Another area where innovative firms are refining their segmentation is through targeting by time. Time-based approaches are currently rare but they are becoming increasingly important. This is clear and obvious in markets such as car insurance whose customers follow steady annual patterns of renewal. However, for many other firms time-related variables are critical because they highlight how customers and their buying phases might change. They allow a company to address the question as to when customers should be targeted, and refine the segmentation with this additional dimension.
Thus the parameters of the offer include an additional element such as:
Table 5.3 Benefits of multichannel segmentation management
Customer care representative Nextel (%) Industry
average (%)
Has correct information 91 93
Can clearly hear and understand the customer 95 94
Representative understands customer needs 89 89
Representative gives their name 96 94
Asks if they can assist with other matters 96 92
Makes follow-up call 20 12
• Which characteristics will create a distinctive segment at a given point in time?
• How often will a customer buy?
• For how long will a customer buy?
• What point has the customer reached in their repeat buying cycle?
• What is the customer’s propensity to buy at this moment?
• What emerging needs will the customer have over some future period such as a week, a month or a year?
• How will the potential profit from customers change over time?
An unprofitable customer at the beginning of an executive career may be someone of high value in five years’ time.
Figure 5.4 illustrates how buying patterns for three example customers might vary, and the sort of questions the company will need to answer in responding to differences in buying pattern.
There are real opportunities to be a first mover in this area of segmentation. In 2003, over 50 per cent of companies were tracking their customer profiles once a year or less. As a result many are missing opportunities. Of course, these opportunities vary by sector.
Asset managers should consider highly dynamic segmentations, while for a distribution company a bi-annual review of customer purchase patterns may suffice. Table 5.4 illustrates the differences in approach that might be used by companies in three different industry sectors.
Segment Characteristics Multidimensional view
• Who are they?
• What do they look like?
• What do they need?
• How do they act?
• What do they buy?
• Why do they want it?
Time:
• Customer life-cycle view
• Recency and frequency of purchasing
• Customer life-stage
• Channel choice
Profitability:
• Share of wallet
• Amount spent per purchase
• Cost to serve
Figure 5.3 Dimensions of segmentation
Source: IBM Institute for Business Value (2003).
$0
0 2 4 6 8 10 12
0 14 16 18 20 22 24 26 28
Weeks Since First Purchase Revenue per customer Revenue per customer
Revenue per customer
Weeks Since First Purchase
32
30 34 36 Time
38 40
$0 $0
Weeks Since First Purchase
Which segment characteristics (life-stage, etc) will drive future purchases?
What communications or promotions should be sent to the customer to prompt a purchase or prevent defection?
When should they be sent?
Does the pattern highlight a potential defection?
What should be done to encourage future purchases?
What can be done to get back this potentially lost customer?
Future purchase predictions Why does the customer
buy differently at different times?
Figure 5.4 Changing patterns: company analysis and interpretation
Source: IBM Institute for Business Value (2003).
Nearly real-time Industry Financial services: top five US asset
manager
Distribution/retailer: international leader in pharmacy products and services
Distribution – consumer products Leading consumer products company
Purpose of segmentation
Increasing share of customer’s wallet Migrating customers to higher value
Identifying attractive new offerings Customer retention
Migrating customers to higher value
Determining optimal positioning Identifying new offerings for different segments
Increasing share of wallet within growth segments
Nature of customer interaction
Through necessary, regular and frequent interaction with customers across multiple touch points (branch, online, ATM)
Through in-store and online shopping for products that are both necessary and nice to have
Through focus groups and customer surveys
Type of data collected
Individual customer data collected at each customer interaction
Demographic data purchased from a third party
Individual customer data collected with loyalty cards
Data about customers as a whole collected from sales data
Demographic data purchased from a third party
Customer data collected in focus groups and customer surveys
Data about customers collected from sales data
Demographic data purchased from a third party
Case study: Luminar Corporation
One company that has successfully implemented strategic and operational segmentations is the fast-growing UK-based leisure corporation, Luminar. It employed two principal techniques for operationalizing its segmentation strategy, using data from its loyalty cards to refine its branding platform.
1. Develop a strategy based on segmentation. As a leading UK developer and operator of themed bars, nightclubs and restaurants, Luminar has many brands, each targeted at different customer segments and different needs. These include:
– The, a new, stylized bar;
– Jam House, a blues jazz concept;
– Life, a trendy club bar;
– Jumpin’ Jaks, a Deep South concept bar;
– Chicago Rock Cafes, provides the 3 D’s: drinking, dancing and dining;
– Liquid, a nightclub with computer graphics that can be changed to set the scene (seascape, tropics, forest, etc).
The CEO recognized that the entertainment business is all about market segmentation, trying to work out what people want, where they want it and how they want it. The entertainment business is very competitive and volatile so the only way to grow the business is to match segments to what they want.
2. Grow revenue by tracking customer patterns:
– The group collects customer data using its loyalty cards. When customers sign up for loyalty cards they provide initial demographic data. Subsequently they are rewarded in the form of discounts for answering questions about their drinking habits. Data are collected throughout each evening in each club so that the company can fine-tune its products or pass on the information to suppliers.
– Luminar then uses online transaction data in real time along with historical loyalty card data and text messaging, to capitalize on current and changing trends. Advertising screens linked to point-of-sale systems payment systems steer customers from low-margin to high-margin beverages. The text message system is used to offer discounts and promotions to coax customers from crowded locations to quieter sites, thus evening out peak loads on resources. They also collect data on 14- to 17-year-olds at their under-18 clubs and non-alcoholic club nights so that contact can be made at a future point. When they turn 18, they get a phone call from the club to say,
‘Now you are 18, you’re legal’, (the legal minimum age for buying alcoholic drinks in clubs in the UK).
Segmentation analysis can also be used to identify potential gaps in existing markets as the basis for developing new products and services.
Case study: Marriott Hotels
Marriott International has a robust segmentation strategy that enables the company to gain a deep understanding of needs and wants of both individual customers and segments of customers. Segmentation data are used to identify service gaps between brands that represent opportunities for brand improvements and chances to introduce new brands. There are two main strands to this process:
1. Make brand improvements and product extensions based on segmentation findings. Brand improvements result when a group or segment of customers expresses a specific need for new services provided at a current lodging brand.
Example: Through customer research Marriott found that customers of Fairfield Inn2 were looking for bigger rooms and more amenities. As a result, Marriott launched Fairfield Suites3 to meet these needs.
2. Develop entire new markets, based on knowing what customers want. New markets are pursued when a group or segment of customers desire a totally new set of services that are not aligned with a current brand
Example: Through customer surveys, Marriott determined that the company was not meeting the demands and expectations of elite travellers. This was a contrib-utory factor to an eventual decision to acquire the Ritz-Carlton group.
Example: Fairfield Suites customers desired additional amenities such as lobbies with carpeting, fireplaces, crown mouldings and breakfast rooms. These addi-tions revolutionized the brand to the point that it was attracting new customers.
Marriott then converted Fairfield Suites into Springfield Suites4(a new brand).
These approaches are open to all companies; the winners will be those who can implement the ideas most effectively. It is often the effec-tiveness not only of the data management that makes the difference but also of the extent to which the results of this analysis can be exploited and put into effect that separates successes and failures.
Since data are often held on multiple databases, the technical complexity of merging different records sets to obtain a cross-customer view of all transactions and touch points often presents technical difficulty. For example, Mr Smith may sometimes identify himself as John Smith or J. Smith, or may undertake transactions through his purchasing office, his wife or even an agent. Thus records relating to the same Mr Smith may be held under several individual or company names. One major insurance company noted that 75 per cent of the segmentation effort is in database merging; data comes from multiple, disparate databases. If the segmentation analysis takes a total of eight weeks, it might spend five of those weeks merging data
to create a single record for each customer. Only then are they in a position to conduct cluster and other types of analysis needed to segment their customers.