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Were there really 132 Artificial Intelligence companies at NRF?

Spotting the next generation of artificial intelligence

Overview

Artificial intelligence and machine learning were a huge buzz at NRF, and it seemed like every other booth had some mention of it. The NRF program listed 132 exhibitors as “AI” providers in their guide. Are AI and machine learning truly that pervasive and are the much-hyped benefits real?

What are Artificial Intelligence and Machine Learning?

Artificial intelligence is most commonly defined as “human intelligence demonstrated by machines”. Yeah, that means just about anything that makes a decision with a computer is AI. While artificial intelligence and machine learning are used interchangeably in common parlance, machine learning is an approach to artificial intelligence. Its definition is also super broad with the most common one being a “system that can learn from experience to find patterns in a set of data.” (1) That includes methods like ages old linear regression. So, it is true that a lot of vendors offer AI and ML, and we’ve seen it for years.

Next generation AI and ML

If we’ve had these things for years, why is it a big deal now? Is it just some marketing ploy by software vendors? There is a very real next generation of capabilities. With the dramatic increase in available computing power, the huge increase in data, and the development of next generation analytical techniques, AI and ML can solve problems it never could before. And, with the digitization of the customer journey, there are entirely new ways to create value for customers from personalized promotions to cross-selling. McKinsey estimates that AI will produce over $600 billion of annual benefit in retail, and results like Adore Me seeing a 15% increase in revenue generated from its promotional campaigns after applying AI validate the potential.

How can you tell old AI from new AI?

So, how can you distinguish next generation AI capabilities from older techniques? Here’s a guide to spotting the differences -

Data volumes

Next generation AI can process huge amounts of data and may make recommendations at microscopic levels. By looking at numerous examples, it can determine the real drivers of a result and then make more accurate forward predictions. For example, older techniques may look at history summarized by location to determine a promotion’s effectiveness. When looking in total, the techniques might reach a conclusion that a type of promotion performs better in suburban areas than urban areas when the promotion resonates with working moms. However, that predictions would be off in urban areas with significant # of working moms. By looking at detailed, customer level responses, the response rate difference by customer type can be spotted, and the prediction could be much more accurate. Not only that, it enables more targeted actions by a retailer like only delivering promotions to customers who would respond and would produce incremental business value.

Data types

Next generation approaches aren’t limited to just simple data sources like sales history. They can take images, written text, customer clickstream data, voice, etc.. and can sift through them to uncover insights. For example, it’s possible to process product images and find commonalities between different products without needing to create an attribute and manually assigning values for each and every product. This can even be used to find common products at other retailers. Similarly, customer reviews can be processed to find the qualities of the product that stand out or could stand to be improved. This leads to a fuller understanding of the product, competitive environment, etc...

The ability to process new data types also enables solving challenges in new ways. The much-hyped Amazon Go cashierless store is a great example of this, but it’s not alone. Walmart is deploying robots to spot empty shelves, and Chick-fil-a is using visual recognition to ensure their food is fresh.

Patterns detected

Next generation AI can find complicated patterns that predict behavior. In the past, AI techniques would use fixed patterns like level, seasonality, and trend and make projections off those. However, we know certain fabric weights only sell well at specific temperatures. Instead of requiring humans to manually assign seasonal profiles that capture these behaviors, next generation techniques can find these differences on their own. And, the patterns it finds aren’t limited to the obvious; Walmart found customers prefer eating berries on days that aren’t windy. Who would have known?

Datasets

Next generation AI systems don't just try to make the most out of what was done in the past. They look at the past, spot potential actions where they have limited experience, run tests to improve its understanding of how it will perform, and then can automatically decide to stop performing the action or expand it because it performed well. In essence, these techniques can automatically perform the limited tests that were manually executed in the past. B2W, the largest online retailer in Latin America, tested setting their prices using these approaches and were able to increase their gross margin by 30% in one of their product categories. These techniques are also being used in digital marketing to personalize emails and evolve website designs.

What can you do with next generation AI?

The ability to understand the customer (via processing of huge amounts of data and direct interaction with them), competitive environment (via text and image processing of websites), and back-office task (via digitization of transportation, invoicing, and other processes) coupled with advanced methods to find the hidden patterns has opened up a whole new world of opportunity. You can use these AI techniques to improve the effectiveness of your promotions, better allocate product to stores, improve your labor planning. And, the cases aren’t just limited to customer-facing decisions. You can use it to better predict arrival times for shipments, delays at your warehouses, if employees will be absent, or what orders and receipts an invoice matches to. Or, you can make your decision processes more efficient by automatically identifying causes of issues like low stock levels or long wait times in store.

Conclusion

While there are a ton of AI providers, there are a limited number applying next generation capabilities. By finding these next generation software and analytics service providers and the right use cases that are important to your business, you’ll be able to apply AI to truly be more relevant to your customers, more efficient in your operations, and make better business decisions.

About Us

Cognira is a boutique consulting and software analytics company that focuses exclusively on retail merchandising and supply chain. Cognira’s goal is to make science easy for retailers, and to help them get the most value from their advanced science solutions. We bring decades of experience leveraging science, analytics and scalable technology to improve retail decisions.

Footnotes

1) If you’re super interested in the definitions, check out this awesome machine learning intro.

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