bulzī Mobile Data Services connecting mobile to out of home

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Beryl Dixon
8 years ago
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1 bulzī Mobile Data Services connecting mobile to out of home There are almost as many mobile phones in use as there are people on the planet. Each day trillions of data packets are sent to servers connected to these mini computers. Buzi has pioneered the use of anonymous mobile data to improve the effectiveness of mobile and out of home advertising. Great Data Lots of firms talk about big data. Our data is not just big, but great. We have associated most mobile devices to the consumers who carry them and we can see where the devices have been. Anonymous All Bulzi data is anonymized. Consumers cannot be identified - only their targeting attributes are processed by our computers. Powerful Bulzi can find your audience, deliver your message, and measure the result with a level of accuracy never before possible.

2 Big Data Bulzī s criteria for data is simple: use the best. That means high quality and large scale. The quality of mobile data is limited by the accuracy of the mobile phone location records that are used. In our case we use mobile ad request data which measures location accuracy in tens of feet. Carrier data measures theirs in thousands of feet. On the scale side, Bulzī s DataMart receives tens of billions of records each month. Analytics By analyzing high-precision mobile location records at such a massive scale, our data analytics team is able to establish highprobability associations between devices and the consumers carrying them. Once that is done any commercially available dataset can be associated to the mobile device. This is very different from analyzing where a device shows up and then associating audience characteristics to the device based on that information. It is also very different from attaching those audience characteristics to a geographic area. While these other strategies can be useful, they are secondary to the first-order goal of associating devices to individuals. Privacy Bulzī has established a privacy-safe model which limits the handling of personally identifiable information ( PII ). Our servers only handle anonymized consumer data while leveraging rich consumer attributes for improved ad targeting, retargeting, and attribution measurements. All PII data is managed by our third party data analytics firms who build and maintain safe haven systems.

3 USE CASES Addressable Ad Targeting Build an audience profile using any combination of our standard 950 consumer attributes or create a custom set for us to associate to mobile devices. Bulzī has activated addressable inventory in Out of Home and Mobile. Buy our addressable inventory we will serve and report based on it. B&M Conversion Measurement Send us a list of your Brick & Mortar locations involved in an ad campaign. Every week during the campaign, and for some period after, we will measure the devices that show up there and compare those against the devices exposed to the ad campaign. Measure the foot traffic lift resulting from your media spend. Competitor Conquest If your goal is to steal business from the competition, send us a list of your competitor s Brick & Mortar locations and we will track the devices that have been there, create a target audience from that, and serve your ad campaign to them. Customer Retargeting If your goal is to deepen the relationship with your existing customers, send us a list of your Brick & Mortar locations and we will run a historical view of devices that have been there, create a target audience from that, and serve your ad campaign to them. Or compare our list of devices to your CRM data to see who else you can extend your marketing campaign to. Cross-Channel Retargeting Bulzī can serve your ad campaign in Out of Home, Mobile, or across both channels to the same set of consumers. Reinforce your message across multiple touch points. Build Your Own Combine these Use Cases or create a new one. Bulzi is changing the rules of the game. Your marketing tool set just got a lot more interesting.

4 CASE STUDIES Summary The following Case Studies illustrate Bulzi s Point of Interest (POI) mobile device analytics capabilities which can be used to support a range of advertising-related Use Cases. Process In each case a group of locations were selected by the client to analyze the mobile device activity at the POI. Bulzi then created appropriately-sized geo-fences (a geographic boundary) around each POI. Finally, all unique mobile devices which showed up in the geo-fence during the reporting period were collected and reported. Data All data originated from mobile ad requests using data from Bulzi s Co-Op of mobile advertising partners. Unlike data originating from carrier cell towers which typically have low precision (+/ feet), this location data is generally much more precise (+/- 10 feet). As a result, Bulzi-measured devices can more reliably be mapped into smaller geo-fences at the POI. Results The results for each Case Study are reported in the charts below. In each case the number of Matched devices and non-matched ( Other ) devices are shown. Matched devices means that the device ID is mapped back to an individual or household. The majority of Other devices are mapped back to some geographic area such as ZIP+4 (which includes about six houses on average). Only unique devices were reported (frequency was not reported).

5 CASE STUDIES Case Study #1 Case Study #1. The POI was a Fast-Casual Quick Service Restaurant (QSR). Ten locations in a single DMA were geo-fenced using a 20m x 100m perimeter consistent with the size of the stores. A total of 94,343 unique devices were reported on, of which 70% were matched. The analysis was done on a look-back period using the current matching analytics version. Increasing trend in the quantity of devices is a reflection of the increasing size of the Co-Op data mart. Over time the percentage matched will also increase. This study was used to compare consumer s exposure to specific addressable TV ads to the increased foot traffic in the stores being advertised, and will also be used to deploy cross-channel ad campaigns in mobile and OOH. Case Study #2 The POI was a Specialty Running store. 627 locations across multiple DMAs were geo-fenced using a 20m x 100m perimeter consistent with the size of the stores. A total of 1,704,359 unique devices were reported on, of which 47% were matched. The analysis was done on a look-back period using the current matching analytics version. Increasing trend in the quantity of devices is a reflection of the increasing size of the Co-Op data mart. Over time the percentage matched will also increase. This study was used to gather existing customer data for serving ads to these customers across multiple channels including mobile and OOH. Case Study #3 The POI was a High-End Sportswear store. Five locations across multiple DMAs were geo-fenced using a 20m x 100m perimeter consistent with the size of the stores. A total of 123,095 unique devices were reported on, of which 20% were matched. The analysis was done on a look-back period using the current matching analytics version. Increasing trend in the quantity of devices is a reflection of the increasing size of the Co-Op data mart. Over time the percentage matched will also increase. Bulzi Media Inc. 895 Dove Street, Suite 300 Newport Beach, CA v This study was used to gather existing customer data for serving ads to these customers across multiple channels including mobile and OOH.

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