Location-Based Data Driving Retail
By Scott Marden / September 2016Guest Contributor, Insights
With the convergence of technology, big data, attribution and the Internet of Things, smart marketers are connecting on and offline behaviors to make meaningful connections with consumers. As CMO for a location-based video network, I, too, focus much of my efforts on surveying my locations (high-rise office towers, hotels, condos and suburban offices) to gain additional insights on the movements of consumers, their buying behaviors and attitudes, and most importantly their response to my content and advertising.
Consumers’ smartphone usage and their online shopping habits are driving this influx of information. According to Captivate’s Office Pulse research on white collar consumers, the growth in retail shopping online since 2012 has more than doubled for most categories (excluding Health and Beauty, Books and Clothing). That means there’s a wealth of data available to retailers looking for smart ways to cash-in on consumer insights.
In retail environments, the need for insights and the types of data collection are very similar to location-based video networks like Captivate. Before looking at the use cases for data, questions remain regarding privacy and appropriate usage. There are different schools of thoughts on data collection at a location level and how to use that data. First, there is the question of consumer privacy. If you collect information from their phone (namely their web surfing and app usage data), are you invading their privacy? Most experts would argue that as long as the data is anonymized (a.k.a you are not attaching that info to their individual name, household or device ID.) then you are not invading their privacy. In almost all cases, consumers are not knowingly opting in. So, there’s also the question of whether they want to be analyzed or targeted. In this case, most experts use the “everybody’s doing it” argument, noting that the usage or purchasing results/lift prove the targeting was warranted. Finally, the debate that most people will initiate is whether or not to redact what’s been collected or keep it for future learnings/applications. This argument, like the others, has advocates on both sides. However, if the data is applied to help the greater good, many agree it should continue to be explored.
Exploring the trends of what location-based intelligence means to retail, the list below highlights much of what’s being used or investigated today (but certainly not everything).
- Survey data is the most often used data by marketers and retailers today, although it has evolved to produce real-time insights and can now be pinpointed by location using mobile technology.
- Enumeration data allows for counting the number of visitors at different times.
- Purchase data has been collected and ascribed to consumer data for many years and continues to pay dividends as companies get better at managing big data.
- Loyalty data still plays a big part in retail but will be challenged as an emerging generation of Millennials enter the picture.
- Digital behavioral data brings online and app usage data to a location based on a shopper’s smartphone usage.
- Visitation data brings insights into other locations that a shopper has visited (in addition to the retailer collecting it).
- Biometrics and facial recognition data allows you to translate the moods, demographics and even attire to useful data that allows for better selling.
Technology and providers of location data
- Cameras and facial recognition software are being employed by governments, stadiums, and now marketers to gather insights into the age, gender, ethnicity, emotions and attitudes of passersby
- Passive Wi-Fi recognition are sensors that collect smartphone usage and report on the apps and site browses by users within a certain radius of the detector (the phone needs to have Wi-Fi turned on)
- Beacons allow companies with an SDK (software development kit) and an app to collect location and behavioral data of the users while using the designated app
- Data aggregators use information from wireless carriers, digital ad requests and tagging/cookies/web pixels across devices to build audience segments
- Third-party data integrators tie-in location data with credit cards, auto registrations, CRM and contact / demographic data
- Social media data aggregators can report on the types of social media likes, posts, shares and key words among groups within a geo fenced area, using the location signals within social media apps
- Geofencing/polygons is a technique to identify users within an area based on the cell service, beacons and other technology using algorithms and GPS data
Examples of Retail Applications:
- Deliver real-time messaging or offers while the visitor is at your location (on their phone or on other Internet connected screens/devices in-store)
- Conquest your competitor by delivering real-time messages or offers while customers are in competitive locations
- Deliver hyper-targeted messages and offers based on the on-camera appearance of an individual in your store (i.e. place an ad for contact lenses for someone wearing eye glasses)
- Deliver hyper-targeted messages and offers based on the proximity to key products in the store or time-spent in store of an individual (at grocery check-out, remind a visitor about dairy products if your technology recognizes they skipped the dairy aisle).
- Deliver hyper-targeted messages and offers based on the interests/recent visits of an individual in your store (if a mass merchandiser sees recent online and brick and mortar visits to home improvement sites, offer coupons to items within the Home Improvement section)
Captivate Retail Example
- Captivate’s Office Pulse research has shown a trend in consumer brick and mortar shopping habits during and after work over the past few years. Presumably this is due to longer work hours, greater acceptance and necessity of work-life-balance and white collars’ need for convenience. This, combined with Captivate’s ability to match address proximity, on screen geo-tagging of messages for retailers and insights into specific brand affiliations has allowed Captivate to use location data to drive purchases and brand awareness. For a grocery chain in the West, Captivate delivered 75% lift in consideration of ready-to-eat lunches and dinners by leveraging data, targeting and time-of-day dayparting.
While targeting offers and messages while in-store is one of the best benefits of location based intelligence and technology, the real key to retail success is knowledge – retailers can take advantage of the knowledge provided by the collection of data at locations and online to build upon the loyalty that retailers have already built.
About Scott Marden
Scott Marden is a seasoned marketing, strategy and media executive. For 20 years he has specialized in driving revenue for media sales groups by consultatively using research, data, creative and content services. Since joining Captivate in 2009, Marden has helped the company develop its leadership position as a digital video media network with data-driven targeting and thought leadership. As Chief Marketing Officer at Captivate, he has built a world-class/sales-oriented Marketing organization, spearheaded product innovation, developed the nation’s leading workplace research panel, and enhanced digital place-based metrics and reporting. Connect with Scott on LinkedIn and Twitter: @ScottMarden1.
Known for its vast network of 10,000 elevator and lobby displays located in 1,600 premier office buildings across the U.S., Captivate connects advertisers with more than 10 million unique monthly viewers through creative, research-driven and Nielsen-measured advertising and marketing programs. By engaging its viewers with timely news and actionable information, Captivate provides advertisers with a highly desirable and difficult-to-reach audience of affluent and influential business professionals. Founded in 1997, Captivate is owned by Generation Partners and Gannett. Learn more at Captivate.com on LinkedIn and Twitter @Captivate.