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2. Moral (i.e. unfraudulent). This means that they stay on the right side of the law in all their dealings with the company. Note that some companies do quite well out of meeting the needs of customers who in other respects are immoral, but stick to the law while interacting with the company e.g. casinos or betting shops used for money laundering.
3. Prudent. This means that they live their lives within the resources available to them. Note that some companies make a very good living out of the imprudent, even if it does necessitate charging usurious interest rates!
4. Punctual. This includes, for example, paying bills on time and arriving at transport locations on time.
5. Responsive in a relevant way to communication. They respond to marketing communications that are relevant to them (in the sense that they are likely to lead these customers to evaluate seriously the possibility of buying the product or service). However, they do not respond to ones that are not (the latter is known as the "schoolboy brochure" phenomenon at the Motor Show, but is experienced by many companies with strong brands – interestingly the World-wide Web has proved to be a very cost-effective way of handling these). One of the major problems faced by companies is to manage communications cost-effectively (whether or not they are solicited in the first place) when they have a low probability of leading to retention or development of revenue. Of course, if the communication is solicited, this does not necessarily imply poor targeting, as the customer may respond in other ways than anticipated by the company.
6. Responsive to other initiatives e.g. willing to try new products
7. Happy to give relevant and truthful information to the organisation and to update information previously given. This allows the organisation to determine the appropriate "treatment" for the individual, but also to save resources by not offering inappropriate treatment. This also applies to complaints (see below).
8. Healthy in habits (e.g. moderate drinking, not smoking) and perhaps even in genes
9. Safe e.g. in driving and as a pedestrian, and perhaps in sporting habits
10. Observing rights and responsibilities (e.g. prepared to learn how to work with the organisation to achieve mutual benefit, such as installing security devices, looking after credit cards, following a healthy life style)
11. Complaining only when "justified", in which case the organisation can improve its service and reduce later complaints
12. Prepared to recommend to other individuals if service/product is good. etc.
13. Persistent – i.e. unlikely to switch – though this of course depends on the product. Persistent customers for undertakers are rare (though if the family is the decision-making unit, persistence can exist). However, there are strange bedfellows, so to speak. Persistent customers for wedding wear might also be persistent customers for lawyers, because they can afford to be , and hence possibly good customers for financial services advisors.
14. Stable or predictable
Bad customers have characteristics largely the opposite of those listed above. For most organisations, the key bad characteristic is current and/or likely future unprofitability, though there are circumstances in which unprofitable customers are highly valued e.g. as recommenders. Companies may include in their definition of bad companies debtors, switchers, liars, or those with court judgements against them. Charities, public sector bodies or private sector bodies acting on behalf of these organisations often focus on such customers.
As companies seek to manage customers more as individuals – but remotely, so some bad customers learn to exploit this tendency. This is most visible in the areas of credit and debit cards and the Internet, but also in insurance and banking. Bad customers learn very quickly because the incentive is often very large (the potential gain) and in some cases transfer their learning very quickly (often to other individuals within an organised criminal network, but sometimes also within their ethnic or professional group or geographical locality). For example, government benefit frauds are often organised within ethnic groups or families. Tampering with utility meters often spreads geographically.
Certain industries always have a relatively high risk/value ratios at the level of the individual customer. These are typically industries where there is a large insurance, credit or consequential liability element involved. They include:
• Insurance itself
• Any industry where maintenance contracts are sold
• All pure credit industries e.g. bank loans, credit cards
• Any continuous supply which takes place under credit terms e.g. utilities, industrial supplies
• Any situation in which claims are hard to validate
• Products where failure or misuse can cause significant damage to the customers, leading to high incidence of product liability claims
However, other areas of high risk include:
• Service industries where complainers – having taken the service – typically ask for reimbursement of the full value
• Products or services where the costs the organisation has to incur to serve the customer after the initial purchase may greatly outweigh the revenues if the customer behaves in a certain way e.g. cherry picks all the high cost, low or zero revenue parts of the service
• Situations where entitlement documentation can be forged
To give the reader an idea of the extent and variety of "bad customer" situations, and their typical correlates, here are a few examples which differ from the usual example.
• Certain apparel shoppers (mostly women – because they are the majority of apparel shoppers) buy merchandise knowing that they are going to wear it once, before taking it back to the shop to exploit the shops liberal returns policy. Shops develop strategies to deal with this, typically taking the merchandise to the back office and smelling it for signs of body odour, deodorant, perfume etc – sure indicators of the garment having been worn for an extended period. If this is suspected, management is called in and the customer is challenged
• A very high percentage of claims on holiday insurance policies are fraudulent, particularly those involving claims of lost cash. Insurers are dealing with this by developing databases of frequent claimers
• Certain flyers forge airline boarding passes to obtain frequent flyer points. One airline found that there was a correlation between forging and choosing ethnic meals
• Certain utility users always pay at the very last minute, when they are about to be disconnected or have a prepayment meter fitted. One utility found this to be correlated with ethnic group
• Certain customers make a habit of claiming that different types of cleaning and washing fluids damage the item being cleaned – or of course their skin! They are traced by keeping properly computerised records of complainers' identities.
• Certain small businesses persistently claim that their suppliers are short-delivering them – maintaining that they were too busy to check the delivery in the presence of the deliverer. Companies deal with this by sending double-checked trial deliveries.
Bad customers are not to be avoided at all costs. Some organisations have as one of their objectives to serve customers defined as bad (by these organisations and/or by other organisations). Examples include police forces, social welfare agencies and charities. As we have mentioned, private sector organisations can design products for bad customers (tamper-proof electricity meters and phone booths, insurance products with specific risks excluded, service products with prepayment tariffs). Similarly, slack product or service design can turn apparently good customers into bad customers, and indeed whole markets from basically good to quite bad. For example, recruitment of customers for cable telephony or motor insurance irrespective respectively of their propensity to pay their bills or to switch on price led to many more customers turning bad.
Here are some simple rules to help you.
1. Define good and bad customer, recognising that most customers are a mix of good and bad attributes
2. Remember that bad customers often occur in groups, may work together, may collude with your staff, and get better and better at being bad if you let them i.e. they learn. So your picture of "badness" should not assume that the situation is static
3. Don't be afraid to "think the unthinkable", in terms of how "badness" may be distributed, but make sure that you base your analyses on hard evidence, not prejudice, and that you stay clearly within the terms of the various laws that determine what you can do. These include laws covering data protection, racial and other types of discrimination, employment, and specific industry regulations
4. Ensure that your databases and data sources allow you to identify good and bad customers
5. If you can, measure the performance of your business in terms of the net value you obtain from each customer – including all exposures and not just routine costs. Where you do not have individual customer revenues and costs, use research-based estimates.
6. Estimate your net exposure to bad customers, and calculate whether it is worth while investing in reducing exposure
7. If it is worthwhile, make sure that your systems at the point of contact with customers allow you to identify bad customers, and also predict whether a new customer will be good or bad
8. Where the data you need to do this is somewhere else, obtain it, and if possible, develop relationships with your competitors which allow you to identify bad customers and warn each other about them – subject to the provisions of the Data Protection Act and any special industry regulation
9. Develop, test and refine alternative strategies for dealing with bad customers, combining limitations on dealing (and in extreme cases refusal to deal) with techniques to reduce exposure to bad customers who have "got through"