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  • Writer's pictureJohn Ennis

Eye on AI - May 7th, 2021

Welcome to Aigora's "Eye on AI" series, where we round up exciting news at the intersection of consumer science and artificial intelligence!


This week, we’ll be addressing the unprecedented rise of AI in retail sales programs by looking at how companies are better utilizing their data with the help of machine learning to produce more accurate sales predictions.


AI Tech the Biggest Opportunity in Retail Sales

We begin with a look at a story out of Forbes, titled “The Biggest Opportunity In Retail Right Now, in which writer/venture capitalist Richard Kestenbaum makes a strong case that the real opportunity in retail isn’t a product, but technology; specifically, AI technology.

“In the past, we’ve seen newly-developed software overtake older products because the new programs are better,” writes Kestenbaum. “But with machine learning and access to giant lakes of data, companies that become the leaders will retain their leadership and they will own their market segment. That makes right now a unique time in the software business. It’s the equivalent of a software land rush, whoever gets there first, will own the territory and it will [be] difficult to impossible to displace them.”

Indeed. Think of apps like Instagram or Facebook, which seemingly had little intrinsic value when they were first launched. Pictures and posts, likes and clicks. No products. No ads (at first). And yet, time and time again, these seemingly worthless apps sold for millions to billions of dollars. Why? The data.

All those cool, free apps on your phone are data-collecting gold mines. Many track every in-app user action, with the sneakiest among them (Facebook, cough) even tracking off-app activity like location history, other apps usage, sleep time, etc. though Apple recently put the kibosh on that. As that data was dissected using machine learning algorithms, individual user profiles were created, mapping out how to best influence each individual user into making a purchasing decision.

Think of targeted ads on Google or Facebook. Is it just me, or do they always seem to pop up when you just had a thought related to that exact product? The computer isn’t reading our minds. It’s building smarter ads based on the data it continuously collects, which is why targeted ads have such high conversion rates.

“Imagine all the times you picked up a product, examined it and put it back, or you looked at a product online in detail and closed the tab without buying,” continues Kestenbaum. “If a retailer could aggregate your activity with millions, or even billions, of other consumers, they would have a much better idea of how to improve performance and your satisfaction. Technology like that requires intelligence and analysis that is so massive it is way beyond human capability.”

As mentioned, machine learning models get smarter as they receive more data, which means those companies that already have robust data collection methods are already well ahead of the competition. Mega retailers and advertisers like Amazon, Google and Facebook continue to increase advertising / purchasing revenue using the mountains of data at their disposal. As that divide increases, smaller retailers are continuously forced to rely on their outsourced predictive models for advertising because they can’t compete with the amounts of data the larger companies feed into their models.

Machine Learning Ushers in a New Frontier in Predictive Sales

Retargeting is one thing. Strategic sales is something else altogether. Marketers want to know which companies and industries they should be targeting. Sales professionals want to know which leads they should be prioritizing. Thanks to machine learning, marketers and sales professionals now have the ability to know which of their prospects are the ripest for conversion before lifting a finger.

“[Machine learning] is ideal to use as part of sales forecasting,” writes CustomerThink contributor Samuel O'Brien in his article, ‘6 Machine Learning Methods to Simplify And Sharpen Sales Forecasting. “This is due to its ability to store, process, and use data much more quickly and efficiently than people can... It also prioritizes leads to a stronger sales approach and forecasts sales for the future. As it can go through years’ worth of data, it can identify patterns that take people a long time. These patterns are turned into predictions and sales forecasts.”

Think of it this way: every company tracks customer data through a database, CRM, or other systems. That data tells a story. Yet it’s extremely difficult (and time-consuming) for humans to dissect all the data in any meaningful way.

Enter machine learning. By looking at a company’s closed/won and closed/lost history, machine learning can conceivably create an ideal customer profile (ICP) model for each individual product, then score incoming leads against that model. This allows sales professionals to better prioritize prospecting efforts, and marketers to understand which predictive model attributes suggest high or low conversion probability (i.e. the industry segment, location, company size, funding, etc. most typically associated with high or low conversions) then adjust their marketing channels and forecasting accordingly.

Predictive sales or marketing is not a cure-all. Model accuracy depends on the quality of the data being used. And not all models are created equal. But if companies choose the right lead scoring and/or predictive marketing vendor, they can potentially increase conversions by remarkable amounts, which is a big reason AI in retail tech is projected to grow 35% year-to-year through 2025.

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