There are a number of analytical techniques that you can use to analyse your data in order to improve your customer relationships. Some are relatively simple to implement, while others require:
- expensive software
- large, relatively ‘clean’ datasets, as well as
- skilled resources to be able to implement them.
We recommend at least gaining an understanding of how each technique contributes to transforming raw data into valuable information, and to implement those that are within the capacity of your business to set up as ongoing processes.
This category has articles in the following sub categories
- Activity Based Costing (4)
- Customer Profiling (5)
- Data Mining (3)
Some of the main analytical processes and methods used to calculate and display customer profitability and value are described below:
Activity Based Costing
Activity Based Costing, or ABC, is a costing technique that emerged in the early 90s – to provide a costs approach more appropriate to modern business. It helps identify the costs incurred by carrying out the various activities in the business, often as a consequence of customer requests or behaviour – which you then apply to determine product costs.
If you apply the various product and activity fees and costs to customer behaviour – in terms of enquiries, sales, and service requests - you get customer profitability, and more importantly, on understanding of what is causing the current value.
Balanced Scorecard
While some may consider the Balanced Scorecard more a way of reporting performance rather than as a tool for analysing it, the Balanced Scorecard framework places Customer Metrics, and internal resouce capabilities (=Staff) at as prominent a level as financial reporting.
The three reporting areas are interlinked:
- Well trained and motivated staff deliver good performance.
- Good performance delivered in a courteous and efficient way makes for happy customers.
- Satisfied customers promotes loyalty and repeat purchases, and lowers the request for refunds - which improves the financial performance of the business.
Keeping an holistic eye on the business measures become a habit to ensure improved customer relationships.
Data mining
Sophisticated analytical routines are used to process relatively clean large datasets to discover potentially interesting patterns in customer data. Potentially interesting in that the identified pattern needs to be investigated to see if it indicates an undiscovered opportunity – to increase sales, or an unknown risk – possibly fraudulent behaviour.
This is not for the faint hearted – a lot of effort is required to clean / prepare the data so that the sophisticated techniques have a chance of returning a meaningful result. Skilled staff, large datasets, and expensive software are required to make this work.
Behaviour Profiling
This is the discipline of applying ABC-derived, or estimated margins per transaction to each and every interaction and sales transaction, and trending these calculated values over many months – 25 months is a useful period – as it provides some understanding of the seasonality of customer purchases.
It is possible to track both financial and non-financial data in this way:
- the number of enquiries
- the sales per month
- complaints
- rejected payments
By setting up some business rules (created by thinking through your business, or even from data mining) to automatically monitor these business measures, you can keep a close watch on each customer, and trigger off a report or email if an appropriate response by the business is required.
Pareto Analysis
Simplistically named the 80/20 rule, this is one of the simpler techniques to apply – but extremely powerful. In almost all businesses, a relatively small group of customers contribute a disproportionately large amount of profits, and another small group submit most of the complaints.
The technique can help focus the attention of your staff on your most important customers – who are often neglected due to the more vocal, less profitable complainers.
RFM – Recency, Frequency, Monetary Value
RFM analysis has been used by direct marketers for years. In some respects it appears counter-intuitive – a person that has just bought from you is likely to buy again – but understandable if you have more than one product. If you have just sold something to a customer, they are indicated that they trust you and your products – so while that feeling is still strong, it is the ideal time to try to sell them more, and further entrench the relationship.
A useful technique, and pretty simple to implement.
Segmentation
Market segmentation is all about grouping similar customers into groups so they can be approached with a similar message – i.e. ‘For entrepreneurs …’, or ‘For Professionals …’.
Various ways of grouping prospects and customers can be applied:
- Demographics – age, gender, etc
- Psychographics – lifestyle indicators – goes to gym, vegetarian, etc.
- Geographic – physical location
- Technographic – technophile / technophobe
- LSM – Lifestyle Measures
There is no fixed set of measures that need to applied to each business or analysis – merely what is appropriate for that particular business.
Used in combination, the above techniques will reveal important insights into your customer base.
