how big data is changing retail marketing analytics
DESCRIPTION
Learn how Smart retailers are using advanced revenue attribution and customer-level response modelling to optimize their marketing spends.TRANSCRIPT
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Retail Marketing Analytics
APRIL 2012
MARKETING ANALYTICS AS A SERVICE
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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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UpStream Suite
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Challenges with Multi-Channel Retail
Multi-channel marketers are unsure where to spend their next dollar.
Messy data with many marketing and order channels, disparate databases, various execution platforms
Don’t understand how spending on marketing affects conversion
No easy way to identify the most profitable channels for every customer
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What is Attribution Modeling?
Assigning creditWhat marketing treatments drove my order? How should they share credit?
TargetingWhich customers are most likely to buy?
Cross-channel EffectsDoes marketing in one channel affect other channels?
Incremental ResponseWhich customers are most receptive to catalog? To remarketing? To email?
Strategic AllocationWhat 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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ACCURACY
ME
THO
DO
LOG
Y
Marketing mix models (CPG)
Complex heuristic rules
Weighted, equal or cascading Attribution
Last or first click/touch
Double count sales
LOW HIGH
WEA
KST
RO
NG
Attribution
Current State: Multi-Channel Customer Analytics
• Simple and flexible methods lack accuracy
• Most tools lack offline and brick & mortar data
• Inability to integrate disparate data sources limits multi-campaign view
• Most tools aggregate data to scale, losing customer level detail
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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 all of a marketer’s data into a Hadoop cluster
Apply the rigor of a medical researcher with patented methodology
Know whom to reach
Identify and attribute the revenue drivers
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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.)
BenefitsNo 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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Customer
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Customer
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Attribution Using Time Dependent Models
catalog 1
PURCHASE
search
JANUARY FEBRUARY MARCH APRIL MAY JUNE
Customer
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catalog email catalog
catalog email catalog email 2
$100 PURCHASE
PURCHASE $100 PURCHASE
catalog email 2
PURCHASE $100 PURCHASE
email catalog 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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Common Attribution Buckets
MarketingCatalogEmailDisplay AdvertisingAffiliateComparison Shopping EnginesLink ShareSearch (Non Branded)Loyalty Programs
BaseCustomer DrivenStore LocationSeasonal
Mass MediaNeilsen Data
Special CasedBranded SearchEconomic Conditions
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Case Study: Top Multi-Channel Retailer
AttributionImpact
Presented results that were contrary to company’s expectation; client validated results internally
Within 3 months, reallocated $5MM marketing budget to another channel with more changes to follow
Insights
Marketing is responsible for ~50% of overall sales (offline and online). The other half account for the customer’s buying habit and store trade area.
Ecommerce significantly more influenced by marketing than retail or call-center channels
Direct Load: UpStream credits marketing activities that drove user “navigation” to website.
Catalog
Catalog
Display Remarketing
Display Remarketing
Search
Search
Other
Other
Direct Load
Customer Driven/Trade Area
0%
20%
40%
60%
80%
100%
120%
140%
160%
180%
Before After
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Case Study: Top Multi-Channel Retailer
OptimizationImpact
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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Exploratory Work
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Results in R
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Example Findings
Google keywords often perform worse than you thinkIn many cases 20-40% worse
Display Advertising performs better than you thinkCertain 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 storeOften 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 homeUnfortunately 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 CenterThe impact of season purchasing is almost double that of retail
Tenure of customers show significant differencesNewer customers are more sensitive to marketing, seasonal factors, and store area than established customers (based on tenure).
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Hadoop – Revolution Integration
• ETL• N marketing channels• Behavioral variables• Promotional data• Overlay data
• Functions to read Hadoop output; xdf creation
• Exploratory data analysis• GAM survival models
• Scoring for inference• Scoring for prediction
• 5 billion scores per day per customer
Current State: Revo v6
UPSTREAM DATA FORMAT (UDF)
CUSTOM VARIABLES (PMML)
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UpStream: Architecture Decisions
Pros• Commodity hardware
• Move the code to the data, not the data to the code
• Scale Infrastructure to meet demand
Cons• Complex to debug
• Lack of standards (but improving)
• Staffing
Pros• Cost effective
• 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
Cons• Nothing major to report
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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 dataBoth online and offline
Be statistically relevantGuess work doesn’t count
Scalable and flexibleMake sure you have the right technology platform and tools
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Connect with Us
We’re Hiring San Francisco & SeattleMasters/PhD in Statistics or Biostatistics
Java Developers
HyderabadOperations engineers: Big Data
Conversations with marketersWe’re happy to introduce attribution and help educateabout process and methodology
ContactJohn Wallace, [email protected]
Brandon Mason, [email protected]
www.linkedin.com/company/upstream-software
@UpStreamMPM
www.facebook.com/UpStreamSoftware