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Multiple regression and optimal mkt mix

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Multiple Regression modelling and mkt mix

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Multiple regression and optimal mkt mix

1. 1. CASE STUDYHow to determine optimal mktmix using a Multiple RegressionModel
2. 2. AgendaQuestions 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. 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. 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. 5. Other RecommendationsHitches & Solutions Entries From All Step1 Funnel Channels Visits Total Sales Entries By Channels Step1 Funnel By Channels Sales By Channels
6. 6. CalculusAn Explanation of the Results
7. 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. 8. Search OrganicBranded 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. 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. 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. 11. SearchA 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. 12. MarketingAffiliates 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. 13. MarketingAggregators 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. 14. MarketingOther 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. 15. MarketingOther 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. 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. 17. MarketingOthers 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. 18. Inconsistencies
19. 19. Major IssuesHitches & 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 didnt 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. 20. InconsistenciesSome Examples
21. 21. InconsistenciesSome Examples
22. 22. InconsistenciesSome Examples
23. 23. Q&A?