零售市场分析(Retail Marketing Analytics)

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Attribution Using Time Dependent Models
JANUARY FEBRUARY MARCH APRIL MAY JUNE
Customer
PURCHASE
$100 PURCHASE
1
Customer
catalog
email
catalog
PURCHASE
$100 PURCHASE
STRONG
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
Attribution
Impact
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
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
MARKETING ANALYTICS AS A SERVICE
Retail Marketing Analytics
APRIL 2012
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1
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
Base
Customer Driven Store Location Seasonal
Mass Media
Neilsen Data
Special Cased
Branded Search Economic Conditions
10
Case Study: Top Multi-Channel Retailer
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
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?
Know whom to reach
7
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.)
180%
160% Direct Load 140% Other 120% Search 100% Display Remarketing 80%
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.
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Exploratory Work
13
Results in R
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Example Findings
Google keywords often perform worse than you think
In many cases 20-40% worse
Display Advertising performs better than you think
ACCURACY
LOW HIGH 6
How do you approach the problem?
Enable retailers to conduct customer-level analysis on big data to understand what motivates individuals to buy.
2
Customer
catalog
email
catalog email 2
PURCHASE
$100 PURCHASE
3
catalog
search
catalog 1
email
catalog 2
email 2
来自百度文库
affiliate
search 1
RECENCY OF TREATMENTS
customer sales catalog email search affiliate catalog
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Common Attribution Buckets
Marketing
Catalog Email Display Advertising Affiliate Comparison Shopping Engines Link Share Search (Non Branded) Loyalty Programs
Locations San Francisco, Seattle, and Hyderabad
John Wallace, CEO
Brandon Mason, CTO
2
UpStream Suite
3
Challenges with Multi-Channel Retail
Multi-channel marketers are unsure where to spend their next dollar.
No easy way to identify the most profitable channels for every customer
4
What is Attribution Modeling?
Assigning credit
What marketing treatments drove my order? How should they share credit?
METHODOLOGY
Marketing mix models (CPG) Complex heuristic rules Weighted, equal or cascading Attribution Last or first click/touch
WEAK
Double count sales
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
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
Current State: Multi-Channel Customer Analytics
Messy data with many marketing and order channels, disparate databases, various execution platforms
Don’t understand how spending on marketing affects conversion
SALES ALLOCATION
email search affiliate
#1 #2 #3
$ $ $
100 100 100
20 20 72
40 15 60
0 0 10
0 0 30
$ $ $
99.98 81.84 40.64
$ $ $
0.02 18.16 0.01
$ $ $
47.03
$ $ $
12.32
Customer Driven/Trade Area 60% Catalog
40%
Other Search Display Remarketing Email Catalog Email
20%
0% Before After
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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