How Machine Learning Will Turn Consumer Data Into Gold

In this modern era, almost everything we do generates data. That includes purchasing goods online and offline. Understanding these huge volumes of data goes beyond human capabilities, hence retailers can deploy machine learning solutions to make sense of these. Let’s explore such developments, methods, and insights that are modernizing the retail sector.
How Machine Learning Will Turn Consumer Data Into Gold



Defining Machine Learning

Machine learning (sometimes referred to as “ML”) is one of the main technologies that is becoming quite valuable for retailers as more and more businesses make use of big data (essentially a large volume of data that has the potential to be mined for information).


Many people are confused by the term “machine learning”. Let’s take the time to decipher it. It is related to artificial intelligence (AI) but the two are not interchangeable. AI, generally speaking, refers to a computer’s capacity to make decisions in a manner that replicates human logic. On the other hand, machine learning is the way in which a computer can “learn” such logical rules without simply being programmed to do things in a certain fashion. Machine learning enables a computer to repeatedly refresh its understanding of the rules as it sees more examples of how humans react to a number of external factors.


Such technology has become more common as modern computing power makes it possible to handle a large volume of data and run advanced algorithms. Machine learning has evolved to make it easier for both retailers and consumers to use.


How Machine Learning Will Turn Consumer Data Into Gold

Use Cases in the Retail Sector

There are numerous ways machine learning is utilized in retail. Through ML, retailers can take into account the leap from past and present data to the future in order to better comprehend and meet their customers’ needs. A shopper may splurge on a premium pret-a-porter item around graduation season, yet their usual buying behavior is typically more humble, rushing to recommend fashion items at your highest pricing point will probably not be effective.


Machine learning algorithms can generate suggestions for complimentary items, rather than pushing an item a customer just purchased that they rationally won’t need to stock up on for weeks, months or even years. You might have guessed that Amazon has one of the most famous recommendations engines of any eCommerce retailer, and rightfully so: its ML algorithms are so efficient that 55% of sales are driven by machine learning recommendations (as of December 2017).



Another crucial use case for machine learning in retail would be dynamic pricing. What is deemed to be the “right price” fluctuates over time and an algorithm can factor in key pricing variables, such as “seasonality”, “supply”, as well as “demand”. Such provides retailers the flexibility to generate the right price at the right time, while staying the course with specific goals, such as profit or revenue optimization.


Algorithms learn based on performance over time, hence they easily adapt to changes within the market. Moreover, there is the added bonus of removing human bias, given that small errors can have a significant impact on the overall result. One of the earliest dynamic pricing success stories took place in the early 2000s when Hilton Worldwide Holdings Inc. and InterContinental Hotels Group decided to eliminate fixed rates in favor of a fluid scheme (including dynamic pricing strategies). At the time, room prices were modified once or twice a day. Current computational power now allows prices to change almost in real-time.


Furthermore, customers tend to search online for visual content before proceeding with a purchase. In certain circumstances, they cannot easily find good keywords to describe what they want. The goal of visual search is to make it easier for consumers to find exactly what they are looking for. Instead of typing a search query such as “combo soft case for Android device”, which could return a lot of general results, prospective shoppers can upload an image to help narrow the search down to more specific items. With the enormous and increasing amount of taking and sharing pictures, ML algorithms can achieve great results. Retailers using visual search include ASOS, Target, Wayfair, John Lewis,, Neiman Marcus, Nordstrom as well as Urban Outfitters just to name a few.


Regardless if machine learning is employed to improve promotions, recommendations, or pricing, it is efficient for finding patterns. When retailers are equipped with the data and capability to act on spending habits, behavior, and market trends, they can personalize their offering to create a customer experience that will drive sales.



Increasing Adoption Rates

In 2018, the Massachusetts Institute of Technology (MIT) surveyed 1,600 senior North American marketing executives and managers on their use of key performance indicators (KPIs) and the role of machine learning in their marketing activities; 653 were from the retail sector. Among these retail marketing executives, 72% believe that their functional KPIs can be better achieved with greater investment in automation and ML technologies. In the overall sample, 74% of respondents stated the same thing.


Sixty-two percent of retail executives thought their organization has incentives or internal functional KPIs to use automation and ML technologies to drive marketing activities. In the overall sample, that number was lower: just 49% of respondents stated they had such incentives.


Last but not least: 72% of retail executives thought that their organization was investing in new skills or training during the year to make marketing more effective in using automation and machine learning. In the overall sample, 63% confirmed such investments.

In the end, retailers that are open to utilizing machine learning tools effectively will be able to differentiate themselves by creating great personalized customer experiences and better understanding their respective markets.