Innovative technology in the retail world doesn’t always mean flashy gadgets. In fact, most of the industry’s most effective form of digital disruption is invisible.
Machine learning technology continues to further integrate with consumers’ daily lives, with new offerings such as Apple’s personalized App Store or Amazon price optimization tools. These platforms are seamlessly providing a personalized online experience through algorithms that are invisible to the average user.
Think about how Facebook, Netflix, Amazon and Google make smart inferences and recommendations for the consumer. The technology operates behind the scenes, but is always evolving to understand the customer better over time. Facebook provides an accurate prediction of the content an individual would like to see on his or her timeline, and understands that these preferences change over time. The same goes for Netflix and Amazon. It’s an invisible, yet complex understanding of the user or customer.
This is particularly effective because the user or customer is completely unaware of what is going on. There is no burden on customers to do anything more than operate as they normally would. The technology simply learns their preferences over time to offer hyper-personalized content.
Why invisible machine learning works so well in the retail world
Consumers will not inconvenience themselves to interact with a brand. They will not provide information or learn a new process or device. Instead, they want marketers to adapt to their buying processes and routines. So for marketers to become more technologically advanced, they do not necessarily need to invest in high-tech devices. The key is using technology to better understand the customer behind the scenes.
Retail-specific machine learning platforms are powered by several algorithms that automatically learn from both offline and online interactions over time. They analyze years of data for millions of customers with hundreds of data points per customer. This ultimately allows the platform to predict exactly what customer will want to buy down to the price, location and device. And the key is that the customer has no idea this is happening yet they receive highly personalised and relevant content.
In the Facebook example above, the social media site does not ask you to choose whose content you prefer to see or which brand you prefer to interact with most. It knows, based on your interactions over time, the best content for you on any given day. It also knows that someone you interact with now may not be the same person you prefer to interact with two or three years down the road. The retail industry can and should operate in the same fashion.
Marketers have attempted to do this manually using traditional data analysis. However, humans are simply unable to complete this type of analysis to the same degree of accuracy. The best a human can do is rely on segmenting large groups of consumers based on simplistic data points such as a previous purchase, resulting in highly inaccurate and inefficient targeting efforts.
Conversely, machine learning platforms are always on, always learning and always improving based on ever-changing data. Targeting each individual consumer with personalized, relevant discounts through the right channel and at the right time is imperative to gaining market share in an incredibly competitive industry. And even more importantly, this needs to be invisible to the customer.
That all sounds great, but how does it work?
The focus should be less on what looks cool and cutting edge, and more on what is engaging and delivers on the overall business goals. Here’s how a marketer can get started with machine learning platforms:
- Understand the business goal: It’s difficult to find the right solution when you have no clear goals. Do you want to drive revenue overall? Or increase sales for a specific product? Determine what you want to accomplish in order to implement the right machine learning approach.
- Get to the core of the problem: Machine learning solutions are not ‘cure alls’, they are designed to solve specific problems and do require expertise to maintain. For example, machine learning algorithms can recommend products to a new customer using historical data from other customers, but would not make a good recommendation for hiring a new associate.
- Choose a partner and/or solution that will allow you to execute on these goals: In marketing, the best results come from hiring the best team for that purpose.
Despite a world that seems to be overtaken by high-tech gadgets, the best use cases of technology today are invisible. They’re the subtle behind-the-scenes machines that truly understand users or customers over time. And in a highly competitive industry, retail marketers must take every possible step to secure new customers and motivate repeat sales.
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