Personalised recommendations powered by analytics

In today’s digital world, consumers are faced with a head-spinning plethora of choices when shopping. Companies in all industries - retail, healthcare, and financial services for example - are chasing the same customers on various channels - online, in-store, via mobile, etc - with a variety of sales deals and endless varieties of products and services.

With this, consumers are faced with a daunting and time consuming task of weeding through the bottomless options to find the right product or solution for them. Recognizing this current state of affairs, many companies are now looking to data analytics to offer consumers with the most relevant products and services quickly.

Targeting customers with generalised campaigns - mass mailings, large advertising hoardings across towns or digital ads - is no longer an effective business strategy in today’s ever growing digital landscape. To reach today’s consumer, businesses are now investing in real-time analytics technologies that will help them to better understand consumer behaviour at a more granular level across a wide spectrum of channels.

The newfound consumer knowledge will help companies to pursue more tailored recommendations for their consumers which can result in increased sales and earn consumer’s loyalty, and help them to forge ahead of the competition.

When looking to deliver timely personalised recommendations across a vast range of products and services, multiple cultures and a growing number of channels, companies need to address four key challenges:

  1. Acquiring the ability to process massive volumes of consumer data, created offline, online, or through mobile channels.
  2. Interpreting the results of consumer interactions in real-time, while drawing insights and recommending products and services that are highly relevant to each consumer.
  3. Creating a holistic, personalised and consistent experience across all channels is essential when making recommendations—not only in terms of products or assortment of products, but also in terms of presenting each consumer with an attractive design layout, offer and price.
  4. Investing in advanced analytical tools and methodologies to gain insights from the sparse data generated by the online (and indeed offline) interactions of new and occasional consumers, as well as for new and infrequently purchased products, to make relevant recommendations.

By understanding customer preferences, analysing purchasing behaviour and developing individual profiles, the multi-tiered recommendation engine helps to direct the customer towards a product or service that meets their requirements.

To make individually tailored and effective suggestions at scale in real-time, next generation recommendation engines must be enabled by several key capabilities. The engines should:

  • Understand context: Know the location of each consumer, and what they are doing at a particular moment.
  • Adjust recommendations based on insight: Make automatic improvements, based on feedback from previous recommendations.
  • Scale and flexibility: Have a robust and dynamic technical architecture to manage the sheer volume of data arising from multiple sources.
  • Test and learn: Allow business users to quickly simulate performance before moving into production, to minimise risk at critical customer touchpoints.
  • Be analytics masters: Balance breadth of data from a diverse customer set—from regular shoppers to infrequent customers.
  • Support business users: Give business users greater control over performance and enable priorities to be set for the business e.g. stock availability, promotional strategy.

Businesses must understand consumers in the context of the world in which they live, in order to provide the right product at the right time through the right channel to each one of them. By learning about their preferences and adapting to changes in their lifestyle, interests, or geography, companies can increase their ability to stay relevant and competitive in today’s digital age. Most importantly, companies that harness the power of advanced analytics to drive their personalised recommendation engines will be able to manoeuver through the vast amounts of data and ultimately win the battle for digital consumers.

Posted by Nigel Paice, Managing Director, Customer and Digital Analytics Lead for EALA, Accenture Interactive


Copyright © 2014 IDG Communications, Inc.

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