Exohind is a start-up in the lifestyle travel sector, utilizing a growth plan based on the Bass diffusion model for sales forecasting and market share analysis. The document outlines various marketing strategies including customer segmentation using decision tree algorithms and the development of brand awareness campaigns through empirical models. Additionally, it details pricing and advertising strategies using several theoretical frameworks to optimize market entry and growth.

1. MARKETING STRATEGY
EXOHIND - HIGHLIGHTS
Presented by
Soumyadeep Chowdhury

2. EXOHIND GROWTH PLAN – EXTENDED BDM
• ‘ExoHind’ is a start-up company that is funded partly
by seed capital of the founding members and partly by
taking loan and going to start a business in the
Lifestyle travel category
• Since this is a start-up so I don’t have any sales data
for it
• In order to forecast our sales I used Brand level
extension of Bass Diffusion Model –
• Then we use the Funds Balance Equation to identify
the expected growth rate
Year 1 Year 2 Year 3
Revenue 21576742 22929742 24282742
Growth Rate 0 6.27 5.90
Expenses 5000000 5000000 5000000
Profit 16576742 17929742 19282742
RONA 2.81 3.04 3.27
debt/equity 0.50 1.50 2.00
retained earning ratio r 0.77 0.78 0.79
g(s) 3.20 5.82 7.79

3. BRAND ATTRIBUTES & IMPORTANCE - FISHBEIN
• We use the reverse fishbein model to find
out the relative importance of the three
attributes that are relevant for us
• We found the rough estimates of market
shares of the various competitors for
previous year
• For this year we use Bass Diffusion model
to forecast category level sales and
calculate market share
• From that market share we find out attitude
and then run a Regression analysis on
Brand Attitudes and beliefs to find out the
‘Importance’ ratings
Attributes
MakemyT
rip Yatra ExoHind Others
Importan
ce
Arts & Cultural value 7 6 9 8 b1 = 5
Website quality 7 6 8 7 b2 = 14
Travel experience in
India 9 8 7 8 b3 = 81
Attitude
A1 =
10.58
A2 =
5.29
A3 =
2.47 A4 = 6.17 100
Market Share 0.432 0.216 0.101 0.252 1.000

4. CLASSIFICATION AND REGRESSION TREE
SEGMENTATION
• Here I have taken a representative data set of
customers, based on various demographic
and psychographic attributes
• Then I use CART algorithm on that data set
• This algorithm basically divides the records in
the data set into various decision nodes
based on the criterion mentioned in the
candidate split
• Each split gives us a set of nodes
• We measure ‘Φ’ – the goodness of split for
each candidate splits
• Φ(s|t) = 2 PLPR 𝑗
# 𝐶𝑙𝑎𝑠𝑠𝑒𝑠
| 𝑃 𝑗 𝑡 𝐿 − 𝑃(𝑗|𝑡 𝑅)|
• The split with highest ‘Φ’ value gives us the
desired segment
Custom
er Income Age Internet
Museum
Attendance
Travel to
India
ExoHind
Prospect
1 30k 20-30 No Yes No No
2 50k 30-40 Yes No Yes No
3 80k 50+ Yes Yes Yes Yes
4 30k 20-30 Yes No No No
5 45k 30-40 Yes No Yes No
6 55k 30-40 Yes Yes No Yes
7 65k 50+ Yes Yes Yes Yes
8 40k 20-30 Yes No No No
No Left child node, t(L) Right child node, t(R )
1 Internet = no Internet = yes
2 Travel to India = no Travel to India = yes
3 Museum attendance = no Museum attendance = yes
4 Age <= 30 Age>30
5 Age <= 40 Age>40
6 Age <= 50 Age>50
7 Income <= 30k Income>30k
8 Income <= 50k Income>50k
9 Income <= 75k Income> 75k

5. CART DECISION
TREE DIAGRAM
• Here the root node had all
the records initially
• I choose the behavioral
variable ‘Income’ and
psychographic variable
‘Museum Attendance’ as a
candidate for split among
many others
• The diagram showed on the
right hand side was found to
be the optimal split for the
given data set
• This tells us that out of the 8
records present 3 records
(record no. 3,6 & 7) are
actually our target segment
of customers

6. KANGAROO SEGMENTATION – BAYESIAN MAP
CLASSIFICATION
• In order to find out similar customer
profile, like our loyal customers, among
non-users I use an algorithm called
‘Bayesian Maximum A Priori (MAP)
Classification’
• Here we find a ‘MAP estimate’ of a
customer using our service given some pre
defined criterion
• Using this I could identify two records in
my data set where the ‘MAP estimate’ is
maximum
• These two customers are our loyal
customers
• Any non-user having same demographic
and psychographic profile as these two
customers has the highest potential to
become our customer
Customer
Museum
Attendance
Travel to
India
ExoHind
Prospect
1 Yes No No
2 No Yes No
4 No No No
5 No Yes No
8 No No No
10 No No No
3 Yes Yes Yes 50+, 80k
6 Yes No Yes
7 Yes Yes Yes 50+,65k
9 No Yes Yes
For
Table 1
(when the customer has travelled to India & goes to
museum)
P(Prospect = true) = P(T ꓵ M|EP). P(EP) = 0.5x0.4 = 0.2
P(Prospect = false) = P(T ꓵ M|E̅P̅). P(E̅P̅) = 0x0.6 = 0

7. VARIOUS ELASTICITY
CALCULATION
Price Promo Online ad TV ad Sales
40 10.5 6.5 6 300
30 10.5 6.5 6 380
30 8 11 7 460
40 11 7 10 540
30 13.5 8 6.5 620
30 14 5 13 700
30 12 10 13 780
40 15 7 19 860
40 15 15 15 940
• The Sales Response function is given by –
Q = K. 𝑃 𝑎. 𝑃𝑟 𝑏. 𝑂𝐴 𝑐. 𝑇𝐴 𝑑 ,
where Q = Sales response function,
K= constant,
P= Price,
Pr = Promotion,
OA= Online advertisement
and TA= Television advertisement
• In order to design the test market to find out the
elasticity values, we use the ‘Before-After’
experimental design methodology
• We get sales data from this test market experiment
• We take logarithms and then run a Regression
Analysis to obtain the values of the various elasticity
co-efficient
• These elasticity values will help me in – calculating
Sales response calculations, pricing strategy and
advertisement budget calculation
Elasticity co-efficient
Price (a) -0.734
Promo (b) 0.727
Online ad (c) 0.361
TV ad (d) 0.570

8. BRAND AWARENESS AND ADVERTISEMENT – EMPIRICAL
HEM
• Since ExoHind is a start-up so we need a lot of Brand Awareness campaign
• Semiotics principles will be used to design advertisement
• Advertisements and their copies will be inserted in various media
• Effectiveness of a single advertisement will be measured via HEM stages –

9. MODEL FOR MEASURING BRAND AWARENESS
• I will use Blattberg and Jeuland,1981 model for measuring the effectiveness of a Brand
Awareness campaign
• This model uses ‘Bernoulli advertising exposure process’ and an ‘exponential forgetting
process’ to model awareness
• According to this model the probability of a consumer being aware at time t, is given by –
f(t) = Σr q. (1-q)r-1. 𝒆−𝜶(𝒕−𝒕 𝒓)
,
where 𝛼= retention rate (calculated from the empirical HEM model)
• Assuming we inserted the advertisement campaign in Television media for a period of 7 days,
we get –
• f(t) = 0.34
• This value of f(t) signifies that an the aggregate level of the entire population we can expect
around 34% of the people to be aware of our brand after a certain television advertisement
campaign run for a time period of 7 days.

10. ADVERTISEMENT BUDGET – VIDALE & WOLFE
MODEL
• To establish a sales-advertisement relationship Vidale & Wolfe proposed a model for
determining ‘Advertising Budget’ –
𝑑𝑆
𝑑𝑡
=
𝑟𝐴(𝑡) 𝑀 − 𝑆
𝑀
− 𝜆𝑆
• Where,
𝑑𝑆
𝑑𝑡
= change in sales rate at time t, r = sales response constant A(t) = advertising
expenditure at time t, M = saturation level sales, 𝜆= sales decay constant
• Putting the value of sales from ‘sales response’ function previously, the solution is given by the
integral –
• =
• Finally we get –
• A(t) =
165.𝑃−0.7(4𝑟2+165.𝑃−0.7.𝜆2.𝑀)
330.𝑃−0.7.𝑟
.
𝑒
𝑡
𝑀
. 165.𝑃−0.7.𝑀(4𝑟2+165.𝑃−0.7.𝜆2.𝑀)
+1
𝑒
𝑡
𝑀
. 165.𝑃−0.7.𝑀(4𝑟2+165.𝑃−0.7.𝜆2.𝑀)
−1
−
𝜆.𝑀
𝑟

11. PRICING STRATEGY – DOCKNER & JORGENSEN
MODEL
• We will set our price using competitive pricing
– using Dockner & Jorgensen model (1988)
• This model assumes an open-loop Nash
equilibrium concept where each firm is
assumed to optimize its price time path, given
knowledge of what all other firms are doing
• Optimal price according to this is given by –
Dockner-
Jorgensen Model
This
Year
Next
Year
Next Year
with EH
Differe
nce
Sales
Lost (λ)
Elastici
ty
MMT Market
Share = 88320
11408
1 102599 11482 0.48 0.72
Yatra Market
Share = 44160 57041 51300 5741 0.24 0.76
ExoHind Market
Share = 0 0 23921 0 0.73
Others = 51520 66547 59849 6698 0.28 0.77
Optimal price
= $ 903

12. CHANNEL STRATEGY
• Our primary marketing strategy is to gain
market share so aligned channel objective
would be to reach as many people as
possible
• Initially we would prefer to operate as a
‘click-and-mortar’ store and along with that a
few ‘brick-and-mortar’ stores.
• We would tie-up with international online
travel agencies present in UK, like – Cox &
Kings etc.
• The number of levels to be included in the
channel: 2 levels
• Initially, in the short run we will start off with 1
channel (e.g. – intermediaries) only as we have
financial constraints
• in the long run when we have more profits and
brand equity then we can use that to add more
channels