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HERE Challenges ‘One Map for All’ Approach to Maps and Location-Based Services (LBS)

by Nitesh Patel | 6月 12, 2014

HERE announced the acquisition of mobile predictive analytics firm, Medio, on 12th June 2014. At the heart of this acquisition is the belief that LBS should be customized to meet the needs of users. HERE believes the one size fits all approach to delivering LBS that consumers are accustomed to must be challenged, as numerous layers of location relevant content threatens to clutter and prevent an optimal user experience. Today, mobile map users can customize their map views based on their mode of transport, e.g. selecting walking, cycling, driving or public transport mode fires up map views and map related information pertinent to those transport modes. Furthermore, users of mobile maps can customize their view by choosing satellite imagery, street views, overlaying with traffic flow information, or cross referencing locations with information from travel guides or Wikipedia.

HERE’s plan to integrate Medio’s predictive analytics engine takes personalization a few stages further. For example, delivering map views and search results that dynamically reflect learned user behavior and patterns, instead of default map views. Unfortunately, I have failed to avoid the often used cliché in my example, but if it is 1pm and I have searched for restaurants serving Pizza’s in the past, then the map view delivered, underpinned by predictive analytics, may well label nearby Pizza establishments (or even offers from those restaurants) to the map foreground – while fading out other types of business in the process. Also, if I click-through on 4-star rated establishments mainly, then predictive analytics should show me those results only.  Speaking more broadly, HERE claims the predictive analytics platform (in tandem with HERE’s map platform) will serve up a map and related content taking into consideration:

  1. Users’ location (lat, long, along with context e.g. indoor, outdoor);
  2. Known information about the user (e.g. age, gender);
  3. The device they are accessing the application from, and;
  4. Other information such as previous search results.

The idea is to enable LBS providers to deliver an optimized user experience that is differentiated from the current one-size fits all model, and which as a consequence is more engaging for the end-user. The acquisition clearly builds on that of Desti, the iOS based planning app that HERE acquired on 30th May 2014, in order to integrate natural speech recognition, but also Desti’s ability to mine the Web for detailed and contextually relevant information about places.

Placed into greater context the acquisition of Medio is relevant for the following reasons:

  • Taps into growing interest from businesses in data driven intelligence: We are having a growing number of conversations with players in the programmatic ad-buying space. This sector relies on a combination of third and first party data to drive automated, real-time purchase decisions around mobile ad inventory. The interest in big data for underpinning operational efficiency and business decisions continues to build momentum across all sectors, not just mobile advertising.  
  • Going beyond the location data cloud to the business intelligence cloud: Customer (current and prospective) will have access to a richer product beyond map and map related content. The ability to capture, measure/ analyze, categorize and predict user behavior (on the fly) is a powerful and complimentary asset for maximizing user engagement across location centric and non-location based apps alike.
  • Boost for HERE’s analytics and platform segment: Enables a broader conversation beyond location based services, and packages it with the other elements mentioned above, e.g. broader app and business intelligence capabilities. That provides upsell capabilities although as I understand it the two platforms will be integrated to create synergies.

The integration of analytics with location platform certainly adds a further string to the bow of HERE’s location platform, above and beyond the ‘look and feel’ customization in Google Maps. However:

·         Will interest in predictive analytics convert to demand from businesses? With predictive analytics a relatively new concept in marketing (and one which extends beyond business intelligence) I expect it will take time to educate even brick and mortar retailers of the benefits of customizable maps. Even though they are trying to differentiate themselves from the surrounding competition, generate foot traffic, and combat eCommerce.  If, as I also expect, the predictive analytics capability commands a higher license fee from map licensees then proving its ROI/ effectiveness will be imperative.

·         Will consumers care? High adoption and regular usage of mobile maps suggests that there are few problems with mobile maps today. Successful recommendation systems from players like Amazon and Netflix highlight the power of predictive analytics.  With 60% of US smartphone owners using maps to find a retail location, a well-implemented system can enhance both consumers’ LBS experience and drive retailer business. However, although personalization reduces serendipity, the promise of targeted ads has been hit and miss so far.

In my view case studies, evaluations, and free limited time API trials (the latter which is part of Medio’s self-service capabilities) will certainly be required to in order for HERE to turn the tide against the prevailing one-size fit model.

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