Download - Marketing return On Investment Modelinig
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Developing a ROMI AnalysisIntroduction and Discussion
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Agenda
Overall Approach
GMAX™ Modeling– Benefits
Case examples
Discussion– Data availability
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Methodology Overview
Build database
Normalize data from various sources
Build parallel models to look at different variables and variable combinations
Refine models (focus on DMAs with enough advertising data to make confident conclusions)
Generate and test hypotheses with models
Find themes that emerge from the models
Translate mathematical results into actionable business recommendations
Drill down to gain better
understanding of relationships
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Why is the OG ROMI approach different?
Proprietary tools and data platforms permit examination of more variables– Typically 3 orders of magnitude more
Identifies complex relationships and patterns in the data– Interactions– Curvilinear functions (multi-order polynomials)
Models the real-world environment
“X in combination with Y”
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MethodologyThe approach/methodology used a combination of analytical techniques
GMAX – Genetic Approach
- 10s of thousands of model combinations
- Determines important variables
Regression
- Seeks to understand and calibrate individual variable influence
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Classic Regression
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Genetic Programming
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GMAX™ Benefits
Cost-effective examination of more variables– Including non-linear relationships– …and interactions between variables
Helps avoid “errors of omission” (filtering potentially useful data based on heuristics or prior experience of the modeler)
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Case example
HP: Small Business Target
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Project Objective
Develop model and understanding of relationships between marketing expenditures and sales
Direct MailCatalogPrint AdsEmailsOnline AdvertisingAdvertisingPricingCustomer AwarenessCustomer ExperienceSalesMarket Share
Total Sales $
ClientControlled
AttitudinalOutcomes
SalesOutcomes
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Print Costs While print costs appear in the GMAX model, the
relationship is not clearly seen in graphical analysis of print costs by themselves
Print Out of Pocket
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Marketing Communications Variable Tree
Share of voice, print, online, and direct mail all have an affect on sales Sales
Shipments
Prod BShare of voice
Prod AShare of voice
PrintOut of pocket
DirectMail
PrintOut of pocket Online costs
Note how Print has an impact by itself AND in combination with Direct Mail
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Catalog Circulation
Higher volumes of catalogs correspond with higher sales volumes
– Diminishing returns at approximately 160,000 pieces– Suggests that higher cost catalogs (i.e. CPM) produce results
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Other variables examined: Supply Chain
An example from one set of modeling …
Variable RelativeFrequency
AvFqcy Variable
1.83000 PRTTOOP Print out of pocket costs
1.07000 WTPcompAP Wtd average pricing vs Comp A
0.75000 Category SOVShare of voice
0.53500 BRANDSOV Share of voice
0.31000 CATTOOP Catalog out of pocket costs
0.15500 PRTCIRC Print circulation
0.08500 SOVSOM Share of voice / Share of market ratio
0.06500 WTD compaPR Wtd average proposed pricing vs. CompA
0.06500 SERSPEND Total spending on XXXX
0.04500 BACKDOL $ value of backlog
0.04500 CATQTY Catalog circulation (quantity)
0.03000 SPCompBPR Proposed price vs CompB
0.02500 DIRMAIL Direct mail circulation
0.01000 SPRICE Price vs CompS
0.00500 DMCOST Direct mail total out of pocket costs
0.00500 BACKQTY Backlog quantity (units)
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This analysis yields a moderately complicated, but understandable and interpretable model
Total Sales $ =
240,000,000 + 1.984 *
((Print Out of Pocket $/Print Circulation) *
(Catalog Out of Pocket $) *
(Weighted Pricing vs. compA/Weighted Proposed Pricing vscompA))
+ $335 * (Direct Mail Cost)
ROMI Model
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Sales per $
For most marketing expenditures it was possible to calculate an estimated of return (SMB sales) per dollar spent
SMB Total Sales Est Return per $
Total SPO Expense 6.6Online Total Costs 77SMB Catalog (per cat) 32Catalog $ 76
1% price change vs IBM 7,644,000 One article 1,339,000
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Case example
U.S. Navy: Recruiting
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Lead Contract Lag
Although the lag between a lead and a contract varies, almost 60% of contracts are signed within 4 months of generating a lead
Lead-Contract Lag
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Cume58.2%
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Positive Impact on Leads
Spending non-Production
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Television
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TV Spend
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Internet CPL
Each of these variables contribute to lead generation at > 85% CL
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Influence on Leads per $ Spend
Spending Category Leads per $100,000 Sig. LevelInternet CPL 3329 83%Internet 1817 86%Direct Marketing (Total) 1239 91%Radio 1072 98%Television 277 97%Total Media Spend 209Out of Home -1880 90%Emerging NSMedia Event NSPrint NSInternet Search NS
Of the variables that have a positive correlation with leads, Internet (specifically CPL), Direct Marketing, Radio and TV have the highest rate of return per an additional $100K of spending
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Positive Impact on Contracts
Spending non-Production
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Cat Television
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DM Total
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Internet CPL
Each of these variables contribute to contracts at > 85% CL
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Contracts
Total media spend
Media Events
War Handling
Media Events
Media Events work by themselves, but also act as catalysts to Print and Direct Marketing
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Contracts: Influence per $ Spend
Spending Category Contracts per $100,000 Sig. LevelInternet Search 200 80%Internet CPL 107 97%Media Event 32Internet 29Direct Marketing (Total) 29 94%Print 21Television 3Total Media Spend 2.4 80%Radio NSEmerging NSOut of Home Negative 99%
Of the variables that have positive correlation with number of contracts, Internet (specifically CPL and Search), Direct Marketing and Media Events have the highest rate of return per additional $100K spent
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Implication: Media Influences
LEADS
CONTRACTS
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Internet CPL
Direct Marketing
Media Events
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Discussion
Data requirements –– As many variables as you are able to identify– OG supplements with secondary data sources and
GIS database information
Time periods – Ideally 2-3 years of back-data depending on
category, and if it is monthly/weekly
Data format– It doesn’t matter …we do the Extraction,
Transformation and data Loading (ETL) work
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In Summary…
Marketing Analytics…not just “market research”– Primary data + secondary data– Integrate and synthesize data from throughout the
company – not just research data but also sales, marketing plan investments, etc.
Using proven tools and templates… – We frame data and convert to actionable information
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Intersecting marketing, science and technology™