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QuantiSense (Private) |
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October 1, 2012 Issue |
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The Most Powerful Name In Corporate News and Information |
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“Big Data” Company, QuantiSense with their Decision Orchestration Platform™ is Delivering Great Value to the Specialty Retail Space using Analytic Solutions that Make Sense out of Numbers and Recommend Productive Actions |
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www.quantisense.com
As a pioneer of retail BI applications, Jeff has spoken at industry events such as National Retail Federation and been published in leading retail media outlets. Prior to QuantiSense, Jeff founded dotVantage, a company focused on creating analytical applications that enabled e-Commerce companies and technology providers to understand customer behavior on the web. Jeff started his career at MicroStrategy as a consultant specializing in retail implementations.
Jeff earned his B.A. and B.E. in Mechanical Engineering at Dartmouth College. While attending Dartmouth, he founded Dartmouth Formula Racing, an organization that builds formula style racecars for competition. Dartmouth Formula Racing continues to compete today. When he’s away from the office, Jeff coaches sports teams for his children, Cooper and Della. |
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Interview conducted by: Lynn Fosse, Senior Editor, CEOCFO Magazine, Published - October 2012
Mr. Buck: Our vision was to build what we called a business intelligence application. We quickly decided to do that in the retail space and got a lot of early adoption within the specialty retail space. We partnered closely with our customers to provide an application that contains all the components that you would need to build a repeatable way to implement data warehousing combined with business intelligence functionality designed specifically for the specialty retail space in an off the shelf solution.
Mr. Buck: Both the analysts and the market give us credit for pioneering the retail analytics space over the last ten years. It has been very successful because we applied rigorous software development principles to our solution. With the testing and bulletproofing that’s built in there is a big difference in quality versus the alternative, which is really building this for yourself. We focused on performance so when you create or run a report, it comes back quickly. That was a big part of it.
Mr. Buck: One example would be a nationwide high-end home goods and furniture retailer. They sell home furnishings online and in their stores. Because of the wide variety of merchandise that they sell like furniture and lighting and the size of this national retailer, they have multiple distribution centers, so they have to worry about balancing inventory between those distribution centers. In the one case I was actually looking at with this retailer, they had 93% of their inventory for a particular item on the side of the country that had only 40% of the demand and 7% inventory on the other side. Now why did that happen? It happened because the overall demand from these to DCs were split, so when they were ordering product, they were ordering product in those larger percentages cycles and then over time you can see how the disparity between the DC inventory and the store sales just snowballs. Because we look at data on a very low level, and we are able to track exceptions at an item/store level, so we are able show them how to catch a problem like this and stop it before it gets any worse. Through our technology and our retail knowledge, we are able to give them tools that they can use to see from an executive level where they may be able to spot and resolve this problem.
Another example is Casual Male Retail Group, the largest specialty retailer of big and tall men's apparel. With over 450 stores that carry about 2,500 styles with up to 54 different sizes of pants alone, they have a real localized assortment challenge. They had a problem where a 5XL man would come into their stores and be so wonderfully appreciative of the first time that he sees a fashion assortment of clothes in exactly the size that he needs. Oftentimes he will buy out the entire fashion assortment of 5XL all across that particular store, and because of the wide array of sizes they need to keep and the small format of their stores, they do not have much depth in any particular size. We provide a Play that helps them identify that problem and a process with which to solve that problem through our interface and analytics.
Mr. Buck: I think it is both. Often times they recognize the problem but it’s too late. We have a lot of retailers who don’t buy enough of an item mix and sell out early in the season. We have a play that is called “What is in Reserve that is Hot.” We also have a play that is similar to that called “Accelerate”. Those two plays are going to work together, so you have stuff in your DC and you can send it out to your stores, and if you do not have stuff in your DC, then Accelerate new orders for it. The point is that you can identify that early in the quarter so you don’t lose sales due to stockouts. Our technology combines a very powerful, highly detailed, low-level look at the retailer’s business that defines discrete exceptions and then values them. Then you see these exceptions through the eyes of the different buyers, planners and allocators that run the business.
Mr. Buck: For those three roles, the plan allocators are the easiest and the most analytical. They are already familiar with analyzing data through spreadsheets so they tend to react well to an analytical approach. Buyers typically are at the other end of the spectrum. It’s not that they do not use analysis but there is a significant art to what they do. When you talk to a buyer about a topic like optimizes assortment planning, that is a very personal process that differs widely from retailer to retailer. We have to fit our technology into their existing business processes. We are not saying that they need to change everything they do, they just need to augment their process with the kind of analytics that we can offer.
Mr. Buck: We have just gotten two new capabilities off the drawing board. We have tackled the area of customer analytics which is a really new area for retailers. Given the explosion of shopper interactions from the store to e-commerce, to loyalty programs and email campaigns, it’s hard for merchants to get a comprehensive view of customer preferences. But we provide powerful analytics that identify the hidden relationships between customers, products, store and web. In a related area, retailers want to optimize local assortments but the volume of data is overwhelming. The QuantiSense solution tackles all that data and analyzes assortments and items, store by store, across a number of performance areas. It provides the knowledge required to make significant improvements in assortment mix and balance within each store.
Mr. Buck: Most of our leads are coming through word of mouth. We work very hard to serve our customers with great technology and a great attitude and that has created a positive reputation in the industry. Because we deliver such a strong value, the word is spreading fast.
Mr. Buck: Right now we are growing rapidly selling to global retailers who are headquartered in the United States and Canada. We have entertained some interesting projects in England and in other places but we do not have a defined presence there yet.
Mr. Buck: Business is great! The focus that we have in retail analytics is obviously hot right now so we are getting a tremendous amount of interest around it. We have a combination of products that serve a very practical need for managing big data such as the foundational product I mentioned earlier, but also a very strategic need which are the playbooks and the retail exception engine. That combination is powerful; but we can sell them independently.
Mr. Buck: We have some companies that have come up that serve a similar technology space but not directly in our suite spot of customers. The biggest competition is really the retailers themselves either trying to do this on their own or through some integration partner. The other competition we have is the inertia that spreadsheet analysis creates for retailers; they are very tied to their spreadsheets, but it’s not a process that can scale. At some point they will hit the limit.
Mr. Buck:
First, we are a big data company. We deal with large amounts of data from
the specialty retail space – all the major areas where they generate data
such as sales transactions, inventory transactions, and customer
information. We have already delivered great value from that data through
our solutions even before somebody coined the name “big data”. That has been
our business from the beginning. Secondly, our analytics take that big data,
really making sense out of numbers and recommending smart, more productive
actions for the retailer. Thirdly, QuantiSense is a company that is
vertically focused, so we have specifically targeted all of our internal
efforts, product efforts, sales efforts, everything in a well-defined
segment of the market and it is a big segment. Those three things are coming
together in a nice way for QuantiSense. |
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We are a big data company. We deal with large amounts of data from the specialty retail space – all the major areas where they generate data such as sales transactions, inventory transactions, and customer information. We have already delivered great value from that data through our solutions even before somebody coined the name “big data”… our analytics take that big data, really making sense out of numbers and recommending smart, more productive actions for the retailer. - Jeff Buck |
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