Learn how Smart retailers are using advanced revenue attribution and customer-level response modelling to optimize their marketing spends.

1. MARKETING ANALYTICS AS A SERVICE
Retail Marketing Analytics
APRIL 2012
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2. Who we are
Company Overview
Experienced team with a proven history of solving difficult analytics
problems for Fortune 500 companies
Cloud-based software to manage marketing’s big data problems:
customer level revenue attribution and multi-channel optimization, triggered
marketing, and planning and reporting
Locations San Francisco, Seattle, and Hyderabad
John Wallace, CEO Brandon Mason, CTO
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3. UpStream Suite
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4. Challenges with Multi-Channel Retail
Multi-channel marketers are unsure where to spend their next dollar.
Messy data with many Don’t understand how spending No easy way to identify the
marketing and order channels, on marketing affects conversion most profitable channels for every
disparate databases, various customer
execution platforms
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5. What is Attribution Modeling?
Assigning credit
What marketing treatments drove my order? How should they
share credit?
Targeting
Which customers are most likely to buy?
Cross-channel Effects
Does marketing in one channel affect other channels?
Incremental Response
Which customers are most receptive to catalog? To
remarketing? To email?
Strategic Allocation
What is the optimal way to spend my next marketing dollar for a
specific customer? For group of customers? Or my whole file?
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6. Current State: Multi-Channel Customer Analytics
STRONG
• Simple and flexible methods lack
Attribution
accuracy
• Most tools lack offline and
METHODOLOGY
brick & mortar data
Marketing mix
models (CPG) • Inability to integrate disparate data
sources limits multi-campaign view
Complex heuristic rules
• Most tools aggregate data to scale,
losing customer level detail
Weighted, equal or
cascading Attribution
Last or first click/touch
Double count sales
WEAK
ACCURACY
LOW HIGH
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7. How do you approach the problem?
Enable retailers to conduct customer-level analysis on
big data to understand what motivates individuals to buy.
Assemble and standardize Apply the rigor of a medical Identify and attribute Know whom
all of a marketer’s data into researcher with patented the revenue drivers to reach
a Hadoop cluster methodology
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8. Advanced Revenue Attribution
What is it?
Data-driven time-to-event statistical modeling used to establish an objective and accurate
revenue distribution, all done at the individual user level
Patent pending methodology for attributing marketing spend per user
“Big Data” platform that handles all of a company’s online and offline data (sales, web analytics
logs, catalog and email send data, display and search advertising logs, etc.)
Benefits
No need to retag your site with more pixels – use existing data sources
Incorporate non traditional elements into your attribution, the methodology is flexible.
Participate in the modeling process
Plan and allocate spend for each marketing channel based on actual performance
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9. Attribution Using Time Dependent Models
JANUARY FEBRUARY MARCH APRIL MAY JUNE
Customer PURCHASE $100 PURCHASE
1 catalog email catalog
Customer PURCHASE $100 PURCHASE
2 catalog email catalog email 2
Customer PURCHASE $100 PURCHASE
3 catalog search catalog 1 email catalog 2 email 2 affiliate search 1
RECENCY OF TREATMENTS SALES ALLOCATION
customer sales catalog email search affiliate catalog email search affiliate
#1 $ 100 20 40 0 0 $ 99.98 $ 0.02 $ - $ -
#2 $ 100 20 15 0 0 $ 81.84 $ 18.16 $ - $ -
#3 $ 100 72 60 10 30 $ 40.64 $ 0.01 $ 47.03 $ 12.32
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10. Common Attribution Buckets
Marketing
Catalog
Email
Display Advertising
Affiliate
Comparison Shopping Engines
Link Share
Search (Non Branded)
Loyalty Programs
Base
Customer Driven
Store Location
Seasonal
Mass Media
Neilsen Data
Special Cased
Branded Search
Economic Conditions
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11. Case Study: Top Multi-Channel Retailer
Attribution 180%
Impact 160%
Direct Load
Presented results that were contrary to 140%
company’s expectation; client validated Other
results internally
120%
Within 3 months, reallocated $5MM Search
marketing budget to another channel 100%
Display Remarketing
with more changes to follow
80%
Customer
Insights 60% Catalog
Driven/Trade Area
Marketing is responsible for ~50% of overall
sales (offline and online). The other half 40% Other
account for the customer’s buying habit and Search
store trade area. 20% Display Remarketing
Email Catalog
Ecommerce significantly more influenced by 0%
Email
marketing than retail or call-center channels Before After
Direct Load: UpStream credits marketing
activities that drove user “navigation” to
website.
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12. Case Study: Top Multi-Channel Retailer
Optimization
Impact
Already field tested head-to-head against industry leading model
+14% lift in response rate
+$270K in new revenue in a single campaign
Reallocated marketing circulation: identified best prospects to not mail that were likely to
purchase without receiving catalog
Scored 22MM households with 9 models all in the cloud
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13. Exploratory Work
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14. Results in R
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15. Example Findings
Google keywords often perform worse than you think
In many cases 20-40% worse
Display Advertising performs better than you think
Certain types of display, such as retargeting, performs better than you think and can have strong influence
especially at retail stores, which most attribution tools fail to pick up
Custom loyalty has the most impact at the retail store
Often retail sales are due to habit and loyalty, but the same trend doesn’t hold online
Retail sales are influenced by the presence of a store near home
Unfortunately the inverse is also true, web purchases are not typically driven by having a store nearby
Seasonal is much stronger at Internet than Retail or Call Center
The impact of season purchasing is almost double that of retail
Tenure of customers show significant differences
Newer customers are more sensitive to marketing, seasonal factors, and store area than established
customers (based on tenure).
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16. Hadoop – Revolution Integration
Current State: Revo v6
• Functions to read Hadoop output;
xdf creation CUSTOM VARIABLES
UPSTREAM DATA
FORMAT (UDF) • Exploratory data analysis (PMML)
• GAM survival models
• ETL • Scoring for inference
• N marketing channels • Scoring for prediction
• Behavioral variables
• 5 billion scores per day
• Promotional data per customer
• Overlay data
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17. UpStream: Architecture Decisions
Pros Cons
• Commodity hardware • Complex to debug
• Move the code to the data, not the data to the • Lack of standards (but improving)
code
• Staffing
• Scale Infrastructure to meet demand
Pros Cons
• Cost effective • Nothing major to report
• Scale & Performance (increase 4x
with Revo Scale R)
• RevoScaleR package on 50MM records
• Brilliant and growing user community, which
positively impacts hiring
• Ongoing Hadoop/Revo support
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18. Summary
The World is Changing:
The way customers are purchasing services is changing
Managing marketing budgets in the multi-channel world is challenging
Understanding attribution is critical to successfully deploy your marketing budget
To Be Successful, Your Attribution Solution Should:
Cover all of your data
Both online and offline
Be statistically relevant
Guess work doesn’t count
Scalable and flexible
Make sure you have the right technology platform and tools
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19. Connect with Us
We’re Hiring
San Francisco & Seattle
Masters/PhD in Statistics or Biostatistics
Java Developers
Hyderabad
Operations engineers: Big Data
Conversations with marketers
We’re happy to introduce attribution and help educate
about process and methodology
Contact
John Wallace, CEO www.linkedin.com/company/upstream-software
jwallace@upstreamsoftware.com
@UpStreamMPM
Brandon Mason, CTO www.facebook.com/UpStreamSoftware
bmason@upstreamsoftware.com
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