FutureProof Your Small Business: Machine Learning In Retail

By: on January 31, 2018

Machine learning in retail is here. If you’re not preparing for machine learning in your retail operation, you’re going to fall behind the competition and lose out on customers whose expectations you can’t meet.

But what exactly is machine learning? And what are the potential impacts of machine learning for retail businesses? The following article answers these questions and helps you futureproof your business to succeed with machine learning capabilities.

What Is Machine Learning?

Machine learning is a subfield of artificial intelligence that automates data analysis. Users feed a data set into a chosen algorithm that analyzes the data and creates a model that can then make predictions about other similar data.

Here’s an example of what machine learning looks like.

Retail Machine Learning in Action: Pricing Optimization

Pricing optimization is important for getting the most value out of products without overpricing them. Let’s take a look at the steps of the machine learning process in the context of a retail pricing optimization model:

machine learning in retail infographic

Let’s go deeper into what’s happening here:

GATHER TRAINING DATA: Users gather a data set of product and pricing information from previous sales. This initial data set is used to pre-train the pricing optimization model.

CHOOSE AN ALGORITHM: The chosen algorithm analyzes various product features included in the training data to make predictions on what the price of the product should be.

TRAIN PRICING OPTIMIZATION MODEL: The model checks the predictions against the actual prices of the product (since the prices at the point of sale are included in the training data).

TWEAK THE PREDICTION MECHANISMS: The algorithm automatically adjusts the prediction mechanism accordingly. This is the “learning” component of “machine learning.”

OPTIMIZE PRICES WITH MODEL: Pre-training is now complete; predictions are based on previous selling prices in relation to the features and quality of the product.

FEEDBACK LOOP: And then when a product is sold, the price at the point of sale can be input to close the loop and help train the model, making it more accurate.

NEW DATA: To use the pricing optimization model, users input product information into the model. The model then predicts the optimal price of the product.

How Machine Learning Can Impact Your SMB

Machine learning is primed to give retailers tremendous advantages over their peers. Here are two impacts of machine learning in retail:

1. Understanding customer behaviors to make revenue-growing decisions

“Trying to figure out how to drive and manage and create more specialized and unique customer experiences is absolutely a machine learning problem,” says Bob Hetu, retail research director at IT research firm Gartner.

Hetu says truly understanding customer behaviors is the holy grail of knowledge for retailers.

“If you can understand why customers behave the way they do, then you know how you can perhaps influence that behavior.”

Pricing optimization is a great example of understanding human behavior. The optimization model cross references purchases (behaviors) with product information like size, condition, availability etc. (drivers of behavior).

By analyzing these previous purchases in relation to product features, we can begin to see trends in consumer purchase behavior.

2. Newfound operational efficiency that impacts bottom-line

Machine learning models will optimize backend operations, such as online order fulfillment.

Consider you’re a retailer with multiple stores across the country. You fulfill online orders with whatever you have in your stores. Typically, you’d assume the store closest to the fulfillment location is the best … but is it?

Machine learning models enable retailers to take more into consideration than just location, and one of these critical considerations is amount of inventory at each location. If one location is overstocked, it’d be ideal to fulfill the order from that inventory.

But what if that overstocked store is further away from the customer than another store with the product? At a certain point, an overstocked store is too far away to reasonably fulfill an order from. But where is that point?

Hetu says you have to balance the cost of fulfilling from a store that’s further away from the customer versus fulfilling from a store that might become understocked after this fulfillment.

“In the human mind, that balance is very difficult to do,” Hetu says. “But with machine learning, you can apply the right set of algorithms to help your model make the best decision. And it might decide that based on the potential for lost sales and the margin that can be gained, it’s probably worth it to spend a little more to ship the product from an overstocked store that’s further away.”

FutureProof for Machine Learning in Retail by Getting Your Data in Order Now

The main advantage of machine learning is the ability to easily analyze and create prediction models based on huge sets of data. These advantages are only realized if you have a significant amount of data to be analyzed.

“The best place to start is always at the foundational data level,” Hetu says. “Make sure your master data is clean.”

Let’s break that down:

  • Master data is the business information shared across your entire organization. This could include data about: sales, customers, products, employees, vendors and more. Machine learning can be applied to analyze any of these data sets.
  • Clean data is data that is consistently organized within rows and columns. For example, if you have collected customers’ zip codes, you’d want to them to be in the same column. Once you get your zip codes into the same column, you’ll want to make sure all the zip codes are actually valid zip codes. You’ll eliminate any invalid inputs. This elimination is basic data cleansing.

unstructured and cleaned data

An example of raw, unstructured and cleaned data

It’s necessary to clean your data so that you can analyze large sets of data to find insights. The sooner you get your data in order, the sooner you can unlock the advantages of machine learning and the greater benefits of artificial intelligence.

Hetu is clear here: “I strongly recommend that organizations, if their data is clean, start to think about how to adopt elements of an artificial intelligence strategy in certain areas like demand forecasting and fulfillment that have a stronger and a longer track record.”

But what exactly does that mean and what comes afterward?

Must-Take Actions to Stay Alive in the Machine Learning Future

There are multiple steps to take to revamp or begin a master data management journey. The first is getting a system in place to actually collect the data. For SMB retailers, this is most likely going to be via your POS system. Most POS systems on the market provide all the data management tools you’ll need for gathering inventory and sales histories.

If you already have existing data scattered throughout spreadsheets, there are standalone database management tools that you adopt to wrangle your data into order. But for most SMB retailers, you’ll be fine with your POS.

If you need some new software before diving into machine learning capabilities, we can help. Software Advice works with tons of retailers everyday to help create a shortlist of the best systems for their business. All you have to do is answer a few questions and we’ll reach out.

And our collection of user reviews from your retailers peers provide firsthand insights into the benefits and challenges with the software systems you’re considering.

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