A method of customer segmentation to improve marketing return quickly

Wang Ming Tam database marketing training Excerpt

Customer segmentation, English "segmentation" is the marketing staff has been doing the work, customer segmentation is the eternal theme of marketing. On the one hand, because of the limited marketing resources, we can not put the same marketing resources for all customers, on the other hand, today's consumer behavior more and more personalized, have different preferences, marketers must cater to the personalized needs of customers. Our question is: is not the finer the customer is better, is not 1 to 1 marketing is the pursuit of precision marketing the highest position?

In fact, 1 to 1 marketing is not a new concept of database marketing, the most typical 1 to 1 marketing is actually the traditional marketing training introduction of the major customer marketing: Every big customer has a dedicated Customer Manager to follow up. This is the most expensive marketing approach. This is impractical for companies with a large number of customers. The other extreme is mass marketing, which is the best way to sell popular products such as Cola.

But the vast majority of businesses are somewhere in between. For these enterprises, with the gradual breakdown of the customer base, marketing will gradually improve the return, but the excessive segmentation is bound to lead to a single customer's marketing costs quickly increase and reduce marketing returns. Therefore, in theory, for a certain market environment, specific business and products, there must be a most appropriate degree of customer segmentation, at this point, the marketing return is the highest. In China, the vast majority of enterprises are still in the stage of extensive marketing, scientific and reasonable customer segmentation, can continuously improve marketing returns. This course focuses on customer segmentation from this perspective.

But how to segment customers? How to develop more personalized marketing on the basis of customer segmentation is the most concerned practice of all marketers.

Traditional customer segmentation methods mainly from experience, and market research, customer segmentation of the way more sensitive and direct, the definition of customer base is more vague, such as the elderly, young people, fashion people, high-income groups. This kind of customer segmentation is suitable for the same reader or audience more obscure traditional media. The traditional strategy of subdivision hides the malady: for example, people who like to travel, and those who do not like to travel are the most different in their consumption behavior, and those who buy more in the past will buy more products in the future. But the reality may be that the difference in consumption behavior between marriage and singles may be the biggest, and those who buy the most often will be more likely to buy more products in the future.

Computer technology, database technology and network technology development, so that we can record the customer's information and consumer behavior, we can through electronic and network channels to interact with customers, which for us to carry out more accurate database marketing laid a technical foundation. At the same time, the database marketing also to customer segmentation methods and technologies put forward higher requirements, in turn, the database marketing technology for customer segmentation has given a new meaning, the pursuit of higher marketing returns possible.

The significance of customer segmentation based on database marketing

The customer segmentation technology of database marketing can greatly improve the feedback rate and sales conversion rate of email marketing, direct mail, telemarketing and network marketing, and integrate marketing, and also improve the traditional marketing customer segmentation method.

The more advanced application of the customer segmentation technology of database marketing lies in the possibility of matching customers, channels and products. The so-called customer, channel and product matching is different customer segments, providing different products, through different channels (such as telephone, e-mail, etc., business Hall) to carry out marketing and services.

Five major customer segmentation methods based on database marketing

1) segmentation based on population

2 segmentation based on consumption behavior (frm method)

3 segmentation based on customer value

4 segmentation based on data mining

1) segmentation based on population

This subdivision method is relatively easy to understand, in fact, is also the traditional subdivision methods. At present, the majority of enterprises to carry out database marketing still stay at this stage. This may be easier because the customer database for the enterprise, or the database that is being outsourced, is the only information available.

Many indicators can be selected based on population segmentation

For the business of the Business-to-consumer industry, the key metrics include:

? Demographic breakdown: Age, Sex, class, marriage, child data, etc.

? Socio-economic Subdivision: occupation, income, assets, etc.

? Geographical subdivision: Cities, streets, neighborhoods, etc.

? Consumer behavior Segmentation: consumption, purchase channels, brand loyalty, etc.

? Mental and personality breakdown: attitudes, interests, and perceptions

? Lifestyle segmentation: Fashion youth, workaholic, petty bourgeoisie, etc.

For business-to-business Industrial Industries, it may include a breakdown of the indicators:

? Industry

? Area

? Number of employees

? Turnover

? per cent agency

? Nature of business and country

? Contact Way

? Key Contacts (title, Department, contact)

With a population-based customer segmentation, personalized marketing can be carried out for different groups of people, such as feedback incentives for men using razors, and feedback from women to encourage the use of yoga cards. Promotion of high value room for high income people, low price room type for low-income people, different words for different customer groups.

Through testing and continuous improvement, feedback rate and sales conversion rate will be significantly more than mass.

2 segmentation based on consumption behavior (frm method)

When customers have generated consumer behavior in your company, and you have recorded their consumption behavior, such as time of purchase, product and amount, it can be imagined that this information must contain the consumer's future consumer behavior prediction information and the rules of feedback on marketing activities. Theoretically, we can get the rules through data mining, and predict the behavior of these customers, but data mining is the people, technology, tools, time required a high data processing process, the cost is very high. For many companies, it is not feasible and unnecessary.

Is there a simpler and more feasible way to help us get the main rules to improve the effectiveness of future marketing campaigns? The answer is yes. This method is RFM, Europe and the United States and other developed countries to use for many years, effective, investment returns very high.

Let us first use our experience to understand the basic ideas of the segmentation method based on consumer behavior. If your boss wants you to list the big accounts, those customers are most likely to buy the most products in your company in the next year, so we can focus our limited sales staff on these customers. One of the simplest (though probably inaccurate, but better than none) is to order the total purchases of all customers over the past year, taking out the top customers who have sold the total to 20% of the annual sales. These customers are your big customers. This is the simplest way to RFM customer segmentation: According to the sales of the subdivision (money), you can also be sold in accordance with the 10 equal, 3, etc., time span can be one year, a quarter, three years can be, sales can use other indicators, such as the site's posting volume, the number of points. Figure 1 is the process of grouping customers by sales.

Research has shown that the frequency of customer purchases (frequently) is also closely related to future consumer behavior, as well, the last purchase time (recently) and the current time interval are also closely related to future activity feedback. The way of data processing is the same, no longer unfolding here.

So that we can, according to the amount of consumption in the past period, the frequency and interval of the purchase are grouped with the customer, and the test will show that the group has the highest feedback rate or conversion rate for a direct marketing campaign (such as direct mail, telemarketing), and that the formal execution activity specifically picks out highly-feedback groups. It is noted that in most products or environments, the greater the sales, the highest purchase frequency, or the closer the purchase time, the higher the feedback, but most of the time is not the case.

Further, the three dimensions together, we give the customer divided into three-dimensional units (add all is 5 equal), each unit is numbered, such as 532, is the R 5th Group, F of Group 3rd, M of the 2nd group of 5x5x5=125.

Test all units before launching a marketing campaign, and calculate the return on marketing investment for a unit group, such as the profit and loss ROI index, the higher the index, the better, the negative is the loss of activity, shown in schematic 4. From the analysis diagram can be seen, we only target 555 units of the customer base activities, its marketing return must be very objective.

These methods each marketing staff only need simple training, can conduct RFM analysis and grouping, but bring the cost of marketing savings and increase in return on investment is amazing. If you often do the same analysis, you can design tools through professional programmers, if necessary, we can also help you design tools, so that each group and analysis can save a lot of time.

Above is only to the RfM method principle Analysis Method Introduction, in the actual work, may according to the enterprise, the product and the budget situation nimble play and the extension. Take a case to see how they rfm ways to improve sales:

The company is a small chemical raw materials trading enterprises, annual sales of more than 40 million yuan, the most core competitiveness of the products are food additives, such as citric acid, the top customer is a few international well-known large-scale beverage manufacturing enterprises in the local factory, by the senior management personnel to follow up, customer relationship maintenance is very good, Can steadily occupy a more ideal customer share. But the company also operates a number of other chemical raw material product lines, other non-top customers nearly 1000, from different industries, procurement scale and habits are not the same, to concentrate limited enterprise resources, maintain high value customer loyalty, to prevent the loss of high value customers is not an easy thing. The use of RFM analysis model can effectively improve the work in this area.

(1) Step one: Calculate the total purchase amount of the customer contribution in the past 12 months, and can calculate by formula:

mxf= TM (totle monetary Value, total purchase amount)

*m= average purchase amount in the past 12 months

*f= the number of purchases in the past 12 months

The total purchase amount can also be obtained by other statistical means (e.g. from invoicing software or sales automation SFA software). After you have calculated the total purchase amount for each customer, you can use Excel to sort the list of customers in descending order by total purchase amount, define the top 1% of the 1000 customers to be the highest customer, define the second 4% about 40 customers as high-end customers, and turn the 15% About 150 customers are defined as midrange customers, while the remaining 800 customers are classified as low-end customers.

The following table is the case of several customers from the 11th place in descending order, which is part of 40 high-end customers:

2 Step two, the focus of the customer relationship is first placed in the former high-end and midrange users there, because the top customer loyalty, high customer share, and then extra energy to the marginal benefit is not obvious; For about 190 customers at the high-end and midrange level, the average period of each purchase to the next purchase in the past 12 months is calculated separately, and the formula is:

365 Days &Pide; f= P

*f= the number of purchases in the past 12 months

*p=period Average purchase cycle

Calculate the date of the last purchase of these 190 customers from D to today's days:

Today–d= R

*d=date Recent Purchase Date

*r= Stop purchasing Days

The P (period average purchase cycle), D (date recently purchased), and R (stop purchase days) are filled in sequentially into the customer list made in step one, and the difference between the two is calculated using the Excel formula:

P-r=

*today Today =2004-11-27

*

Statistical cycle = Past 365 days

As you can see from Figure 4:

>0, the number of days to stop purchasing is less than the average procurement cycle of three companies;

=0, the number of days to stop purchasing is equal to the average procurement cycle of the Ding Company;

<0, the number of days to stop purchasing is greater than the average purchase cycle of three companies. b, C, three companies need immediate attention

。 For some days they have not continued to purchase according to their past behavior habits. Further observation, we found that B Company only more than its average procurement cycle of 4 days, relative to the 46-day average procurement cycle is still a normal range of fluctuations, do not have too much anxiety, the company can now be treated as the same as the ding, a little attention to its recent trends can be. and C, the two companies not only exceeded their average procurement cycle, and more than the number of days (26 days and 22 days) than the average procurement cycle (17 days) is more than one times. This shows that these two customers the risk of loss is relatively large, need to focus attention. When the census officer discovers this kind of situation, should assign the work task immediately to maintain the sales representative who is responsible for the maintenance of C, own company, let the sales representative take the necessary way to understand the reason that the customer suspends buying.

Customer suspension of purchase may be due to the customer's own difficult to purchase, it may be the customer to adjust the direction of the industry, but also may be due to some incidents caused by the decline in satisfaction of competitors to take advantage of, it may be the normal light season fluctuations. After investigation and follow-up, the sales representative will judge the reason and put forward reasonable suggestions to the company to avoid the financial risk and maximize the customer life cycle value. If the reason is that the customer suffered a temporary difficulty, you can control the risk of receivables on the basis of appropriate adjustment strategies to help customers tide over the difficulties, when the customer business back on the right track, the inevitable reciprocate, more loyal; if the reason is that the customer adjusts the business direction, to a certain kind of raw material purchase decline, we should study the company's product line , what is applicable to the customer's new development direction, targeted to recommend marketable products, prolong the customer's life cycle; if it is due to the improper handling of certain incidents resulting in a decline in customer satisfaction, we should try to explain to customers, make up, and take effective measures to avoid recidivism, as far as possible to recover the impact; Be aware of the customer's next purchase time, and make a sales plan in advance to remind the sales rep to follow up at the appropriate time in the future.

Summary of RFM analysis model

RFM analysis model is not omnipotent, it also has its drawbacks. For example, for M average purchase amount, and the total amount of TM purchases based on FXM is not minus the cost of the product, one-to-one sales, service and marketing costs, and certain administrative costs that should be apportioned, so the total TM purchase amount is not a more accurate customer-current net value (NPV, Net a Value). If the customer lifecycle value prediction can be implemented in an operational manner, the current ranking of customers based on M will be more scientific, thus minimizing the risk of missing key customers.

Easy to get the customer's higher value, the loss of a large risk of the customer record list, and for the list of customer records directly set up and assign follow-up tasks, the loss of valuable customers to eliminate in the bud, and even to prevent the situation.

The above case analysis, in fact, has put Frm method creatively applied to the customer early warning and customer retention.

If we think that frm is not enough, it is necessary to introduce data mining tools, data mining can find RFM unable to obtain a special customer base, these customer groups have special consumer behavior. And to our original customer segmentation methods to improve. Due to the length of the relationship, we'll talk about it next.

Author Introduction:

Wang Ming Tam

China Interactive and direct marketing consulting and training first person, the new generation of China with international vision and local practice of the design of novel marketing model optimization master. Wang Ming Tam Data marketing information, Team Chief advisor, Ceibs MBA.

Wang Mingyu Summary of direct marketing techniques and methods are extracted from the consulting project and research results, pay attention to the actual combat, has a strong operational and targeted. Wang Mingyu is currently a number of consulting consultants, to help enterprises from the traditional extensive marketing to accurate, quantitative, multi Media marketing transformation.