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The Guide to Demystifying In-Store Analytics
 

The Guide to Demystifying In-Store Analytics

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Learn how in-store analytics can help brick and mortar retail compete. Overview of four top vendors: RetailNext, Euclid Analytics, Nomi, Swarm.

Learn how in-store analytics can help brick and mortar retail compete. Overview of four top vendors: RetailNext, Euclid Analytics, Nomi, Swarm.

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    The Guide to Demystifying In-Store Analytics The Guide to Demystifying In-Store Analytics Document Transcript

    • Building Stronger Customer Connection The Guide to Demystifying In-Store Analytics Solutions An In Depth Review of the Top 4 In-Store Analytics System Providers Aaron Moskowitz Paul Conder pconder@lenati.com
    • Building a Better Understanding of Your Customer’s Experience Retailers have always strived to create memorable, unique and engaging experiences for their customers with the goal of improving sales performance in their stores. Over the past decade, many powerful tools have become available to help retailers better understand their customer’s in-store shopping patterns and behaviors. The extensive information gathered and analyzed through these systems can be enormously helpful for design teams and retail planners as they try to find more effective ways to engage the customer. But with so many tools available, and with so much data being generated, how is a retailer to know which system would be best for them? And what is the best way to frame up all that data so that it provides useful guidance to designers, planners and strategists on the ground? spend their time, what might have brought them in, or if they browse but don’t buy. Benefiting from analytics is a three part process: collection, analysis and utilization. In many ways, the collection of the information is the easy part. It should be kept in mind that the collection method filters the data a great deal. Solutions strategies also differ in the way in which the various software options analyze the data, and in turn what a business is then able to do with that information. Is the goal simply greater depth of reporting and knowing what is happening? Is it making current store layouts more effective? Is it pushing out targeted marketing based on specific items searched for by a customer? Or is the goal to track a customer throughout the journey, from online, mobile and in store? Answering these questions is the first step, before choosing a data collection method and service provider. Goal: Happy, loyal customers. This is the first of a two-part report, aimed at demystifying the most common systems in this emerging field and offering some guidance on how to choose which system may be the best fit for your organization. In Part 2, we will discuss how this data can be combined with other tactics to build a more compelling retail concept for your customer. Understand customer needs and behaviors Make the shopping experience more enjoyable In-Store Analytics “ 59% of Amazon’s sales are from repeat customers, in part due to advanced analytics to improve the customer experience” While online retailers like Amazon have been using analytics for years to increase sales performance, this type of information has only recently been made available on the physical side of retail. The key to understanding the customer journey is in knowing precisely where, when and for how long customers are interacting with each part of the retail environment. Most retailers have basic store traffic data such as door counters and POS sales information. But this information alone will not show how a customer interacts with a store once inside; POS data might be able to track how often a customer buys, but not how long they might spend in the store, where they 2 Provide better service and product displays Add value to the instore experience
    • Mapping the Customer Journey Euclid Euclid is a passive measurement tool, monitoring the WiFi pings of enabled smartphones. This simple technology tracks a customer’s smartphone in a similar way to how online analytics use cookies; each phone has a unique MAC address and can be traced both within the store and over time across multiple visits and ultimately linked to a specific customer. Euclid estimates that this method captures 40-70% of customer traffic. The software can tap into existing store WiFi devices for minimal set-up costs to capture presence analytics:       RetailNext primarily uses video to track the customer path, but combines this information with Wi-Fi detection, on-shelf sensors, and data from POS systems and other sources for applications beyond marketing metrics, such as loss prevention and queue optimization. It also differentiates men from women and children from adults. It is a more robust and expensive option than a WiFi-only solution, and offers a deeper dive with more thorough data points. The software can tap into most current digital camera systems, however if additional cameras are required they must be purchased ($500/ea). The basic RetailNext set-up package starts at around $3,000/store, though this is a one-time charge and not recurring. Capture Rate Visit Duration Repeat Visitor Ratio Visit Frequency and Recency Walkbys Engagement and Bounce Rates A free version of the software measures simple metrics like traffic counts, capture and bounce rates and individual frequency. For nominal fees ($50-$200/month/zone), other analysis can be added on, for example incorporating metrics to a marketing calendar, or doing zone analysis for a larger store. The raw data that is collected can also be expanded into customized dashboards created by internal BI teams or other analysis providers. One high-profile drawback is the growing privacy issue related to mobile phone tracking. While people have grown accustomed to being watched by video, there is a lack of knowledge about the depths of information available through MAC addresses and how this information is used, so there is the possibility of running into negative PR through the use of Euclid or similar opt-out WiFi tracking systems. See here for more. Creating an opt-in app – or utilizing an existing one – might be a better longer-term solution to this problem. If the customer can be shown that the shopping experience can be enhanced through the app, they are more likely to accept limited tracking. Another drawback to this type of technology is that it filters out customers who do not carry a WiFi enabled smart-phone. While the penetration of smart-phone use in the US continues to increase, this may lead to skewed data sets especially for retailers whose customers are less likely to be carrying this type of traceable device. 3 RetailNext The RetailNext system uses video footage to study how shoppers navigate, for example measuring that men spend only one minute in the coat department, which may help a store streamline its men’s outerwear layout. At the high end of their product offering is a full path analysis, which uses a multitude of cameras, depending on store size. These video feeds are then stitched together for a full view of the entire space. This enables the software to track a customer path from entry to exit; perhaps the shopper is 70 percent likely to go right upon store entry, or 14 percent likely to linger at a display. The software is also capable of incorporating data from shoppers’ smartphone WiFi pings as well. Retail Systems Research: "Creating a consistent customer experience remains the most valued capability for retailers, but 54% of our respondents indicate that their biggest inhibitor is that they do not have a single view of the customer across channels"
    • Nomi Swarm While basic tracking systems are enabling retailers to gain as much information from offline behavior as online behavior, there is still not a way to accurately link the two. Nomi is a new start up attempting to do just that. Through an opt-in system, Nomi is creating software that will link a customer’s phone MAC address to their email, which enables the system to track their activities in-store and online and create a single timeline for all customer interaction. Swarm is another start up attempting to provide customers with a turnkey solution. The goal of Swarm is to link POS transaction data to WiFi information. Unlike Euclid and Nomi, they are a 100% opt-in system, and part of their package is a solution to help stores encourage customers to opt-in, by delivering information in HTML5 via WiFi, which gives customers the appearance and utilization of a mobile app without having to actually download and install software. They offer the same presence analytics as the other WiFi solutions like Euclid and Nomi, while utilizing either active (opt-in) collection at check out, or by analyzing dwell data around the POS to link MAC addresses to purchase history (which means no on-phone app is required) They currently have partnership deals with many major POS suppliers in the US to have their software work natively with the system, and also to sell the hardware if necessary. This differs from Nomi, who requires the retailer to export sales information and send it to Nomi, where it is then processed and packaged into the dashboard, slowing down the actionability/response time of the information. Nomi uses a similar data collection method to Euclid, measuring the WiFi pings from enabled phones, but goes one step further by matching a phone with an individual. The value proposition is not in the collection of the data but in the presentation and the analysis. When a shopper has volunteered some personal information, either by downloading a retailer’s app or providing an e-mail address when using in-store Wi-Fi, the Nomi software pulls up a profile of that customer — the number of recent visits, what products that customer was looking at on the Web site last night, and purchase history. The store then has access to that profile so that all of the customer behavior information can be seen in one place, with similar metrics, rather than current setups where online and mobile data are stored in different places and used by different teams. The dashboard also natively connects sales histories to shoppers in the store, and can send individualized messages with promotions based on history, or for example if a customer returns to the store but does not buy anything it can send a message about that as well. Swarm seems to offer a more complete end-to-end turnkey solution for retailers. They are able to package the hardware and software necessary to help a retailer see results. They have distribution deals with both POS and WiFi hardware manufacturers and their dashboard includes native outbound marketing distribution options, like direct customer couponing. 4
    • Scenarios PATHING One of the main benefits of an in-store customer tracking system is the ability to precisely follow and understand the typical customer path within the store. This allows for precise display placement, testing and optimization. Any of the available WiFi collection systems, and especially RetailNext’s video system, are capable of producing heat maps within the store. This feature is actually built into the RetailNext dashboard. This information can be used to figure out where customers tend to head when entering and which displays either catch their attention (more dwell) or don’t (less dwell). Depending on store size and pathing options, different layouts can be tested in order to perfect the path. ANSWER TO SHOWROOMING Beyond the obvious applications like measuring display effectiveness or customer path optimization, the system can also be used to combat showrooming. For example, if customer dwell in a certain area is high based on video heat maps, but sales from that area are low, it is possible to then try to track WiFi usage and page visits within the store to see if items in that area are being researched by customers while in the store. In this way, items that are being showroomed can be properly targeted. Click here for more STAFF OPTIMIZATION Another benefit of knowing who your customer is and what their history might be is enabling your sales staff in real time to have that information and to be able to interact on a more personal level. Some of the dashboards have mobile capabilities, giving the employees insight into who is in the store and what their entire shopping history looks like. Dwell data can also be used in real time to assign employees to specific areas of a store based on need. SALESPERSON INCENTIVES Having a link between a person and their online persona can also help employers align salesperson incentives to that of the store. For instance, if a customer works with an associate in store, but then goes home and buys the item online, from your store, it would be possible to pay a commission to the employee with whom the customer worked while in the store. This encourages employees to keep a full view of the shopping experience rather than just thinking about the instore channel 5 DIRECT MESSAGING Knowing where your customer is in the store and the items they have looked at online can help a retailer micro-segment and send specific offers to specific devices as a call-to-action for the customer. Nomi and Swarm have messaging enabled in their dashboards, so specific customers can be targeted with specific deals. Different marketing messages and plans can be tested to see what is most effective. ACTIVE ENGAGEMENT AND FEEDBACK Another interesting use of WiFi engagement is in sports stadiums. Utilizing a robust stadium WiFi system, the New England Patriots have been able to actively engage with fans through an app in real time, adding an additional incentive for a fan to see the game live and also to provide operations with feedback on flow through the concession areas. As stadium competition with giant HDTV home systems grow, additional features to get people off of the couch will grow in importance.
    • Bottom Line For basic presence analytics, using Euclid or other WiFi tracking systems should suffice. If more detailed customer analysis is required, or if the business is interested in uses outside of evaluating strategies, RetailNext could be a better solution. To move beyond in-store insights into setting future strategy and actionable campaigns, Swarm or Nomi would be the options of choice. WiFi collection Video analysis Presence Analytics POS integration Shopper CRM White Label/OEM Online/mobil e integration PART II COMING SOON: LEVERAGING BIG DATA TO DRIVE BETTER DESIGN In the second part of this report, we will be discussing how to use new analytical tools, combined with other tactics, to better inform a store and service design team – resulting in more compelling, targeted and memorable retail experiences for your customer. HOW LENATI CAN HELP As a leading consultancy focused on sales and marketing, Lenati’s retail team can help you choose the most appropriate system for your organization’s needs, and then integrate the information from any of the systems discussed in this article into a larger retail strategy package. Visit lenati.com to find out more. ADDITIONAL RESOURCES http://www.retailwire.com/discussion/16818/compete-blog-how-well-do-merchants-understand-consumers-di http://logisticsviewpoints.com/2013/06/03/omni-channel-momentum/> http://www.retailtouchpoints.com/retail-crm/2346-creating-omnichannel-strategies-that-resonate-with-todays-empowered-consumers> http://www.retailnext.net/analytics-technology/big-data http://www.economist.com/news/briefing/21581755-retailers-rich-world-are-suffering-people-buy-more-things-online-they-are-finding http://www.nytimes.com/2013/07/15/business/attention-shopper-stores-are-tracking-your-cell.htm 6