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Direct Marketing Data Analysis

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Our Approach

Understanding

the Data

Analysis using

Statistical Tools

DrawingInferences

MakingRecommendations

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Our Goals

Our consultation aims to help achieve the following business goals

Increase Revenue

Expand existing customer base (both acquisition and retention)

Identify problem areas ( and propose solutions for them)

Capitalize on untapped opportunities

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Data Comprehension

303

697

No of Customers

New Existing

506494

No of Customers

Male Female

498502

No of Customers

Unmarried Married

710

290

No of Customers

Close Far

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Data Analysis

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0.74

0.84

0.38

Prev Year This YearExisting New

64.3%

Observations

Overall, increase in revenue over

last year has been 64.3%

If we filter down to just the exitingconsumers, the increase in

revenue comes out to be 13.8%

The company seems to be getting

adequate revenue from new

clients. They should tap theseconsumers as a potential segment.

AUD Millions

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Observations

4%

9%

20%

26%

21%

14%

5% 1%

Revenue Segmentation based on

Salary

81% of revenue generated comes

from 65% of population

Salary between $ 40,000 and $1,20,000

As the salary/income of a family

decrease, we observe a decline in

the amount spent. Targeting this

audience with enticingpromotions is likely to change the

trend.

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Observations

13%

24%

29%

35%

Catalogues sent & Revenue

6 12 18 24

The amount spent by a consumer isdirectly proportional to the number of

catalogues he/she receives

Correlation value of 0.473 between

Amount Spent and No of catalogues

R-Square = 0.71, Coefficient = 42.71, p-

value ~ 0

More aggressive the direct marketing

campaign, higher the revenue generated.

Since this seems to be the only marketing

activity to draw reference from, the

company should increase the number of

catalogues sent to potential segments

(after considering the costs involved).

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Observations

Married population contributes 69% of

the total revenue as opposed to

singles, who contributes a 31% share.

For the married couples, the combined

income data is likely to influence the

higher salaries and further, the higher

amount spend.

31%

69%

Revenue & Marital Status

Unmarried Married

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Observations

15.1113.7

Marketing Effort across Customer

Base

Existing New

As stated before, total increase in

revenue is 64.3%

New Consumers: 50.5%

Existing Consumers: 13.8%

However, average number ofcatalogues sent per consumer is

slightly higher for existing consumers.

While the significance of client

retention cant be overstated, it could

prove hazardous to overlook thepotential of client acquisition, as a

majority of increase in revenue is

coming from latter.

Average Catalogue sent per consumer

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Observations

Out of the 697 existing consumers

the revenue from 294 consumers

decreased over last year, thus a

change in approach is required for

the segment.

Correlation value of0.535 between

Amount Spent and Previous Spent

R-Square = 0.71, p-value ~ 0

Last Year

Revenue

Decreases

Revenue

Increases

697294

403

This Year

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Opportunities

Correlation value of-0.222 betweenAmount Spent and Number of Children.

R-Square = 0.71, Coefficient = -203.47, p-value ~ 0

Revenue generated is inverselyproportional to number of kids in family.

The company can tap the segment byintroducing lucrative offers related tokids products.

The company should aim to maintain abalance between consumer acquisitionand consumer retention. It might be agood idea to approach both segments

with different marketing strategies. The existing clients could be offered

loyalty bonuses whereas, newconsumers could be brought on-board byoffering newbie discounts.

1,407

1,220

941831

0 1 2 3

No of Children

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Threats

It is observed that people living in

vicinity of similar shops tend to spend

$ 535 less per person on an average as

compared to consumers who live In

the middle of Nowhere.

It is likely that these consumers have

other options to choose from, which

results in a decrease in average

amount spent on this company

1,596

1,062

Close Far

50%

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Methodology

Correlation

Regression Analysis

Descriptive Analysis

Pivot Table Analysis

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Thank You