7 AI Use-Cases For The Retail Industry

Use Cases

This post details 7 common AI use-cases that can help retailers improve their business with AI.

Artificial intelligence (AI) is no longer an emerging technology. It is a proven solution that is already being used by some of the world’s largest companies. 

The retail industry is not immune to AI’s disruptive potential. In fact, executives in the retail industry expect to see at least 70 percent of retailers adopting AI in the next two years. In fact, AI can help retailers achieve their business goals by improving the customer experience, increasing sales and reducing costs.

In this article, we’ll explore 7 AI use-cases that can help retailers improve their operations and ultimately, their bottom line.

1. Predicting Churn

Retail churn is unique, in that customers don’t typically quit the product cold turkey, unlike with a subscription. Instead, customers may gradually buy less from you, which is also known as “sliding.”

Using AI, you can predict which customers will slide, to prevent losing them before they quit entirely. In order to identify these customers, you’ll need a historical dataset of customers and their purchases.

Given that existing transactional data, you can upload it to Obviously AI, and predict churn in minutes. Then, you can deploy the model on new customers to predict if they’ll churn.

Knowing what customers are likely to churn arms you with the power to make proactive decisions, such as offering those customers discounts, sending them personalized messages, or asking for their feedback (and taking it to heart).

2. Predicting Marketing ROI

In the United States alone, retailers are expected to spend around $35 billion on digital ads in 2021. In fact, retail is the industry with the largest digital ad spend in the US.

Every dollar spent represents a “bet” that there will be positive ROI. Historically, however, positive ROI on ad spend has been exceedingly rare, especially in the short term.

Internet users are being bombarded with more ads than ever before, and are experiencing “banner blindness,” resulting in even lower digital ad ROI.

Using Obviously AI, you can upload a historical dataset of retail ads (or any form of marketing promotions), to predict ROI. You can then deploy the resulting model on a new marketing campaign idea to predict the likelihood of positive ROI, creating a data-driven approach to marketing.

3. Optimizing Assortments

Optimizing retail assortment isn’t easy, yet it’s immensely important to retail success. 

As Harvard Business Review writes, even the big players get it wrong, and Walmart had to roll back a chance in assortments in 2008 after sales declined significantly.

One of the key drivers of efficient operations is understanding which products are more likely to sell, and at which locations. Given a historical dataset of product purchases, you can use Obviously AI to predict sales and optimize retail store assortments.

4. Determine Floor and Shelf Space For New Products

Retail stores commonly sell hundreds, or even thousands of products. Allocating floor and shelf space for retail products based solely on gut feeling is a recipe for disaster.

At the same time, retailers can’t assume that more space means more profits. Allocating floor and shelf space requires careful consideration of historical data, which can be done using AI.

By uploading a dataset of historical product sales, and their associated floor and shelf space, you can build a model to determine the right amount of space to give new products.

5. Pricing Optimization

How should you price your products? Try to beat your competitors? Always ensure that you profit? Use industry averages?

Determining the right pricing is down to a number of factors, and a data-driven approach will always beat using gut feeling. With Obviously AI, you can predict how demand will change in response to different prices for each unique product, and determine the right pricing accordingly.

6. Predicting Daily Foot Traffic

Accurately predicting daily foot traffic can help retailers optimize their labor schedule and costs, and meet the needs of customers.

As with many retail metrics, however, daily foot traffic is complex, and due to a number of factors, such as the weather, date and time, holidays, and marketing activities.

With Obviously AI, you can upload this historical data to build models that accurately predict daily foot traffic, and deploy them to optimize labor scheduling. 

7. Maximize Employee Retention

The retail industry has notoriously high employee turnover, at around 60%, or four times the attrition average across industries.

This cuts into the bottom line, as retailers are constantly in the process of hiring and training new employees.

By using Obviously AI, companies can upload a historical employee attrition dataset to calculate the probability of new employees quitting, and take proactive steps to prevent attrition, such as personally reaching out to at-risk employees.

Summary

At Obviously AI, we see a rising wave of interest in AI platforms by retailers, and we’re already working with some forward-thinking retail brands on next-generation AI applications. We hope you enjoyed these 7 interesting AI use-cases for the retail industry.

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