Analyzing Dynamic Consumer Behavior? ML-based Predictive Analytics Gives Retailers An Edge

But machine learning (ML), a subset of artificial intelligence (AI), is the new savior on the block.

ML models can be used to predict the future, and get a sense of why individual consumers behave the way they do, so progressive retail businesses need to deploy it.

A recent study shows that the global artificial intelligence deployment in the retail market is expected to grow at a CAGR of 34.4 percent from 2020 to reach $19.9 billion by 2027.

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 Why Predictive Analytics?

Why predictive analytics is important? Data around a customer constantly stream in from point of sale (POS) machines, social media channels, website visits, and so on. Understanding how best to use that information is the task assigned to predictive analytics.

Checking out past trends helps this kind of analytics to determine what will happen next. Armed with the information they need to retain customers and meet their goals, retailers can chart the future.

Having the right information on hand, retailers can use it to help predict customer behavior. What it allows retailers to do is figure out the next moves in a customer’s journey, helping them to manipulate the buying experience.

How Does Predictive Analytics Help?

To be clear, predictive analytics and ML are two different disciplines and are not dependent on each other. Together, they offer a significant tool in what they can offer to benefit retailers.

Machine learning helps a predictive analytics scientist decrease the time needed to collect data, clean it, and analyze it using various technologies and algorithms.

ML techniques include Regressions, Classifications, and neural networks. Using these, ML analysts can predict outcomes in different departments of retail such a customer behavior, digital marketing, financial planning, and inventory control.

Adding ML to Predictive Analytics

Traditional data analytics worked fine for decades in the retail sector, but the need for speed coupled with the requirement to be able to track consumer behavior in almost real-time has exhausted those methods.

The pace of data inflow can be scorching, and traditional analytics can no longer keep up. That’s why the added dimensionality of AI/ML has brought in a new level of data processing.

To Understand Dynamic Consumer Behavior, ML-based Predictive Analytics:

• Uses algorithms such as Decision Trees or Random Forest
• Is self-learning. Suggests automatic improvement in response to changes in the training dataset

Benefits of Predictive Analytics With Machine Learning

Here’s a quick look at some of the key benefits that ML-based predictive analytics brings in:

Price Optimization: Price, after all, is the key in retail. In today’s fiercely competitive e-commerce world, knowing what discount to give which customer when is the key. But that can be tricky.

You may want to win the deal, but you do not want to leave money on the table, at the same time.

While traditional analytics may not be of much help, an AI algorithm could inform you of the “ideal” discount rate for a product or a commodity for a particular customer.

Writing in The Harvard Business Review about how artificial intelligence is changing sales, AI researcher Victor Antonio wrote, “An AI algorithm could tell you what the ideal discount rate should be for a proposal to ensure that you’re most likely to win the deal by looking at specific features of each past deal that was won or lost.”

Consumer behavior: Consumer behavior is the dynamic interaction of humans with a brand, product, or service that includes cognition, conduct, cause, and effect.

It includes their thoughts, feelings, and actions, which brings in a degree of emotion. Read on to learn how ML-based predictive analytics helps deal with dynamic consumer behavior.

How ML-based Predictive Analytics helps Retailers Understand Dynamic Consumer Behavior and Sell Better

By now, you would have come to appreciate how complex the understanding of consumer behavior can be, so much so that traditional predictive analytics starts faltering in the process.

Consumer behavior is fluid, like shifting sand on a beach, and making sense of such a changing scenario is not an easy task for humans unless you bring in machine learning to stay ahead of the curve.

Plus, retail marketers have to not only understand customer behavior but also competitive offerings and the reasons customers purchase rival products or services. Monitoring consumer behavior gives the knowledge to understand them, allowing marketing strategies to be better defined.

But do remember this that AI will encourage consumers to spend more than ML algorithms can make better sense of those copious volumes of data coming in, so investment here is worth it.

Just imagine a tool that helps your customers have prior knowledge about when and how the price for a certain product will change? Price prediction is but one achievement that ML-based predictive analytics helps achieve.

Here are Some Other Aspects:

Retailers who go by ML-based predictive analysis take consumers through a much smarter funnel, allowing them to buy before distraction hits or buying fatigue sets in.

It also helps understand what is the “right” price an individual buyer is willing to pay for a product, yet believing that he/she is getting value for money.

Helps to forecast the exact period in time when a customer is most likely to convert.

An increase in customer loyalty is another benefit of ML-based predictive analytics. Because of dwindling attention spans, marketers must be able to time the period in time to capture a customers’ attention with the right product.

One example is to remind a particular customer of requiring a product once again. Or sending promotional emails at optimum times that convert into a sale.

Increasing a marketing campaign efficacy is what can be achieved with AI. When retail marketers are able to recognize customers’ purchase behavior, the associated data can be used to draw up insights for marketing strategy development. It helps develop a personalized relationship with customers.

Bettering customer experience is another perk. Happy customers are loyal customers, so using AI to say automate the simple customer interactions with the brand can go a long way in satisfying your customers.

In conclusion: Consumers can be fickle. Or can be wooed by the competition simply by better pricing. In order to deal with dynamic customer behavior to customize products or personalize customer interaction in order to retain them, predictive analytics is a favorable option.

But traditional models fail, so machine learning models are the answer. It not only helps in keeping a close watch with customers at every stage of their buying process, but personalizes the entire experience, eventually offering them a better service, and so buying their loyalty.