how big data is changing retail marketing analytics

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

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Page 1: How Big Data is Changing Retail Marketing Analytics

1

Powered by:

Retail Marketing Analytics

APRIL 2012

MARKETING ANALYTICS AS A SERVICE

Page 2: How Big Data is Changing Retail Marketing Analytics

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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

3

Customer

1

Attribution Using Time Dependent Models

catalog 1

PURCHASE

search

JANUARY FEBRUARY MARCH APRIL MAY JUNE

Customer

2

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.

Email

Email

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