Confronting Retail Competition With the Help of AI

Confronting Retail Competition With the Help of AI 1

Artificial Intelligence and Machine Learning have slowly become a staple of competitive industries, from retail to logistics. Simply put, they have become more of a key tool than an alternative technology, proving their worth with results. Here are some of the ways that AI-oriented resources have been helping companies stay competitive in the fast-moving, consumer-oriented economy.

Optimal Pricing

With the advent of the Internet and its effect on widespread advertising, it is easier than ever before for consumers to find the best prices for products. Simply put, buyers can immediately disregard a retailer if a competitor is offering a better deal. Similarly, regularly having the best prices on the market can attract frequent customers and build brand loyalty. As a result, dynamic pricing is vital to consistently edge out the competition.

Research done in 2014 by Forrester Consulting found that 81% of American buyers consulted online pricing information before making a purchase, with that figure up to 85% for Australians. The power of convenience and wide availability cannot be understated, and buyers are often spoiled for choice when it comes to online shopping. Considering this, it’s no surprise that a large portion of retailers (22% in 2014) are relying on dynamic pricing for a competitive edge.

Artificial Intelligence is the key to optimal competitive pricing, as the sheer volume of consumer data available is often too much for analysts to digest. Machine learning algorithms take this data and couple it with parameters given by analysts—categorized competitors, quality of data, product constraints—and return recommendations for pricing strategies. Amazon, perhaps the biggest name in retail, took dynamic pricing to another level, changing their products’ prices every 10 minutes on average. 

Identifying Value

Pareto’s Law, or the 80/20 rule, is a very important thing to remember in any industry. It essentially states that 20% of the input results in 80% of the output. In terms of retail and sales, it follows that 20% of customers contribute to 80% of sales, and 20% of products provide 80% of the revenue. This isn’t limited to just retail, either; this rule can be found in nearly any industry. For example, as a rule of thumb, 80% of materials are procured from 20% of suppliers, and 80% of shipments are made to 20% of facilities.

This is where the volume of data comes in again. Since practically everything related to retail is digitized today, tracking all sales and all customers is simply the next step. Following this principle, it is vital to identify who that 20 % of consumers are and, similarly, what 20% of stock leads to the largest chunk of revenue. Thus, keeping track of repeat buyers and frequently sought-after products can open up windows of opportunity.

Quantifying all this data and allowing AI to do its thing can help massively in cost-reduction, optimized marketing, supplier-buyer communication, etc. If and when a retailer identifies the valuable components of their business—like finding a way to cater to this “20%” group—maximizing revenue becomes a whole lot more straightforward.

Building Loyalty

This final point ties in to the previous two; upon analyzing consumer behavior for pricing analysis and identifying value, companies automatically get a clearer picture of who their customers are. Finding any patterns in buyers, such as identifying the largest demographic, can be a massive aid in future marketing efforts.

Even if the picture still isn’t clear, artificial intelligence in the form of chatbots and webpage tools can still pull in customers and earn their loyalty. Particularly, implementing machine learning in chatbots can make customer service not only quick and convenient but also personalized. With a regular chatbot, users often need to communicate via a series of buttons or scripts, and oftentimes not even reach a resolution. Some companies have begun to humanize their bots and equip them with machine learning tools to maximize their appeal and utility.

On the other hand, the collection of product sales data can find “matching” products to boost overall sales—this is best reflected in those “customers also buy” lists on product pages. Finding which products are frequently or will potentially be bought together allows retailers to bundle products in an attempt to build customer loyalty—of course, all of this can be done with the power of AI. 

Written by Sony T

Sony is a passionate bloggers writes on Futuristic technologies ...

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