Company News • 13.11.2013
Tracking Footfall Further: Bringing Traffic Analysis Inside the Store
By Adam Rodnitzky, ShopperTrak
For more than a decade, monitoring store traffic has been a best practice for data-driven retailers. Apparel retailers in particular have embraced these perimeter foot traffic counts as an indispensable tool in understanding conversion, staffing and marketing effectiveness.
But now, in-store analytics are giving retailers even deeper insight into the store – literally and figuratively.
Of course, no new technology is without its challenges. As retailers implement these new systems, they dive head-first into big data. To succeed, they must understand how to integrate these new data streams with data from traffic counters, POS terminals and other sources.
Two key in-store insight technologies
Retailers employ two methods to capture in-store insights while preserving shoppers' privacy and anonymity: video analytics and mobile phone analytics. Each comes with a unique set of capabilities that allows retailers to capture and present actionable insights for marketing, merchandising and operations. Video analytics can provide specific insight into consumer behaviors that can be captured only through visual analysis. Cell phone analytics can track single shoppers through vast spaces and over multiple visits.
For marketers, understanding the effectiveness of in-store and out-of-store media in converting browsers to buyers is critical. Prior to the advent of in-store analytics, marketers relied only on sales and traffic data to gauge the general effectiveness of campaigns had on driving incremental traffic and sales. Now, in-store analytics allow marketers to gain deeper insight into whether promotions have been effective at moving consumers further along the path to purchase.
For instance, retailers now have the tools to measure the performance of a campaign that promotes a specific clothing line or sweater by reviewing incremental traffic driven to that chosen category or product. If poor conversion rates persist, then a marketer can look to in-store tools, such as point-of-purchase materials or discounting, to motivate consumers. Once implemented, in-store analytics also can help the marketer to understand the impact of these changes on shopper conversion.
Like marketers, merchants and category managers traditionally have relied on store sales data to understand the performance of particular products and categories. With in-store analytics, however, they now can understand more deeply how planograms and displays impact shopper conversion within the category or even at the shelf.
For instance, a merchandiser can segment a group of stores into test and control layouts to determine how a new jeans display captures consumer interest — before choosing to invest in rolling it out across all stores. And, as in the marketing case, a merchandiser with high category traffic but low sales can test strategies such as discounting to see how they impact conversion rates.
Video analytics deliver a wide array of operational insights that reveal how store execution strategies improve conversion and consumer experience.
The best retailers have focused on speed of service as a key differentiator for years. Traditionally, this metric has been difficult to capture reliably at scale. Video analytics now make it easy to capture this critical insight daily at every location to develop benchmarks against which all locations can be compared.
Similarly, retailers who invest heavily in sales staff have had little insight into how effectively their staff has converted browsers into buyers. Now, retailers can track the time between when consumers enter a store and when salespeople assist them, along with the percentage of consumers receiving sales assistance.
In-store analytics in the age of big data
The modern brick-and-mortar retail store is perhaps one of the most data-rich environments ever known. When incorporating in-store analytics, retailers are served well when they utilize proper big data practices to create actionable insights while minimizing data overload.
In-store insights are most powerful when viewed in the context of store conversion rates. After all, the common goal is to convert more shoppers into buyers. Because store conversion rates require transaction data and traffic counts to be calculated, the best in-store insight systems are being designed to incorporate these other data streams to show the correlations between new insights and store conversion.
Take speed of service, for instance. Analyzed alone, speed of service can gauge how effectively employees serve customers at the cash wrap. However, when speed of service is combined with store conversion, a retailer can see how slow service may cause customers to balk instead of buy. Improving speed of service then can become a chain-wide priority to improve customer experience and sales.
The why behind the buy
New in-store analytics systems can reveal much of the "why" behind what makes shoppers buy. Yet, retailers must choose a system that can integrate many of their existing data streams to ensure they get the most value from these new insights. Retailers that properly implement an in-store analytics system now will gain a significant advantage. They will compete more effectively, both online and offline.
How is big data created in retail?
Big data is defined by the "Three V's": volume, velocity and variety. "Volume" refers to the amount of data generated. "Velocity" is the speed with which new data is produced. "Variety" is the multitude of sources from which data may be generated.
Thinking through a retail store, for instance, we can identify a number of different devices and activities that contribute to the "Three V's": the POS terminals capture each transaction; the credit card terminals tie profile data to sales; traffic counters measure foot traffic; workforce management systems note who is working; inventory and replenishment systems indicate stock and sell-through; and more advanced retailers now include smart surveillance systems that further capture consumer and employee behavior in real time throughout each location.
All of these devices generate high volumes of data. As a retailer taps into more and more of them, they are producing this data with increasing velocity. And, again, as more devices' data streams are monitored, there is more variety in the gathered data.
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