How do you measure the effectiveness of your marketing action? Do you take into account dynamic effects and interactions of multiple marketing-mix variables?

Dynamic effects:

Response to marketing actions does not often take place instantly. The effect of an ad campaign does not end when campaign is over; the effect, or part of it, will continue in a diminished way for some time. Many customers purchase more then they can consume of a product during a short term price promotion.
Carryover effects - is the general term used to describe the influence of a current marketing expenditure on sales in future periods. We can distinguish several types of carryover effects.

Delayed-response effect , arises from delays between when marketing dollars are spent and their impact. Delayed response is especially evident in industrial markets, where the delay, especially for capital equipment, can be a year or more.

Customer-holdover effect, arises when new customers created by the marketing expenditures remain customers for many subsequent periods. Some percentage of such new customers will be retained in each subsequent period; this phenomenon gives rise to the notion of the customer retention rate and in its converse, the customer decay rate (also called the attrition or erosion rate)

New trier effects, in which sales reach a peak before settling down to steady state, are common foe frequently purchased products, for which many customers try a new brand but only a few become regular users

Stocking effects occur when a sales promotion not only attracts new customers but encourages existing customers to stock up or buy ahead. The stocking effort often leads to sales trough in the period following the promotion

The most common dynamic or carryover effects model used in marketing is:
Y_t=a₀+a_tX_t+λY_t-1

Says that at time t (Y_t) are made up of a constant minimum base (a₀ ) an effect of current activity a_tX_t  and a proportion of last period sales (λ) that carries over to this period.
Managers can easily guess λ directly as the proportion of sales  that carries over from one period to the next or estimate it by using linear regression.


Multiple marketing-mix elements: interactions
When we consider multiple marketing-mix variables , we should account for their interactions.
Interactions are usually treated in one of three ways:

1. by assuming they do not exist
2. by assuming that they are multiplicative
3. by assuming that they are multiplicative and additive
   

If we have two marketing- mix variables X₁ and X₂ with individual response function f(X₁) and g(X₂) then
assumption (1) gives us :
Y=af(X₁)+bg(X₂)
assumption (2) gives us :
Y=af(X₁)*g(X₂)
assumption (3) gives us:
Y=af(X₁)+bg(X₂)+c*f(X₁)*g(X₂)
In practice when multiple marketing-mix elements are involved, we can resort to one of two forms:
(1) The full linear interactive model
Y=a+b*(X₁)+c*X₂₊ d*X_1*X2
(2) The multiplicative form is :
Y=a*X₁^b*X2^c
b,c are the constant elasticities of the first and second marketing- mix variables, respectively, at all effort levels X1 and X2.

Some forms of the common models used in marketing :

The linear model:

Y=a+b*X

• The model is easy to visualize and understand
• The model can approximate many complicated functions quite well
• It assumes constant returns to scale
• It has no upper bound on Y
• ∆Y/∆X  is constant everywhere and equal to b
• It often gives managers unreasonable guidance on decisions

 

The power series model:

If we are uncertain what the relationship between X and Y, we can use a power series model:

Y=a+b*X+c*X^2+DX^3+…

• The model can take many shapes
• May fit well within the range of the data
• Normally behave badly (becoming unbounded) outside the data range

 

The fractional root model:

Y=a+b*X^C

• Has a simple but flexible form
• There are combinations of parameters that give increasing, decreasing, and (with c=1) constant returns to scale
• When c=1/2 the model is called the square root model, when c=-1 it is called the reciprocal model, Y approaches the value a when X gets large
• If a=0, the parameter c has the economic interpretation of elasticity (the percent change in sales, Y, when there is a 1 percent change in marketing effort X). When X is price, c is normally negative, whereas it is positive for most other marketing variables

  The semilog model:

Y=a+b*ln X

the semilog model handles situation in which constant percentage increases in marketing effort result in constant absolute increases in sales and can be used to represent a response to advertising spending where after some threshold of awareness, additional spending may have diminishing returns.

The exponential model:

Y=ae^bX    where X>0

characterizes situation where there are increasing returns to scale (for b>0) ; however is most widely used as a price-response function for b<0 (increasing returns to decreases in price); when Y approaches 0 as X becomes large

The modified exponential model:

Y=a(1-e^-bX) + c

It has an upper bound or saturation level at a+c and a lower bound of c, and it shows decreasing returns to scale. The model is used as a response function to selling effort.

The logistic model: Of the S-shaped models used in marketing, the logistic model is the most common. It has a form

---

Y=a/(1+e-^(b+c*X)   )+d

this model has a saturation level at a+d and has a region of increasing returns followed by decreasing return to scale; it is symmetric around d+a/2, it is easy to estimate and it is widely used

---

The Gompertz model:

A less widely used S-shaped function is the following Gompetz model :
 
Y=ab^cX +d, a>0, b>0, b<1, c<1
Both the Gompetz and logistic curves lie between a lower bound and an upper bound; the Gompetz curve involves a constant ratio of successive first differences of log Y, whereas the logistic curve involves a constant ratio of successive first differences of 1/Y.

The better known logistic function is used more often then Gompetz because it is easy to estimate

---

The ADBUDG Model:
  Y=b+(a-b)*X^c  /(d+X^c  )
The model is S-shaped for c>1 and concave for 0<c<1. It is bounded berween b (lower bound) and a (upper bound). It is widely used to model response to advertising and selling effort

Customer profitability has emerged as a key metric in financial services firms, driving decisions about individual customer relationships ranging from service tiering and product pricing to cross-sell and retention efforts.

*tiering-service plan in which customers can choose from several service packages with different prices and channels

Activity based management   is a system that enables an organization to manage activities and processes. Activity based management enables managers to get a true understanding of the costs and profits that are associated with a product, customer, service or business process.

Activity-based costing (ABC) is the basic tool of activity-based management.

 Activity-based costing more accurately tracks costs than traditional methods because ABC assumes the following:

Activities cause expenditure of resources

Cost objects (the results of activities or products and services produced) create the demand for activities

Question:

How profitable are your company’s products? If your current cost-management methodology includes spreading the costs of each product over an entire product line, how do you know the true profitability of any single one? To get beneath the spread and unravel the profit mystery, consider activity-based costing/management (ABC/M).

Answer:

Sales revenue alone is an insufficient indicator of profitability. But an activity-based approach to identifying costs associated with each product or service can disclose how profitable each one really is.

Question:

How will a performance management information system employing activity based costing ("ABC") improve my bank's process of planning for, reporting about and analyzing retail customer profitability?

Answer: 
 
Your retail bank's financial success is determined by how well it chooses, attracts, and profitably serves and retains its customers. Profits derived from customers indicate how successfully this is being done. The process of planning for, reporting about and analyzing customer profitability is referred to as "the Customer Profitability Process" or "CPP".

1. What is the profit contribution from each of the served retail market segments (e.g. small business, high net worth, middle market consumers)?
2. How does customer profitability from the middle market vary by delivery channel (e.g. branch type or location, telephone, online banking)? Is there a clear mix of products/services and channels that maximizes current customer profitability (e.g. wealth management and mortgages through upscale branches and payments via online banking for the high net worth segment)?
3. Are there groupings within the customer classifications that show distinctly different profitability characteristics by product/service and channel combinations?
4. Is there significant variance from target profitability? If so, is it coming from a particular segment of customers? Is there a cost or revenue problem in serving particular segments? If so, is where is it located?
5. Does a given customer contribute enough profitability to merit enhanced service?
6. In what customer segments is the bank gaining or losing business?

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