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Using AI to gain a competitive advantage over the next few years is not about better forecasts. What is often missed about predictive analytics is the power to learn more about your customers, and how it helps us provide them with better service. Understanding your consumer base is the real benefit of AI, as it empowers decision making that drives loyalty and brand engagement.

Today, algorithms are constantly crunching data, predicting market trends, and responding to market changes in real time. Such advancements are only possible because of AI. All the data from the consumers, the market, and the competition can be consolidated and analyzed. It can be examined historically and now, with the help of  AI technology, forecasted as well.

To gain insight from AI, you need data. Most companies now routinely log every visit to a product page, every call made to an inquiry response center, and every email received. We are seeing data come faster in almost real-time, we are seeing exponential growth of data, and we are also seeing it come from more places.

Loyal Customers & AI

One of the keys to digitalization of consumer insights is the loyalty card, the likes of which were first made popular by grocery and merchant stores. With this kind of card, retailers can now retrieve a record of everything the consumers have purchased and other useful data. Companies have data from millions of users and, along with other correlating data, they have very real and actionable insights.

Gerri Martin-Flickinger, the Chief Technology Officer and EVP at Starbucks, said in 2016 “With about 90 million transactions a week, we know a lot about what people are buying, where they’re buying, how they’re buying. And if we combine this information with other data, like weather, promotions, inventory, insights into local events, we can actually deliver better personalized service to other customers.”

Starbucks customer loyalty and predictive analytics

Starbucks use the mass of data from their customer loyalty cards to understand when and why people buy what products, so they can deliver personalized experiences.

Moving Beyond Just Forecasting To Delivering Personalized Experiences

Starbucks along with many other retailers is going from just forecasting what may happen, to using predictive analytics and Artificial Intelligence (AI) to deliver a more personal experience. Predictive marketing is clearly a very big deal right now, and the benefits are clear [Ed: more on Starbucks’ approach to using customer data is available here.]

Companies can claim with ever greater confidence that they know what we’re going to do next

Research conducted in 2018 by the Institute of Business Forecasting (IBF) to  gauge the future of technology, asked “What are the top technology advancements in the next 7 years that will have the largest impact on forecasting and demand planning?” Not surprisingly, close to 70% of the respondents answered Artificial Intelligence and Machine Learning as either their first, second or third choice. We’re looking at a perfect storm of technological and cultural factors, driving companies to claim with ever greater confidence that they know what we’re going to do next – and why.

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Many companies are now turning these insights and forecasts into actions and driving a stronger brand and even new opportunities. They are taking their customers on a journey they want to participate it, and earning their trust and loyalty while improving the organization’s bottom line. What companies are finding is that when it comes to the ways customer experience is delivered (through email personalization, digital advertising or targeted suggestions) consumers are not concerned about being marketed to by AI, as long as the content is personalized.

AI Is Making Things Personal

Micro-targeting and personalized sales are fast becoming the Holy Grail for retailers, as this attention to detail is key to keeping consumers coming through their doors or visiting their websites. Personally relevant, value-added, individualized interactions lead to better customer experiences and retention. With AI and micro-targeting, brands are not only delivering personalized content on a variety of channels, but they are also become more proactive in engaging customers and drawing them into the brand-storytelling process.

Knowing why sales occurred allow us to drive demand patterns instead of just waiting for them to happen

In addition, to strengthen existing shopper-retailer bonds, AI can also speed up the process of acquiring new customers. Algorithms out there are continually learning and can cross channels and consumers, and explain why certain behaviors and sales occurred. It is using these algorithms and a wealth of other variables to not only better predict but also nudge and drive demand patterns instead of just waiting for them to happen. We can use AI to pick the right channels and customize the content being delivered to audiences. Machines are learning to tell stories across a variety of channels, in order to ensure that customers are actively engaging.

It is not just about what sales will be next month but using technology to enable decision making that adds value in a multitude of different areas

The return on relationships generated by establishing this engagement is invaluable. Companies that home in on customer needs and wants through predictive analytics have increased their organic revenue by up to 21% year-on-year, compared to an industry average of 12%. It is not just what sales will be next month but using technology to enable decision making that adds value in a multitude of different areas.

By handing over much of the heavy lifting to AI, organizations can concentrate on developing insights which help differentiate their brand and drive more sales. By successfully combining technological innovation with a real desire to improve customer-brand interactions, companies will finally be able to gain the loyalty of their audience.

I’ll be speaking at the Predictive Business Analytics, Forecasting & Planning Conference in New Orleans from May 6-18, 2019. It’s a 2-day conference with an optional data science workshop, designed to get you up to speed with basics of predictive analytics, or get you to the cutting edge of the field with the the latest methodologies, tools and best practices.