Using Location-Based Services to increase consumer engagement April 2010
contents 1. Executive Summary 3 2. Introduction: Mobext and Cadio study 4 3. Benefits of Marketing using GPS-Based Mobile Consumer Analytics 5 4. Brand Challenges 8 5. Brand Application Process 9 6. Conclusion 10 contributors Phuc Truong Managing Director, Mobext US phuc.truong@mobext.com Sharon Bernstein VP, Insights Director sharon.bernstein@mediacontacts.com Jared Hopfer Mobile Marketing Manager, Mobext US jared.hopfer@mobext.com Dr. Thaddeus R. F. Fulford-Jones CEO, Cadio US thaddeus@cadiomobile.com 2
1. executive summary Location-Based Services (LBS) utilizes a mobile device s geography to deliver relevant information to a consumer, and is creating a new means of mobile marketing. Advertisers can now overlay location patterns with existing customer data to deliver prospects custom messages at the right time by serving unique, relevant, time-targeted offers based on shopping patterns, consumer segmentations, and travel history. Mobile consumer analytics is not limited to consumers who have high-end smart phones; a majority of standard feature phones in the US have GPS hardware that can transmit location data with a consumer s optedin consent. LBS technology allows an advertiser to yield various insights, including shopping preferences, competitive store visits, time and frequency for shopping activities, as well as travel patterns. Armed with additional mobile consumer analytics, advertisers can enhance their marketing efforts by strengthening the value of existing customers while using the data to supplement competitive intelligence. Mining consumer location data Predicting behavioral patterns Protecting users privacy MOBILE CONSUMER ANALYTICS 3
2. Introduction: mobext and CADIO STUDY In late 2009, Mobext, the mobile marketing network of Havas Digital, partnered with Cadio, a mobile consumer analytics firm, to analyze GPS data from opted-in mobile phones to better understand consumer interests and habits. time stamped and returned to Cadio s servers in real-time. The maximum data acquisition frequency was 10 GPS data points per hour. In the study, over 200 retail or lifestyle-relevant participant destinations were mapped. These destinations included: airports, hotels, train stations, large national retailers, supermarkets, and selected other categories. Mobext recruited Sprint subscribers to share semi-continuous GPS data (once every 10 minutes) with Cadio via their mobile devices. Participation was entirely voluntary and no incentive was offered to candidates. In order to participate in the study, the volunteers signed a consent form, in effect opting into the study. The participants were all between the ages of 25-54, 58% male and 42% female. They resided in three different metro areas: Boston, MA, Chicago, IL, and New York City. The location data was collected for two weeks, from November 25, 2009 to December 9, 2009. This time period was chosen specifically to capture travel and shopping patterns associated with the long Thanksgiving weekend. Participants were not required to download an application onto their phones, but instead location data was requested and acquired automatically via the Sprint network. Cadio s servers transmit a request for GPS data from an optedin Sprint handset, and the request is forwarded to the mobile network via an aggregator. Sprint initiates a network-based request to activate the GPS hardware on the handset. Once the handset acquires a latitude-longitude fix, the data is Female 42% Male 58% 4
3. Benefits of Marketing using GPS-Based Mobile Consumer Analytics The study revealed that GPS location data can deliver actionable insights that inform brand decision-making: Increase the value of a marketing panel Brands can append their existing marketing panels with inferences from mobile consumer analytics to understand the travel patterns, preferences and lifestyles of their customers, and to determine how often they are near store locations. Brands can also determine where consumers shop (including whether near home or work), and what days of the week and times of day they go shopping. They can establish the lifestyle patterns and brand affinities of their customers to create offers and marketing messaging that are most likely to resonate and improve consumer engagement. During the study we discovered that the panelists who preferred Dunkin Donuts were 33% more likely to dine out than those panelists that preferred Starbucks. Conversely, participants who went to a Wal-Mart were 60% more likely to dine out compared to Target customers. Adding onto the behavior of shopping preference and dining out, of the Target customers who dined out, approximately 25% of Target customers went to a restaurant prior to going to Target and an additional 25% of customers went to a restaurant after going to Target. Armed with this information, retailers like Target and Wal-Mart, who have snack food options within store premises, might consider expanding their menu to include foods items beyond snacks. Smaller retailers may benefit by partnering with nearby restaurants in driving complementary traffic between stores. The panel revealed that participants who dined out had a lower tendency to engage in fitness activities than those who did not. Conversely, the average frequency of fitness activities for individuals who went to quick-serve coffee or doughnut locations was 50% higher compared to those that did not visit such locations. The data also unveiled a link between shopping and behavioral preferences. For instance, participants who visited Whole Foods were twice as likely to engage in fitness related activities compared to individuals who shopped elsewhere. Additionally, half of the participants who visited Whole Foods also frequented other grocery stores during the study. An obvious application of this insight would be for Whole Foods Market to create co-marketing programs with gyms or yoga studios to increase acquisition rates; similar to the retailer and restaurant example above. Such joint marketing programs that offer complementary services/ products are not new. However, mobile marketing tactics can further enhance such programs by improving the relevancy for targeted consumer segments. Our experience shows that delivery of offers via a mobile device is more impactful because users are more likely to acknowledge such messages. 5
Strengthen the value of existing customers Advertisers can send offers to customers at the optimal time for them to respond based on their location/proximity to a store location, knowing when they are likely to shop, and what they like to buy. This makes the offer more relevant to consumers. Saturday 31.8% Sunday 36.4% By leveraging location information from existing customers, retailers with retention programs (i.e., loyalty cards) can create programs that focus on increasing the recency, frequency or spend among the customer base. When personal and work travel patterns are included in the mix we are able to help brands select offer expiration dates, or limited-time incentives. Furthermore, advertisers can determine which stores consumers prefer in their areas and provide higher incentives for consumers to travel to farther locations if sales are down. An advertiser with shopping pattern information from its customers is able to tailor its messaging based on the times in which their customer segments choose to shop. During the study, the data showed that close to 70% of all visits to big-footprint retail locations took place on Saturdays or Sundays; surprisingly, only 11% of visits took place on Black Friday. Conversely, 25% of the people from this study chose to shop on the Sunday following Thanksgiving. Participants got a late start on the weekends, as shopping commenced after 2pm on Saturdays, and close to 1pm on Sundays. They also only visited two stores on average each Weekend day. The research also showed that Sears shoppers did not visit any other department store. In contrast, individuals who visited department stores other than Sears always split between multiple national department store chains. Armed with this type of insight, for retailers whose customers display higher loyalty compared to their other segments, it would be beneficial for them to reward these customers above and beyond the typical rewards milestones. Friday 13.6% Thursday 2.3% Wednesday 6.8% Obtain competitive information Tuesday 6.8% Monday 2.3% Advertisers can understand which competitors are in the vicinity of customers homes and offices, where consumers spend their time, and most importantly, which customers visit competitor stores. This can help a brand determine where they should open new locations, or on the flip side, potentially close unsuccessful locations (due to the competition s footprint). Recommendations derived from mobile consumer analytics can also help determine the right time for a high value special offer or promotion, to de-incentivize customers from patronizing a competitor s store. If an advertiser wants to drive awareness or gain competitive share, it could determine where its prospects are traveling so they aren t wasting marketing spend on existing customers. As an example, an advertiser like McDonald s who has aggressively introduced their McCafe menu items might use competitive location data to understand consumer habits relating to morning versus afternoon visits to other cafes. In addition, if the data shows that segments of customers visit multiple coffee destinations in the morning, McDonald s can ultimately determine whether consumers visit their restaurants for food purchases versus coffee purchases (assuming in this example the data shows the other visits being Dunkin Donuts or Starbucks). 6
:: Using location-based services to increase consumer engagement :: We found that about 50% of Starbucks customers visited Dunkin Donuts locations. However, if an individual visited Dunkin Donuts there was a 67% chance they would visit Starbucks. Therefore, it appears that the volunteers in this study preferred the Starbuck s product more so than Dunkin Donuts as the increased visit frequency was 13% higher. Travel frequency During the study we found that on average, the most on-the-move group was from New York (New Yorkers spent 80% of their time in 2.3 zip codes), followed by Chicago (2.1 zip codes), and those from Boston (1.5 zip codes). On average, commute times were 20% longer for participants who lived in or near Chicago than for those who lived in or near New York City (median 72 minutes versus 60 minutes). With this level of insight, among the questions those competitors could consider: is the quality of coffee better? How is my product mix compared to my competitor? How important are the customer experience factors contributing to increased frequency? Use as a Media Planning Tool Average Commute Time (minutes) 80 64 10 72 9.4 8.5 8 60 48 6 32 4 16 2 0 Average Commute Distance (miles) Understanding consumer travel and work patterns is crucial to creating the optimal media mix (either for outdoor, digital out of home, or radio advertising). Brands should determine precisely when and where customers are traveling via car for radio or out-of-home advertising (what roads they travel, what time of day, etc.). Using work schedules can determine when target consumers are likely to be watching television or using the Internet. If out of the home or office, brands can extend their message frequency via mobile advertising. 0 Illinois Commute time (minutes) New York Commute distance (miles) Massachusetts participants were most likely to travel long-distance (defined as trips of more than 100 miles in each direction) during the study, but New Yorkers were most likely to travel long-distance for business purposes (midweek trips were classified as business-related, and travel during the Thanksgiving holiday period as vacation-oriented). Further, when New Yorkers traveled long-distance, those trips were shorter than trips taken by Massachusetts or Illinois residents. As a consequence, New Yorkers were more likely to travel long distance by ground rather than by air. Armed with work and travel data, advertisers can implement creative integrated media executions that begin with traditional and mobile media (during commuting times in the morning); on the PC-based Web (during office hours), and back to mobile media (when traveling). Additionally, determining store impressions (i.e., how many target consumers pass a brand location) and frequency (i.e., how often a target consumer passes a brand location) can also improve marketing programs. 7
By appending mobile consumer analytics to customer profiles, travel-related advertisers can determine when and what types of offers to make (especially for leisure travel). For example, if a consumer travels for business every other Monday, provide a weekend discount/incentive for the weekend after he travels for business so as not to interfere with his work schedule. Travel-related brands can also make travel easier by providing local guide content and travel directions. Consumer shopping patterns can be determined by work hours and days at the office. During the study, we found that people in New York were more than twice as likely to work past 7pm compared to people in Boston and Chicago. Restaurant advertisers can use this data to deliver ads at times of the day or week that match consumer habits. For example, a fast food restaurant chain could use mobile location data to engage consumers only if they are leaving work after 7:30pm and normally drive within 0.5 miles of a restaurant location. Digital Advertising Effectiveness Measurement Measuring the effectiveness of digital out of home advertising has traditionally been challenging. However, with consumer travel pattern information in areas where out of home placements are located, mobile consumer analytics can now be used to accurately measure the effect of advertising in driving foot traffic to targeted stores. By measuring consumer behavior before and after exposure to a (mobile) ad, a retailer can precisely assess how many more people are visiting a store because of a new campaign. Brands can use this data to measure return on investment the real-world equivalent of online cost per click metrics. 4. Brand Challenges In order to gain access to location data, advertisers must keep consumers at the core of this initiative; the program s success starts and stops with them. To successfully create programs that provide location data, advertisers must consider the factors below: Incentivizing consumers to continuously share GPS data GPS information is sensitive in regards to privacy, and consumers have a variety of different perspectives on whether and how this data can reasonably be shared. Younger consumers who are technology-engaged, and who use social networking sites such as Facebook, Twitter and Foursquare to name a few, are generally most likely to share their GPS data with brands in return for appropriate incentives. Other demographics may be more sensitive, in which case it may be necessary to offer more attractive incentives or higher-value rewards to encourage participant opt-in. Some experimentation with incentive structures may be necessary to define an optimal approach that will adequately secure the participation of all required demographic segments. Safeguarding privacy Brands should comply with the CTIA s Guidelines for Location-Based Services in order to guarantee consumer rights and a defined minimum level of privacy control. Specifically, consumers must have the opportunity to opt-out of GPS data sharing at any time, and inferences derived 8
from mobile consumer analytics must be appropriately safeguarded through the use of modern encryption and firewall technologies. Technology limitations Technology can be a barrier for LBS marketing initiatives. Programs in less urban areas may be more successful as there will be fewer challenges to collecting data in areas without tall buildings or signal-blocking concrete. Another challenge for marketers involves sorting through the massive amount of data to determine which data points are relevant to their marketing efforts. Stringing together the GPS paths of thousands of participants, overlaying time of day, day of week, as well as targeting advertising by content, quickly becomes a large task. It will be important for marketers to have a defined focus for this type of program. 5. Brand Application Process Once advertisers address the challenges, they need to create a framework for their LBS program. As such, advertisers are recommended to follow the steps outlined below: Step 1 Partnering with the right provider Providers of mobile consumer analytics technologies, such as Cadio, and agencies, like Mobext, can help advertisers create the backend foundation needed to develop a LBS mobile advertising program. Step 2 Identify questions of interest Advertisers should reference Section II above to determine the types of actionable insights that they wish to receive from a mobile consumer analytics program. Step 3 Choose project parameters In collaboration with partners such as Mobext and Cadio, advertisers should select the following parameters: Program duration (number of weeks or months) Desired sample size (determined by required statistical significance) Geographies of interest (suburban or semi-urban areas are more GPS-friendly than densely urban environments) Step 4 Determine incentive structure and secure consumer opt-ins Leveraging GPS data through a LBS program starts and ends with the consumer. Brands must obtain explicit opt-in permission both for consumers to share their GPS data with a firm such as Cadio and for consumers to agree to receive marketing messages via their mobile device or through another channel. In order to increase the probability of customer opt-in, incentives or rewards for individuals must be offered. The form of currency varies based on the type of program, the targeted demographic segments, and the program s duration. Currency types include: Cash reward Loyalty points 9
Free merchandise Discounts and coupons Customer recognition Customer preferential treatment Advertisers should start their LBS marketing programs with existing marketing panels (with consumers who have already opted in to share their information). Appending inferences derived from mobile consumer analytics to existing customer profiles will allow advertisers to iron out any kinks, and also capture valuable locationbased inferences with which to build improved segmentation profiles. Advertisers can obtain opt-in consent via any channel including Web-based sign-up, text message opt-in or consent via a smart phone application. Brands that already have retentionbased programs, such as points-based loyalty cards, may find it convenient to simply offer bonus points to those who register, as an incentive to participate. 6. conclusion Deep data mining of GPS traces from mobile phones provides new types of inferences that are robust and reliable. Advertisers can use mobile consumer analytics to uncover both lifestyle-relevant and commerce-relevant characteristics of existing segmentations, helping advertisers engage in more effective conversations with existing consumers. Mobile consumer analytics can also bring Internet-style click through metrics to the real world. Now it is possible to build a bridge between digital ad exposure and real-world offline consumer behaviors. Step 5 Activate During the program, GPS data is acquired and processed and actionable inferences are derived accordingly. Depending on the scope of the analytics, results may become available in real-time or after the end of the data-sharing period. Step 6 Close the loop Mobile consumer analytics provides actionable insight into the effectiveness of each digital advertising campaign. Return on investment data can help guide strategic decision-making to optimize the marketing mix and engage in more relevant conversations with the consumer. 10
About Mobext www.mobext.com Mobext is a specialized mobile marketing agency operating within the Havas Digital family of agencies. With offices in Europe and the Americas, Mobext is recognized as an agency leader in bringing brands to engage within the mobile channel. Its core offering includes mobile strategy, consumer activation and media. Its roster of clients are globally recognized brands ranging from many sectors including automotive, finance, retail, entertainment and consumer packaged goods companies. About Cadio www.cadiomobile.com Cadio, Inc., headquartered in Cambridge, MA, is a pioneer in the emerging field of GPS-based mobile consumer analytics. Cadio s proprietary consumer analytics engine processes semi-continuous streams of GPS data to generate actionable inferences about consumer interests, habits and behaviors. Cadio s approach protects consumer privacy while maximizing value for brands and advertisers. 101 Huntington Avenue - Boston, MA 02199 www.mobext.com :: www.mobext.mobi