How Small Retailers Can Personalize The Shopping Experience

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With the rise of automation and next-gen technology all around us, it was inevitable that big data would go through a “smarter” evolution, too. This idea that structured data (with the help of machine learning) helps businesses more intelligently shape the customer experience has actually been around the last few years:

  • Google’s made good progress in using structured data and machine learning to reduce spam in Gmail (as well as across their other applications).
  • Music, search, and social media apps already use machine learning to provide customers with more personalized suggestions and results (think of Facebook’s face tagging or Apple’s Siri).
  • According to an MIT Sloan Management Review study, 76 percent of executives believe that machine learning has helped them improve their sales prediction accuracy.

At the root of it, structured data helps businesses more intelligently and accurately predict what may otherwise have seemed like an unpredictable future. Imagine knowing how to stock your inventory so well that you can cut down on product loss, increase sales, and always have your customers’ favorite products in stock.

With the help of machine learning and structured data, this is the future the retail industry can look forward to.

Structured data: building a landscape for the ‘perfect’ customer experience

eBay took steps earlier this year to give their inventory a “smart” makeover in the hopes of providing customers with a more personalized shopping experience.

“We have solved the hardest problem of delivering e-commerce at scale, which is to have robust and vast inventory. Millions of items are listed on our marketplace in any given month, and millions go away. Knowing that inventory is the most important component to deliver on eBay’s brand promise,” said eBay CTO Steve Fisher.

In eBay’s quest to gain clearer insights into their inventory, they devised a Structured Data Initiative. Through this initiative, they assessed, re-categorized and tagged hundreds of millions of marketplace items. By consistently and more granularly tagging each product, they were able to create a reliable set of data that machine learning could then derive insights from.

Machine learning is a form of artificial intelligence whereby computers can change and adapt on their own once they have a proper foundation from which to learn. In this case, the restructured data served as the base—and it is how eBay anticipates being able to more accurately and efficiently predict supply and demand.

This is how eBay has gone about creating the “perfect” shopping experience for customers and, ultimately, it is a process other retailers will seek to replicate. However, as we watch mega brands like Amazon, Google and eBay pave the way with machine learning, how realistic is it to assume that smaller retailers will be able to feasibly adopt this smarter data solution, too?

Stepping stones for a structured data future

Before businesses adopt this “smarter” form of inventory predictability, it is important to understand there’s a big investment—in time and money—needed first in order to make machine learning work properly. That is not to say that smaller retailers cannot look forward to a profitable and successful future with structured data and machine learning, but it will require some baby steps in the meantime.

As large retailers get the hang of structured data, smaller retailers can ramp up their own use of big data to better personalize the customer experience. Data insights can be gleaned from places like:

  • Website traffic
  • Web page conversion rates
  • Product views
  • Heatmap tracking software
  • Customer purchase history
  • Shopping cart abandonment trends
  • Social data
  • Customer surveys

By gathering a more comprehensive, 360-degree view of customers’ identity, shopping habits and general preferences, retailers can use this data to deliver personalization in the form of:

  • Product recommendations
  • Life event and milestone reminders and congratulations
  • Discounts and other special offers
  • Loyalty program benefits
  • Local retargeting
  • Marketing emails
  • Live chat conversations
  • And more

With more accurate means for forecasting sales trends, stocking inventory and providing customers with personalized recommendations, there is a lot the retail industry can look forward to as they ramp up data collection and processing methods in preparation for making the leap into structured data.

Personalize your customers’ shopping experience with care

At the end of the day, your customers’ shopping experience is what matters most. If they are not satisfied with the search results you send their way or the level of service they receive, they will find a different retailer who will do it better. That is why smart machines and data processing will play a big part in the future of the retail industry.

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