Using Location-Based Services to Increase Consumer Engagement

Using Location-Based services to increase consUmer engagement

april 2010

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:: Using location-based services to increase consumer engagement ::

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

contents

Phuc Truongmanaging Director, mobext [email protected]

contriBUtors

Sharon BernsteinVp, insights [email protected]

Jared Hopfermobile marketing manager, mobext [email protected]

Dr. Thaddeus R. F. Fulford-Jonesceo, cadio [email protected]

mailto:[email protected]

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1.execUtiVe sUmmary

mining consumer location data

predicting behavioral patterns

protecting users privacy

moBile consUmer analytics

Location-Based services (LBs) uti-lizes 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 opted-in consent.

lBs technology allows an advertiser to yield various insights, including shopping preferenc-es, competitive store visits, time and frequency for shopping activities, as well as travel patterns. armed with additional mobile consumer ana-lytics, advertisers can enhance their marketing efforts by strengthening the value of existing customers while using the data to supplement competitive intelligence.

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2.introDUction: moBext anD caDio stUDy

male 58%Female 42%

in late 2009, Mobext, the mobile marketing network of Havas Digit-al, partnered with cadio, a mobile consumer analytics firm, to analyze gps data from opted-in mobile phones to better understand con-sumer interests and habits.

mobext recruited sprint subscribers to share semi-continuous gps data (once every 10 min-utes) with cadio via their mobile devices. partic-ipation was entirely voluntary and no incentive was offered to candidates.

in order to participate in the study, the volun-teers 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 loca-tion data was collected for two weeks, from no-vember 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 lo-cation data was requested and acquired auto-matically via the sprint network. cadio’s servers transmit a request for gps data from an opted-in 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 hand-set acquires a latitude-longitude fix, the data is

time stamped and returned to cadio’s servers in real-time. the maximum data acquisition fre-quency 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 sta-tions, large national retailers, supermarkets, and selected other categories.

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3.BeneFits oF marketing Using gps-BaseD moBile consUmer analyticsthe 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, pref-erences and lifestyles of their customers, and to determine how often they are near store loca-tions. Brands can also determine where consum-ers 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 pat-terns 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 pan-elists 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 prefer-ence and dining out, of the Target customers who dined out, approximately 25% of Target cus-tomers went to a restaurant prior to going to Tar-get and an additional 25% of customers went to a restaurant after going to Target.

armed with this information, retailers like tar-get and Wal-mart, who have snack food options

within store premises, might consider expand-ing their menu to include foods items beyond snacks. smaller retailers may benefit by partner-ing with nearby restaurants in driving comple-mentary 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, par-ticipants who visited Whole Foods were twice as likely to engage in fitness related activities com-pared to individuals who shopped elsewhere. Additionally, half of the participants who vis-ited 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 res-taurant example above. such joint marketing programs that offer complementary services/products are not new. However, mobile market-ing 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.

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monday 2.3%

tuesday 6.8%

Wednesday 6.8%

thursday 2.3%

Friday 13.6%

sunday 36.4%saturday 31.8%

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.

By leveraging location information from exist-ing 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 consum-ers prefer in their areas and provide higher in-centives for consumers to travel to farther loca-tions 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 seg-ments 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. con-versely, 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 vis-ited 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 nation-al department store chains.

armed with this type of insight, for retailers whose customers display higher loyalty com-pared to their other segments, it would be ben-eficial for them to reward these customers above and beyond the typical rewards milestones.

obtain competitive information

advertisers can understand which competitors are in the vicinity of customers’ homes and of-fices, where consumers spend their time, and most importantly, which customers visit com-petitor 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). recommen-dations derived from mobile consumer analytics can also help determine the right time for a high value special offer or promotion, to de-incentiv-ize 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 deter-mine whether consumers visit their restaurants for food purchases versus coffee purchases (as-suming in this example the data shows the other visits being Dunkin Donuts or starbucks).

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We found that about 50% of Starbucks custom-ers visited Dunkin Donuts locations. However, if an individual visited Dunkin Donuts there was a 67% chance they would visit Starbucks. There-fore, 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.

With this level of insight, among the questions those competitors could consider: is the quality of coffee better? How is my product mix com-pared to my competitor? How important are the customer experience factors contributing to in-creased frequency?

Use as a Media Planning tool

Understanding consumer travel and work pat-terns 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 consum-ers 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.

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 Chi-cago than for those who lived in or near New York City (median 72 minutes versus 60 minutes).

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, deter-mining 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.

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4.BranD cHallenges

By appending mobile consumer analytics to cus-tomer profiles, travel-related advertisers can de-termine when and what types of offers to make (especially for leisure travel). For example, if a consumer travels for business every other mon-day, provide a weekend discount/incentive for the weekend after he travels for business so as not to interfere with his work schedule. travel-re-lated brands can also make travel easier by pro-viding 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 com-pared to people in Boston and chicago. restau-rant 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 con-sumers only if they are leaving work after 7:30pm and normally drive within 0.5 miles of a restau-rant location.

Digital advertising effectiveness measurement

measuring the effectiveness of digital out of home advertising has traditionally been chal-lenging. However, with consumer travel pattern information in areas where out of home place-ments are located, mobile consumer analytics can now be used to accurately measure the ef-fect of advertising in driving foot traffic to tar-geted stores. By measuring consumer behav-ior 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.

in order to gain access to location data, adver-tisers 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 pri-vacy, 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 re-turn for appropriate incentives. other demo-graphics may be more sensitive, in which case it may be necessary to offer more attractive in-centives 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 guaran-tee 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

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5.BranD application process

from mobile consumer analytics must be appro-priately 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 deter-mine which data points are relevant to their mar-keting 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 fo-cus for this type of program.

once advertisers’ address the challenges, they need to create a framework for their lBs pro-gram. as such, advertisers are recommended to follow the steps outlined below:

step 1 partnering with the right provider

providers of mobile consumer analytics technol-ogies, such as cadio, and agencies, like mobext, can help advertisers create the backend founda-

tion needed to develop a lBs mobile advertis-ing 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 follow-ing 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 con-sumers 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 pro-gram, the targeted demographic segments, and the program’s duration.

currency types include:

• cash reward

• loyalty points

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6.conclUsion

• Free merchandise

• Discounts and coupons

• customer recognition

• customer preferential treatment

advertisers should start their lBs marketing pro-grams with existing marketing panels (with con-sumers who have already opted in to share their information). appending inferences derived from mobile consumer analytics to existing cus-tomer profiles will allow advertisers to iron out any kinks, and also capture valuable location-based 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 retention-based programs, such as points-based loyalty cards, may find it convenient to simply offer bo-nus points to those who register, as an incentive to participate.

step 5 activate

During the program, gps data is acquired and processed and actionable inferences are derived accordingly. Depending on the scope of the an-alytics, 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 ad-vertising campaign. return on investment data can help guide strategic decision-making to op-timize the marketing mix and engage in more relevant conversations with the consumer.

Deep data mining of GPs traces from mobile phones provides new types of inferences that are robust and reliable.

advertisers can use mobile consumer analyt-ics to uncover both lifestyle-relevant and com-merce-relevant characteristics of existing seg-mentations, helping advertisers engage in more effective conversations with existing consumers. mobile consumer analytics can also bring inter-net-style click through metrics to the real world. now it is possible to build a bridge between digital ad exposure and real-world offline con-sumer behaviors.

about Mobextwww.mobext.com

mobext is a specialized mobile marketing agen-cy operating within the Havas Digital family of agencies. With offices in europe and the ameri-cas, mobext is recognized as an agency leader in bringing brands to engage within the mobile channel. its core offering includes mobile strat-egy, consumer activation and media. its roster of clients are globally recognized brands rang-ing from many sectors including automotive, fi-nance, retail, entertainment and consumer pack-aged goods companies.

about cadiowww.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-con-tinuous streams of gps data to generate action-able inferences about consumer interests, habits and behaviors. cadio’s approach protects con-sumer privacy while maximizing value for brands and advertisers.

101 Huntington avenue - Boston, ma 02199 www.mobext.com :: www.mobext.mobi

Using Location-Based Services to Increase Consumer Engagement

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