Conducting an Effective Return-on-Marketing-Investment Analysis
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Conducting an Effective Return-on-Marketing Investment Analysis
Jeff EwaldOwner/Founder
“I know that half of my advertising budget is wasted.
I just don’t know which half.”John Wannamaker
Why is Marketing ROI so important?
• ROI provides immediate and long term benefits
• Focus on results – increased growth and profitability
• Compare marketing investments to other opportunities
• Compare results to expected results (variance analysis)
• Extraordinary focus on every dollar spent during downturn
• Multi-channel marketing makes interrelationships more critical (and difficult) to measure
Why is Marketing ROI so difficult?Complex environment to
analyzeInternal Variables
Product/ Service
AttributesPrice
Positioning
Marketing Activities
DistributionSales force spend
AdvertisingDirect Marketing
Social MediaEvents
PR Promotion
External Variables
Competitive ActivityEconomic Variables
Political StatusSeasonality
General mediaWord of mouth
Consumer tastes/behaviorsConsumer cycles
Trigger points
Situation ofRelative Certainty
Informs range of choices
Situation ofRelative Uncertainty
Informs “best choice”
Test market
Regression
STMsStructural equation models
NeuroNet Models
SimulationGenetic modeling/
Tool Comparison
Putting together a ROMI analysis
1. Crystalize goals
2. Develop hypotheses
3. Determine required data
4. Determine appropriate analysis methodologies
5. Gather data – ETL
6. Conduct analyses to test hypotheses
7. Construct/evaluate/select predictive models
8. Implement … Simulators
My Goal Today:Emphasize the critical importance of clear analysis goals and articulation of specific hypotheses
Goals & Hypotheses• Goal: Determine the key factors, and their
relative weights, which are contributing to increases in sales
• Specific factors hypothesized as possible contributors include: • Weather patterns• Overall economic health• Agricultural and construction industry
employment levels• Changes in cost of critical production input
(i.e. cotton)• Advertising spending
Determining data and analysis requirements
Average Precipitation
S&P 500
Marketing Spend
Average Temperature
Consumer Sentiment
Employment
0 20 40 60 80 100 120
37
38
48
51
61
100
Relative Variable ImportancePredictors of $ Sales• Jobs and consumer
attitudes about the economy
• Weather • Overall marketing spend• Important to note that the
specific industry sector data did NOT emerge – generalized measures of the economy outweigh the ag and construction verticals
• In each market, sales (demand) increases when average monthly temp drops below a specific value
• Influence of weather is a local market phenomenon
Albany Denver
MinneapolisSt. Louis
510
300
650
Impact of temperature varies dramatically by market
.67
Variable importance is similar across DMAs
Live Cattle Futures
Agriculture Employment
Ave Monthly Temperature
Marketing Spend
Wheat Futures
S&P 500
Feeder Cattle Futures
0 20 40 60 80 100 120
34
35
37
50
53
98
100
Minneapolis
Ave Monthly Temperature
Consumer Sentiment Index
Marketing Spend
0 20 40 60 80 100 120
19
86
100
Albany
Marketing Spend
Ave Monthly Temperature
Employment
Labor Force
Consumer Sentiment Index
0 20 40 60 80 100 120
56
72
76
88
100
Denver
CPI
Precipitation
Consumer Sentiment Index
S&P 500
Ave Monthly Temperature
Marketing Spend
0 20 40 60 80 100 120
32
46
83
89
90
100
St. Louis
Goals & Hypotheses• Goals: determine the most
effective marketing efforts and a strategy to improve Leads and Enrollment numbers
• Key Hypothesis: different tools impact different stages of “the funnel”
LEADS
STARTS
ENROLLS
7.5%
65.6%
Leads Coeff Sig Leads per $1000 Cost per LeadTV 0.007 0.981 * 7 142.86$ Website 0.007 0.574 7 142.86$ Web Based Initiatives 0.02 0.988 * 20 50.00$ Outdoor 0.164 0.95 * 164 6.10$ Newspaper -0.027 0.961 * -27Yellow Pages 0.019 0.658 19 52.63$ Radio 0.039 0.953 * 39 25.64$ Direct Mail -0.052 0.935 * -52Event Marketing 0.009 0.159 9 111.11$
• Separate analyses for Leads, Enrollments and Starts
• Example: Leads
Preliminary Results
“Dual Path” Model
16
TVWebsite WBIRadio
LEADS
Out door
Newspaper
Direct Mail
Enrolls
.171.053 .053.376
.369
.268
.983
.059
.908
.-.324
(Structural Equation Modeling)
Goals & Hypotheses
• Goals: determine the optimal levels of key tactics to:
• Drive higher margins/mix-up
• Drive more product sales/volume
• Across a wide range of marketing tactics
Marketing Drivers Ad spend & mix
Tire line promoted Rebate levels
Credit card rebates SPIFFS
External Drivers Gas prices
Miles driven Car park
New vehicle sales Consumer confidence Weather
Outcome Measures Brand $ Sales
Brand Unit sales Brand mix (% of Corp)
Outcome Measures Margin Contribution
Ave Line Margins
Analytical Plan Overview
Goals & Hypotheses (Revised): • Do rebates work to drive volume and/or margin?
• Do some tire lines demonstrate better response to rebates than others?
• How does the rebate AMOUNT impact response rate and margin?
• Do rebate responses change with changes in the external environment?
Original scope too broad … revised project focused on understanding consumer rebates
Stimulus Response• Independent Variables • Dependent Variables
Rebate at a specific $-level
On a specific tire line
Tires Per Day
Ave Line Margin
Almost all rebates impact TPDRelative Impact Index
Non-rebate factors stronger than specific tire line rebate
$ REBATE LEVEL
Tires Per Day
Consumer Sentiment 100Consumer Confidence 98Ad Spend 97Car Park 92Gas Price 91Circulation 90Eagle F1 Asym A/S (new) 20 51Wrangler SR-A 20 49SP Sport Signature 20 47Other brand 20 46Eagle EMT/ROF 80 45Other brand 80 45Assurance ComforTred Touring 80 44Eagle F1 Asym A/S (new) 60 43SP Sport Family - no SP Sport Signature 20 43Graspic DS-3 40 42Wrangler SA 40 41Wrangler Duratrac 20 41Graspic DS-3 20 41SP Sport Family - no SP Sport Signature 60 41SP Sport Winter 3D 80 41Assurance CS Fuel Max 40 40Other Dunlop tire 60 40Assurance CS Fuel Max 80 39Rover HT 40 39Rover HT 80 38
$ REBATE LEVEL
Tires Per Day
SP Sport Winter 3D 40 38Assurance Fuel Max 20 37Wrangler SR-A 40 37Wrangler Duratrac 40 37Assurance CS Fuel Max 60 37Eagle F1 Asym A/S (new) 40 37SP Sport Signature 60 37Assurance Fuel Max 80 36Assurance TT AS 40 36Ultra Grip Winter 40 36Other brand 40 36Assurance ComforTred Touring 40 35Wrangler SA 80 35SP Sport Signature 80 35Wrangler SR-A 80 34Assurance TT AS 80 33Wrangler Duratrac 80 33Wrangler SR-A 60 32SP Sport Family - no SP Sport Signature 40 32Ultra Grip Ice WRT 60 31Graspic DS-3 80 31Assurance Fuel Max 40 30Wrangler SA 20 30Other Goodyear tire 20 30Graspic DS-3 60 30SP Sport Family - no SP Sport Signature 80 30Wrangler Duratrac 60 28
$ REBATE LEVEL
Tires Per Day
Other Dunlop tire 20 28Other brand 60 28Assurance Fuel Max 60 27Signature CS 40 27Other Goodyear tire 60 26Assurance CS Fuel Max 20 23Rover HT 20 23Other Dunlop tire 40 23SP Sport Winter 3D 20 20Other Goodyear tire 80 19SP Sport Winter 3D 60 19Rover HT 60 17SP Sport Signature 40 16Other Dunlop tire 80 16Assurance TT AS 60 13Assurance TT AS 20 12Signature CS 80 8Assurance ComforTred Touring 60 6Eagle F1 Asym A/S (new) 80 5Assurance ComforTred Touring 20 3Wrangler SA 60 2Eagle GT 0Assurance CS TTAS 0Ultra Grip Ice WRT 40 0All Kelly tires 40 0
Fewer rebate offers impact average tire margins …Ranked by Relative Importance Index relative to margin contribution(Random Forest)
Tire Line REBATE LEVELMargin Per Tire
Assurance TT AS 80 97Assurance ComforTred Touring 80 91SP Sport Family - no SP Sport Signature 40 58Eagle F1 Asym A/S (new) 80 43SP Sport Signature 40 42Other Goodyear tire 60 36Assurance Fuel Max 40 34Eagle F1 Asym A/S (new) 60 31Signature CS 40 30Wrangler SR-A 60 29SP Sport Signature 80 26Other Goodyear tire 20 21Wrangler SR-A 40 18Other Dunlop tire 80 18Assurance Fuel Max 80 16Assurance Fuel Max 20 14Wrangler SA 40 14Wrangler SR-A 80 13Wrangler Duratrac 60 12Assurance CS Fuel Max 40 11Other brand 80 11SP Sport Winter 3D 60 10
ROMI Recommendations
• Clear and concise ROMI objectives, coupled with specific hypotheses frames the required data and analysis methods
• Keep each analysis as simple as possible
• More actionable
• Easily repeatable – iterative learning and updating
• Implement the learning – feed it into a process change