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How your marketing can benefit from a calculated risk

How your marketing can benefit from a calculated risk

By Steve Denby, CEO, JaywingDMG

In the Christmas clamour to sign-up customers to mobile broadband contracts, many brands offered high-value freebies such as laptops as incentives.

Following high-profile advertising campaigns and promotions on-line and in-store, consumers rushed to take them up on the offers, only to find that many were turned away, disappointed.

So what went wrong, and how can telcos use customer data to inform future campaigns, increase acceptance rates, and minimise the long-term impact of these too-good-to­-be-true-offers?

Against the backdrop of financial crisis and recession, the promise of a free laptop with every new mobile broadband contract was too good to refuse, and consumers applied in their thousands in the pre-Christmas period.

But, many applications were unceremoniously turned down following credit checks – as many as three in four according to Broadband Expert.

Clearly these brands are right to be cautious in these difficult times, and need to ensure customers can afford to pay up for the lifetime of the contract.  But, are they doing enough to ensure the accuracy of their accept/reject decisions?

A combination of the right data and decision rules could increase acceptance rates without taking on additional risk – maximising return on investment and ensuring a better consumer experience into the bargain.

Customers are often rejected, quite rightly, if a poor credit history is revealed during the application stage. However, a lack of available information is also a common reason for rejection, meaning there could be many profitable consumers being turned away.

Not only is there an initial loss to the business by rejecting so many applicants, but refused applicants will have a negative impression of the retailers, and the mobile brands they represent; a negative association that is likely to remain for many years to come.

Such a desirable offer is tailor-made to appeal to people who couldn’t normally afford to buy a laptop, so there will be a natural tendency to receive a large proportion of applications from less ‘desirable’ consumers.

For instance, students are likely to be attracted by such a deal, but are most likely too young to have any history on file with the credit reference agencies.  Some customer rejections are therefore inevitable, but with more data at their fingertips, mobile brands can make more robust decisions about whether to accept, and how to handle those they reject.

By changing their approach to risk decisioning, mobile brands can significantly improve their customer acceptance rates.  In addition to information provided on the application form, data from one of the three UK credit bureaux (Experian, Equifax and Callcredit) are usually solicited, to check the applicant’s credit-worthiness.

However, there are some significant differences between these agencies, both in the way name and address data are cleaned and matched, and in the completeness of each individual’s record. If a record can’t be found (a non-confirmation) the application is usually rejected, and similarly if there isn’t enough information on that person to make an accurate credit decision (they have what’s known as a ‘thin file’).

But by drawing data from all three credit reference agencies, it is possible to build up a more complete picture of each customer. So, where one agency alone may not find their record and another may only provide a thin file, by combining the data from all three you’ll have all available data at your disposal.

Taking this multi-bureaux approach can reduce the number of thin-file rejects by 70%.  Furthermore, compared with using a single bureau, accessing all three can reveal around 40% more County Court Judgments (CCJs), and reduce the number of non-confirmations by around 15%.

So, by reducing the number of good customers you reject and rejecting more high-risk customers, you’ll get a much bigger return on investment from your marketing budget.

Not only that, but this extra level of detail can be used to decide what to do once an applicant has been rejected for the initial offer. Maybe they are too risky to give them a free laptop, but that doesn’t necessarily mean you don’t want any of their business.  Detailed bureau data can be used to differentiate between applicants, so you can pick the ones you’d still like to have as a customer.

They are in your store or talking to your call centre, they’re ready and waiting to buy your product – are you going to let them walk away, muttering about never doing business with you again?  Or, do you apologise, but offer an alternative deal instead?

The opportunity for mobile brands to market ‘free gift’ offers well is in taking a more dedicated approach to risk decisioning. In doing so, brand and consumer alike can experience a positive interaction by entering into a contract based on a real understanding of risk and the customer’s suitability.

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