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Becoming the Netflix of retail

  • In association with VMob
  • October 7, 2015
  • Scott Bradley
Becoming the Netflix of retail
Image: Kerryn Smith

In this difficult environment, marketers are no longer focused solely on advertising and promotions; they now own customer experience and all its massive impact on the bottom line. Having only just got to grips with the idea of 1:1 communication using targeted emails, retail marketers have had to rapidly evolve and invest heavily in customer-centric innovation, starting with mobile.

The retailers that succeed in this increasingly mobile age will be those that best meet the needs of their customers, and those best equipped to do that will be the ones that can actually anticipate what their customers want, whenever and wherever they want it. This drive towards personalization has led to data-savvy organizations using unprecedented amounts of live customer data; building relevancy algorithms that drive engagement by delivering the content most relevant to each individual. And the company blazing the personalized service trail? Netflix.

What Netflix does well

Netflix analyzes thousands of data variables to classify users based on their preferences for film and television; enriching each piece of content with huge amounts of metadata to help describe it in very granular detail, then combining that with customer data. When a customer opens Netflix  its algorithms instantly process a huge range of facts about that person including time of day, the day of the week and what they have previously watched or previewed.

The algorithm can now look at all the content that similar people have watched under similar circumstances and suggest the shows and films that are most relevant to that customer right now. The more relevant it is the more likely they are to watch, the more they watch the more engaged they become, and the more engaged they become the less likely they are to churn. Today, more than 75 percent of all the content viewed on Netflix was suggested by the Netflix algorithm.

The key to ongoing engagement is continuous refinement of the algorithm and ongoing data collection; Netflix must constantly personalize its service and recommend relevant content. If I've watched everything in my queue and there's nothing new being recommended, where's my incentive to come back? Powered by its intelligent use of massive data, Netflix successfully meets the expectation of a personalized experience at every touch, and critically, provides that service without making the customer work for it.

Becoming the Netflix of retail

Retailers should be focused on using technology innovation to invisibly enhance the customer experience. Relevancy algorithms powered by real-time customer data from a multitude of connected devices can ensure that shoppers encounter relevant recommendations at every possible interaction point: the experience I have is shaped by every experience I've had before, and different from every experience everyone else is having – it's completely personalized to me.

Applying the Netflix lesson to the real world

The most unique opportunity mobile brings to retailers comes at the very end of the purchase process - the checkout. When we combine the data collected along the customer journey with that last critical piece of transaction data, we can do really useful things. Now we know which promotion resulted in a sale, how long it took to close that sale, and what else a customer purchased at the same time. We also have the contextual data that shows us what other factors played a part in that sale - location, weather, app usage, social, public transport and local events.

This means that within this one data set we now have a true picture of the cost, value and context of each promotion a retailer is running. And it’s with that data that the marketing program can be extended to actually predict outcomes and recommend promotions that will best engage customers and achieve bottom line goals – a la Netflix.

This story was originally published in NZ Retail magazine issue 739, August/September 2015.

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