By Jeff Huckaby, market segment director, global retail and consumer goods at Tableau
Business intelligence practices are changing quickly across the retail industry. The result? Leading retailers are prioritising analytics programmes more than ever. Increasing numbers of retail and consumer-goods companies are opening up their data to executives and front-line employees to improve their overall offering and customer experience. Because of this, the call for faster, simpler, and mobile-friendly tools is growing.
As this data movement continues, below are the top retail analytics trends we expect will make an impact in 2017.
1. Advanced analytics Is no longer just for analysts
With the self-service boom, non-analysts throughout retail organisations are becoming increasingly data-savvy. Almost every department is becoming more involved in data, from store managers to bookkeepers. Interactive data visualisations now allow all employees to ask and answer their own questions at the speed of thought.
Most large retailers are also leveraging advanced predictive analysis to allocate labour during peak times and provide improved quality customer care.
Advanced analytics functions such as clustering and outlier detection help store employees make data-driven decisions. By analysing customer purchases and shopping behaviour, the resulting insights empower retail employees to choose the most efficient store layout, enhance the shopping experience, and ultimately, increase the bottom line.
2. 2017 is the year of mobile analytics
For retailers, identifying insights in store with a mobile device is no longer just a pipe dream. Instead of using outdated legacy business intelligence systems, modern mobile analytics is faster and more accessible- allowing retailers to place analytics at the core of stock decision-making for major brick and mortar stores and their distribution centres.
More than ever, retailers are leveraging their in-store Wi-Fi investments to empower cashiers and distribution associates with live analytics. For example, if a customer wants a product that isn’t in stock, an employee can often use a mobile analytics tool to quickly find out the right information for a customer – e.g. is it available in another store, online etc.
More and more retail employees in back offices and distribution centres have also switched from working with desktop PCs and paper trails for mobile tools. Working with live mobile data on tablets on a daily or even hourly basis is the new normal. Merchants, regional managers, loss-prevention associates, and even vendors have all ditched their stacks of spreadsheets to instead collaborate using interactive visualisations on their mobile devices. This model enables them to make on-the-fly decisions about inventory, omni-channel supply chain, and operational efficiency – opening up an ability to provide immediate solutions for the customer.
3. The Internet of Things will help improve data accuracy
With IoT-connected devices set to triple by 2020, and the data produced poised to grow in prevalence for retailers in 2017, connectivity really is everywhere.
It seems that almost everything —products, merchandising displays, and even foot-traffic pathways—now have sophisticated sensors that collect and relay information for analysis – which is becoming crucial for retailers.
With the omnichannel boom, customers have grown accustomed to knowing exactly which items are available regionally and when a product may be ready to be picked up at the nearest store. To further entice customers into buying, companies are exposing live IoT data about product counts both in-store and online. They’re sharing the exact location of the product, down to the isle and bin at a specific store – making the customer journey and experience as hassle free as possible. This year will also see an influx of beacons and radio frequency identification (RFID) tags, which will help retailers track items throughout the supply chain and improve accuracy for in-store inventory levels.
Major brick-and-mortar stores are also utilising improved IoT data to understand shopper behaviour. Mobile data helps retailers see which in-store marketing techniques are working, and which walking pathways shoppers use the most. Marketing teams then use this information to ensure they’re reaching customers at key points throughout the store, as well as online.
4. Omni-channel data integration gets exciting
Retailers want and need agile analytics. Because timing is everything, it’s essential to get the right data sets to the right people, and quickly. This is no small challenge since data now lives in many different places; from legacy systems to different database platforms that include both on-premises and cloud data.
Successful retailers want and need to be able to see and understand, in one holistic view, commerce-channel data, supply-chain data, and customer data. This is the promise of omnichannel.
Working across multiple channels and data sources can seem tedious and even at times, impossible. In 2017, we’ll see many new players in the data integration space. With the rise of sophisticated tools and the addition of new data sources, companies will stop trying to gather every byte of data in the same place but instead connect to data sets where they live. They’ll combine, blend, or join other data sets with more agile tools and methods.
By analysing the trends with data from multiple sources, teams can set operational and promotional strategies, and continue to improve efficiency and performance.
5. Robots bring big opportunities to retail data
For years, major retailers have been using robotics in distribution centres, but in 2017 robots will take centre stage as part of the in-store experience. We’ll see machines, robots, and artificial intelligence begin to help retailers with routine tasks such as taking physical inventory, offering promotions, and even taking surveys and orders. These robots will begin to serve as new data touchpoints, gathering vital information about customer behaviours and interactions. This information will eventually help companies understand the customer even more. Retailers will continue to work to extend loyalty way past point of purchase, and customer-service data gathered from robots will be one of the differentiating factors between success and failure.
As social robots encourage customers to interact, they’ll offer additional value such as advice, recommendations, reviews, and real-time information for each customer, creating a more authentic relationship between shoppers and retailers.
6. Augmented and Virtual Reality add more insight to retailer analytics
Ever wonder what a new couch would look like in your living room? In 2017, customers will be able to harness augmented reality (AR) and virtual reality (VR) to experience potential purchases – things like the look, size and form of a product – in reality. Taking guesswork out of a purchase cycle will likely improve sales, increase customer satisfaction rates, and minimise costly returns. If you add analytics to the mix, retailers can also use data to provide customers with real-time inventory, visualised on store location maps, to show where products currently exist in-store, making a chance of purchase even more likely.
Merchandisers will also leverage AR and VR to visualise in-store scenarios. For example, instead of retailers creating product plans for shelves and store layouts, employees can review various arrangements and alternates via virtual reality, saving both time and money.
The enterprise will pair these virtual reality cases with embedded data analytics to optimise revenue and profitably. Retailers will go through mock trials of stocking shelves with virtual products, and also use data to predict the outcome of each scenario.