The objective of this exercise is to showcase your ability to mine data and present actionable insights to non-technical business stakeholders. This file includes 6 tabs, including the instructions tab. Traffic Tab: Includes web site traffic by day Market Tab: Includes Competitors traffic and social media mentions (for the automotive category) Sales Reports: shows sales, by day, by transaction and SKU SKU tab translates SKUs into friendly product names and provide $ values Media Spend Report including spend by channel and day Data is somewhat organized, but will require merging multiple sources together to maximize insights. Data may require some cleansing. The exercise: You have been tasked to providetop actionable insights to address one, or all, of the following business objectives. - Increase awareness - Increase revenue - Reduce/optimize media costs - Sales forecast and media spend for 2010. Please include what channels to continue or discontinue spending on. Also, explain the methodology and reccomended approach to forecasting and marketing mix modelling The audience: mid-senior level, non-technical stakeholders The output: 5-7 slide presentation in PPT, which includes assumptions, approach, analysis and insights. 30 min max.

1. Assumptions
• The data set given is assumed to be an ecommerce sales and revenue model
• This is a B2C module for 2008 and 2009
• The approach to the suggestive revenue model (6th and 7th slides) – Ratio of spend in each media is the same ratio to the Return Of Investment (ROI)
Research Methodology
✓ Data Mining – Cleansed and rearranged data scale into months and quarters for clear vision
✓ Analytics – Performed various calculations to bring out metrics such as
▪ Total revenue & spend, Gross & Gain, PPC, Display & Email Ad Revenue, Ad frequency, Target Audience, Revenue/pageview, SOV- Visits, etc
✓ Data Visualization – Created charts and infographics based on the data parameters for better understanding to the business model
✓ Insights – Co-related various metric parameters to bring out actionable and suggestive insights to improve the business objective
✓ Forecasting – Generated sales and revenue forecasting for 2010 predictions using regression methodology
✓ Case Study – Based on the exisiting spend to yield ratio of Ad campaigns, developed actionable revenue model to reduce, optimize and increase revenue for the forthcoming years
Executive Summary
Introduction–Antuit Assignment
Brand awareness of the company has scope to increase as remaining market
share to be 52%
The Brand Awareness of the competitors is significantly high assuming
because of pricing and Ad strategy advantage compared to the company
Keeping up the High revenue yielding 1st quarter will help optimizing the yearly
ROI
Based on the suggestive revenue model sales and revenue can be
increased by optimizing or reducing the media spend
Created by Sathish Kumaar

2. Brand Awareness
Target Audience
On an average 48%of the total
audience is being targeted
Market Share Visits to
competitor
sites
Visits
46%of
the market is still
available to
increase exisiting
target audience
0k
50k
100k
150k
200k
250k
0m
20m
40m
60m
80m
100m
120m
140m
160m
180m
200m
2008 Qtr1 2008 Qtr2 2008 Qtr3 2008 Qtr4 2009 Qtr1 2009 Qtr2 2009 Qtr3 2009 Qtr4
Frequency of Ad Visitors Visits Page Views Social Media Mentions
Approach – Co-related ad frequency to cast impact on brand awareness among total audience
• According to years 2008 and 2009’s 1st and 4th quarter
• Both year had high audience scope and high exposure of ads.
• The customer behavior suggests that the audience is actively looking to purchase.
• This is an ideal scenario for high conversion rate to increase sales.
• According to years 2008 and 2009’s 2nd and 3rd quarter
• Both year had moderate audience scope with low ad frequency. Thus shows a trend
• Right time to increase the media spend to target audience from the competitors market
• Building an ad promotion would then increase conversation rate against its competitors audience
Note: All metrics are measured in millions and ad frequency is measured in thousands
Created by Sathish Kumaar

3. 0k
50k
100k
150k
200k
250k
0
200
400
600
800
1000
1200
2008 Qtr1 2008 Qtr2 2008 Qtr3 2008 Qtr4 2009 Qtr1 2009 Qtr2 2009 Qtr3 2009 Qtr4
Product Sales Quantity vs Ad Frequency
Windshield
Tires
Tail Lights
Shocks
Radiators
Mirrors
Hoods
Headlights
Ad-Marketing vs Sales
Approach – No. of product sold in each quarter is compared against the frequency of Ad-exposure. This gives a perspective on how the expense in advertising
has impacted the sales
Assumption – It is evident that the ad frequency is sinusoidal to the no of products sold in each quarter where ad campaigns are channelized seasonal to gain
selective product based sales and revenue
• On 2008 quarter 1 , quarter 4 and 2009 quarter 4 has high ad frequency and also reflects high sale figures (more than 1000 units) of windshield, tires and taillights thus
relating ad-campaigns were targeted on those particular products and yielded a total profit of $2290k
• It shows that ad expense can be reduced or optimised in the first and the fourth quarter but can be increased in other quarters to nominalise the budget
and ROI for the whole year
Suggestion – Similar Products sold extensively in competitors market should be targeted on Ad campaigns to grow target audience and sales numbers which
directly increases yearly revenue
Note: All product metrics are measured in SKU – (Stock Keeping Unit) and ad frequency is measured in thousands
Created by Sathish Kumaar

4. Sales & Revenue Forecasting 2010
850
900
950
1000
1050
1100
2008
Qtr 1
2008
Qtr 2
2008
Qtr 3
2008
Qtr 4
2009
Qtr 1
2009
Qtr 2
2009
Qtr 3
2009
Qtr 4
2010
Qtr 1
2010
Qtr 2
2010
Qtr 3
2010
Qtr 4
Sales Forecasted
$0k
$200k
$400k
$600k
$800k
$1,000k
$1,200k
$1,400k
$1,600k
2008
Qtr 1
2008
Qtr 2
2008
Qtr 3
2008
Qtr 4
2009
Qtr 1
2009
Qtr 2
2009
Qtr 3
2009
Qtr 4
2010
Qtr 1
2010
Qtr 2
2010
Qtr 3
2010
Qtr 4
Revenue Forecasted
Approach – The sales forecasted is plotted against the years 2008 and 2009 units
sold data in each quarters using a regression method
• Above forecasted line shows a high sale number in 2010 quarter 1 following
the seasonal wave of previous years
• 2010 sales slows down to a gradual plot during quarter 2 to quarter 4 but did
not drop low as 2009 figures
• The linear regression method thus extrapolates it to a predicted sales of 3967
units for the forthcoming year
➢ Which is 35 and 101 units lesser than the previous years respectively
Approach - The revenue forecasted is plotted against the years 2008 and 2009
revenue earned data in each quarters using a regression method
• The revenue plot is directly proportional to the sale numbers
• The linear regression method thus extrapolates it to a predicted revenue for
the forthcoming year is $3100k
➢ Which is $500k more than the previous years respectively
➢ Thus explaining the number of higher value products such as windshield
and tires are expected to be sold more than the other products offered
Note: All product sales metrics are measured in SKU (Stock Keeping Unit)
Created by Sathish Kumaar

5. 2008,2009 Media Revenue
PPC Ad Campaign
Display Ad
Campaigns
Email
Campaign
$0
$200
$400
$600
$800
$1,000
$1,200
$0 $200 $400 $600 $800 $1,000
Revenue/1kvisitors
Spend/1k visitors
Spent vs Profit
0%
10%
20%
30%
40%
50%
60%
70%
80%
$-
$200
$400
$600
$800
$1,000
$1,200
$1,400
$1,600
$1,800
PPC Ad Campaign Display Ad Campaign Email Campaign
Revenue/1000 visitors Spend/1000 visitors
Profit $ % Profit Share
Media Profit Index
Media profit index bubbles – describes the size of profit earned and the space where it stands between investment and revenue
Spent vs Profit – explains the ad campaign Spend to Earn ratio and the profit shared among other media campaigns
• PPC Ad Campaign
➢ The most profitable campaign Sharing 75% of the profit and stands at 38% of money spent
• Display Ad Campaign
➢ Very low profit earning campaign sharing 6% of profit share but stands tall at 49% of money spent
• Email Ad Campaign
➢ Moderate performing Ad campaign sharing just 13% of money spent yet still returns 19% of profit earned
Created by Sathish Kumaar

6. To Reduce/Optimize Ad Spend for 2010
PPC Ad Campaign
Display Ad…
Email
Campaign
$0
$200
$400
$600
$800
$1,000
$1,200
$1,400
$0 $200 $400 $600 $800 $1,000
Revenue/1kvisitors
Spend/1k visitors
Spent vs Profit
0
10
20
30
40
50
60
70
80
$0
$200
$400
$600
$800
$1,000
$1,200
$1,400
$1,600
$1,800
PPC Ad Campaign Display Ad Campaign Email Campaign
Revenue/1000 visitors Spend/1000 visitors
Profit $ % Profit Share
Media Profit Index
Note: This model is a suggested to reduce and optimize Ad expense
Approach – Display Ad expense share of expense is controlled to 25% whereas PPC and Email Ad campaign expense is optimized to 50% and 20% respectively
• PPC Ad Campaign
➢ This remains to be the most profitable campaign Sharing 76% of the profit and stands at 50% of money spent
• Display Ad Campaign
➢ Being moderately invested campaign sharing 25% of amount spent earned 2% of profit share
• Email Ad Campaign
➢ The lowest invested Ad campaign sharing just 20 % of money spent yet still returns moderately 22% of profit earned
• This approach returned $2064 /1k visitors as revenue leaving $27 more profit and running cost is $84 cheaper than the existing model
Created by Sathish Kumaar

7. To Increase Revenue Spend for 2010
PPC Ad Campaign
Display Ad
Campaigns
$0
$200
$400
$600
$800
$1,000
$1,200
$1,400
$1,600
$1,800
$2,000
$0 $500 $1,000 $1,500 $2,000
Revenue/1kvisitors
Spend/1k visitors
Spent vs Profit
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
$0
$200
$400
$600
$800
$1,000
$1,200
$1,400
$1,600
$1,800
PPC Ad Campaign Display Ad Campaign Email Campaign
Revenue/1000 visitors Spend/1000 visitors
Profit $ % Profit Share
Media Profit Index
This model is a suggested to increase Ad revenue significantly:
Approach – PPC Ad expense share of expense is boosted to 65% whereas Display and Email Ad campaign expense is controlled to 20% and 15% respectively
• PPC Ad Campaign
➢ This becomes most profitable campaign Sharing 85% of the profit and stands at 65% of money spent
• Display Ad Campaign
➢ Discontinuing display ad will cost a drop in brand awareness thus optimum investment of 20% is shared for a return of 2% profit share
• Email Ad Campaign
➢ This Ad campaign was controlled to 19% to maximize to PPC performance yet still returns moderately 14% of profit share
• This approach returned $2,227/1k visitors as revenue leaving $190 more profit than the existing model at the same running cost
Created by Sathish Kumaar