Machine Learning Solves Retailers’ Biggest Challenges — Part 1
The COVID-19 pandemic has sent an already competitive retail market into complete overdrive. Margins that were previously stretched thin by pricing wars and highly discriminating consumers are now additionally burdened by a global crisis, and retailers know they have to act fast to respond.
As a result, there has been a surge in brands and stores investing in new technology innovations to stay ahead of the pack. One solution leading the way is machine learning. A subset of artificial intelligence (AI), machine learning involves advanced software that can learn to recognize patterns in data, draw conclusions, provide prescriptive actions, and even prescribe corrective actions without specifically being coded to do so. It empowers retailers to mine their own retail data to identify improvement opportunities.
Retailers might hesitate to add machine learning to their already packed suite of solutions, but the reality is that AI can positively impact almost every aspect of a brand’s operations. For those on the fence, this two-part series will examine seven common questions retailers share and how machine learning can be the answer.
1. What market trends can help boost sales?
In retail, market trends are the keys to success, from the latest in coupon promotions to regional influences on consumer buying. Retailers who capitalize on the latest industry trends position themselves to be market leaders but having the agility and the foresight to recognize which trends they should follow is difficult. Historical data cannot be leveraged to forecast these changes, as retail trends over the last few months would rarely repeat twice. Relying on what’s been previously successful could therefore be a disaster.
Advanced machine learning can provide a solution to this challenge by identifying valuable patterns of behavior. Rather than using historical data to make predictions, machine learning models compare ongoing activity against previous averages, combined with how “like” stores perform (there’s a saying, “all tides go up together,” which nicely sums up how similar stores typically exhibit similar behaviors) to help retailers leverage positive trends to maximize sales. This is done using a strategy called clustering, wherein entities (such as cashiers, vendors, stores, districts, etc.) are organized into clusters based on similarities in sales behavior. From there, machine learning models determine average sales benchmarks for each cluster, and when sales exceed this established benchmark, the machine learning solution identifies the direct cause and automatically instructs stakeholders to effectively repeat this behavior. The result is a brand that continually capitalizes on new trends based on current, proven data.
2. How much sales revenue do out-of-stocks cost my brand?
It’s retail 101 that sales are lost when a product is out of stock. However, retailers risk losing more than just that sale, though they often don’t realize it. Machine learning algorithms can identify the notoriously difficult-to-calculate “hidden demand” to determine the exact extent of the damage. Here’s how:
First, the algorithm analyzes retail data to determine the true demand of a product when it was in stock across every store and online. Next, it leverages this calculation as a benchmark to predict demand during out-of-stocks, even if your supply chain system doesn’t recognize it as such (also known as phantom inventory). Finally, the algorithm translates this hidden demand into a monetary value to demonstrate the exact amount of revenue lost.
In addition to highlighting the negative impact of an out-of-stock item, this calculation enables retailers to improve inventory accuracy, reduce shrink and maximize sales through specific tasking, simple prioritization and expedited product entry. As a result, the retailer earns maximum revenue and the consumer is satisfied by the ease of purchase.
3. How can online reviews prevent a public relations disaster?
In the digital age, where any consumer can go online to make their opinions heard, and where consumers ultimately trust each other the most when making decisions, online reviews are critical to brands’ public perceptions. All retailers know that monitoring these reviews can glean crucial insights but doing so can be a full-time job.
Machine learning is leveraged to easily harvest these reviews for actionable insights that can save a brand in crisis. Machine learning algorithms perform data-mining methodology called “sentiment analysis” on online content like reviews, web comments, and even product ratings. The algorithm then analyzes this content to identify customers’ general sentiment when writing their comments, whether positive or negative.
Based on these findings, the algorithm can quickly alert retailers to any especially problematic content that indicates high risk of a PR incident and provide instructions for resolution. Even after action is taken, the algorithm can continue to monitor the situation to ensure the problem was properly resolved. By implementing this solution, brands and companies alike can ensure they are exceeding customer expectations every day, and act quickly in case of a PR crisis.
Machine learning is here to stay
Machine learning is more than just the latest innovation in retail; it is quickly becoming the industry gold standard for analytics technology, and often separates the winners from the losers. This will be many brands’ first foray into AI, and there is more pressure than ever to get it right. By thoughtfully applying machine learning to their everyday challenges, retailers are empowered to make the correct decisions that drive greater revenue and customer satisfaction.
Stay tuned for part two of this series in which we’ll explore more of retailers’ burning questions, and how machine learning can answer them.
