Data-driven decision-making: 3 ways to drive retail resilience
Retailers can improve CX and strengthen the bottom line by taking a fresh approach to data.
Machine learning in retail takes the industry beyond the basics of big data. For years now we’ve been told that data is king and that it should be tapped for all decisions; what to stock, how much to buy, what products to suggest to repeat customers. But doing more with that data using machine learning is just what retailers need to really succeed in the current market.
A study by McKinsey found that U.S. retailer supply chain operations who have adopted data and analytics have seen up to a 19% increase in operating margin over the last five years.
Data is clearly effective for retailers, but it’s all about putting it to work in the right areas and adding in predictive capabilities.
McKinsey cites real-time pricing optimization as a high potential use case for machine learning based on responses from 600 experts across 12 industries. The study pointed out retail activities that could effectively utilize machine learning, which include recognizing known patterns and optimizing and planning. Let’s go over a few of the key uses for machine learning in retail.
Use cases of machine learning in retail
There are numerous ways data has been employed in retail. A few use cases include:
- Personalization
- Predicting demand
- Price optimization
- Inventory management
- Logistics support
A top priority for retailers today is personalization. All retailers want to know their target buyer, but understanding the past and present of their interactions simply isn’t enough.
The next piece of the puzzle is being able to project what customers will do and need next in order to optimize assortment and offers. After all, most shoppers won’t need sunscreen all year long. So it would be a waste to keep suggesting it in the winter after they already bought it a few times in the summer.
On top of that, shopper demographics aren’t permanent. Just because someone has an infant and buys teething toys for them online, doesn’t mean that you should continue suggesting them forever.
Customer needs change over time and retailers need the data to understand what a customer bought in the past, which of those items they’re likely to need again soon (compared to suggesting they buy shampoo over and over when a bottle will last a while), and which of those items are clearly a temporary or one-time purchase.
With machine learning, retailers can take the leap from past and present data to future in order to better understand and meet their customers’ needs.
If someone splurges on a luxury briefcase around graduation season, yet their buying behavior is typically more modest, switching gears to recommend fashion items at your highest pricing tier won’t be effective.
Machine learning algorithms can generate suggestions for items customers actually might want, instead of pushing things they don’t care about or just bought.
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The price is right
Another key use case for machine learning in retail is dynamic pricing. What is considered the “right price” changes over time and an algorithm can take into account key pricing variables, like seasonality, supply, and demand.
That gives retailers the flexibility to generate the right price at the right time, while staying on track with specific goals, such as profit or revenue optimization. Algorithms learn based on performance over time, so they easily adapt to changes in the market.
There’s also the added bonus of removing human bias, since small errors can have a big impact on the bottom line.
Whether machine learning is employed to improve promotions, recommendations, or pricing, it’s extremely effective in finding patterns. Once retailers are armed with the data and capability to act on spending habits, behavior, and market trends, they can personalize their offers to create an experience that will drive sales.
With insight into purchasing patterns, retailers can optimize their supply chain operations, inventory management, and logistics. Shoppers can get what they need and retailers aren’t burdened with stock that doesn’t move.
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Faced with ongoing uncertainty, retailers are strengthening their supply chain to keep shelves stocked, inventories healthy, and customers satisfied.
Beyond just big data
Machine learning allows retailers to automate data analysis and go beyond the surface to really get to know their customers, discover patterns behind the data, and make data actionable by incorporating predictive analytics.
Instead of just understanding what their competitors’ assortments consist of and what their customers have bought in the past, they can figure out how to better plan their offerings to provide what shoppers want before they even know that they want it.
Machine learning in retail takes big data to the next level and pieces together the fragmented puzzle we’ve been looking at for years.
It accomplishes this by combining customer data with market trends to give retailers a holistic action plan to target customers better. Then retailers are able to optimize pricing and predict buying behavior with a greater degree of accuracy.
The ultimate goal of machine learning in retail is to drive revenue growth in a more efficient way and it is certainly effective in accomplishing this. To say the least, machine learning is changing retail for good. It makes hyper-personalization possible, as it takes big data based on demographics further. Machine learning improves decision making by bringing in more accurate data to inform crucial business decisions.
