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  1. How to win in the data economy

How to win in the data economy

Many keen observers of technology would readily acknowledge we have now entered the machine age—an age driven by self-learning algorithms, where large volumes of disparate datasets are analysed to make thousands or even millions of decisions every day.

Published: March 12, 2018 1:12 AM
data economy, data, algorithm, data networks Many keen observers of technology would readily acknowledge we have now entered the machine age—an age driven by self-learning algorithms, where large volumes of disparate datasets are analysed to make thousands or even millions of decisions every day.

Many keen observers of technology would readily acknowledge we have now entered the machine age—an age driven by self-learning algorithms, where large volumes of disparate datasets are analysed to make thousands or even millions of decisions every day. The world we live in is changing, and changing at a rapid pace. Those companies that fail to adapt are at risk of being replaced by companies that can compete smartly on data and algorithms. For example, Uber, which upended a number of legacy taxi operators across many countries by being able to adopt and ride the multiple emerging global technology trends such as the widespread adoption of smartphones, improving coverage of LTE data networks, sophisticated mapping technology, availability of Big Data infrastructure and advancement of machine learning algorithms. By leveraging these so-called exponential technologies to create a convenient solution to a common problem, Uber was able to become one of the most valuable start-ups in the world. To illustrate the point, let’s take the upfront pricing decision that Uber or any app-based cab aggregator takes when a user opens the app to get back home from, say, office. The aggregator, at that point of time, has at its disposal most of the relevant data and the context including: it is a trip to home, the time of the day, the availability of cars, the traffic situation and, most importantly, the user’s own price sensitivity based on her past responses to historical quotes. With all this data and the ability to analyse it in real-time, the aggregator can offer a quote that is personalised for her for that moment in time—a self-learning algorithm that has the potential to capture the customer’s willingness to pay.

This is analytics at the edge of decision-making. What does this mean for traditional enterprises such as banks, telcos, insurance, retail and consumer packaged goods companies? A number of stand-out companies in these industries have begun to start taking data and algorithms seriously to improve customer experience. Analytics-based decision-making has moved from a support function in marketing and risk departments to taking its due place in Board conversations. Boards have started to ask how do we leverage analytics, algorithms and artificial intelligence to compete and win in the market. Enterprises need to start looking at data as an asset and should begin to actively invest in it and develop it. Data strategy should include considerations for both first-party data which is captured by in-house systems (such as CRM, loyalty, web and apps) and external data. It should include strategies for both offline and online data acquisition and development. As the walls between the traditional industries are collapsing at a rapid pace, consider the below sample use cases that offer some exciting possibilities of data strategy and algorithms:

  • A fashion retailer can leverage the location data from the telco or app data partner to identify the profile of the people who walked into the store but did not make any purchase, and engage with them proactively. Going a step further, the retailer can run marketing campaigns at scale by identifying lookalikes of these audiences by tapping into a large data management platform with execution capabilities.
  • An insurer can identify the life-stage changes of their customers and potential customers by leveraging the basket data from a food and grocery retailer. Life-stage changes are excellent opportunities for insurers to cross-sell or up-sell their products and improve their customer engagement. While this is exciting, there are some obvious pitfalls that enterprises need to consider while they embark on their data and algorithm strategy—the importance of designing these analytics applications with customer privacy at the core. Enterprises need to do the right thing considering all relevant privacy considerations including taking customer consent on what data is captured, to what degree and for what purpose in order to stay a step ahead of what the current regulations mandate. Clearly, the possibilities of algorithm economy are endless.

Lakshmana Gnanapragasam
Vice-president, Strategy & Insights, Epsilon

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