Data Analytics


Retail and Consumer Packaged Goods

In today’s digital economy, retailers must constantly rethink and evolve. Many factors influence this evolution – demands for personalized experience and consistent engagement across all channels and more transparency to guard consumers' trust. A nimble and an optimized supply chain with insights embedded at every decision point will become a must-have for any enterprise. An advanced logistics leveraging machines, external data and insights blended seamlessly with supply chain and commerce is key to turn logistics into a competitive differentiator.  These industry trends are driving retailers to adopt disruptive technologies such as wearable, mobile, big data, virtual reality, sensors and robotics. Data ingested through such technologies when effectively leveraged can help retailers build a new age consumer experience and product marketplace. A winning strategy for retailers would lie in building an analytics driven enterprise that monetizes its various data assets (both internal and external) in a boundaryless fashion. Here are some specific opportunity areas for retailers:

  • Build a supply chain process that is responsive to market and geo dynamics with real-time predictive and prescriptive analytics
  • Augment machine learnt insights with human intelligence to anticipate, curate, personalize and inspire product and shopping experience across the consumer journey without diluting privacy
  • Enable targeted marketing by leveraging intimacy mapping developed through micro-segmentation
  • Design and consistently enrich an integrated product marketplace that resonates with consumers’ taste by analyzing data for patterns and correlations
  • Provide uniform brand personality across the ecosystem through new intelligent digital assets such as wearable, smart apps, collaborative microsite, visual immersive experience, and social channels
  • Improve logistics operational efficiency by correlating machine / sensors generated data with external data such as weather, traffic and supply chain data

While every retailer has their vision of integrated analytics and insights ecosystem to be successful in the omni-channel era, very few are able to lead the pack in conceptualizing, designing and implementing them. Infosys offerings are designed to help retailers rethink, evolve and achieve their vision through a three-pronged strategy:

  • Boundaryless Information: Retailers will continue to adopt multiple niche technologies that are optimized for unique use cases. Consider a retailer’s personalized digital marketing use case that pursues to enhance and strengthen consumer intimacy with the brand & product. An effective implementation of such a use case would involve a comprehensive solution leveraging campaign management tool, planning tool, big data, EDW, analytics, consumer MDM, virtualization, discovery, digital apps and response data streams. The retailers’ information landscape will evolve with composition of next gen consumer & product MDM, data lake, augmented DWs, In memory analytical marts and multiple data products. The retailers require to weave the information across their landscape and analytics leveraging the right mix of those data, technologies and aligns with the business strategy to facilitate management as well monetize data at scale. Boundaries between data platforms will continue to blur and cloud adoption will only make it more interesting. Retailers will win as they keep this evolving information and analytics fabric seamless and transparent to its consumers of data in a boundaryless fashion
  • Pervasive Analytics: BI/ analytics platforms will no longer be the ultimate downstream in the information lifecycle. Insights derived out of these platforms will pervade across the enterprise and shape the thought process of front line decision makers. What will be even more crucial is how well that insight is contextualized to the actual touch point (includes the consumer of that data) that attempts to operationalize that insight. The entire analytics lifecycle including the consumption and operationalization of the insights derived through data needs to embrace an open and adaptive framework to realize the vision of seamless integration and pervasiveness.
  • Progressive Organization: Changing market dynamics coupled with the evolution of boundaryless paradigm for information will propel retailers to move away from the traditional BI analytics organization and governance structures. Organizations will evolve into product focused knowledge tribes that aligns BI/ analytics teams with various business processes. This structure needs to be supported by horizontal platform and tools team with focus on building new tools and automation. This structure need to be further strengthened with strong data governance tailored to next-gen data sets and associated policies around security and business semantics.

Success Stories

Helped them in servicing retail clients across the US and EU to generate, collect and analyze customer data and generate actionable insights for a leading marketing and loyalty analytics company

Created a flexible solution that disseminates semi-structured market survey data from external agencies into relational tables, using metadata-driven, regular expression-based and file transformation techniques. This increased brand perception score and consumption by 17% for a global beverage leader.

Helped create a seamless consumer interface based on data from stores, mobile applications, wearable, and online marketplaces for a leading sports footwear and apparels major. This has helped monetize customer data and increased the revenue for direct channels by 35%.

Helped create a 360-degree view of products across the brand and market to improve product visibility, build positive brand sentiments and increase profitability for a leading CPG player. This has reduced the time-to-market by more than 40% and eased collaboration with start retailers.

Helped apply and modify the heuristic patterns that identify the strategic value of customers through a research platform, which has profile resolution and refinement components for a leading fashion retailer. This helped increase consumer attributions by more than 40%.

Helped build loss prevention (LP) and a shrink analytics platform that tracked critical drivers necessary in preventing loss and improving profitability for a leading retailer in the US. The time taken to identify loss cases is reduced to days from 5-6 weeks earlier in addition to reducing shrink by 5%.

Helped increase customer engagement and ensure timely targeted marketing through stronger audience recency signals and sharper propensity scores by leveraging clickstream and consumer experience data across all channels for a leading retail super-store. This has helped reduce customer interaction from 5 days to few hours and increased traffic conversion by more than 15%.

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