Real-time modeling drives inventory optimization
Optimized inventory is a must in an omnichannel landscape. By leveraging real-time modeling to uncover customer patterns, retailers can adjust assortments — and amounts — of merchandise based on when customers are ready to make a purchase, according to Kevin Stadler, president and CEO of 4R Systems.
Chain Store Age recently spoke with Stadler regarding how forecasting, risk assessment, machine learning and predictive analytics are paramount in optimizing inventory and positioning retailers for maximum profitability.
What is profit optimization of inventory?
Most retailers today realize that inventory can be an asset (if it’s in the right place at the right time) or a liability (if it’s in the wrong place at the wrong time). Through a holistic model that combines forecasting, risk assessment, machine learning and predictive analytics, we can provide a future look at inventory productivity by item, by demand location and by fulfillment location to optimize inventory for maximum profitability.
Why is this important for retailers?
With the shifting dynamics of online versus in-store behaviors, it’s clear that inefficient retailers are closing locations and going bankrupt at an alarming rate. Wall Street is rewarding both growth and profitability, and punishing retailers that have neither of those. Today, in peer retailer investment, the leaders are getting rewarded with investment, so you must stay ahead of your peers to survive.
How has the game changed as the retail landscape becomes more digitally influenced?
Patterns shift faster than they used to. Consumers move in “packs of influence” today and change direction fluidly.
Older methods of target service level availability and slow supply chains are becoming antiquated because they are
expensive and slow to respond to a digitally influenced society.
What struggles do retailers have on the road to profitability?
The older perspective of inventory was very siloed and did not have a unified view that could provide profit and risk perspectives. It used to be that you would make a forecast, set a fairly high service level and then look at item profitability. An integrated model uses all of those factors simultaneously to goal seek rather than use a trial-and-error process that uses large staffs and a top-down category management approach. Today’s advanced retailers are going bottom up with big data and finding patterns using machine learning to quickly adjust on a very granular basis.
What tools do retailers need to solve these issues?
Having a real-time modeling environment that correctly weighs all factors is important. This takes a lot of data and a large, fast data structure. Then you need algorithms that can weigh risk and optimize against a number of variables all at once. To visualize all of this requires some data science and multidimensional techniques. As an example we use the Markowitz modeling formula, which is based on mean-variance analysis, to provide multiple variables to be integrated then visualized via risk curves.
What role do predictive analytics and machine learning play?
A common question we ask is “Do you remember what you had for lunch two weeks ago on Thursday?” The answer is that almost no one does.
To optimize the retail environment of thousands of items over many stores, and over a number of years, you have to use machine learning to find patterns. Then advanced predictive analytic algorithms reveal patterns to ensure you have the right product in the right place at the right time. To move to this proactive prescriptive technique, you have to have both machine learning and predictive analytics.
What results are possible?
The results vary widely from several percent improvement in revenue to double digits. Existing staffs can be more proactive and strategic. Profit improvement of course depends on the margins and turns of the product categories, but are significant as there is both an improvement in margin and turns at the same time.
How can 4R Systems help retailers in this journey?
4R has been a partner to retailers driving sales and profits for many years. We have a unique cloud optimization environment that uses modeling, predictive analytics, machine learning and goal seeking against very large data sets.
In addition, we can use our techniques to build the business case for change up front, not after you have invested. We reduce risk and maximize return for our partners at each step of the process.
TechBytes: POS trends
As retailers continue to combine online and offline elements of the shopping experience, point-of-sale systems are helping to bridge the gap at the store level. To support this transition and drive a more frictionless commerce experience, POS functionality is taking a new turn.
Here are four POS trends that can help transform the store experience:
Expanding mobility options: Mobility is a must in the road to frictionless commerce and more configurations are becoming available. Mobile POS, which enables associates to tender orders from anywhere on the sales floor, continues to gain traction due to its expanded flexibility, increased functionality, device simplicity and its lower cost of entry.
And look for more retailers to let customers skip the checkout line entirely via mobile checkout. Macy’s is ramping up its mobile checkout option, with plans to launch it in all full-line stores by year-end.
Customers can use the Macy’s app to browse and purchase merchandise and apply relevant offers and rewards. They can then pay through the app with a pre-registered credit card.
Before leaving the store, customers visit a dedicated mobile checkout counter where associates verify the purchase, remove security tags and bag their items.
Cloud-based POS solutions: Retailers are turning to cloud-based solutions for more than product scanning. Cloud solutions give cashiers access to online and store inventory levels, updates on BOPIS orders — as well as the ability to create ship-from-store orders — and order merchandise from online channels or other stores.
Nordstrom runs its POS in the cloud. Besides enabling associates to assist customers more quickly, cloud solutions provide access to rich product and inventory information and allow users to quickly locate and order the right products across the supply network on any device. Cloud software also connects commerce platforms with back-office systems and analyzes data.
The need for speed is increasing: No one wants to wait in a long checkout line — especially today’s on-demand shopper. Modernized POS functionality that streamlines access to pricing and payment systems can speed up what can otherwise be a cumbersome checkout process.
“Understanding the customer experience you are trying to achieve defines the technology needed to speed up the experience,” said Greg Buzek, founder and president of IHL Group.
There are things you can do to shave off steps, and ultimately, time at the POS, advised Steve Rowen, managing partner of Retail Systems Research.
“It comes down to thinking the way your specific shopper does while checking out,” Rowen added. “Taking some of the sting out of what makes that process unpleasant is where all of our heads should be right now.”
The emergence of unified commerce strategies: Delivering a true omnichannel experience requires leveraging customer data and driving personalized customer service across all digital touchpoints — and POS must be part of the mix.
“POS must leverage all customer data that a chain has, as well as all inventory and product knowledge,” Buzek said. “If you do not have a single view of your customer and inventory data across channels, then you are only operating your POS at 50% to 75% of its usefulness.”
Modell’s Sporting Goods is one of the latest retailers to invest in unified commerce. The chain has transformed its historically proprietary and closed POS solutions into a single open architecture that enables a seamless experience across channels. Using a scalable architecture and data model, Modell’s stores can now combine its front-end experience within its web and mobile channels.
Joann collects customer feedback—in real time
A fabric and craft retail giant has a new tool that is helping it improve the customer experience across all channels.
Joann has deployed a solution that provides a real-time view into actual customer feedback across its 870 stores, e-commerce site and mobile app. By implementing the new tool, the retailer can now collect holistic, actionable customer feedback in real-time.
The solution, the Medallia Experience Cloud, supplements Joann’s existing analytics and CRM systems. It captures feedback across all channels and centralizes data from customers across all stores, online and the call center. The technology processes one customer comment every minute, giving associates the information they need to take action, and improve the customer experience.
Early results already show high engagement from Joann customers, with an initial 50% open rate and 20% survey response rates for solicited feedback. Team members are able to consistently monitor their stores’ comments, and an average 75% of actionable incidents are closed within 72 hours.
The company is continuing to explore rolling out the platform across other channels, including its buy online, pickup in-store and education programs, according to the retailer.
“Although we’ve served the craft industry for 75 years, we are constantly learning from our customers and looking for new ways to improve their experience,” said Steve Miller, senior VP, marketing & e-commerce at Joann. “To align and engage our tens of thousands of team members across the nation, we needed Medallia’s powerful platform that offered a holistic view of the customer experience, as well as the ability to easily see, drill down and take action on specific incidents.”
Miller noted that Joann recently launched a new concept store, which features new experiential offerings, merchandising and custom services, “and getting clear feedback on those changes will help determine how we move forward.”
The store, which opened in June in Columbus, Ohio, combines technology, dedicated community and learning spaces and new custom services.