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Machine Learning for Retail: Driving Innovation and Growth

Explore the power of Machine Learning for Retail. Discover how machine learning revolutionizes retail with curated product discovery, personalized engagement, and automated tasks.

Explore the power of Machine Learning for Retail. Discover how machine learning revolutionizes retail with curated product discovery, personalized engagement, and automated tasks.

Retailers, large and small, are starting to turn to machine learning to boost sales and provide better service to their customers.

At F33, we were talking in the office last week about some recent inquiries we’ve been seeing from retailers who want to make the most out of the data they’re sitting on.

They also are aware that there is a lot of data from their customers constantly being generated during the sales process. They have heard that organizations use machine learning, an aspect of automation, to comb through this information.

We tell them that it’s true that automation and machine learning can be quite disruptive in the retail sector. ML helps you analyze customers and sales to inform better business decisions.

With that in mind, we put this post together to outline how retailers (and enterprises in general) can deploy machine learning to get more done with less.

Curated Product Discovery

Occasionally, we field inquiries from retailers whose chief complaint is that their customers feel overwhelmed by all of the choices available to them, which results in customers not making the purchase they came for in the first place.

Narrowing down the selection in an intelligent way helps your store respect the intelligence of shoppers. Inside Big Data cites the case of the Wish app, which takes data mining information to show shoppers only products they are most likely to purchase (or to bookmark or share with someone else).

With less clutter, the retail shoppers enjoy a streamlined experience. For example, Wish won’t show belts, pants, skirts or shoes if data shows the shopper isn’t interested in those types of items. About 500,000 shoppers use Wish daily, indicating its popularity among retailers and shoppers they connect with.

Direct Engagement With Customers

Machine learning collects vast amounts of historical data to analyze and compare, gaining more insights into shopper thinking and behavior. Amazon is an obvious contender in this category. It uses ML to develop what Software Advice calls “hyper-personalized customer profiles in real-time.”

Profiles include what customers like, what they’ve purchased in the past and how long they linger on site and where they’ve lingered. This paves the way for more personalized engagement with each shopper, resulting in higher conversion rates.

Automate Back Office/Warehouse Tasks

In brick and mortar retail establishments, workers are often cross-trained to pick up the slack in one department to help another. For example, salespeople may be tasked to sort and place new inventory in an all-hands-on deck situation. Similarly, you can have machine learning tools do any of the rote work that involves data.

This is particularly so in so-called multichannel retail efforts encompassing a brick-and-mortar storefront with online sales and marketing, such as through social media channels. Using ML in retail, you can automate back office activities to free up staff, per Forbes. You can also rely on an ML system for objective quality checks, improving store operations.

Determine Ideal Prices

Machine Learning for Retail: Driving Innovation and Growth

With machine learning, you can improve your pricing strategy. ML will consider seasonal demands, shopping trends, information on current customer demand and even compare current items with competing products.

All of this information is hard to process on the go by people, but machine learning makes quick work of this. You can test different pricing models with your ML simulations and pick the one that works best for you.

Predict Inventory Levels and Needs

Maintaining the correct amount of inventory is always a tricky affair. Retailers need to use machine learning to get the numbers to be more precise.

With a system built to forecast demand based on historical shopping details and other data, a retailer can waste less, predicting in real-time the inventory requirements.

In-store Assistance to Customers With Chatbots

Chatbots work autonomously without need for a manager to look over them, so they’re a useful machine learning tool to use in retail. Use them to answer customers’ questions 24/7/365.

It will reduce the time your human sales representatives need to spend on routine, rote questions and answers, so they can focus on providing high-end assistance.

Source Suppliers With Better Deals

How is your business doing in negotiations with suppliers? Use an ML system to scour the internet for what suppliers charge for their items, seeing in real-time if there are better deals.

Criteria will include delivery speed trade-offs for better pricing, who has a reputation for the best quality, and an overview of which suppliers have the most reasonable deals.

Personalized Shopping Experiences

The more personal you can get with shoppers, the more connected they will feel, as long as you address their needs. Sometimes a store, with the help of machine learning, can identify truths about customers before they realize it themselves.

For example, Inside Big Data reports that statistician Andrew Pole studied pregnant women for Target. Target wanted to understand more about new parents, who are big shoppers at the retail store chain.

Target analyzed women’s shopping habits in their 2nd trimester to develop a “pregnancy prediction” score. This enabled Target to predict better what quantity of baby items it needed flowing through the supply chain, and to better meet the needs of guests with a baby on the way.

Track the Shopper’s Journey

Inside a brick-and-mortar retail store, you can install smart vision cameras that use machine learning algorithms to keep track of each customer walking around inside, as noted by Software Advice.

ML watches to see where customers are looking at items on shelves and will keep tabs on how long they linger in different parts of the store. You can also get insight when you see what items they pick up and examine and then put back on the shelf.

Visual Search

It’s good to let people discover things through visual search instead of just text. To that end, Wayfair began using a visual search tool. It lets customers shoot pictures of the products they like, and then the website will show them products similar to the ones they made photos of.

Searching via images gives customers better options than asking them to use specific keywords related to what they want to buy, per Inside Big Data.

Using Machine Learning to Boost Your Retail Business Year

Retailers often work with a hybrid model, with a brick-and-mortar store (or more) and an online component. So, you may see customers inside your store and serve them better with your machine learning system, but there will also be customers you follow with ML when they engage with you only online in a browser.

To learn more about your organization’s options for deploying machine learning in retail, please connect with F33 today.

Behind F33
Greg Bigos, CEO

Greg Bigos is the CEO of F33, bringing over decades of experience in delivering ERP solutions for manufacturing, logistics, and retail industries.

Contact Greg
Behind F33
Wit Jakuczun, CTO

Wit Jakuczun is the CTO/Chief Data Science Officer at F33 with a PhD in Applied Mathematics and over 18 years of experience in mathematical modeling, data analysis, and simulations.

Contact Wit

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