November 30, 2015
|By Consulting Group|
Whether we are talking to large corporates or fintech start-ups we get asked the same question in many different ways – who are our future clients?
A product should always be designed to be engaging and inspire loyalty, but market penetration, rather than loyalty, will always be the biggest driver of growth. The average number of products held by Australian personal banking customers is 2.0.1Think about it from your own experience and this probably rings true. I personally only hold a current account and a credit card with ‘my’ bank. The home insurance, mortgage and FX services went to other brands a long time ago, and while I generally try to follow a ‘Black Swan’ approach, in this respect I suspect I’m fairly typical.
Big multinational consumer brands invest in extensive (or expensive) external qualitative and quantitative research to help them understand the future, but smaller brands, faced with similar penetration and frequency of buying challenges need to generate their own insights.
So given a highly competitive marketplace, the need for increased penetration to grow a brand and limited cross selling opportunities, how can we analyse existing customer data so we can tell a start-up CEO or a corporate divisional director with any level of confidence who their new clients will be over the next 3, 12 or 48 months?
Early stage start-ups don’t have existing customer data and therefore we help them to analyse publicly available data, insight from research reports and initial data they have gathered to build out a model of their most probable client demographic. With this information to hand, we recommend a process of rapid prototyping. This means the concept can be tested with a relevant group of potential users and then (with appropriate regulatory approval) the reach can be extended to newly identified prospects and adapted according to their feedback. After further iterations, timely use of services such as ‘google surveys’ and solid WOM marketing techniques the product can begin to build its customer base by focusing on units of 10, then 100, then 1000.
For established businesses the data analysis opportunities are more significant, especially if the company can combine data from the often present legacy silos. If both customer details and product usage data is available, then it is possible to combine the two elements to get to an answer.
Part 1: Customer Segments
Retail finance companies have an advantage because of the amount of customer data they are required to capture. Services such as Experian’s FSS or CICI’s Acorn customer classification tool makes unlocking this data and mapping it against the third party classifications a relatively simple process and enables the business to confidently identify existing customer segments.
Part II: Product Adoption
Remember we are trying to predict who the future clients will be, not who they already are and this is when product usage and customer behaviour analysis comes in. In retail finance customer purchase frequency is an issue, whether trading equities, investing in an ISA, setting up a SIPP, getting a loan or exchanging currencies frequency varies a lot. Analysing how customers use both established and new product to interact however frequently and their behaviour can give significant insight to a business.
We still think one of the best ways to illustrate this is by looking back at the adoption of, first the internet, and then mobile by retail finance customers. Remember when customers using the internet were in a minority? Understanding that group of customers and their behaviour early enabled a brand such as Hargreaves Lansdown to secure a competitive advantage and grow their market penetration. They now account for close to 35% of the DIY investing market. This opportunity for new providers to penetrate the market has now arisen again through mobile. Just look at the speed of growth of an app such as Revolut in the mobile payments / FX space compared to other finance brands.
Confident Strategic Decisions
The result of this analysis is clearly defined customer demographic segments, with subsets relating to product adoption and behaviour, for example early adopters of a new mobile service or product functions such as ‘social sign on’. These newly defined behavioural clusters can be used to enable a business to target clearly defined audiences with new product developments.
The point is that excessive and expensive analysis of customers from a loyalty and cross selling perspective is not the holy grail, but knowing who your future customers are likely to be based on existing customer subsets behaviour, enables company executives to make more confident strategic decisions and ultimately increase market penetration.
1Mundt, Dawes & Sharp 2006