Marketing Strategy- A Complete Guide

 Marketing Strategy- A Complete Guide


 Lets start with Five C analysis  for formulating any marketing strategy:-

1.Customers
2.Company
3.Competitors
4.Collaborations
5.Context


All the elements of Five C Analysis are described in the Image Below:-

Marketing Strategy

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After 5C analysis, an economic analysis should be put into it to make sure everything adds up to the viable business proposition.

What is a Marketing Process?

1. Marketing Process--A marketing process is defined as a way how marketers do their work utilizing marketing mix.
2. The marketing process can be defined as
Implementation---> Programming, Allocating and Budgeting---> Analysis and research--->Marketing Planning--->Strategy formation--->Monitoring and Auditing.
3. Strategy and Implementation should match in a way and help marketers analyze the marketing implementation.

Preferential Mapping, Perceptual Mapping and Joint Mapping

4. Consumer is sensitive to value and not price--> the marketer sends the signal and consumer picks it up.
Consumers are not  price insensitive.

Share of Voice and Share of Market for the analysis of advertisements

Share of Voice and Share of Market

Promotion Matrix

               Promotion Matrix

Customer Value

Customer Value

There are three components of value

 1. Economic Value----> Price = Quality but the actual value comes from savings at the consumer's end. Economic value we associate with low price products.Just because you have car at $100 does not mean that a Car gives you economic value.The difference is in absolute values---->Economic value is required at all levels. Economic value is required for every product

2. Functional Value---> Relationship between features and applications.The application of feature gives value.Air-Bag is a feature and a solution is in case of collision, it will save lives.


3. Psychological ---> Relationship between brand and every intangible----> " Peace of Mind".

 Your Marketing Strategy should be aligned to these values:-

Marketing Values

Marketing Strategy

Market. Analysis----> Product Company-fit, Product Competitor- fit, Product Customer- fit.

Business Strategy and Marketing Strategy Link

Gross Profit Ratio= Profit/Sales--> Customer is paying high value-->Differentiation
Capital Turnover ratio= Sales/Capital Employed-----> Cost Leadership

ROCE= Profit/ Cap.Employed =You can increase your ROCE either by increasing your sales or increasing your sales/Assets ratio.As a company, you can follow two strategies, we can say that in a matrix as shown:-

                             Marketing Strategy

Marketing Strategy

1. Segmentation--> Target Market Selection----> Positioning

2. Segmentation is always based on consumer variables


Marketing Program---> Marketing Mix---> Price, Place, Products and Promotions

Customer Satisfaction---> Performance and Expectations---> CSAT


Mass marketing---> We don't believe that there are segments

Undifferentiated---> We believe that there are segments and we don't act according  to these segments

Differentiated Marketing

Product variety mktg

Bathing soaps---> Assortment kind of shopping behavior------>Product Variety Marketing

Product Concept---> A new product development process

1. Idea Generation
2. Idea Screening
3.Product Concept development and Starting
4. Simulated Test Marketing
5.Test Marketing
6.Commercialization

Conjoint analysis is a statistical technique used in market research to determine how people value different features that make up an individual product or service.

There are different types of  Conjoint Value Analysis
1. Full Profile Method and Pair Wise Method

2. Adaptive Conjoint Analysis

3. Choice Based Conjoint Analysis----> Four Different options

4. Adaptive Choice Based Conjoint(ACBC)

5. Menu Based Conjoint(MBC)

6. ASEMAP

Objectives of Test Marketing

1. Predict national  level in share
2. Efficacy of Marketing Mix
3.Positioning Credibility
4. Market Potential Index--> Country like india has 40 towns but how much resources i should put in these towns...What could be index which would give relative buying power?
 
For Example City with highest buying power gets the index of 100 and then every city gives the relative index
Latitudinal and Longitudinal Studies

***First Purchase you make is trial rate

Customer Share == p*r

Market Share = p*r*buying index

where p= cumulative penetration
          r= Repeat Purchase rate
        BI=  to measure the usage---> light user, heavy user

For Example---> 900 people---- 45000 units--- 5 unit per family

but for brand---> 50families--> 300 units--> 6 unit per family

Buying Index= 6/5=1.2

The Parfitt & Collins Model


A model for predicting the market share of a new product, based on early panel data sales results. The model views market share as the product of three quantities: the brand’s penetration level (i.e., proportion of buyers of this product class who try this brand), the brand’s repeat purchase rate (i.e., the proportion of repurchases going to this brand by consumers who once purchased this brand), and the buying-rate index of repeat purchasers of this brand (where the average rate across consumers = 1.0. This index shows the extent to which the consumer is a relatively heavy buyer (rate > 1.0) or light buyer (rate < 1.0) of the product category).

Structure of the Model.

Parfitt and Collins conceptualized a simple model that has a great deal of intuitive appeal and has greatly influenced the structure and development of other product models. It predicts ultimate market share for new repeat-purchase consumer products using input data from consumer panels. Although the model requires actual market data (which is expensive since it presumes that the new product is at least in a test market), its ability to predict national share prior to national distribution can help management avoid future losses.

Cumulative penetration (the total number trying the brand, over time) and repeat purchasing rates over time from the time each buyer first bought the product (along with a buying-level index) form the basis for predictions of future share. 

Parfitt and Collins represent the ultimate brand share as a composite of these three dimensions: Share = T x R x B where,

T = Projected percentage of triers of the new brand,

R = Projected percentage of those who tried and will repurchase the brand, and

B = Buying-level index of repeat purchase of the new brand, compared with an index of 1.0 for the product class average.

To illustrate, suppose we had developed a new lemon-lime cake mix and introduced it in test market. As consumers buy it, the number of triers of our product accumulate, growing in number, but at a diminishing rate. A few months after introduction the shape of this growth curve should become fairly well defined, and a (freehand or computer-aided) extrapolation can be made to the ultimate penetration level (illustrated by the dotted line in Figure 8-1). Similarly, the repeat purchase rate for the brand can be examined.

For example, assume that Figures 8-1 and 8-2 represent the cake mix penetration and repeat, and that average repeat level for our product is equal to the product category. Then our ultimate share is projected to be,

Share = (0.34)(0.25)(1.0) = 0.085

That is, if 34 percent of the potential market tries this new product and 25 percent of the triers repurchase it, and they buy neither more nor less than other brands in the product class, the share for the new product will settle at 8.5 percent.

An appealing feature of this model is that the predicted share can be estimated well before stable shares have been reached, and even while the company is in test market with the product. Too, the diagnostic value of the model should not be ignored. Share estimates below expectation may suggest to management that a change in promotional strategy is necessary to increase penetration (trial) rates, or that a change in product strategy is necessary to increase repurchase rates.

 Market Share Penetration

Trial Repeat Model
Trial- Retention Model
 Preference Modelling
Brand Switching Models
Choice Models

 Switch Back rate---> At what rate people are switching to your brand

Retention Rate--->   SB/((1+SB)-r)
## Sample Size is determined only by heterogenity

In Regression, the no.

of data points should be one more than that of variables

Correspondence Mapping

Diffusion Modeling

-> Product- Company fit


-> Product Competition fit
a) Differentiation
b) Herfindhal Index- Concentration of players in the market.
A commonly accepted measure of market concentration. It is calculated by squaring the market share of each firm competing in a market, and then summing the resulting numbers. The HHI number can range from close to zero to 10,000. The HHI is expressed as:

HHI = s1^2 + s2^2 + s3^2 + ... + sn^2 (where sn is the market share of the ith firm).

The closer a market is to being a monopoly, the higher the market's concentration (and the lower its competition). If, for example, there were only one firm in an industry, that firm would have 100% market share, and the HHI would equal 10,000 (100^2), indicating a monopoly. Or, if there were thousands of firms competing, each would have nearly 0% market share, and the HHI would be close to zero, indicating nearly perfect competition

-> Product Customer fit

Low Involvement

Value of Purchase is low

Positioning should be consumer based...

Continuous Planning Advertising Program...


Conjoint Analysis
When you asked consumer what they want the result is very vague to the consumers- This form the basis of conjoint analysis. Consumers always want more than the given characteristics. When consumer make choices they mostly do trade off so that we can design a conjoint analysis.

We develop concepts cards based on the attributes and the design the utility graphs..In Conjoint analysis we give an option of either car pick up or 10hrs internet- Check the importance....

 So the entire concept of conjoint analysis is to decide the trade off between different attributes and find out the corresponding results

Conjoint Value Analysis

Conjoint Designer- What are the attributes that we are going to take in our study

 Market Share Predictor
there are two concepts
Arul Data--->CA= 2.4; CB= 2.1
Sahil Data---> CA= 2.9; CB= 3.2

So we can generate the market share

Segment Profiler
200 respondents and there are two concepts we are pushing and we also know the demographics and other sets of data-> So we can group the customers accordingly and for that group of customers we can run the conjoint in the one go.
Coefficient of Concordance---> Spearsmen Data

When we have ratings---> We can use ANOVA and Cluster Analysis

 Sensitivity Analysis


Design of Experiments-- Orthogonal Array, Randomized Design.

Nominal Property is the labeling property. 5 is different from 7-> Mode is the measure----> We use Non metric scale
Ordinal Scale- 5 is different from 7 --> Median is the measure---> We use Non- Metric Scale
Interval Scale---> We also know 7-5=2. Rating Scale is the interval Scale---> Average is the measure.e.g Temperature is the interval scale---> We use Parametric Scale
Ratio Scale--->50/10=5-----> We use parametric scale


Coefficient of Concordance---> Spearsmen Data

When we have ratings---> We can use ANOVA and Cluster Analysis

Regression is done on ratio and interval data.

There are three theories on consumer motivation:-

1. Herzberg--> Satisfaction factors and Dissatisfaction factors--> Seller should try to reduce dissatisy factors
2. Sigmund---> Acc. to Sigmund, there are unconscious factors that describe the buying behavior of a person--
3. Maslow---> There are certain steps in life of a human

Positioning

Consumer Self Selection:- Consumer decides himself the self selection- target market selection and actual user profile--->It will give insights for further changes in positioning

User Profile and Target market Profile

Example of Propecia-

Segmentation---> Resource Optimization-->


Market Segmentation from marketers                         Market Segmentation from User Side

Market Segmentation                                                   Product Segmentation

So the tool of Product segmentation which user are using to segment product; marketers start entering into product segmentation; this is called as positioning..

Product Positioning is the  perception of user relative to the competitive brand.

Analytic Measurement of Positioning

Types of Maps

1. Perceptual Maps---> Perception of Brands
2. Preference Maps---> User Preference
3.Joint Maps---> Perception( Marketers)+ Preference( USers)

Joint Maps we use in Markstrat---> Perceptual Maps

Consumer Distortion

Analysis:-
Difference between types of data used
Compositional analysis Technique-
De compositional Analysis Technique:-

Techniques we used to do Positioning Maps:-
Multidimensional Scaling
Correspondence Analysis
Factor Analysis Based Positioning
Discriminant Analysis based Positioning

Different Types of Data that are used for Positioning

1. Perception Data

A. Similarity Data---> Pairwise Similarity data

A B C D E- Make 10 pairs using combination
Ask the consumer which two brands are similar and dissimilar using rating and ranking

B. Anchor Brand--->Rather than making pairs, we make a matrix







B Perception Data on Attributes---> Ranking or Rating

a)Yes/No Data---> Whether this is present in the product













b)Rating Method
c)Ranking also
d)Chip Allocation--> Mileage is the attribute and you are doing the technique- On mileage allocate 100 for each of the five brands
Seating Comfort, Mileage, Power ----> Rate the data

2. Preference Data-->

For existing brands, either you take rank order or rating scale

Least preferred ----------------------------------------------Most preferred

Handbook 1 Marketing Scales----> VOl 1 , Vol 2 and VOL3 

Multidimensional Scaling

Let us consider that there are three objects--A, B, C and we have to find out which kind of projects are similar or dissimilar
AB- 1; BC-2 and AC-3

For two things to be similar, they have to be same on some attributes and considerations
AB< AC< BC



If there are n objects for which we have pair wise relationship available, I can always plot an n-1 dimensional map where all the relationships can be captured.It keeps on decreasing the no. of dimensions till we get the required dimension( We lose some data)

VIBGYOR- we can make 21 pairs then we can ask to find similarity between different pairs.This input is given in the multidimensional scale and therefore we get the results

If there is only data --> Pairwise/Anchor Method( TORSCA, INDSAC) then I'll get only location of brands on the perceptual map.


Similarity Data( Pairwise)+ Attribute Data
In example A1 is the vector which is defined by vector 4 because it has more importance in the entire research study

Enter the diagram

One which is closer to x and y axis we can lable the brand

How different attributes are correlated with each other

Attributes are positively correlated are called leveraging attributes
Attributes are zero related are neutral
Attributes are negatively correlated and therefore difficult to manage







Preference- Multi Dimensional Scaling( Pref Map)

PrefMap- It is derived measure of Ideal preference

Problem- There is no average of preference

Ideal data is calculated by preferences

Let us take an example of Project Scorpio- Positioning Problem

Cold, Allergy and Sinus Problem


----->Suggestion While devising a marketing plan, always keep two strategies---->

Low Cost Strategy and Entrepreneurial Cost Strategy


Demand Forecast for Non- Durables- Product Category

-----> Durable
------> Product Category- first time it is launched

Diffusion Model

In year 1966 by Frank Bass---> Bass Diffusion Model

Any durable product that we launch in country- Since different people have different probabilities of adopting the product

Cumulative Probability of Adoption is a function of time


St= Sales at time t
p= Innovation Coefficient
q= Imitation Coefficient
m= Market Potential-1
y(t-1)= Cumulative sales till the product( t-1)

Rt= Rate of Adoption

Rt= p+q( y(t-1)/m)

St=( m-y(t-1))*Rt

St = ( m- y(t-1)( p+q( y(t-1)/m)
St = pm+ (q-p)y(t-1)- q/m(y(t-1)^2
St= a+by(t-1)+c(y(t-1))^2

We now need to calculate the values of p, q and m
y(t-1) is the cumulative sales for previous periods....
p is influenced by product category
q and m is driven by country's buyers( And the sales in initial years is very small and therefore forecast becomes accurate)

How to estimate p,q and m initially

p---> Using the same product data from another country where it is already launched

q, m----> Using some other product data from same country.

Since initially sales volume in loss, the error value does not influence much.

Lets us take an example to illustrate the Diffusion Model
Year  St    Y(t-1)       Y(t-1)^2
1        3        0              0
2        6         3             9
3       12        9            81

St = a+ by(t-1)+ c(y(t-1))^2----> Simplifying the equation
m= (-b-sqrt( b^2- 4ac))/2c; q= b+p; p= m/a

The Bass diffusion model was developed by Frank Bass and describes the process of how new products get adopted as an interaction between users and potential users.