In the field of marketing, demographics, opinion research, and social research in general, psychographic variables are any attributes relating to personality, values, attitudes, interests, or lifestyles. They are also called IAO variables (for Interests, Attitudes, and Opinions). They can be contrasted with demographic variables (such as age and gender), and behavioral variables (such as usage rate or loyalty).

When a relatively complete assessment of a person or group's psychographic make-up is constructed, this is called a psychographic profile. Psychographic profiles are used in market segmentation and advertising.

Some categories of psychographic factors used in market segmentation include:

• Social class
• Lifestyle
• Behavior

In business, "lifestyles" provide a means of targeting consumers as advertisers and marketers endeavor to match consumer aspirations with products. Lifestyles refer to patterns in which people live, spend time and money. These patterns reflect by demographical factors (the habits, attitudes, tastes, moral standards, economic level and so on) that together constitute the mode of living of an individual or group); that include things such as the individual’s activities in addition to their interests. As a construct that helps consumers interact with their worlds, lifestyles are a subject to change every time. Consumer behaviour research uses lifestyle data to determine which consumers by products.

Past research.

The product class of interest was the retail banking market in which was dominated by 6 banks. Each respondents sought benefits were measured by ratings of the importance of 17 attributes associated with banks. Subjects were asked: “When choosing a bank with which to business, how important the following attributes:

1. large
2. wide variety of services
3. good advertising
4. conveniently located branches
5. good reputation in your community
6. high interest on savings
7. modern
8. pleasant offices
9. encourages financial responsibility
10. convenient banking hours
11. concerned about the local community
12. plenty of parking
13. friendly atmosphere
14. loans are readily available
15. quick service
16. low interest rates on loans
17. a bank for most everyone

 Results:

The importance of 17 banks attributes were factor analyzed, and cluster analyses of  each of set of factors were performed. The factor analyses were done in order to avoid redundancies in intercorrelated variables.

 The 4 factors were named:

1. Convenience and saving interest
2. Loans
3. Facilities
4. Size and advertising

The resultant factor score were used as inputs to the Howard and Harris clustering algorithm. 5-group solution were chosen:

1. Front runners (3 %) - in comparison with other segments, this segment considered the attributes of large size, a bank that was for all, good advertising and modernness to be important. Demographically, they were younger then average and more likely to rent their living quarters

 

2. Loan seekers (17%)- consumers who placed relatively high importance on easy availability of loans, low loan interests, encouragement of financial responsibility, friendliness. This group was concerned with the availability and cost of credit. They had higher then average income, smaller household size and tended to have moved more often then average within the general area. In relation to other group, this group tended to favor Commercial Bank B, which was of considerable size and Saving Bank X, which was very small, but which heavily advertised lending services          
3. Representative subgroup(39%) – this group wasinteresting in that the responses to the benefit-sought questions were all about average. They favored the larger commercial banks, especially for checking and credit card services. Otherwise nothing distinguished them from the population as the whole.

 

4. Value seekers (17%) - compared to other consumers, they considered high saving interest, quick services and low loan interest to be important benefits. They tended to patronize the two largest savings banks more than other banks. They tended to own their own home, where slightly older when average, lived in area older then average, and the husband’s occupation tended to be blue color. They were conservative in their outlook on life in general and also in their views about the ease with which credit should be given and the use of credit cards

 

5. One-stop bankers (28%)  -they considered great variety of services, convenient hours, parking adequacy, quick service, high saving interest and availability of loans will relatively be important. They most likely to use Commercial  Bank A (the largest in the area) but also use Commercial Bank B (the second largest) more then other segments. Demographically, they had no distinguishing characteristics, they more conservative towards the use of loans and credit cards and felt less available to manage their own finances.   

One advantage of benefit segmentation over more traditional forms of segmantation, its greater potential for directly translating segment description into marketing strategy

• 
  Robinson (1938):-Market Segmentation involves viewing a heterogeneous market as a number of smaller homogeneous markets, in response to differing preferences, attributable to the desires of consumers for more precise satisfaction of their varying needs. He also emphasized that market segments arise from managers conceptualization of a structured and partitioned market, rather than empirical partitioning of the market on the basis of collected data on consumer characteristics. However, even if market can be partitioned into homogeneous segments, market segmentation will be useful only if effectiveness, efficiency and manageability of marketing activity are influenced substantially by discerning separate homogeneous groups of customers.
  
  6 criteria for segmentation concept:
  
  1. Identifiability –is the extent to which managers can recognize distinct groups and able to identify the customers in each segment on the basis of variables that can be easily measured.
  2. Substantiality- is satisfied if the targeted segments represent a large enough portion of the market to ensure the profitability of targeted marketing programs and closely connected to the marketing’s goals and cost structure.
  3. Acessibility –is the degree to which managers are able to reach the targeted segments through promotional or distributional effects.
  4.Responsiveness is critical for the effectiveness of any marketing segmentation strategy- because differentiated marketing mixtures will be effective only if each segment is homogeneous and unique in its response to them.
  5. Stability
  6. Actionability
  7. Measurability -the marketing firm should be able to identify and quantify the potential of each segment
  
  Segmentation – is essentially grouping task, for which a large variety of methods are available and have been used.
  
  The method employed in segmentation research can be classified in two ways:
  
  1. A-priori and post-hoc approaches.
  
  Segmentation is called a-priori –then the type and number of segments are determined in advance by researcher and the post-hoc –then the type and the number of segments are determined on the basis of the results of data analyses.
  For example, a manager may decide to segment the market for the fast-food chain by usage situation, into the breakfast, lunch and dinner sub-markets. Often, multiple segmentation bases are used to form the segments, and the segments obtained from each of those criteria are assessed by looking at the associations between grouping arising from the alternative bases. In the example the manager, might in addition segment the market by usage situation and location.

2. The second way of classifying segmentation approaches – is according to whether descriptive or predictive statistical methods are used.

• Descriptive methods analyze the association across a single set of segmentation bases, with no distinction between dependent and independent variables.
• Predictive methods analyze the association between two sets of variables, where one set consists of dependent variables to be explained/predicted by the set of independent variables

	a-priori	post-hoc
Descriptive
	Contingency tables	Clustering methods:
	Log-linear models	nonoverllapping
		overlapping
		fuzzy techniques
		ANN
		mixture models
Predictive
	Cross-tabulation	AID,CART
	Regression	Clusterwise regression
	Logit	ANN
	Discriminant analysis	mixture models

The most important segmentation methods:

• Cluster analysis
• Mixture regression
• Scaling

  Cross-tabulation - appears to have been the popular technique for the evaluation of bases in the earlier years of segmentation research. A problem of this method is that higher order interactions are difficult to detect and interpret in the tables. Green and Carmone suggested to use Log-linear models for this purpose.

The main objective of cross-tabulation and log-linear analyses in such cases is to test segments arising from alternative bases, and to predict one segmentation base from other bases. For example to compare heavy and regular users of a brand by lifestyle (e.g VALS) . Although they are not very effective, they continue to be used, especially in hybrid segmentation procedures that combine a-priory and post-hoc methods. Often it is desirable to obtain segments for two separate strata in a population defined a-priori, such as a business and private users, users and nonusers, new customers versus current customers.

In this case a two-stage approach is taken .

1. A sample is partitioned a priori on the basis of variable in the question.
2. Within each of the strata that arises, a post hoc, mostly clustering-based procedure is used.  

  Post-Hoc descriptive methods :

In lifestyle segmentation, for example, consumers are first measured along several demographic and psychographic characteristic; a clustering procedure is then applied to the data, to identify groups of consumers that are similar in terms of their values, activities, interests and opinions.

Clustering methods:

• Non-overlapping – each subject belongs to a single segment only.
• Overlapping  - a subject may belong to multiple segments
• Fuzzy – the hard membership or nonmembership of a subject is replaced by the degree of membership in each segment. For example a subject may belong partly to segment A (0.6) and B (0.3) and C (0.1)

  Non-overlapping clustering – are the most used in marketing research.

  Two types of Non-overlapping clustering are distinguished :

• Hierarchical -start with single – subject clusters and link cluster in successive stages. Two consumers who are placed in the same group at an early stage of the process will remain in the same segment up to the final clustering solution. Several hierarchical methods can be distinguished : single linkage, compete linkage and minimum variance linkage (Ward’s method)
• Nonhierarchical –methods start from a random (initial) division of the subjects  into a predetermined number of clusters and reassign subjects to a clusters until a certain criterion is optimized. Two consumers who are placed in the same group at an early stage may end up in different segments. A large number of nonhierarchical methods is available; k-means is the best known and most widely used.

 Several extensions have been proposed for both hierarchical and nonhierarchical clustering methods.

 De Soete Desarbo and Clark extended the k –means algorithm in a similar way. The method also allows for the analyses of several groups of a-priory weighted variables.

  Benefits of marketing segmentation:

Marketing segmentation should result in benefits for both the marketinf firm and its customers.

If no such benefits accrue to either party, then the segmentation exercise is a meaningless waste of time.

Possible benefits:

1. Greater sales and profitability
2. Allow the producers to design products and marketing appeals that are more "finely turned " to the needs of the market.
3. Greater consumer satisfaction
4. Focus on sub-markets with the greatest potential
5. Allows greater product differintiation and variety as firms seek further market opportunities by developing new segments
6. Better competitive position for existing brands
   

In data mining and treatment learning, association rule learners are used to discover elements that co-occur frequently within a data set consisting of multiple independent selections of elements (such as purchasing transactions), and to discover rules, such as implication or correlation, which relate co-occurring elements. Questions such as "if a customer purchases product A, how likely is he to purchase product B?" and "What products will a customer buy if he buys products C and D?" are answered by association-finding algorithms.

This application of association rule learners is also known as market basket analysis. As with most data mining techniques, the task is to reduce a potentially huge amount of information to a small, understandable set of statistically supported statements.market basket analysis is often promoted as a means to obtain product associations to base a retailer’s promotion strategy on.

Associated products with a high lift/interest can be promoted effectively by only discounting just one of the two products. Implicitly, the assumption that market basket analysis automatically identifies complements. Academics , however, have shown that one should be careful with this conclusion. They show that this implicit assumption does not hold. Their empirical analysis reveals that market basket analysis identifies as many substitutes as complements. Therefore, market basket analysis should not be used to build a promotion expert system for retailers, unless supplemented by other, more empirical, methods of product relationship determination.

RULES

After reviewing the straight frequencies, clicking back to the Rules tab provides information about the relationships between the items. By default, the rules are expressed as “item A implies item B”, and are listed with the following measures: Expected confidence is the percentage of times item B occurs in the data.

Confidence is the percentage of cases in which item B is present when item A is present.
Support is the percentage of records containing both item A and item B.
Lift is how much more likely item B is if item A happens. A rule has lift when its confidence is higher than its expected confidence.
Count is the frequency of item A and item B occurring together.