Vodafone NZ drives market success through use of advanced analytics

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Vodafone New Zealand used data analytics to overcome limitations in tailoring the marketing and sale of prepaid mobile plans to specific market segments, particularly the country’s youth market.

Prior to using Teradata Aster analytics solutions, Vodafone was limited in its ability to deliver relevant offers to its prepaid mobile customers because it did not have access to the demographics it now collects through the Teradata solution.

Vodafone New Zealand Director, Data Analytics and Strategy David Bloch said iTWire, “We were limited in our ability to deliver relevant offers to our prepaid customers. This has prevented us from capitalizing on up-selling and cross-selling opportunities for services and products. Customers could also miss out on offers and offers that could improve their experience ”.

As Bloch explains, in New Zealand, prepaid customers are not required to register details or provide ID to obtain a SIM card for their phones. This meant that Vodafone did not have access to demographics that could be used to effectively market to this customer base.




Instead, Vodafone could only develop marketing campaigns based on call time, TXT, or data consumed by the user on the network. customers.

Vodafone New Zealand chose the Teradata Aster appliance, which leverages the open source Big Data Hadoop ecosystem, and added R analysis software to identify likely demographic groups, by combining network data from the appliance Hadoop with customer profile data from their DataMart.

Bloch says that Vodafone’s team of data scientists were then able to analyze immense amounts of data to identify patterns.

And, according to Bloch, the resulting predictive model was very accurate at identifying young customers in their prepayment base.

Now, Vodafone claims that after testing and adjusting the model, they achieved a “mind-boggling” correct prediction ratio of 89%.

Bloch says the new solution has allowed Vodafone New Zealand to identify a large number of young customers within its base – a segment which he says is now successfully transitioning to the new youth proposition, Vodafone Mates.

“We have had great success recently in a highly competitive market. We attribute this to knowing more about our clients and building market-leading propositions that are attractive to them, without necessarily being the cheapest option available in the market.

“Our competitors have lowered the prices, but we are now able to better target customers and increase average revenue per user, while still offering them the right mix of plans. “

“The youth market has been great for us,” says Bloch.

“When we started building our role models within the Teradata ecosystem, we had a number of known young people within our base through contracts and previous surveys. We had a rough idea of ​​the total size of the market and we knew this was an area of ​​opportunity for us to grow against our competition. We were able to create profiles on how a young guy was using our network, and then through our anonymous prepayment database, understand the likelihood of these unknown customers being young or not.

“To remedy this, we offered our Vodafone Mates prepaid package and targeted young people between the ages of 18 and 24.

“Through the analysis, we understood what is important to these guys and we knew, for example, that they were browsers at night and they were using more data, so that’s how we developed our plan.

“We collected a lot of behavioral data on how young people were using our network. We were able to model them as young customers based on predictive analytics models.

“It was very precious. This means that we are able to get the right message and the right topic with a plan built around their use.

“We were able to learn more about our customers through analytics and target our communications to them. This allows us to migrate customers to the right plan and they are more likely to stay with us, reducing the churn rate.

“Through a combined effort of our marketing and data science teams, we have used our analytical models to guide how we target these customers and to ensure that the brand, message and propositions are all clear and engaging. for these customers. We were able to double the total number of young people using our prepaid products and services in 12 months, which we believe to be an exceptional result.

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