Telco Use Cases

Data-driven insights can be used to differentiate when operating in a very competitive space. For example, churn is a critical metric for Telco companies, whether it relates to an account or a particular product or service. Recognising the factors that increase churn and identify customers at high risk provides the opportunity to take steps to prevent it.

Customer Insights

Operational Efficiencies

Data Monetization

  • Real-Time Geo-Fenced Marketing Campaigns

 

A Deeper Dive:


Churn Prediction and Prevention

Customer churn is a priority focus for all consumer brands but is heightened challenge for the telecoms industry where telecom regulations mean it is easier than ever for customers to switch providers, putting critical subscription revenue at risk.

ChurnTelco providers run sophisticated analytical programs to identify consumers who may be at risk of churning. These will look at patterns of behaviour across basic factors such as analysing changes in customer spend levels, changes in product usage (data levels, call levels), length of contract lock-in, etc., through to more sophisticated analysis such as network effect – what effect does one customer in a network switching providers have on a second customer’s likelihood to switch?

The Challenge
Churn prediction requires significant analysis of customer behaviour and customer network behaviour to deliver optimal results. The analysis needs to take a significant longitudinal perspective to understand how churn behaviours progress over time and with respect to differing contract lengths.

GDPR and e-Privacy regulations can place significant limitations on a Telco’s ability to do this type of analysis without explicit customer consent, particularly as churn analysis becomes more sophisticated and moves beyond the individual customer into their network, raising the need for multiple levels of consent. Regulations also impact Telcos’ ability to retain persistent identifiers over time for analysis purposes, making medium to long term longitudinal analysis challenging.

The Solution
By using the Trūata Anonymization Platform a Telco can perform extensive longitudinal analysis and predictive modelling across their entire customer base. As both individual customers and all the related customers in their network are anonymized, Trūata completely removes the heightened risk associated with more sophisticated levels of customer network analysis.

Telcos can be assured that they can run analysis over several years’ worth of data. Anonymized, persistent identifiers are maintained, allowing the Telco to perform behavioural analysis and train predictive models effectively over the full longitudinal view of data.


Intelligent Customer Journeys

Access to customer location data provides a unique opportunity to Telcos to drive operational efficiencies but also to generate new revenue streams through monetization of location-based services, particularly when blended with additional insights which the Telco has with respect to the customer.

location based dataUnderstanding customer journeys and linger times, such as what locations specific customer segments frequent at specific times, and predicting future traffic locations are of significant interest to traditional bricks and mortar retail businesses, media planning companies, event management companies, etc.

Compelling new product propositions can emerge from demographic and location-based insights such as “Where can I typically access the highest concentration of higher income 24 to 35-year olds on Saturdays between noon and 2pm?”.

The Challenge
Location data is sensitive personal identifiable information and the use of this data under GDPR and e-Privacy regulations is extremely restricted. Telcos are frequently reluctant to use this data outside of necessary operational situations due to the risk and potentially high-profile reputational impact that can result through misuse or exposure of this data.

The Solution
Using the Trūata Anonymization Platform and the privacy controls that we apply to data insights generation, location data can safely be used to gain new insights and monetize data.

The platform anonymizes location data so that maximum utility is maintained to support your analytical and data insight use cases. Privacy controls and tests ensures that data insights for aggregated customer segments based on the location data will only be generated where the re-identification of a data subject is not possible.

Follow the links below to learn more about the use cases for the Trūata Anonymization Solution.

Automotive     Financial Services     Retail     Travel and Hospitality