HIGH POINT — Furniture retailers face a fiercely competitive landscape, tight margins, along with a slew of marketing and promotional considerations to account for in determining pricing and placing it within their larger business strategy.
Further complicating the matter is an increasingly informed customer base who can comparison shop with ease. Retailers often rely on instinct, historical data or matching the competition when setting prices, but these older approaches are quickly proving outdated in today’s environment.
That’s the view of Matt Pavich, managing director of global strategic consulting at Revionics, a software-as-a-service vendor with partners in the home retail sector including Home Depot.
“We help retailers with their base pricing needs, their promotional needs, their markdown needs. We also offer a lot of consulting and analytical services to help them refine and improve their pricing strategies,” Pavich told Furniture Today.
“Our solution is powered by really good AI science that’s constantly learning about consumer demand based on their transactional history. … If people are making certain decisions with their spending behavior, we’re learning from that, and we’re understanding” which products and product types are preferred and performing better than others.
“And we’re really able to then apply a lot of different rules and strategies based on what the consumers want, or (what) the retailers want.”
‘Big-picture’ personalization
Pavich addressed what he believes is a bit of a popular misconception around the kind of data that Revionics is harnessing.
“We don’t collect any personal data or anything like that,” Pavich explained, but rather the tools add personalization to pricing strategies that target different groups of consumers in different markets based on a variety of factors including their preferences and past behavior.
“Take groceries as an example: People eat different things in Miami than they eat in Kansas. So if you offer a better deal on Goya black beans in Miami, that’s a form of personalization that is market-based. So (we’re offering) retailers more personalization by making it even more local.
“For furniture retailers, if you are in an area with more apartments vs. more big homes, you’re going to want to offer different kinds of furnishings. So there’s a lot of opportunities to move along the spectrum of personalization, based on an individual retailers goals and needs,” Pavich said.
Strategizing promotions
Home and furniture retailers rely heavily on promotional sales at key times of year, and this is one area where data science can help ecommerce vendors move the needle. Pavich said that one of the first things that Revionics can do when partnering with any retailer is to analyze the performance of their past promotions.
“The very first thing that we can do, even without looking forward, it’s just looking at the promotions you’ve run in the past. To say, these were smart promotions, these were not smart promotions,” he said.
Pavich went on to explain that many retailers, including furniture, are often too locked into past practices, and whether they worked or not at the time, they may not have the same impact in a changed set of circumstances. This is where big data can come into play in a big way.
“You have to be really careful with when looking at your promotions. If you’re not applying really good science and analytics, heavy cannibalization (is possible) especially for big ticket items like furniture. I’m not going to buy a sofa and then go and buy another sofa. So, if we decide to put a sofa on sale, that could actually be costing a better sale of a different sofa.
“It’s really important to understand the side effects of writing a promotion, not just the direct impacts of that promotion. How did it impact my business overall? Was it highly cannibalizing things?”
In addition to addressing the possibility of self-cannibalization, he said, a big-data driven pricing strategy can help retailers apply the lessons of past promotional programming and optimize future offerings.
“Then the next step is taking that information and having a powerful tool that can really help you understand (how to make better promotions) and find a solution that benefits the customer, benefits (the retailer) and benefits the vendor. We sell more units, and customer gets a lower price (while) I hit my numbers.
“There are a lot of ways that you can leverage the analytics and tools to help make prices lower for customers, help improve your vendor relationships while also hitting your business objectives, and really just stop doing ineffective promotions.”
Zoning-in
Zone pricing strategies are another point where an analytical solution can help optimize things for retailers, and this goes for brick-and-mortar retailers as much as e-commerce.
Pavich notes that Revionics can help retailers account for factors like the proximity of competition, store performance and supply chain costs in determining pricing, and he said they ask several questions when going about this.
“It’s usually: How close is your nearest competitor? What are we seeing with the actual science of that store? Is that store a store where the customer is more elastic, meaning they’re more dependent on low price prices?” Pavich said. “Then we might want to offer lower prices in that store, or we might want to invest in our price position more in stores where customers want it more.
“It could be other things like supply chain costs, or it could cost more to just run that store because that city might have a minimum wage law. So there’s a lot of different reasons why you would have a different price in different stores. It’s really critical, and we help retailers with this,” Pavich said.
AI’s role: Beyond the buzz
AI plays a central role in Revionics solution, but Pavich noted that there’s a need to look past the buzzword to understand its real applications. It has also been a part of their solution for quite a while, long before generative AI (gen-AI) burst onto the scene.
“We have been in AI solution since 2002, but during past couple of years, gen-AI took it to another level. So I think it’s important to differentiate pricing AI, which we’ve done for more than 20 years and we continue to evolve, and gen-AI. We just made significant platform investments to accelerate how smart our AI is on the pricing side. And that’s really the analytics and the quantitative side.
The company’s pricing AI is able to glean actionable, big picture data based on an aggregation of individual transactions. “Every time a consumer makes a purchasing decision in one of your stores, it’s another data point that makes it a little smarter,” Pavich explained.
“Then gen-AI is a cherry on top. So the way it works in our solution is this: If you’re a furniture retailer, and you’re using our solution, and we give you all these great price recommendations. You can quickly type into a chatbot: ‘Did Ikea change the price on (their equivalent product)?’ And then it will tell you in real time … and make strategic recommendations that are coming from the AI.”