There are four main types of customer data used to construct a database. These are:
1 Behavioural data. This type of data is derived directly from the actual
behaviour of the customer and the interactions between the customer and the organ- isation. The data may come from a wide range of sources, including:
• advertising coupon responses; • order forms;
• enquiries; • letters;
• competition entries; • telephone calls;
• sales order processing systems; • accounting systems;
• payments; • complaints.
More recenty, information on online behaviour has become available and can be added to this data. The frequency with which customers access or transact Internet sites, the web pages they visit and the online enquiries they make can also be added to the database.
2 Volunteered data. This is data that customers have volunteered by filling in a
form, web page or questionnaire (note that this is not a marketing research question- naire) with the intention of updating the information that an organisation holds about them. They may do this to ensure that they get regular information about products or services that are of specific interest to them. An organisation will use
such information to fill in missing fields within a database and also to provide trig- gers for future marketing campaigns.
3 Attributed data. Although marketing research respondents’ identities are
confidential and cannot be used to add to customer databases directly, the results from a marketing research study (for example, people who are in their 20s are more likely to have an MP3 player) can be extrapolated throughout the customer file. Therefore each person within this age category will have an entry on the database saying ‘high likelihood of MP3 ownership’ or a score that reflects a high probability of MP3 ownership. Whereas a person in their 80s may have a very low score reflect- ing a very low probability of them owning an MP3 player.
4 Profile data. This is obtained by linking the data with other sources such as:
• Geodemographic profiling systems (e.g. Super Profiles, ACORN, MOSAIC),
sourced from bureaux such as CACI and Experian. These profiling systems are based on the principle that where people live is a predictor of what people buy. Profilers have found that the neighbourhood in which you live is a good indicator of your income, size of family, stage of life and even your interests, lifestyles and attitudes. You may argue that you are nothing like your neighbours; however, it is important to remember that the basis of these profiling products is in descriptions of groups of people, not individuals. So it is about there being a higher likelihood of certain behaviours and lifestyles ocurring in certain neighbourhoods. The neighbourhood-type data is linked to database addresses via the postcode. CACI started geodemographic profiling with ACORN, which clusters addresses accord- ing to the Census in addition to electoral roll data, consumer credit activity, the Post Office address file, the shareholders’ register, house price and council tax information. The census provides information on small groups of houses, which are then clustered with others according to common characteristics and given a particular ACORN label (see page 5). MOSAIC is a similar system, which has 61 postcode types in the UK constructed from 400 variables such as average time of residence, household age, occupational groups, financial data and housing standards. In an international context, Mosaic Global covers over 284 million of the world’s households. Using local data from 16 countries and statistical methods, Mosaic Global identifies ten distinct types of residential neighbourhood, each with a distinctive set of values, motivations and consumer preferences, which can be found in each of the countries (see www.business-strategies.co.uk).
• Lifestyle databases(e.g. Claritas, Equifax Dimensions, CACI Lifestyle Database): such data is derived from questionnaire responses to ‘lifestyle surveys’ or product registration forms. Lifestyle surveys rely on questionnaires being distributed in magazines and respondents being recruited through the offer of prizes or shopping vouchers. Such surveys should make it clear to respondents that the data is being collected for the creation of a database rather than for marketing research pur- poses. The product registration forms are often provided with new products and customers are invited to complete them in order to register their product for a warranty. These forms frequently ask about the purchaser’s interests and lifestyle, rather than simply noting product details for warranty purposes. The information gathered in such lifestyle databases can be used to predict the behaviour or charac- teristics of an organisation’s customers based on the extent to which their profile
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matches those on the lifestyle database. The main advantage of lifestyle data over geodemographic data is that it is based on individuals rather than the aggregates of individuals that exist in geodemographic data. However, it may be biased towards people who like filling in ‘lifestyle surveys’ or product registration cards because they have the time to do so, or are attracted by the incentives to fill them in. Geodemographic data providers claim that the official nature of the Census and their other sources improves the validity of their data. However, the argu- ments about the various advantages and disadvantages are likely to become less important as the different approaches converge and the data is fused together.
• Company databases (e.g. Dun & Bradstreet): profiling of organisations can be
undertaken using information relating to number of employees or the Standard Industrial Classification (SIC), which sets out the industry sector that the organ- isation is involved in. Relative to the consumer profiling products, the business-to- business profiling products are still underdeveloped.
Data fusion
Data fusioninvolves the fusing together of different types of information to present a more complete picture of an individual or a group of individuals. The combination of behavioural data, volunteered data and attributed data based on marketing research and profile data provides companies with a better insight of their customers and their requirements.
An example of profiling: Dawes Cycles
Dawes, a UK manufacturer of bicycles, produces a full range of cycles that are sold directly into independent cycle shops. Following a recent management buyout, Dawes challenged CACI to help it to focus its limited sales and market- ing resources on to those areas of greatest return.
As part of a new customer loyalty scheme, Dawes was collating customer information through cycle guarantee cards. The postcodes on these were profiled with CACI’s consumer classification ACORN. Built using Census data, ACORN segments the UK population into groups that share similar lifestyle and demographic characteristics.
The resulting profile enabled Dawes to determine the profile of its current consumer base and helped it to understand who its consumers were. It also helped to reshape the sales process. By analysing the catchment areas of each of the independent cycle shops using ACORN and overlaying this with typical consumer profiles and sales information, Dawes was able to rank the cycle shops to highlight those retailers that offered the greatest potential for growth. This also helped Dawes to decide on the sales team’s call frequencies necessary for each cycle shop.2