by Kevin Rands

9 companies using data to make retail more efficient

Opinion
Jul 16, 2017
AnalyticsRetail Industry

Here's how big data is disrupting the industry.

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Credit: Martyn Williams/IDG

Retail isn’t exactly in it’s most comfortable space right now. With companies like Amazon on the rise, physical retail locations are being called into question. Are they actually valuable anymore? Are consumers willing to break away from their computers and go to shop when they can just order from their living room? Questions like these can easily be answered with the right information.

Retail behemoths like Sears, JCPenney, and Macy’s are closing left and right with no backup plan in sight. But their problem isn’t that they exist in physical locations, otherwise many other thriving retailers would be suffering the same fate. Their problem is failing to adapt to the latest retail tech trends and consumer demand. The retail space is constantly changing, and if they want to stay ahead of the curve, they’ll have to adapt with it.

This brings us to our list of the companies using data to make retail more efficient. These companies were all chosen because they take a unique approach to the many problems retail is facing. Instead of simply guessing at solutions, they ask the right questions, and collect useful, actionable data to solve everyday retail problems. While physical retail spaces seem to be losing popularity, many of these companies are not only making them more attractive but more efficient as well.

Lesara

Lesara is a digitally native online fashion and lifestyle retailer and pioneer in Agile Retail, a technologically advanced version of Fast Fashion. By using big data and data-backed research, the company is able to understand its customers at a higher level. Tech-driven retailers are able to curate more tailored products, have extremely efficient supply chains, be more intentional with production cycles, and do it all within days. Where traditional fashion companies produce what their stylists think will be in style that season, companies like Lesara can leverage algorithms to produce what people are actually searching for.

RetailNext

RetailNext took serious steps in 2013 to shape the industry by acquiring NearBuy, a company specializing in in-store data collection via visual analytics and IoT-style touch points throughout physical retail locations. They’re transforming stores from shopping centers to data collection hubs that monitor every possible data point. All the important things that customer service people might miss, RetailNext doesn’t. It collects it all and compiles it in a useful way to better tailor the shopping experience.

Infinite Analytics

Infinite Analytics is taking a more personalized approach and putting the human touch into the digital retail space. They’d like you to meet ian, their AI-powered retail assistant. Ian runs on a platform that collects data from all sources (text, meta-data, visual recognition, etc.) and combines it to aid with product search and recommendations. This model of Conversational Commerce has many consumer benefits, but also arms companies in the retail space with useful data and analytics to better target their shoppers.

Quri

Sales opportunities come and go every second, and if you don’t know how to spot them, you lose them. Quri has recognized the problem and developed a solution. Instead of waiting weeks to see if your latest campaign worked, Quri delivers real-time results to track the performance of your products and promotions. It gives you the opportunity to adjust your approach before a customer walks out the door.

Vend

Retail isn’t just done in-store and online. It’s often done on the fly, at kiosks, markets, and pop-up stores. Anywhere you can make a sale, retail can exist. Sadly the tech of retail is usually not conducive to this type of commerce, but Vend is bridging the gap. They’ve created a POS (point of sale) software that can not only process sales but track inventory and manage customer data on any device. They remove the headache of inputting data by handling it automatically while making the sale.

Mobee

Mobee is taking consumer data to a whole new level while at the same time getting back to the basics. They understand that customer data lies in the hands of the customer, but they’re making it easier to extract. Instead of surveys and secret shoppers, they’re using apps and touch points. They’ve created a crowd-sourcing mobile platform that let’s the customer tell you what they think instead of relying on a machine to guess. Not only that, but it’s such a convenient solution to the old ways that they’re much more inclined to actually tell you what they think.

ShopAdvisor

ShopAdvisor is pulling out all the stops. E-commerce, proximity-based promotions, in-store analytics – they’re doing it all. As an industry leader in e-commerce, ShopAdvisor is already powering the digital shopping experience. Their recent acquisition of Retailigence is now powering them to shape the physical retail space as well. They’ve developed a platform to simultaneously qualify and target customers and drive their business into local stores. This will allow companies to take the value they possess and put it in the face of their potential clientele.

Density

Density is giving physical retail stores an inside look at what’s going on, well, inside. Managing multiple locations simultaneously is nearly impossible when you can only be in one place at a time. Density eases that burden by measuring how busy any given location is in real time. With all this information at their disposal, management can decide what steps to take to drive more traffic into a specific location.

Signpost

Signpost goes beyond just data collection. Big data is an incredible tool to have but only if you know how to use it. If you don’t, Signpost is what you need. Their AI-based platform collects consumer data from hundreds of millions of unique data points and creates campaigns from the data. This allows small business owners to keep up with the evolving landscape of retail and avoid spending hours reading and analyzing piles of data.