Bankcard revolutionised the way Australians paid for goods and services from its introduction in 1974 until its withdrawal in 2006. Within 18 months of its issue approximately 6% (one million) of the Australian population held a bank card (one million). By 1986 there were more than 5 million card holders. Bankcard was not accepted outside of Australia which was a major barrier to its further growth prospect. Today there are approximately 50 million credit/debit cards issued that account for an average of 17 million transactions in Australia every day.

Today there are many hundreds of cards available with NAB offering 8 types, CBA 9 types of cards, each with different fees and charges, interest rates, many different features, rewards points, fees and rates, credit limits, etc.

As a consumer it is no longer a case of having just a bankcard not since there are basically 1,000’s of different credit/debit card types/options available. With so many choices available and varying supply costs it is vital for banks to understand what drives demand and card choice and to establish if there is room in a crowded market place for yet another card offering.

The strategy team at a leading bank had identified some gaps in their current offering and wanted to know how to optimise to maximise acquisition demand without cannibalising the existing card offerings. The costs of failure are large but so too is ‘success’ if the banks customers swap over to a ‘better’ card

arnold&bolingbroke were commissioned to construct a behavioural market model that allowed the bank to build a simulated market model in order to test all possible outcomes in the market place, measure possible demand and fully optimise an offer.

The size of the problem

The study used a relatively large sample size in order to cover the range of possible card offerings and to produce enough response data for modelling. In total over 80 factors were tested in parallel which amounted to 1018 unique combinations of possible credit cards on offer.

We were required to set up a unique experimental design that allowed for an efficient testing to occur that didn’t exhaust respondents and render the experiment redundant.

A range of card types were tested that included multiple levels of card providers, type of card, different annual fees and interest rates, incentives, bonuses, photo on card.

The point of the study was to model the market places so that the optimal product bundle could be determined that would maximise consumer interest and needs yet not cannibalise the existing offer. By doing this of course we would need to unravel the drivers of card choice to understand which features and benefits individually and combined were most valued by consumers.

The experimental design was determined and the card options were shown as completed visual representations of the card on offer and compared with 3 other card options – a bit like a comparator site for credit cards



It was clear that consumers were partially confused by the complexity of cards available and that they could not easily disaggregate the cost-benefit significance of the individual card features. This produced some interesting business opportunities that the bank was able to exploit after receiving the study results.

(1) Interest Rate and Interest-Free Days

  • By undertaking some modelling analysis we were able to determine the relationship between interest rates and the annual fee and the amount that consumers were prepared to trade-off between the two. We were able to identify a core segment that could withstand an increase in the annual fee that had limited effect on market share or demand for the new card.
  • The interest-free period feature of the card had a low level of price sensitivity as most cardholders could not work out its true cost and therefore were miscalculating the real value to themselves.
  • Hence the bank was able to reduce the number of interest free days while still maintaining a high level of market share due to the inclusion of features and benefits in the new card that were more highly valued.
  • The bank was able to identify the elements of the card’s offer that were most valued by the consumer. Profitability was optimised by reducing the (costly) interest free days yet maintaining the overall attractiveness of the bundled offer that the card was perceived to offer.

(2) New Cardholder Segments

  • arnold&bolingbroke was able run the study across 8 different segments and card types making it quite a large set of data. We were also able to identify one higher-income segment of particular interest that warranted designing a specific tailor-made card.
    We were able to clearly measure and calibrate the optimum combination of features that achieved maximum uptake.
  • Understanding the trade-off interactions that cardholders make allowed our client to structure an offer to particular segments that were most attractive yet still maximised the ROI of the product.


Detailed Strategic Implications

The net result was that the bank implemented the key findings of the study and released a new card that was very similar to the optimum card produced by the model.

It gave the bank the confidence to identify and abandon those benefits that lacked adequate consumer appeal by identifying the direct value in dollar terms what each benefit was worth to the customer.

The model was able to validate the viability of the new cards. A number of new card components were included in the model and we were able to show which cards containing new elements were both viable and would take share from competitors, while not cannibalizing the banks existing offer.

The study was commended for its ability to accurately test the complex interactions of new product features. We were able to disaggregate the value of the product into component parts that could then be more clearly costed and included in the product design.

The model showed that a specific set of changes to the interest rate, annual fee, interest free days, etc would improve profit without reducing market share or overly cannibalizing their current card. The changes were implemented and independently estimated to increase profits by approximately $8m per new card.

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