Retail Marketing Analytics

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1 MARKETING ANALYTICS AS A SERVICE Retail Marketing Analytics APRIL 2012 Powered by: 1

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 2

3 UpStream Suite 3

4 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 4

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 ? 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? 5

6 Current State: Multi-Channel Customer Analytics WEAK STRONG METHODOLOGY Complex heuristic rules Marketing mix models (CPG) Weighted, equal or cascading Attribution Last or first click/touch Double count sales Attribution 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 ACCURACY LOW HIGH 6

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 all of a marketer s data into a Hadoop cluster Apply the rigor of a medical researcher with patented methodology Identify and attribute the revenue drivers Know whom to reach 7

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 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 8

9 Attribution Using Time Dependent Models JANUARY FEBRUARY MARCH APRIL MAY JUNE Customer 1 PURCHASE catalog catalog $100 PURCHASE Customer PURCHASE $100 PURCHASE 2 catalog catalog 2 Customer PURCHASE $100 PURCHASE 3 catalog search catalog 1 catalog 2 2 affiliate search 1 RECENCY OF TREATMENTS SALES ALLOCATION customer sales catalog search affiliate catalog search affiliate #1 $ $ $ 0.02 $ - $ - #2 $ $ $ $ - $ - #3 $ $ $ 0.01 $ $

10 Common Attribution Buckets Marketing Catalog 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 10

11 Case Study: Top Multi-Channel Retailer Attribution 180% Impact 160% Direct Load Presented results that were contrary to company s expectation; client validated results internally 140% 120% Other Within 3 months, reallocated $5MM marketing budget to another channel with more changes to follow 100% Search Display Remarketing Insights 80% 60% Catalog Customer Driven/Trade Area 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 40% 20% 0% Before Other Search Display Remarketing Catalog After Direct Load: UpStream credits marketing activities that drove user navigation to website. 11

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 12

13 Exploratory Work 13

14 Results in R 14

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). 15

16 Hadoop Revolution Integration Current State: Revo v6 UPSTREAM DATA FORMAT (UDF) Functions to read Hadoop output; xdf creation Exploratory data analysis GAM survival models CUSTOM VARIABLES (PMML) ETL N marketing channels Behavioral variables Promotional data Overlay data Scoring for inference Scoring for prediction 5 billion scores per day per customer 16

17 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 Cons 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 17

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 18

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 Brandon Mason, CTO 19

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