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Transforming Consumer Behavior through Mobile Data Science

Unlocking mobile data insights is crucial to understanding the omni-channel, always-on consumer (AOC). According to Vivaldi Partners, 48% of US consumers are already “always-on,” using three connected devices daily, going online multiple times a day from at least three different locations. Mobile plays a key role in this always-on environment: 61% of smartphone owners use mobile search on a daily basis and, according to Place IQ, 48% are reachable through app-based messaging. As hyper-digital consumers rely on their devices, apps, and social networks to reach a purchase decision, mobile platforms offer the most connected, holistic view of behavior across all lifecycle phases — from discovery through purchase, usage, and loyalty. Mobile is a key enabler to delivering AOCs a highly personal, omni-channel experience that encapsulates their evolving needs, delivers the right content/message at the right moments, and does so seamlessly across all interactions in the customer journey.

Through its advantage of precise, geo-location data, mobile is uniquely positioned as a catalyst for connecting the physical world with digital and offline CRM data; it enables marketers to understand where the consumer has been and more importantly, where he is going next. Leveraging predictive analytics, companies are adapting mobile marketing strategies to proactively mold and nurture the behaviors that will impact their bottom line in the short term and ultimately breed more profitable customers over time.

Below are five examples where data-driven, location-aware mobile technology is changing the future omni-channel customer and commerce experience.

1. Mobile Operators:

In response to current challenges of addressability, measurement, and poor customer experience in mobile advertising, Verizon’s Precision Market Insights group launched PrecisionID. This is an individual, anonymous identifier matched to devices on the Verizon network that helps advertisers improve targeting accuracy and attribution.

2. Retail:

Using mobile foot traffic technology, a big box retailer determined 60% of traffic occurred in 30% of their store. Mobile analytics informed optimal placement of promotional materials and positioning of most profitable brands to drive sales from the remaining 70%. From kinetic heat maps to in-store mobile analytics, retailers are analyzing shopper browsing, search, social behavior, and in-store navigation to optimize store performance.

3. Insurance:

State Farm’s Drive Safe & Save program uses in-dashboard apps (OnStar®In-Drive®, SYNC®) that sync with smartphones via Bluetooth to collect data on miles driven, speed, time of day, and acceleration/braking patterns to tailor auto premiums based on individual driving habits. SYNC’s smartphone link enables State Farm to pull metrics directly from (partner) Ford’s Vehicle Health Report. Mobile insights drive sustainable changes in driving behavior that generate increased net revenue per customer.

4. Automotive:

INRIX, a leading traffic intelligence services provider, partners with Ford, BMW, Porsche, other auto manufacturers as well as government entities to deliver real-time and predictive traffic flow and incident data to improve the daily commuter experience in major metropolitan areas. INRIX mobile app consumers enjoy optimized navigation, incident reporting, traffic forecasts, and other driver services to minimize typical road frustrations. Geo-location technology combined with real-time traffic insights provides additional mobile services such as POI search, optimal fuel pricing, and parking info, saving time and eliminating guesswork for end-users.

5. Public Sector:

INRIX also partners with mobile operators and local government agencies (Department of Transportation) to aid urban transportation projects and planning efforts in the US and around the globe. Relying on a crowd-sourcing approach, the INRIX analytics platform is strengthened by its ever-expanding network of vehicle/smartphone data.

The examples above highlight the incremental revenue streams and cost reduction resulting from mobile big data collaboration initiatives across operators, brands, advertisers, agencies, and governments. Consumer value is driven by merging traditional and unstructured digital data sets, often owned across these different entities, and pairing them with geo-location data to drive real-time decisions. However, many organizations continue to struggle with mobile data monetization because: a) mobile media budgets are on the rise, but do not match level of consumption (~2 hours and 51 minutes per day per US consumer in 2014), b) addressability, measurement, and attribution in mobile ads are difficult without identity/cross-device mapping solutions, c) mobile is an afterthought – companies are not invested in a robust strategy or analytics to optimize the mobile customer experience.

There is an imperative to address and anticipate the complex needs of today’s omni-channel consumer through smarter investments in mobile data and analytics. From retail marketing and connected cars to solving for urban mobility and traffic flow to population analytics that help build smarter cities, the applications of mobile data science extend beyond everyday micro-decisions of the individual consumer to broader macro-economic and environmental implications.

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