As e-commerce picks up pace, and competition heats up, e-tailers are mobilizing the best of their pricing weaponry. Amazon is a good example. The seasoned e-tailer changes its prices every 10 minutes on an average – thereby taking a clear lead in terms of differentiation. Amazon’s pricing and customer engagement strategies exemplify the importance of basing pricing decisions on actionable insights.
In the customer-centric world of retail and consumer packaged goods (CPG), pricing is considered one of the most important levers for driving the organization towards profitable growth. Pricing the product right not only increases profitability, but also generates demand, builds brand image, strengthens customer connect, and fosters brand equity.
Today’s connected world throws up consumer data in structured and unstructured forms. Organizations that leverage this data and transform it into readily deployable information have the power to outperform the competition and volatile market dynamics. Retail and CPG companies can harness the power of this data to catalyze their pricing decisions, and gain the much coveted competitive advantage.
The challenge here lies in how retailers apply this data and information to create effective pricing mechanisms. The answer can be found in analytics. Pricing tactics, coupled with analytics, can provide retailers with a catalyst, which can give them an edge over competition in taking prudent pricing decisions.
Listed below are some of the key imperatives that retailers and CPG players need to take into consideration while developing a robust pricing analytics practice:
Domain knowledge: Every category is unique and there needs to be a strong emphasis on the ground work to understand category and market dynamics
Deep understanding of inputs: It’s essential to develop an appreciation and understanding of the consumer, market and business information sources, and their application in pricing analytics. Being information-agnostic gives companies an edge over competition
Exemplary modeling capabilities: One analytics modeling fits all’ approach seldom works. Categories, brands and clients could be at different stages of analytics readiness. A modular approach to pricing analytics puts retailers at ease.
Customized productization: Products provide scale. However, rigid standardization creates boundaries and restricts adjustments for typicalities of categories and client environment.
Plug and Play: A plug and play ‘simulator’ allows stakeholders to check their own hypothesis and see if the model indeed provides actionable insights.
The above-mentioned pricing analytics weaponry enables retailers to address pricing-related challenges such as:
The journey to pricing analytics begins at the data stage, as retailers have a plethora of untapped data at their disposal. Point of Sale (POS) data is one such goldmine, which provides the starting point for a pricing analytics journey. Predictive econometric modeling on POS data helps retailers decode the impact of marketing interventions and pricing on brand volumes. Adding information about the competition and the industry makes the model more robust.
This journey is ably supported and further embellished by data sciences. Retailers can leverage data sciences to understand price elasticity – both, their own and that of the competition (cross price elasticities), price corridors to operate with respect to competition, and the lift that various promotional activities provide to volumes.
But can these data sciences be tested on real-time, real-world pricing problems? The answer is an emphatic ‘yes’. Pricing sciences can be socialized through pricing simulators that can be leveraged by retailers to test their pricing hypotheses. The pricing simulator allows retailers to create their own scenarios, choose a brand and competitor of choice, select pricing corridors, and arrive at a target price range with a clear visibility into profitability.
Pricing analytics can thus enable retailers to make informed pricing decisions.
Research and Analytics, WNS
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