Apr
29
2014

Growth through intelligent marketing

Today’s consumers expect a tailored customer experience which complements and supports their needs. And why wouldn’t they? When you go on Facebook the adverts are tailored to your interests and cookie tracking online means you are constantly presented with ‘complementary’ offers and relevant information. It is part of the new age of personalisation.

Personalised and tailored communication is a critical component of any good, modern day, customer service.  Effectively managing this service is of huge benefit to businesses and customers alike with customers more likely to be loyal, have lower management costs and higher retention rates.  It’s a no-brainer.

The huge advances in technology which evolve daily, (including smart phones and social media), provide many powerful ways in which to communicate with customers easily.  This kind of marketing can be done alongside traditional methods such as email. 

Clearly though you can over communicate so that people dismiss material too easily, or a lender could pick the wrong time to send an offer to a potential customer who would otherwise respond positively.  These are symptoms resulting from a poor understanding of customer needs.

Historically lenders have used analytical techniques to identify which people are both eligible and likely, to respond to an offer for credit.  The first challenge (checking eligibility), is a vital component to a responsible and successful marketing campaign.  It’s important to avoid inviting people to apply for credit they cannot afford (and would therefore probably fail the application process).  These kinds of scenarios are extremely unhelpful and potentially very harmful to lender / customer relations.

Once a customer has been screened for eligibility, the next step is to focus contacting only those people likely to respond to a given marketing campaign.  This helps to reduce costs and avoid needlessly bombarding people with unwanted offers on a regular basis.

Although this approach will predict response we do not however truly understand customer propensity / needs.  We don’t for example know if they would take up the product had the price / offer been slightly different or if the campaign were a few months later.

The approach therefore is no longer sufficient in today’s world where people focus on a mixture of price, customer experience and personal circumstance.  Personal circumstances can change quickly and with easy access to credit the best targeting solutions are likely to be those applied regularly, triggered by key events.  Scoring is still incredibly powerful but models should be developed in a manner which removes the issue of price or the nature of the offer from consideration and focuses on customer appetite. 

To better understand customer appetite, an alternative approach is to build a solution which identifies take up across any lender.  We would need to analyse customer behaviour over a longer time period, say 6 months for example, and use external credit bureau data to derive the modelling objective.  By analysing behaviour over time we remove the issue of response to one specific campaign thereby generating a strategy that caters for evolving consumer credit profiles.  We also capture customer requests for credit in addition to lender driven campaigns.  Such an approach will provide a more holistic understanding of customer needs allowing you to tailor offers at the right time.

However, this isn’t something which will be effective if done once. It requires an on-going strategy to manage the customer relationship effectively. What’s more, it needs to be a combination of credit bureau and external customer data to give the intricate detail and individual understanding you need.

The power of such solutions depends heavily on the level of data that is available. Plus, the use of external credit bureau data comes with its own legal & compliance restrictions and some information isn’t available to use during outbound marketing activity. Although when the usable data is aggregated into a ‘score’ there is more flexibility for use across marketing to existing customers.

Ultimately, when using analytics for marketing you will naturally begin to optimise your conversion rates.  How these models are developed however can make a significant difference to the potential value you generate. Deploying such tools alongside an existing risk strategy, enables lenders to design individually tailored and pre-approved offers in a responsible, yet competitive, manner for a much smarter customer experience.

The question to ask yourself is; how are you managing your customers? Optimally or not?


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