Strategic pricing analysis

Strategic Pricing Analysis

Pricing plays a central role in consumer demand analysis across all sectors of the economy, ranging from Consumer Packaged Goods (CPG) to Financial Services.

Consumer response to long-term regular and short-term promotional price change is conventionally summarised in the form of price elasticities, which play a key role in optimal pricing and demand management. 

Marketscience consulting provides strategic advice on regular and promotional pricing at varying depths of data granularity. The precise level of analysis ranges from market through to household and customer level depending on the types of business question.

Aggregated analysis

For an understanding of optimal price points, pricing thresholds, promotional ROI and competitive structure across products and brands, regular and promotional own and cross-price elasticities are sufficient. Dynamic marketing mix models are ideally suited for this purpose, with CPG, consumer electronics and retail sectors typically involving aggregated purchase data at the market, chain or store level. Some general guidelines concerning the appropriate modelling level are as follows:

  • Market

Appropriate if the focus is on market and brand level effectiveness, price and promotional strategies are common within chain and there is a high incidence of store switching. More

  • Chain

Ideal if the focus is on differential effectiveness by chain and promotional tactic, price and promotional strategies are common within chain and store switching between chains is minimal.

  • Store

Necessary if the focus is solely on Universal Product Code (UPC) or Promoted Product Group (PPG) elasticities, price and promotional strategies differ by store and store switching is minimal.

Marketscience Consulting provides insight and advice on which level of analysis is most appropriate for your business and construct the optimal model structures accordingly.

Household panel analysis

Aggregated elasticities measure the net impact of price changes and subsume several key elements of consumer response ranging from within-brand cannibalisation and store switching through to stockpiling. Disaggregated household panel data are required to uncover this information and optimal strategies will differ depending on which elements are driving the overall effect. Some key examples are as follows:

  • Within-brand cannibalisation

If consumers respond to a uniform regular price increase across the brand by trading down to smaller items or larger pack sizes at lower price per volume, an optimal pricing strategy would be to consider differential UPC price increases to maximise revenue for the manufacturer and retailer.

  • Store switching

If consumers respond to regular and promotional price changes via store switching, the manufacturer should consider the optimal assortment within different retailers in order to maintain a healthy customer relationship.

  • Stockpiling

If consumers respond to promotional pricing by stockpiling, the manufacturer needs to manage promotional frequency. Continued deep cuts in the wake of hoarding behaviour are wasted due to pantry space constraints. 

Marketscience Consulting has extensive experience in Hierarchical Bayesian techniques on household scanner panel data that get to the heart of consumer response to pricing. Using these approaches, we provide advice on optimal pricing strategies at detailed levels.

Customer level analysis

On many occasions, the client focus is on understanding individual customer response to prices. The financial services sector provides a good example, where banking institutions seek to optimally set interest rates in order to retain existing customers as products expire. Owing to factors such as inertia and loyalty, it is often unnecessary to pay the same rate to retain as to acquire the customer. Consequently, knowledge of the precise rate sensitivity is critical in optimal rate setting for maximum revenue.

Marketscience Consulting provides highly detailed customer equity modelling to address these and similar issues surrounding customer acquisition and retention.

Conjoint analysis

In circumstances where historical data are sparse or exhibit insufficient variation, alternative approaches are required. Conjoint analysis is a powerful tool that uses discrete-choice research data to infer the trade-offs consumers make between product attributes. This allows a detailed understanding of customer response and willingness to pay under proposed price strategies and changes.

Marketscience Consulting provides a suite of solutions to advise clients on optimal regular and promotional price setting policy. We utilize data at varying degrees of aggregation depending on the client issue, employing techniques ranging from dynamic marketing mix and hierarchical Bayes choice modelling through to discrete choice research.