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Vodafone Global Enterprise

Case studies

You are here: Home > News & Insight > Case studies > Extracting insight and value from network data

Extracting insight and value from network data

Case studies 18 September 2012

Mobile analytics (mAnalytics) can provide great insight to an organisation, allowing them to understand both employees and customers intimately.

One Vodafone Global Enterprise customer in particular is keen to exploit mobile analytics (mAnalytics) by converting raw data from the Vodafone network into meaningful insight. It aims to drive more efficient and effective working practices internally, but first and foremost, it aspires to generate additional value through the applications it licenses based on business intelligence (BI) services and contextual awareness.

To support this, an internal trial was established, following the movements and usage activity of a statistically significant number of Vodafone employees. A similar study is also planned for the customer’s organisation.

We track communications records, data usage and location-based information to the nearest town, but we don’t monitor the content of calls and messages or data, personal web information, weekend usage, precise location or productivity.

The data is aggregated and analysed at a numerical level and participants can choose to remain anonymous when they sign up, so they aren’t personally identifiable from their data.

The trial focused on four key areas:

  1. Workforce productivity patterns
    Measuring variables such as commuting, office movements and inferred working hours. At aggregate level, this insight can be used as a foundation for decision-making and as a valuable input into business cases for flexible working.

  2. Contextual awareness
    Assessing the opportunities for context-aware services and how this intelligence could be used to enable creation of workflow and processes based on location and device.

  3. Privacy
    To gain insight into participants viewpoints on how their data is used and how to balance the value of insight with privacy.

  4. Business Intelligence
    Understanding how large enterprises can create meaningful correlations with the data e.g. linking socioeconomic grouping with footfall analysis to inform a retailer’s promotional tactics or store expansion strategy.

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