is to simulate purchase situations in the same way as the human brain processes information in payment scenarios. These situations are difficult to achieve in an experimental setting
  • Representative samples are required to achieve market coverage
What method to implement?

Data integration:

  • Creation of data models to integrate information that revolves around the business
  • Automation of data ingestion and ETL processes to build efficient data models
  • Data quality techniques

Analytics:

  • Supervised analysis: Methods for predicting sales volume in different price scenarios
  • Unsupervised analysis: Segmentation of SKUs and channels for higher accuracy

Visualization:

  • Dashboards showing the analytical process
  • Automate the process so that dashboards always show the analysis in a timely manner

Optical price study

  • PSM Price Sensitivity Meter
  • Garbor Granger
  • BPTO Brand Price Trade Off
  • PVP: Perceived Value Price

Optimal price study

Conjoint + Analytics = Pricing

It is essential to recognize the virtues of each approach:

    • Conjoint: It allows us to analyze the consumer's response to the pricing strategy and to explore the consumer's "trade off". This makes it easier to analyze the impact of our movements on the competition and vice versa. It also allows us to issue short-term data that can be used to simulate the strategy in the future.
    • Analytics: It allows us to analyze the volatility of price indicators, including the point-slope elasticity. With this historical volatility, the conjoint data can be fed into the future, providing a 360° view of the price.

Our consultants will be ready to listen to your needs. Let's make an appointment to explore together the benefits of this methodological approach for your research goals. Find below the location that is most convenient for you:

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