Analytics vs Research. How to drive towards Pricing Strategy?

• 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

• 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

• 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

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

• Full Profile Conjoint (FPC)
• Choice Based Conjoint (CBC)
• Descrete Choice Model (DCM)
• Adaptative Conjoint (ACA)
• Adaptative Choice Based (ACBC)
• HILCA
• Menu Based Conjoint (MBC)