Vodafone New Zealand has used data analytics to overcome limitations to tailoring marketing and selling of pre-paid mobile plans to specific market segments, particularly the country’s youth market.
Prior to using analytics solutions Teradata Aster, Vodafone was restricted in its ability to provide relevant offers to its prepay mobile customers because it lacked access to the demographic data it now garners through the Teradata solution.
Vodafone New Zealand manager, analytics and data strategy David Bloch told iTWire, “we were restricted in our ability to provide relevant offers to our pre-pay customers. This prevented us from capitalising on opportunities to upsell and cross sell services and products. Customers were also potentially missing out on deals and offers that would improve their experience”.
As Bloch explains, in New Zealand, pre-pay customers are not required to register any details or provide identification to get a SIM card for their phones. This meant Vodafone did not have access to demographic data that could be used to effectively market to this customer base.
{loadposition peter}Instead, Vodafone could only develop marketing campaigns according to how much call time, TXT or data the user consumed on the network, and that’s when, Bloch says, Vodafone turned to Teradata to help it use big data to accurately predict the traits of their prepay customers.
Vodafone New Zealand chose the Teradata Aster appliance, which leverages the open source Hadoop big data ecosystem, and added R analytics software to identify likely demographic segment groups – combining network data from the Hadoop appliance with customer profile data from their DataMart.
Bloch says the team of Vodafone data scientists was then able to analyse immense amounts of data to identify patterns.
And, according to Bloch, the resulting predictive model was highly accurate in identifying youth customers in their pre-pay base.
Now Vodafone claims, after testing and tuning the model, they achieved a “staggering” 89% correct prediction ratio.
Bloch says the new solution has allowed Vodafone New Zealand to identify a large amount of youth customers within its base – a segment it says is now being successfully transitioned into the new youth proposition, Vodafone Mates.
“We’ve had a lot of success recently in a highly competitive market. We attribute this to knowing more about our customers and building market leading propositions that are attractive to them, without necessarily being just about the cheapest option available in market.
“Our competitors have been dropping prices but we are now able to target customers better and able to grow average revenue per user, while offering the right combination of package for them.”
"The youth market has been tremendous for us,” Bloch says.
“When we started to build our models within the Teradata ecosystem, we had a number of known youth within our base through contracts and previous surveys done. We had a rough idea of what the total market size was and we knew it was an opportunity area for us to grow against our competitors. We were able to build profiles of how a typical youth was using our network, and then across our anonymous prepay base, understand the likelihood of whether or not these unknown customers were youth.”
“To counteract it, we came up with our pre-paid plan Vodafone Mates and targeted 18- to 24-year-old youth customers.
“Through analytics built an understanding of what’s important to these guys and we knew, for instance, they were browsers in night and using more data, so that’s the way we developed our plan.
“We collected a bunch of behavioural data of how youth were using our network. We were able to model them as youth customers based on predictive analytics models.
“This was very valuable. It means we are able to get out the right message and the right topic with a plan built around their usage.
“We have been able to learn more about our customers through analytics and to 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 churn.
“Through a combined effort of our marketing and data science teams, we’ve used our analytical models to drive the way we target these customers and to ensure that the brand, messaging and propositions are all clear and engaging to these customers. We’ve been able to double the total number of youth using our pre-pay products and services within 12 months which we think is an outstanding result.”