Investment in AI and machine learning within e-commerce is at an all-time high. But for too many retailers, the investments that are made toward machine learning are funneled into a machine that continues to learn the wrong thing. Retailers have good intentions when investing in machine learning, but are forced to assess new technologies they often have little experience using. To ensure their investments in machine learning are fully optimized, retailers must have five key principles in place:
Have clean product data
A very common breakdown in machine learning is the absence of “clean” product data for the machine to ingest. Many retailers are very much aware of their deficiencies in online data quality, yet leave that data untouched for years. The result? About 9 in 10 consumers say that they would be unlikely to make a repeat purchase with a retailer that provided inaccurate or incomplete product information, and 10-20% of a company’s profits are tied up in dealing with returned merchandise. If a consumer has made a return or made the decision to not visit a retailer’s website due to poor product information, it is easy to see how important clean data is toward e-commerce success.
Capture every behavioral data point available
Personalization remains the ultimate pinnacle that has yet to be fully achieved within e-commerce. When managing an e-commerce business, you don’t have the ability to stand within a store to assess how people behave, but you do have the luxury to tap into how consumers navigate through your site. More often than not, there is much more navigation that occurs on a website than just a consumer finding a product and making a purchase. Navigational steps should be analyzed in detail to determine the common behavior between shoppers and how well a site is working to support a buyer’s intentions. Beyond data collection, retailers need to understand how to tailor a site to convert more shoppers.
Ensure you have enough data
The successful use of big data is all about connecting the dots. While generating a large volume of data for even modest-sized retailers doesn’t take very long, having the systems in place to analyze data is still a difficulty for many online retailers. A data-driven retailer needs to plot out all of the sources where data is being tracked—such as payment data, site usage data and conversion data. The key is to also ingest data from external sources that support your business objectives. A retailer that can leverage external data sources with their own information puts the organization on a whole new level.
Enhance findability within search
When integrating a new e-commerce platform, leveraging the out-of-the-box search functionality certainly feels like an easy add-on but it won’t rise to the expectations of today’s online shopper who expect quick and accurate search capabilities. Many e-commerce search queries bring back an assortment of results based on a single keyword buried within a product description, or a common false positive generated from previous search deficiencies. Fixing these errors will only work to compound the defects of your machine learning investments.
Leverage Data to the Maximum
Organizations often have plenty of data, but it can be daunting to link, match, correlate and interpret data coming in from different sources. In a business’ struggle to mine data, they leave plenty of opportunity for leveraging insights behind. Retail organizations must determine the metrics that actually matter. While it is true that one of the principles should be to collect as much data as possible, retailers must be highly selective as to what data to focus on. In the end, it makes the difference between e-commerce success and revenue flat-lining.
Successful retailers understand the value of the investment behind machine learning, and understand how technologies work to advance their business. Without these five key principles in place, however, retailers have all too often seen that the machine they are utilizing will take them down a wrong road, and that often it is nearly impossible to make a U-turn.
GroupBy provides product data, site search, personalization and analytics software for e-commerce sites.Favorite