The pervasive adoption of mobile devices has driven an explosion of contextual user information, including geolocation data, which has become a valuable resource for marketers. However, a lack of technical skill sets among marketers has made it difficult for them to use this data (when they have access to it) effectively. Plus, changing regulations mean it’s more important than ever for marketers to understand what data they have access to and how to properly leverage it.
Currently, most brands, agencies, website publishers, and other marketing entities use location data to engage in a variety of marketing applications, such as proximity marketing, among shoppers with the brand’s app. Many retailers and proximity marketers have deployed beacons inside stores that have resulted in up to a 15% lift in retail foot traffic and a 73% increase in the likelihood of purchase among shoppers. Beacons are battery-powered wireless sensors installed in retail stores or event venues that detect nearby consumers who have opted in to alerts through Bluetooth or other technologies and that relay information to consumers’ mobile devices. For example, a store like Macy’s can build its presence on a beacon platform that can be downloaded by shoppers as a mobile app. After that, each time shoppers with the app enter a beacon-enabled store, they can receive promotional messages or deals on their device about products in the aisles they are browsing.
Brands also use geo-fencing, or creating a zone around a business for advertisement targeting, in different locations for targeted promotional offers on mobile devices (via any digital platform the firm as access to, such as social media, email, or text). For example, Whole Foods developed geo-fences around its stores, as well as its competitors’ stores, to target relevant audiences and achieved a post-click conversion rate that was three times higher than the national average.
Some brands are using location data for improved attribution analysis to assess marketing effectiveness. This entails identifying whether exposure to a certain promotion, ad, or specific touchpoint (such as a sales encounter) for a demographic can generate future sales. For example, Placed is a firm that provides in-store attribution analysis representing consumer visits to physical store locations. It measures both promotional tactics and audience characteristics of targeted audiences who have opted in. It uses customer location data to ascertain which promotions work, and for whom.
It’s important to understand that this type of increased monitoring warrants a corresponding increased attention to privacy needs. Once a customer chooses to participate in a social media sharing system, attention has to be dedicated to securing data storage and providing the user access to information that has been collected by brands and processed on their behalf. A brand like Starbucks can monitor posts from its stores nationwide to deploy resources directed at customers who have voiced relevant needs while still inside the business premises. For example, customers irritated with long wait times can be delivered special deals to keep them from switching to other stores. But before companies engage in any kind of location data analysis, they need to have a privacy policy and be internally clear about what data they are using and why. New GDPR guidelines in Europe will grant individuals the right to access, restrict, correct, or transfer data that companies have gathered about them and to know how their personal data is being used.
With the right guidelines in place, there’s a much greater potential for geolocation data that remains untapped. We propose combining geolocation data with social media data to create what we call vigilant marketing intelligence (VMI), a conceptual framework based on our prior academic research and observations. VMI can help firms to better use location-based social media posts for enhanced data-driven marketing.
What Is Vigilant Marketing Intelligence?
Broadly, the rising gap between new customer acquisition costs and retention costs for existing customers necessitates continuous vigilance of consumers’ purchase journeys and their satisfaction from the same. In some specialized industries, such as pharmaceuticals, monitoring consumer behavior can be a legally mandated part of post-purchase experiences, with the ultimate goal of vigilance for brand and consumer safety (such as tracking adverse drug reactions). While brands do attempt to forecast customer-related outcomes based on social media posts, the availability of location-based social media data further enhances the predictive power of future unfavorable outcomes, including customer dissatisfaction, brand switching, and churn. When such vigilant intelligence is operationalized, it can help improve customer relationships, retain customers, and expand customer lifetime value.
VMI creates a framework that integrates incident reporting data from social media posts with geolocation data of the report —that is, the physical location that the post is emanating from. For example, this happens when a consumer checks in with an app, such as Foursquare, at a location, such as a store or a restaurant, and then also tweets about what is happening in terms of an experience, incident, or service encounter. While the term “incident reporting” is frequently used in media and journalism, for marketers, a close parallel is customers’ interactions with brands, which can indicate important incidents or events, also referred to as touchpoints, micromoments, or “moments of truth.”
Many companies already monitor social media networks for posts from customers. However, adding location data for monitoring consumer behavior makes the firm’s responses more actionable in the short run and adds value in the long run. For example, tracking activity on a platform like Foursquare not only can inform a brand when customers visit specific stores and complain about wait times or products being out of stock but also presents a firm with an opportunity to respond (digitally or physically) while the customer is still inside the store. The company can then open a new counter or activate an inventory transfer between stores. Additionally, in the long run, a customer’s presence in nearby businesses or establishments can help brands cross-promote their own products and services. Knowing that a loyal customer of TGI Fridays checked in at a movie theater next door can initiate special offers to attract them to the restaurant. This can help increase short-term sales as well as build long-term brand loyalty.
Mapping adds a new layer to this type of monitoring. Several African and Asian countries have used Ushahidi’s crowd mapping technology for crisis monitoring during natural disasters, post-electoral violence, and other crises. Researchers have designed early warning systems at London’s Heathrow and Gatwick airports to estimate flight disruptions, delays, and breakdowns by harvesting complaints from location-based social media. One novel use of this location-based data is KLM Royal Dutch Airlines’ surprise campaign, where the company identified passengers who checked into its flights on Foursquare and tweeted about waiting to board. KLM conducted social media research to find out more about why the customers were waiting at the gate, whether their flights were delayed, why they were traveling, and then surprised them at their gates with personalized gifts.
Integrating such social reports with geolocation delivers two added advantages. First, the content of the communication can be interpreted within the context of physical surroundings, thus identifying if the user is sharing specifics of an ongoing service experience. For example, this data could tell you if a customer is still waiting to board a delayed flight at the airport, or if they are tweeting about a bad experience after the fact. Second, knowing the consumer’s location gives a brand an opportunity to take timely corrective actions when a customer is having a problem. For that customer still waiting at the airport, for example, the airline could reach out with text updates to keep the customer informed about updated flight departure times, continued delays, or alternative travel options.
Integrating geolocation data with social media content also helps ascertain the accuracy of shared content to validate if restaurant ratings, such as those on Yelp, are consistent with emotions embedded in tweets from restaurant locations. Significant deviations or inconsistencies at certain times or days of the week can make the ratings of the restaurant from such review platforms questionable. Location-based posts can also help monitor user satisfaction dynamically. For example, users riding in different modes of transportation — buses, trains, boats, and bicycles — can report their experiences in different cities. Information gleaned from the location-based social media posts of the travelers can then show traffic patterns, such as whether certain routes are overcrowded.
To use this location data most effectively, companies need to monitor business locations for shared social media content, identify topics of conversation and the sentiments expressed, follow time-based patterns, and either promote positive remarks from customers with the help of PR teams or have customer service teams follow up on complaints.
Challenges of Building a VMI Framework
There are three major challenges to implementing a VMI framework.
The first challenge is the precision and accuracy of available location data. While a person might be located at a specific spot with geographical coordinates, the real location is often a distance from where they are shown to be. The extent of this deviation depends on the source of the data, whether it is cell towers, Wi-Fi, Bluetooth, or GPS, as well as external factors such as urban construction density and the ways consumers update device settings. The average deviation in one study was found to be 93 feet. This deviation can make a big difference in how well marketers can execute their plans, especially in crowded cities where a small distance can change the consumer’s physical state, as well as their state of mind.
The second challenge is the voluntary nature of the shared content. It becomes necessary for service providers who are harvesting the data to ascertain its validity and define the minimum volume of feedback they consider important before triggering responses. For example, feedback from a single customer about wait times may not be sufficient to generalize the operational efficiency of the staff.
Finally, the simultaneous optimization of trust and relevance is an inherently difficult balancing act. While timely interventions or offers can make customers happy in the short run, recent awareness of Facebook’s data exposure and social media practices of data sharing with third parties such as Cambridge Analytica have led to long-term concerns about the safety of their personal data. New regulations such as GPDR in the EU aim to give consumers control over their personal data. In such an environment of heightened concern about data privacy, assurances — such as better end-user license agreement design, opt-ins, limited third-party sharing, and better deidentification processes — need to be designed to alleviate concerns about storage of data, identifiability of users, and terms of sharing with other entities. Only then can VMI succeed in fully capitalizing on consumers’ location-based social media data for better data-driven marketing and a better customer experience overall.
Mobile technology allows firms to know where the consumer is located. Integrating such location information with social media posts that the consumer shares from that location enables a better marketing intelligence system. Such a system can help firms better understand consumer journeys and also address consumer needs in the moment, provided that consumer privacy and security concerns are adequately addressed.
[“Source-hbr.org”]