Re-imagining the 5Ps in a hyper data analytics world order

Re-imagining the 5Ps in a hyper data analytics world order Opinder is a seasoned executive with eighteen years of combined consulting & industry experience in digital & integrated marketing, pricing strategy & execution, and technology transformation at Fortune 500 companies in Consumer Goods, Health, Retail, and Technology verticals. At Infosys, as Associate Vice President, Opinder lead strategic northeast region Consumer Goods & Retail clients. He is responsible for bringing contextual thought leadership and enabling our clients for growth via his industry experiences, Infosys innovation, and transformation capabilities.

The retail industry needs to adapt to serve consumers in an omnichannel commerce environment. The amount of data generated across customer touch points and retail channels provides an opportunity for data scientists to revisit the five Ps of marketing – place, people, product, price, and promotion – and deliver a seamless shopping experience.

New generation data analytics enable retailers to calibrate an optimal mix of the 5 Ps and transform the producer-oriented business into a customer-centric model.

Significantly, design thinking paves the way for a bespoke shopping experience while machine learning and algorithms enable rapid testing, thereby maximizing value in every P.


Retail and fast-moving consumer goods (FMCG) enterprises need to adopt an anytime, anywhere model to serve millennial shoppers. While omnichannel is a business imperative, the age-old question of ‘what to place where’ needs to be addressed, in the physical, online store or whichever digital channel shopper may choose to engage.

using smart planogramming to help shoppers find what they are looking for

For example, Shopper data and behaviour can accelerate ‘path to purchase’ using smart planogramming to help shoppers find what they are looking for at physical and online stores.

Big data and web analytics track the online journey and physical trail of shoppers. Significantly, brick-and-mortar retailers can optimize and improve the usability of their stores by analysing customer data and distilling business insights of shopping behaviour in almost real time.


Marketing campaigns revolve around people, including customers and employees who serve them. Concierges at the retail store can help shoppers with personalized service and tips on how to navigate the store on a tablet device, which stores a repository of customer information.

Global retailers can engage with customers meaningfully and offer proactive service when they gauge requirements correctly. Big data tools analyse demographic data, in-store shopping behaviour, social interactions, online browsing trends, transaction history and post-purchase trends to understand customers and predict preferences. They help improve customer retention and, at the same time, cultivate customer loyalty.


Advanced demand management techniques – powered by data – help retailers and FMCG enterprises increase return on investment by accurately matching demand with supply.

Demand-driven forecasting models combine real-time data with historical sales data to make projections for future demand, thereby optimizing sourcing, supply chain management and inventory management. They help create prediction tools which require minimal sales data to predict store-specific sales across product categories.

The accuracy of such predictions only increases with the frequency of data updates and quality of inputs. Prediction tools reduce the likelihood of a stock-out incident significantly since stock replenishment is triggered automatically.

prediction tools reduce the likelihood of a stock-out incident

Data analytics is a catalyst for accelerated product innovation via product design support and streamlining of product testing. Early feedback on the product and market sentiment enables retail enterprises to make appropriate course corrections.

Big data also automates market basket analysis. The analytical tools combine structured and unstructured data to empower merchandisers with visibility into the composition and size of shopping carts. They can then be integrated with historical sales data to customize the product assortment for each store.


Data science applied correctly can significantly optimize pricing strategies. Analytical models evaluate variables influencing the financial performance of retail and FMCG enterprises. These enterprises can correlate data and identify patterns between demand for a specific product, sales of complementary products, cost of sales and history of competing brands.

Data insights help enterprises adopt a dynamic pricing system which can maximize profit. For example, end-of-season sales can be replaced with price discounting approaches that capitalize on demand, while simultaneously providing value to customers.

Since e-commerce thrives on price optimization, enterprises should use big data platforms to map real-time merchandising data with customer demand & behaviours and uncover business insights for dynamic pricing.


Digital technology provides retailers with the ability to personalize sales and marketing techniques. It empowers retailers to design customized promotions, which not only boost sales, but also help deliver a superior customer experience.

Smartphones, social media, geospatial technology and analytics

Smartphones, social media, geospatial technology and analytics have transformed advertising and campaign management.

For example, smart analytical models enable enterprises to crunch product, customer data, and supply chain metrics to create targeted offers. They can help retailers and FMCG brands cultivate customer relationships by reaching out to the right customers, at the right time, through the right channel.

Big data and analytics help retailers and FMCG enterprises address fundamental questions:

Will this new product line work in the market?

What is the best channel to sell a specific product?

Are products priced correctly?

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