Multiple Regression modelling and mkt mix

1. CASE STUDY
How to determine optimal mkt
mix using a Multiple Regression
Model

2. Agenda
Questions that needed to be addressed
• What major trends can you discern from the data?
• What relationship is there between paid search and organic search?
• Please explain any anomalies or inconsistencies you see in the data.
• Which marketing channel (paid, affiliate, display, aggregator) do you
think is most effective at driving sales and why? Please include internal
marketing banners as part of your analysis. Assume all external media
is equal in terms of cost per visit and all internal banners are effectively
free.
• What would you recommend to help increase sales?
• Anything else interesting that you see within the data.

3. Snapshot
Seasonal Trend for HP and Prod1
Channel Visits
Metrics
Avg. Weekly Visits (Channel) 23,393
Avg. Weekly Visits (Funnel) 6,275
Total Annual Sales 36,607
Avg. Weekly Sales 704 (508)
% CTR 11%
Declining CTR Qualities rather than Quantities

4. Recommendations
Prod1 Sales f(x) = ������������. ������ + ������. ������������ ������������ + ������. ������������ ������������ + 0.2 ������������
Where: Constraints being:
������0 = Organic Non-Branded Search 1.7 K ≤ ������0 ≤ 2.2K
������1 = Paid Branded Search 1 K ≤ ������1 ≤ 1.3K 690 Sales pw
������2 = Aggregators 1 K ≤ ������2 ≤ 2K
������3 = Other External Referrers Sites 750 ≤ ������3 ≤ 1.2K
 Any policy that increases Organic Search over 2.5k but down 3.75K visits pw will create a detrimental
effect over Prod1 Sales. For value over 3.75K the growth ratio will be 72:27. Organic Branded Search
follows a too random path.
 Paid non-Branded Search negatively affects Prod1 Sales both directly (negative covariance) than
indirectly creating an unwanted raised on aggregators, affiliates and display campaign associated costs.
 Affiliate contracts need to be reviewed as a matter of urgency. Only level of visits lower than 750pw
won’t negatively affect Prod1 Sales.
 Any In-House Adverts produced a negative impact on Sales and it’s likely to deteriorate our customers’
experience.
 Display Campaign and Direct Entries trends are directly related to other advertising tools, likely to
represent duplicate prospects.

5. Other Recommendations
Hitches & Solutions
Entries From All Step1 Funnel
Channels Visits Total Sales
Entries By Channels
Step1 Funnel By Channels
Sales By Channels

6. Calculus
An Explanation of the Results

7. Search Organic
Top Sales for values between 1k and 2k Insights
or 3.7k and 4.5k
• Organic Search Visits are likely to generate
sales for value in the range of either 1K to
2K or 3.7K to 4.5K.
• Increasing the Visits from Organic Search
negatively affects Step1 Apps Visits and it’s
likely to skew %CTR.
• The relation between organic search and
Total sales is 72:28for value over 3.7K.
Independent variables

8. Search Organic
Branded vs. Non-Branded
Insights
Optimal Values between 1.75K and 2.25k
• Visits from Organic Non-Branded Search
rather than any other organic search
channel are more likely to generate Prod1
sales.
• 3 in 4 of those sales are likely to be “e-
signature” sales.
Too random

9. Search Paid
High Level of Paid Search will Insights
negatively affect Prod1 Sales
• High Level of Paid Search might negatively
affects prod1 Sales for value over 2k.
• This is due to the increasing percentage of
budget being spent on Non-Branded Paid
Search that has a negative covariance with
all Sales options but the most expensive “at
home” printing.

10. Search Paid
Insights
Top for values in the range of 1k to 1.3k
• Tot Sales Prod1 is optimal for values of
Branded Search between 1k to 1.3k.
• Increasing ad spend in Paid Branded
Search will increased Tot Sales, whilst
decreasing our affiliates spending.
• Visits from Paid Branded Search are less
likely to include an interactive action with in-
house Advertising.
• Increasing Paid Branded Search ad Spend
will also generated slightly less costs
associated with Aggregators and Display
Campaign.

11. Search
A Comparison
Insights
• Organic and Paid Search are independent.
(slightly negative for higher value of org.
search)
• Increasing Paid Branded ad Spend will also
produce a benefit effect over Org. Non-Branded
Search enhancing this channel sales.
• Increasing Org. non-Branded Visits will also
help us in limiting affiliates, aggregators and
display adv spends.

12. Marketing
Affiliates
Insights
Sales increased with visits < 750 pw
• Entry Visits from Affiliates are the least likely to
produce sales compared with other MKT
channels.
• Increasing ad Spend on Affiliate marketing will
deteriorate our organic search positioning
(especially Non-Branded) and is likely to
produce a counterbalance growth in our
aggregators cost per visits.
• Visits from Affiliate will also generated additional
Step1 Apps visits and thus flattering our %CTR.

13. Marketing
Aggregators
Insights
Aggregators (Visits range 1K to 2K)
• Visits from Aggregators are more likely to
conclude with a e-Signature Sales .
• Increasing aggregators costs per visits will lead
to increased to paid non- branded and affiliates
costs per visits.
• Maintaining Aggregators entry visits in the
range of 1k to 2k per week will also lead to a
potential increased in Direct Entry Visits and so
to an increased in additional sales at no costs.

14. Marketing
Other External Referring Sites
Insights
Top Values: 750 to 1.2k
• Increasing Visits from Other Referring Sites
will lead to Increasing entries via Search
Engines.
• Additional entry visits via this channel is
likely to positively affect e-signature sales.
• Optimum values in the range 750 to 1.2k.

15. Marketing
Other External Referring Sites
Other External Referring Sites Insights
• For value over 1K will probably negatively
impact aggregators (that has a better
conversion rate) and affiliates entries.
• Nonetheless, for value over 1k will act as
Organic Non-Branded Search spin-over.

16. Marketing
Internal Referrers Insights
• Internal Referrers impact negatively on
Sales target for any values but around 6K.
• Nonetheless, Sales via this channel are
likely to be “home printing”.
• Whoever navigate via Internal referrers is
also more likely to engage with any Internal
Display Ad.
Internal Display Ad
• It has a typical hyperbole curve.
• Has the highest negative covariance with
both Total and e-signature Sales.
• For a better understanding how its variation
influence sales, we need to test its
effectiveness according to its location on
site.

17. Marketing
Others
Insights
• Data are too random to have a clear
pictures.
• It has high impact on Paid non-branded
search, affiliates and aggregators.
• Although it has a positive covariance with
Prod1 Sales this might be due to a kind of
cannibalization effect.
• Direct entries increased in the same
direction of aggregators and paid search.
They are probably duplicate prospects.
• It has high impact on Prod1 Sales, but it’s
more likely to driven both home/central
printing rather than e-signature sales.

18. Inconsistencies

19. Major Issues
Hitches & Solutions
 67-Week Timeframe reduced to 52-week for behavioural & Trending
reasons.
 Missed or inconsistent data (e.g. Tot Sales Prod1 figure omitted in cell
36) resolved:
 Last 4 entries for Tot Org. Search Entry Visits didn't match the sum of Branded and
non-Branded Org Search Entries due to a bad tagging issues now resolved.
 Missed entry for Paid Search (All) in Cell 30 (and errors in Cell 20 – or weeks
20/10/08 and 27/10/08) – resolved comparing Atlas and Google AdSense stats for
the same period.
 Missed entry for Internal Referrer for the week 26/05/08 – compared stats with log-
file and applied best-fit methodology.
 Total sales Prod1 data missed – highly correlated with Visits to Step1 Apps – used
regression modelling, double checked results using log-files.
 Sales (at home printing) data missed to: proportional methodologies using Sales
central printing stats.
 Inconsistency in trend for External Referrers during the first 4-5 weeks – solved
used best fit modelling.
 Inconsistency with Affiliate and Aggregate figures (from Oct to Jan) due to bad
tagging – solved comparing Atlas data and applying best fit methodologies.
 Still some issues unresolved (high level of Avg. Delta on Median) due to overlapping
stats

20. Inconsistencies
Some Examples

21. Inconsistencies
Some Examples

22. Inconsistencies
Some Examples

23. Q&A?