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Common challenges

Revenue analysis (ARPU)
Evaluating Average Revenue Per User (ARPU) in a dynamic fashion is key to better decision making.
- Opaque customer information
  If you don’t have the right level of granularity in your data, you can’t get a real sense of how your telecommunications services are impacting users at a customer level. This lack of visibility results in one-size-fits-all solutions that can’t be practicably applied in a modern setting.
- Suboptimal service offering
  A poor understanding of your revenue implications means you can’t deliver an optimised offering that best leverages your resources and experience to meet the demands of the market. Instead, you’re stuck following others because you can’t pull original insights yourself.
- Dynamic ARPU calculations
  Cubewise and IBM Planning Analytics / TM1 offer dynamic and highly sophisticated tools that assist you with your ARPU calculations and analysis at every level. Whether you’re assessing an individual customer, a region, a sector or any other slice of your customer base, you can rest assured you’re making a robust decision backed by real data.
Marketing effectiveness
- The market is saturated
  The telco sector is notoriously competitive and largely commoditised, which makes differentiation through marketing all the more important. However, if you don’t have the means of tracking and assessing the effectiveness of your campaigns, you’re bound to fall behind.
- Correlation versus causation
  Tracking maketing impact is tricky and relies on a savvy process of data generation and analysis. Without it, you risk attributing success to correlated factors rather than identifying the driver responsible for moving the needle. This then makes it impossible to replicate the success you worked so hard to achieve.
- Driver-based marketing analysis
  Cubewise and IBM Planning Analytics / TM1 can help you craft workflows that offer real insights into your marketing effectiveness by tracking core drivers under the hood in all their complexity. By having all your data in one place you can arrive at insights that never before were possible, using them to push your marketing forward and delivering better results time and again.
Customer churn
- Retaining customers is difficult
  The lifetime value of telecommunications customers is significant. With such significant friction in switching providers, businesses must work hard to retain the customers they acquire. If you find your churn rate is too high, you risk having underutilised infrastructure that creates tremendous fixed-cost vulnerabilities.
- Poor sentiment
  The higher your churn rate, the worse your company is perceived in the marketplace. Identifying why your customers are churning and making the right moves to mitigate against those factors is key to maintaining a strong reputation and a positive outlook across your customer base.
- Sophisticated churn analysis
  Cubewise offers tools for detailed churn analysis that can examine every aspect of your business and identify those factors causing customers to look elsewhere. By mining your data for these factors you can then go and do something about it, thus strengthening your business’s Customer Lifetime Value (CLTV) and capitalising on the customer acquisition costs you have already invested.

Opaque customer information
If you don’t have the right level of granularity in your data, you can’t get a real sense of how your telecommunications services are impacting users at a customer level. This lack of visibility results in one-size-fits-all solutions that can’t be practicably applied in a modern setting.
Suboptimal service offering
A poor understanding of your revenue implications means you can’t deliver an optimised offering that best leverages your resources and experience to meet the demands of the market. Instead, you’re stuck following others because you can’t pull original insights yourself.
Dynamic ARPU calculations
Cubewise and IBM Planning Analytics / TM1 offer dynamic and highly sophisticated tools that assist you with your ARPU calculations and analysis at every level. Whether you’re assessing an individual customer, a region, a sector or any other slice of your customer base, you can rest assured you’re making a robust decision backed by real data.

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Amazon Web Services case study

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