It is tempting to stay on familiar ground, especially when times are tough. But it is at times such as these that the unknown should be explored. If it isn’t, golden opportunities could be missed.
It is amazing how many businesses still use a blanket approach to marketing, in the hope that their campaign might hit the right businesses at the right time. But how much of the marketing budget is being wasted by sticking to a method that has proven only ‘somewhat’ successful in the past?
Exploring the unknown
Sometimes the great unknown has to be explored in order to enjoy a more profitable result. What are the things about your customer that you don’t know? By looking into these factors and reviewing them against what you do know, a clearer picture of your current and potential customers can be achieved. This will help you understand when and why a customer actually buys; how much they spend; and how to find similar prospects to target. It will also enable you to refine your understanding of existing customers’ needs and wants. It’s called customised propensity modelling.
By identifying characteristics within customers or prospects that will help predict high take-up rates, low service costs, high rates of customer retention and low levels of debt, marketers can target resources more effectively. If you could identify the high profitability customers and prospects, would you still continue to focus your commercial resources on all your customers and prospects regardless?
Customised propensity modelling allows marketing and sales to pinpoint where their commercial resources should be focussed, also giving direction on the channels to use. It can help identify which prospects are worth a high value consultant-led approach; which ones may be a telesales prospect; and which would be best served by an email marketing campaign.
Identifying high value customers
So how are high value customers and prospects identified? The first step is to take a customer base that is known to use or have a need for the product or service being promoted. Compare this customer population against a universe of businesses that do not use or have a need for the specific product or service, identifying as many pertinent predictors as there are available, to give a score on the need a business with certain characteristics has, for that product.
For example, a company car fleet provider would analyse businesses that are heavy users of company cars against those which do not have as great a need for company cars. The basic predictors here would be industry sector and size of business. Employees within a small clothing retail business, for example, tend to stay within the vicinity and so have lesser need for a company car. There may be a greater need within a much larger clothing retail company, but the need would still be smaller than say, for a building and construction company with a direct sales team. Furthermore, a building and construction company with 50 employees will have a greater need for company cars than a small building business of only five people.
The next step would be to investigate the value of the spend amongst these building and construction companies. For example, which company would be most likely to select a Mercedes or BMW as opposed to a mass market brand? By looking at the financial variables and measures of performance, including current profitability, financial strength, and productivity relative to competitors, marketers will be able to distinguish a company that can afford new cars from one that can’t, and even the type of car it might opt for.
Other factors to consider
Furthermore, dynamic measures such as growth are strong predictors of immediate appetite and would be taken into account. In addition, behavioural variables come into play. The age of the business, international activity and group structure can all be predictive of product appetite and purchasing patterns.
Customised propensity modelling is about exploiting the unknown to help it find profitable prospects and target existing customers more effectively. It’s about using the budget you have more effectively and for this, you have to go into the unknown to know more. The cost involved in generating a customised model is far outweighed by the cost of deploying the sales and marketing assets of the business in a less efficient and sub-optimum way. In the current economic environment, taking this kind of scientific approach to marketing has never been more relevant.
