Customer relationship management (CRM for short) is a marketing strategy whereby firms concentrate on existing customers. Customer relationship management gained prominence in developing marketing strategies, in part, because existing customers are 5 times more valuable to a business than new customers, according to market research. They are also more likely to be loyal and to spread positive information about your firm to their social network.
There are two marketing strategy approaches to CRM — data mining and relationship building.
Data mining involves gathering massive amounts of data about your customers, especially their buying behavior and demographics. Commonly, this information comes from your own sales information, such as scanner data or credit card purchases. Internal data might be supplemented by data purchased from other sources. For instance, credit card companies commonly sell purchase data about cardholders. Even the government sells data such as DMV records of car ownership. It’s important to note that this data is NOT market research about customers, but secondary data originally collected for some other purpose.
Data are aggregated, then statistical analysis gleans insights into behavioral linkages. A number of companies sell enterprise software to analyze data or software to help build these massive databases, such as SAP and SAS.
Data mining is fashionable, made more so by software and skills to analyze massive amounts of data.
Benefits of data mining in your marketing strategy
The more you know about customers, the better you are at building marketing strategies to reach them. For instance, if your scanner data shows your customers prefer certain brands of orange juice, you might benefit by stocking additional varieties of that brand or you might use a sale on this brand as a loss leader to get customers into your store for their weekly grocery trip.
You can also use individual-level data mining. For instance, you might have a baby week sale, where you offer discounts for baby-related items If you identify which customers have babies based on prior purchases, you can reduce your costs dramatically by only sending flyers to those customers.
More sophisticated data analysis builds forecasting models that predict the probabilities of behavior among your customers. Such forecasts form the basis for budgeting. You can then build marketing strategies to maximize success based on these models.
For instance, noting that advertising in a particular media two days before an event yields maximum sales is useful for planning future media buys. Knowing that sales of a brand are higher among young males allows you to better target them in developing advertising strategies. A good example is a domestic beer where a dominant buyer is a young man working a blue-collar job. Using messages appealing to this target market drive increased sales, even if the message doesn’t resonate with other types of buyers.
Data is also the starting point for developing marketing strategies based on customer lifetime value (CLV). The graphic below shows how to implement customer lifetime value in decision-making. You can use this data to segment buyers allowing you to focus on those who represent the most value to the organization or to highlight opportunities to increase revenue by increasing things like customer retention.
An alternative to data mining is building a strong relationship with customers. Some strategies for accomplishing this are:
- creating customer loyalty programs, such as frequent flyers
- marketing to customers, such as newsletters and coupons
- e-mail marketing
Probably the most successful relationship-building strategies are those designed to develop interpersonal relationships — being friendly, using customer’s names, exchanging non-commercial conversation, being empathetic, handling customer complaints effectively, listening to customers, building trust, etc.
Benefits of Relationship Building
Customers who believe they have strong interpersonal relationships with commercial enterprises give the enterprise more of their business, they complain less, they recommend the business, they are loyal, and they are willing to help the business by defending them against critics, pointing out situations where the firm might lose money, suggest improvements, etc.
Dangers of Customer Relationship Management
While there are enormous benefits to CRM, some issues need attention.
First, data mining dangers:
- provides only historic behaviors which are imperfect predictors of future behaviors
- is more appropriate for predicting how a group might respond, not a single individual or smaller group
- behavior is only partially indicative of attitudes
- consumers purchase behavior might not be indicative of their own needs, but for gifts, etc. which complicates projections
- data may be more costly to obtain than the value-added
- customers may be more loyal to the provider than the company, so when the provider leaves, they take the customers
- it’s time-consuming
- it might create expectations that the firm can not deliver on
- some consumers don’t want a relationship with commercial enterprises (this has important implications for those employing social network marketing strategies)
- relationship-building efforts often come across as insincere, which is worse than not doing relationship-building at all.
As you can see, both marketing strategies have enormous benefits for your organization, although they represent some dangers. Unfortunately, firms rarely implement both data mining and relationship building, which is unfortunate. It seems to be firms buy into either one paradigm or the other. The best alternative is to employ BOTH data mining and relationship building, as the two strategies are very complementary and eliminate many dangers inherent in the use of either one separately.
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