At Boomerang, we believe in building a culture of innovation and continuously innovating — with and for our customers and other retailers and brands — to modernize commerce and the consumer experience.
I often get asked what drives our passion of “innovate or die” to help the retail industry. With our customers in mind, in today’s era, retailers must “grow fast or die slow”. Those retailers that have embraced our technology to grow are thriving while others struggle. Partnering with and seeing our customers succeed is what drives us every day.
But, why has it taken this long for retailers to have great technology that just works?
To put it bluntly, over the past 10 years, merchants have been slowly left behind when it comes to technology.
While they’ve had systems to manage information like selection, inventory, price, promotions, and markdowns, they haven’t been provided tools to actually help them do their jobs better and make better decisions. Meanwhile, marketing teams have gotten advanced predictive platforms to understand customer behavior; digital teams have e-commerce suites that can make helpful suggestions to customers on associated items; and even administrative functions like accounting have tools that automate the tedious and error-prone parts of their jobs so they can focus on more valuable tasks. Yet, for merchants, the number one tool they must use to do their job is still Excel, a blunt-force tool that wasn’t designed specifically with their needs in mind.
How did this happen? The problem lies in software having to provide enough value and build enough trust with merchants for them to actually use it. Solutions have been on the market for years that attempted to give merchants analysis and reports that they could use to make better decisions, but those reports took a significant amount of time to read through and synthesize. Then, these reports needed to be manually cross-referenced with other reports to start to see the full picture, and make a somewhat informed decision. This takes more time than merchants can afford to give.
On the other side of the coin, there have been systems that provide answers for merchants like what to assort or how to price, but those answers were opaque and often didn’t perform well in the market. Because these systems made decisions by only factoring in small amounts of information, like historical data, they made uninformed decisions, earning them the distrust of merchants, whose categories were actually harmed by the software’s suggestions.
What it came down to was that the merchants ended up just settling and using Excel to manage their business since legacy software was so esoteric and hard to use.
Last year, Google announced Google Assistant, an AI platform that packages Google services into personalized, relevant insights for anyone using its platform. One example of this is Google’s relevant search results. They allow the user to approach the system passively, not wasting time trying to find something that isn’t there.
Let’s say you want to keep up to date on local events that might interest you. Rather than searching everyday for new food and wine events locally, Google knows you like food and wine events, and surfaces these to you when it finds them, saving you the hassle of having to continuously search. Likewise, a good AI system will never require you to search for opportunities in your data to improve your business, it will surface insights to you to help you take the next best action.
Now, imagine applying this concept to retail. This style of interaction would help merchants to become comfortable with the platform without having to invest months in learning it, or having to wade through reports each time they want insights. By doing this, AI unlocks usability with merchants who are frustrated with outdated systems.
For AI to work successfully, it needs LOTS of data, but it also needs diverse data so it can make accurate predictions. The good news is retailers have massive amounts of data stored in their transaction and catalog systems. But this data is only enough for an AI to make naive decisions. By pairing a company’s performance and catalog data with other external signals like competitor data, market data such as product demand, and other contextual data, advanced AI can help merchants find opportunities to improve business performance and outmaneuver the competition.
The best decision in the world is only valuable if put into action. Even if an AI can make a perfect decision, the merchants have to trust that decision. Opaque solutions that don’t explain why they’re making a suggestion or what the outcome of that suggestion will be, will simply be rejected by any savvy merchant. There needs to be a clear path of logic that the machine took that’s explained in human language, not complex reports, so that the merchant can understand and trust the machine’s decision making.
To make solid, trustworthy decisions, software engineers need to build advanced AI that can go deep into specific use cases, not just a general AI that can do “everything.” Take pricing for example. Whether setting initial price, pricing to stay competitive, or moving an item to clearance, a myriad of factors plays into the dynamics of pricing. You might have something overstocked in a particular region, but your competitors might be short on stock, allowing you to achieve optimal sell-through without the need for discounting. Or you might be advertising an item online that has significant shipping costs associated with it while your competitors drop prices on that item — but use their shipping policies to make up the difference — making it hard for you to compete on pure price. Building an AI that can understand and work with all of these nuances is the key to producing insights that merchants can trust to drive growth.
Today, we take another major step into the future of modern retailing.
To help merchants be more productive and successful, we’ve continued to innovate over the last several months alongside our customers. Our customers have partnered with us and challenged us to develop a “merchant cockpit” that combines our machine learning / AI algorithms with intuitive, easy-to-use enterprise software that just works and enables them to make better pricing decisions in minutes vs. the hours or days spent using Excel and a lot of analysts’ time.
With thanks and much gratitude to our customers and our innovative employees, earlier today, we proudly announced our next generation Price Performance Management (PPM) application as part of our Retail Performance Management solution. By combining external market signals with our customers’ multitude of data sources, PPM applies modern machine learning algorithms — purpose-built for retail — to help merchants (1) easily identify opportunities, (2) test and simulate recommended changes, and (3) make better decisions and take data-driven decisions to improve performance at the category, brand and product levels.
While this is an important milestone for Boomerang and our customers, our team isn’t done yet — this is just the beginning. Building a culture of innovation means that we never stop innovating. We’ll celebrate today, but tomorrow is a new day to do something amazing!