Measuring performance impact of a campaign management system

If you’re thinking of implementing a campaign management system for your network, the ultimate goal is to increase Average Revenue per Unit rate of the network. A campaign management system allows you to micro-segment, gain insights, send concurrent campaigns and monitor all subscribers in real time. The goal of a campaign management system in general is to improve business outcomes.

Measuring campaign management system

But how do we measure the campaign management system? How do you know if a campaign management system is deriving better results? Calculating the increase in ARPU before and after implementing a campaign management system can tell you this, however, can it be considered the most optimal method of measurement?

The above method has one major shortcoming – revenue does not remain constant over a period of time – short or long term – due to multiple factors:

  • Introduction of new services
  • Evolving plans
  • Competition in the market
  • Holiday and seasonal variances
  • Above the line marketing

Thus, a more robust method to estimate the revenue is required; one that is immune to environmental effects.

The Test and Control Groups method will work effectively for measuring individual campaigns. Here, we create test groups and start the campaign while a control group is not exposed to the same campaign. However, to measure the performance of the campaign management system itself, you will face challenges like –

  • We cannot test on multiple campaigns at once
  • The test and control groups may be too small in number to gain an appropriate statistical inference
  • Reserving large control groups will affect ARPU

What is Universal Control Group

A Universal Control Group is created from a normal distribution of subscriber base population by applying T-test and Z-test data analysis method. It is important to consider a bigger sample size to decrease the error rate. With Pelatro’s mViva, for a typical subscriber base, we try to use a sample size such that the expected error rate is less than 1% of the population mean.

The first step is to analyse three months of subscriber usage data to understand the ARPU distribution of the population and also patterns leading to churn. It is important to label subscribers as churned in the second case.

Step two involves stratification. When sub-populations within an overall population vary, it is advantageous to sample each sub-population. We divide the population into various homogenous groups and sample them independently. Stratification gives a clear representation of the population in smaller segments. It also reduces variation as compared to the original population and also reduces sampling error.

Subscribers are assigned to a strata based on their age in the network. Simply because long-time subscribers have more stable behaviour.

The final step is Sampling and Verification. Once we have computed the sample size and the strata, we generate about 100 samples. This is done randomly and without replacement. We compare each of the samples and their corresponding test groups and choose the most similar to the UCG. Additional verification of the reliability of the UCG is carried out by checking how well it corresponds to the historical behaviour of the population.

Multichannel Campaign Management

Importance of campaign management system for your network

Studies show that well thought out and intelligently designed campaigns can ensure increased revenue and customer retention in the long run for networks.

A campaign management system is a holistic business intelligence tool that enables networks to make better and well-informed decisions. It caters to the promotional needs of networks while ensuring the capability to analyze and calculate the efficacy of campaigns over time and generate data in real time. This can be leveraged in the value of a campaign and to calculate ROI.

This is an obvious tool of choice for telecommunication companies and service providers. It improves revenues by increasing operational efficiency for the user. It also helps networks derive value from focused and customized promotional campaigns leading to customer loyalty and retention in the long run.

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