Retailers are in the midst of difficult times, with ongoing economic uncertainty, the rising power of the consumer, and an ever-growing influx of competitors meaning that they are having to constantly adapt. In the battle for survival, data is proving one of the most effective weapons in their arsenal. By using data properly, retailers can improve operating margins by in excess of 60%. The majority of retailers have realized this. In a survey of retail executives by JDA Software Group and PricewaterhouseCoopers (PwC), 86% of retail executives polled said they plan to increase investment in big data tools over the coming year.
The benefits that can be achieved from data in retail come in many forms. In a recent Forbes Insights report, ‘Data Elevates the Customer Experience: New Ways of Discovering and Applying Customer insights,’ respondents cited the most useful of these as improved revenue generation and lower cost reduction, better understanding of customer buying patterns and behaviors, and accelerated process efficiencies and quality improvements. Another recent study by FICCI, in association with PWC, found that analytics could generate in-depth insights in everything from procurement and supply chain through to sales and marketing.
In 2017, we are set to see retailers again increase their focus and investment in data analytics as they bid to stay ahead of the competition. Here, we’ve looked at some of the trends that data practitioners working in the retail space should be aware of this year.
Location analytics becomes vital
Forrester estimates that the adoption of location analytics will increase to more than two-thirds of data and analytics decision-makers by the end of 2017, up from less than 50% last year. It can benefit businesses in a variety of ways, but retailers in particular stand to benefit. They can, for example, send geo-targeted push notifications to mobiles, which research has found to be 6-8 times more effective than other notifications. It can also be used in-store to help better understand people’s purchasing behavior. US fashion retailer Nordstrom, for example, has spent millions introducing technologies like sensors and Wi-Fi signals into its stores that enable them to track such information, and as IoT explodes the number of data points is only going to increase. One customer could generate more than 10,000 unique data in a single visit from various sensors placed throughout a store, indicating where they will go, at what point they make the decision to pick up an item, and so forth. This information could be used by the retailer to get an idea of where products and promotions can be placed to maximum effect.
There are a number of firms making this easier. Foursquare, for example, has recently released Foursquare Analytics, which CEO Jeff Glueck claims is ‘kind of the Google Analytics of the world’. It is a dashboard designed to provide retailers and restaurants with greater visibility into location intelligence, changing store visit patterns among demographics, and their share of consumer visits in relation to competitors. Another making waves is Euclid Analytics. Euclid Analytics is a US-based company that uses location analytics to monitor consumer traffic in shops and malls, using WiFi signals from smartphones to track and analyze everything from how many enter a store to how long they stay, and the number of times they return.
Data from in-store tech grows
The key to enabling a better customer experience in-store is to incorporate as many technologies as possible to collect data, essentially by re-creating the online shopping experience. This is being greatly aided by two emerging technologies set for mass adoption in the coming years - Augmented Reality and IoT.
Augmented reality has been used in store for a number of years. IKEA, for one, introduced augmented reality to their catalogs in 2013 for customers to ‘virtually’ place furniture in their houses before purchase. Japanese beauty retailer Shiseido’s Tokyo stores also has ‘cosmetic mirrors’ installed that allow customers to scan product barcodes and see a virtual image of their faces with the product applied to it to get a better idea of how it looks. Such products improve the shopping experience for the customer while also helping the retailer collect more information so they can improve the experience again in the future. As DreamSail VR game developer Cindy Mallory noted in an interview with us, ‘The way a user interacts with VR allows for extremely pervasive information collection,' and the potential here is truly limitless.
Another technology set to explode in-store is the IoT. Almost 70% of retail decision-makers are ready to make the necessary changes for adoption, according to the 2017 Retail Vision Study from Zebra Technologies. Meanwhile, according to a 2015 report from Juniper Research, retailers will spend $2.5 billion on connected devices by 2020. Aside from the ability to monitor customer movements in store, this will provide particularly in the supply chain, where efficient data collection and analysis can provide real-time analysis of supply and demand and help organizations retain an appropriate stock level. Tom Moore, Industry Lead of Retail and Hospitality at Zebra Technologies, told Retail TouchPoints that, ‘Retailers’ number one business challenge to compete in this dynamic marketplace is inventory visibility. Best case, most retailers are 50% to 60% accurate from that perspective, so if they’re going to have any kind of e-Commerce strategy and leverage their in-store resources and inventory, they need to make sure that product is there.’
Cross-platform analytics helps track and improve consumers’ entire experience
Customers now engage with a range of touch points before purchase. They may view a product on several social media platforms, on the company website, and in-store before they make a purchase. However, in the earlier-mentioned PWC’s survey, only 12% of CEOs surveyed said they provide a seamless shopping experience across channels.
One of the main problem analysts having with tracking data as it moves across platforms is that the data gets trapped in silos. Analysts need to break down these silos to track audiences across platforms so they can understand where products are being purchased and to understand the purpose of online visits before purchase in store, which will then help them to better target offers. Retailers can also take this real-time data to help sales teams to communicate with customers at any stage during the customer journey, whether before, during, or after the store visit, and on any medium, from text to email.
Shift from Predictive Analytics to Explanatory Analytics
Investing in predictive and software analytics for loss prevention and price optimization is still on the up. Predictive analytics makes informed guesses about what customers will want in the future and it is incredibly useful. It is, however, an incomplete approach because it only gives you a likely outcome if nothing changes. It can only tell you what the future will probably be like, it does not tell you why outcomes are likely, the correlations driving those outcomes, or how to intervene to affect them. In order to alter an outcome, you have to be looking to explain why it will happen.
This can only be done with explanatory modeling. So, for example, in retail, predictive modeling is useful in that it would be able to give you an accurate estimate of, say, which shop requires certain stock. It doesn’t offer an explanation of why the shop will require it, and would subsequently do little to address root causes and leave the retailer vulnerable in terms of inventory management. Explanatory modeling, on the other hand, will identify things that are having an impact. For instance, it would identify that the weather is driving an uptick in coats, people buy more coats in the cold, and that the weather will get better next week so you will need to change stock levels.