2. ATTRIBUTION
One attribute of marketing model is market mix.
• Sales
• Market share
• Distribution
• Price
• TV
• Print
• Radio
• Outdoor
• Cinema
• Website
• Search
• Social Media
• Online Adds
These two are strongest KPI’s of marketing mix
Mass Media
These are investments which are in control of marketers.
Digital Media
3. • E-mail
• Mail
• Telephone
• Events
• Sales Force
• Season
• Trend
• Competition
• Environment
Oil
Gold
GDP
Population
Face to Face Media
These are some attributes of marketing mix on which there is no control over
this for a marketer.
4. • Marketers looks for return on marketing investments(ROMI),so marketers only try
to optimizing where we can invest and where not to invest.
Database Marketing:
When we go for data base marketing then following KPI’s are important.
No of responses: Here we need to count how many no of responses.
Response Rate(RR): RR = Responses
Cost per response: Cost
For example: 1000
Cost per response means how many responses divide by total cost. So we can
calculate what is the cost involved per response.
Cost per contact: Cost
It means how much the cost involved per contact.
Total Contacted
* 100
Response
20
= 50 Rupees
Contact
= 1000
100
= 10 Rupees
5. Conversion Rate: It means how many responses converted as a customer.
For example: 8 out of 20 = 40%
1000
From the above calculation we can understand that finally the investment per
customer is 125 rupees .
Average order value: From the above conversion rate we can calculate the average
order value.
Days to Closure: It means the duration of the first day when we call a customer up
to the day when we receive a transaction or cash or cheque from the customer.
• So data base marketing is simple way to check the attributes for what to do or
what not to do.
• Database marketing is a form of direct marketing using databases of customers or
potential customers to generate personalized communications in order to promote
a product or service for marketing purposes.
• The DBMS construct customer profiles based on their personal , demographic ,
geographic, psychographic to direct its marketing effort with greater accuracy.
8
= 125 Rupees
6. Regression:
study the influence of independent variables on dependent variable is known as
regression.
• The regression equation is presented in many different ways, for example:
S
S
• This is a differential equation because one variable depends on the other variable
means if one variable moves then other variables affected.
• This is not a deterministic formula as Length * Breadth = Area.
• So there is some elementary error.
• If elementary error =0 then its deterministic otherwise we go for minimal error.
t
=^ B0 + B1 Distribution + B2 TV add – B3 Price – B4 Competition
+ B5 Season + e
t t t t
t t
t
^
= Predicted Sales
7. Sales Distribution TV Price Season Competition Predicted
Sales(S )
1 0
2 0
3 0
4 1
5 0
6 0
7 0
^
t
The techniques to draw the above data from table is called
Min(S - S ) = e
Min(S - S ) : It means distance between actual sales and predicted sales is min.
e : This is the least possible value.
t t
^ 2
t
t t
t
In the above table 1 means the product is seasonal others remaining 0.
8. Hypothesis:
A explanation (theory) that is provisionally accepted in order to interpret
certain events or phenomena, and to provide guidance for further investigation.
• All B(beta) values of distribution,tvadds,price,competition,season are the factors
which affects our sales.
• These factors are combine in a linear equation which produce a predictive sales
close to the actual sales.
From the previous regression equation only , we can not calculate the sales by
adding some factors and by subtracting few factors.
If we do so then the calculation of sales is not true.
So in this case B(Beta ) plays the crucial role because the properties of B(Beta) is
that when we multiply with the input of the factors then it takes the unit of the
sales in the above case which is in the left side of the equation otherwise it will
take what ever unit is available in the left side
If it is happen then my hypothesis is correct.
So from the above model we can form hypothesis like
9. The expression of B(Beta) is
δS
δd
‘ δ ‘ is the rate of change.
• So it is the rate of change in ‘S’ with the unit change in ‘d’ keeping other factors
constant .
S = B + B D
δS
δd
δS
δd
This equation is stochastic means probably right.
= B
0 1t
= 0 + B
=> = B
10. T S S e
.
.
.
.
Average=0
^
t
We expect the error should normally distributed means the mean or average of
errors is zero.
So the average of error should be zero so we can make an error at any point of
time.
Error Properties:
11. •From the above normal distribution diagram it is clear that little error frequency is
high as -1 to +1 and big errors are very small which are in between -3 to +3.
•So in case of normal distribution there is no reason to believe our positive errors
more then negative errors ,which is a bias.
•Unbiased means the average of error is normally zero.
• +1 is always smaller then +3 so possibilities of +1 is always more then +3.
•As it is a semantic model we can not say that +3 is occurred more then -3.
•If errors are not normally distributed then we can add more variables to the
equation otherwise we need to reject the model.
12. • If actual sale – predicted sale = 5
• Then actual sale is under predicted means no one wants to use this model.
S = B + B TV+ B Dist – B Price + B season + e
B TV : This is the contribution of TV to total sales.
Similarly , B Price : This is the loss of customer due to increase in price.
• This is all to know the contribution of each factor because if we know the
contribution then we can take action accordingly.
• In data base marketing we know all details but in mass media marketing we don't
know the contribution so we need to calculate contribution.
Return on Investment(ROI)=
• If ROI is greater then 1 means good ,else ROI is less then 1 means it is not good.
• Example: Spend on TV is 1rupees and return due to TV is 0.80 rupees.
t43210
1
3
Sales due to TV
Spend on TV
13. Optimization
Definition: Making best use of available resources to get optimum output is known as
optimization.
• Minimum and Maximum are two key performance indicators of a optimization
technique.
• Subject to constraints (STC) for optimization like budget , capacity,etc.
• For example constraint for maximizing sales(KPI) is budget.
Understanding the optimization problem.
Solving the problems.
Max S = B + B TV + B Distribution – B Price + B Season
So STC = 0<=TV <=10000 ( this is a budget constraint because we need to invest)
0<=Distribution<=120000(this is business constraint because if no of stores itself
available is 120000 then how can I put more?)
Price>= 7.25(this is a cost constraint because if my manufacturing cost is 7.25 so i
can not sale less then 7.25.)
So, CPR*TV + CPU*Print<= Total Budget (CPR = cost per rating)
t 0 1 2 3 4
14. we can also get optimization technique by using linear programming.
Linear Programming:
Time Sales TV Dist. Price Season S S
1 1
2 1 1
3 1 1
4
5
t t
^ ^ ^
S is the optimized sales which is much more different then S .
S - S -> Infinite
S : is the observed sales.
Always Subtract observed sales from optimized sales because we want to maximize
the sales.
So , S > S and the difference is called Lift.
t
^^
t
^
tt
^^
t
t t
^^
15. • So always compare the average of data from the data sheet what we get from the
clients with our optimum result S and the difference is called Lift.
• For example observed spending on TV average is 9000/- for sales and optimized
TV average is 5000/- fro same sales. So we can prove that using same budget how
can we maximize our sales .
What is B ?
• It is the intercept concept which cut the y-axis.
• It is the value of Y when X is ‘0’ .
y = mx+ c
• intercept is the sales due to brand good will or brand equity.
• Slope: Slope is the change in y (dependent variable) for the unit change in
x(independent variable).
t
^^
0
x
y
Intercept(B )
0
y =sales
16. • Example: B , B , B are independent variables.
• So y or B is constant means in marketing it is the sales which happened due to
brand equity.
• So big brands always a high intercept but new brands may zero intercept or
negative intercept.
• Example: If Colgate company stop advertising for a month then their sale may not
decrease because of their brand good will or brand equity.
• Price and competition is an opportunity to lose customers which added to the
sales it may be due to the sales .
• It is like the marketing economics that if price increases then the sales decreases.
1 2 3
0
17. • So Data integration involves combining data residing in different sources and
providing users with a unified view of these data.
Types of awareness in marketing:
Top of mind Awareness: It means which comes first into your mind about a brand.
Spontaneous Awareness: It means which brand comes into your mind after the
first one.
Aided Awareness: We remember about a brand when some one prompting us.
GRP: Gross Rating Point
Data IntegratioData Integration
Platform
Sales and Market Share
Brand Track
Media Spends And Creative
Distribution And Inventory
House Hold Panel
Data Integration:
18. Adstock:
The amount of advertisement that already in consumers mind is called adstock.
• It is measured on two parameters based on return of sale .
1. Diminishing Returns: It means we keep on advertising but sales not increasing so if
we increase in advertisement that does not mean our sales also increase.
2.Carry over Effects: It means today's advertising may be tomorrows sales. So if I
increase in advertisement then my sales also increases propotionally.
Sales
GRP
GRP
Sales
19. Time Gross Rating Point(GRP) Adstock
1 300 300
2 450 450+(90/100*300)=720
3 600 600+(90/100*720)=1248
4 450 450+(90/100*1248)=1573.2
5 300 300+(90/100*1573)=1715
6 100 100+(90/100*1715)=1643
a = g + λa
Adstock today = GRP today + Percentage(%) of previous add.
0 <= λ <= 1
• It is called carrier over effect .
• In marketing mix model we need to go for adstock not GRP.
t t t-1
In the below table I consider 90% as carry over effect.
From the above table we can understand how the adstock value increases by
considering 90% carry over effect which is from previous add.
20. a = g + λa
0 <= n <= 1
t t
n
t-1
y=x y = x
2 y = x
.5
It means we increase the GRP power .
GRP
Sales
GRP
Sales
GRP
Sales
From the above diagrams we can understand that sales line changes depending
upon the increase value of GRP.
21. . . . . . . . . . . .
• In the above diagram 0.5 is the significant as most of the sales fall around it. So we
need to go for trail and error method to get a optimum result.
Non Linear Programme:
Objective condition is non-linear: As our curves no longer linear because we have
adstock data.
Constraint is non linear
max S = B + B Dist. + B Adstock95 + B Price +B Season
Sales
GRP
0.9
0.5
0.3
. . . . . . .. …
…. . . .. . …. .. . .
. . .
0t 21 3 4
Where Adstock = grp + λ Adstock
After this we need to mention subject to constraints and then run.
t t
n
t -1
22. • What we can infer from R in regression ?
We can interpret the amount of variation in sales variable which can be explained
by the variation in the input variables of our model.
Example: in the previous model TV, Distribution, Season and Promotion explains
65% of the amount of variation in sales remaining part explained by other
variables.
• What we need to check for a good model ?
Variation should be explained properly.
Influencing factors should add as much as possible.
Actual vs. Predicted sales
Errors should be normally distributed means not biased.
MAPE = Mean Absolute Percentage Error
From error take the mean from absolute value then present it in percentage.
Absolute: It means the magnitude of a real number without regard to its sign.
2
S - S = Error (Error may be +ve or -ve
t t
^
