how we did it: the case of the retail tweeters

How We Did The Investigations

The Case of the Retail Tweeters

Brought to you by

And Our Partners

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We’re Getting A Lot of Questions …

Hi Everybody,

We’re the brains behind the scenes and wanted to answer your questions about “how we did the Buzz Experiments so fast.”

This write-up will give you an idea of our clients’ architecture and some details of the BI screens.

Take a look, and if you still have questions, shoot them to us! We’re both on Facebook.

Yours truly, Neuman Hitchcock & Chi Tylana


Brizio Fashion Debate Between Giorgio (CEO) and Martina (CMO)

...Yes, it’s real!And I need some budgetto prove that to you!

Is Social Media real or a fad? And what does it mean to our company?


Martina Kicks Ideas Around with BSI

It’s all about viral marketing!

• Giorgio gave us $150K to do experiments• Can we pick out Products with “hot buzz”? • Can we use that info to drive forecasts / pricing

up and down (or discontinue)?


We Put Three BSI Investigators on This Case

• Chi was the Lead Investigator> Discovered profitable customers> Who also are using social media

• Mathieu – Buzz Experiments > “Hot and cold buzz”> Used sentiment analytics

• Cody – Viral Marketing> Created “Fashionfluencer” social graphs> Drive a campaign to see if early

adopters drive extra sales


Brizio Fashion Gave Us Access to their Active Data Warehouse from Teradata

This company has• 21M customers• Average revenue per customer $62.58/month

Teradata• 24 TB Active Data Warehouse at our fingertips

> 2-node dev/test system> 4-node DW system, 5 yrs of data> Teradata Retail Industry Logical Data Model - contains integrated

enterprise data, including POS, Contact Center, Web Clicks, Social Media feeds (new – Tweets, blogs, email responses and forwards)

> Right-time active data feeds from order entry, contact center, and web/mail systems (< 15 minute latencies)


CRMCRM

Corp LAN

Brizio’s System Architecture

Teradata Production4 Nodes 5600H

Teradata Production4 Nodes 5600H

Dev – 1 Node 5550H Test – 1 Node 5550H

Dev – 1 Node 5550H Test – 1 Node 5550H

ReportingReporting

24TB

Sentiment Analytics Sentiment Analytics Teradata

Relationship Manager


Social Media Data ModelSegment of Teradata’s Retail Data Model

Buzz-Based Marketing Experiments – Matt

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Pricing Experiments

• Based on Tweet and Blogs, can we pick out new products with “hot buzz” and “cold or no buzz”?

• Possible Actions: > Don’t drop price as fast

(or increase it) on “hot buzz”

> drop faster than normal for “cold buzz”

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“Sentiment Analytics” on Tweets

• We can use any of these Teradata Partners:> Attensity – Response for Social Media Teams, Attensity 360,

Analyze, Exhaustive Extraction™ on Twitter Firehose (90M/day)> SAS – Social Media Analytics, Sentiment Analytics> Clarabridge – Social Media Analysis

• We could have also used these other Software Products> ViralHeat (based on SAS)> Crimson Hexagon (Voxtrot Opinion Monitor) = ForSight “listening

platform”> Google Buzz, OpenSocial> Twinfluence, Twendz, TweetPsych, Twitoaster, etc.

We used Attensity’s Firehose “Respond” product for this study

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Buzz Monitoring Experiments

• Mathieu watched 4 new product introductions

• Ran sentiment analytics from Attensity:> Trentino Handbag --- VERY HOT!> Toscana Handcream --- Weak Positive> Abruzzo Cosmetic Line --- COLD! > Lazio Cleansing Scrub – Positive

Sample Tweets for New Product Introductions

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Sentiment Scores from Attensity

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Base Price Analysis: Week 4

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Differential Pricing Analysis: Week 4

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Matt’s Work after8 Weeks

Increased price created extra margin on Trentino

Decreased price to clear the Abruzzo Cosmeticline

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How Buzz Drove Pricing Actions

ACTIONS:

• HOT BUZZ> Increased pricing on Trentino Handbags by 10%> Held high for 4 weeks> Dropped only 5% for next 4 weeks> 8900 units sold (forecast 4300)> Impact: $72K of extra margin

• COLD BUZZ > 12,000 units of Abruzzo not moving> Dropped price to clear within 8 weeks> Stopped replenishment order> Impact: $27K savings

NET IMPACT

+ $72,000+ $27,000+ $99,000

Viral Marketing Experiment

“FashionFluencer Study” – CODY

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The Fashionfluencer ExperimentsViral Marketing Analytics

• Idea: based on Emails, Tweets, Blogs, pick out “The Fashionfluencers”> Early adopters of new

products, influencers> We pick out the happy ones

who email/blog/tweet a lot

• In the experiment, we’ll market to the FFs and let them market to their followers> Don’t market to the followers

– save $$$

Social MediaNetwork

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Chi and Cody Studied Influencers Used Tableau to see “Twinfluencers”

Some people have huge influence – the Tweeting Influencers

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Core Issue: Identity

• To do individual social media monitoring, you have to be able to link up all the various “identities”> CustomerID in the Brizio Database> Email address – we already have this, used for order and shipment

confirmation> Facebook ID – need this!> Twitter ID – need this!

• Cody launched a “Friends of Brizio” campaign for both Facebook and Twitter> Offer: people get free samples and a heads-up on new products if

they sign up by providing Facebook and/or Twitter IDs

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Influencers - Analyzing People who Tweet Find the “TwInfluencers” (Tableau)

We can analyze: • How much the

Influencers tweet

• What kind of links or retweets are happening

• What keywords they are using in the tweets

Perfume

Florentino

Handbag

Trentino

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Cody Finds the “Queen” Fashionfluencer

Who is this Mystery

Woman who drives

the Universe of Fashion?

Hint: It’s not …

Sample Tweets on the Florentino Perfume of the Month Offer

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Experiments Using Teradata Relationship Manager

TARGET IS Past Purchasers Who Are High Value/Margin

Who are on Social Media Who have Influence and Who caused Past Purchases in their Influence Group

CommunicationsSuppressions

Create the Target Group for the Experiments

Create an Email Campaign with a special deal for this Fashionfluencer group to try out the Florentino Perfume we are introducing, and forward their Opinions

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Email Campaign and Response Architecture

InnerFirewall

TeradataDatabase

TCIM ClusterTCIM

Load Balancer

Teradtata RelationshipManager and Integrated WebIntelligence

TRM/TCIM Administrator

OuterFirewall

DMZ

Corporate Intranet

IWI

Reporting User

Marketing User

Emarketing Server

Social MediaInfluencers

Social Feed Social Feed

EmailOffer

Send Email Offer

Po

st M

essa

ge

s

PostSocialData

1. Cody Sets Up The Campaign

2. SendsEmail Offer

3. Influencers Get Email, Buy/Try Product

4. Tweets, Blogs, Emails

5. Attensity monitors Tweet data

6. ETL key data

7. Cody analyzeswhetherFollowers buy

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How He Did It: Create A Fashionfluencer Segment

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Cody’s Next Steps on this Brizio Experiment Measure Acceleration and Cannibalization effects

Acceleration: Did we just pull sales forward? Or net additions?

Cannibalization: If people bought Influencer products, did they NOT buy other things?

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Business Impact: Fashionfluencers

• Average of 120 Fashionfluencers: > Drove 6.3 incremental purchases at the first level (6.3x)> + 18.9 incremental purchases at the second level (3.0x)> +26.4 incremental purchases at the third level (1.4x)> -6.4 overlaps in influence spheres> 20 net purchases per influencer

• Rollup> 2400 extra purchases> $216 purchase price ($18/month), $71.67 total margin

NET RESULT: $172,000 of incremental profit

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A Cool Idea from Chi

“Let’s put the results and some RT feeds on sales and tweets on an Executive Dashboard for Martina to show Giorgio …”

“It’s so easy!”

DashboardsApple iPAD

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Martina Takes the Results Back to Giorgio

• Martina is Happy – intuitions are confirmed - social media analytics will pay off for Brizio Fashion

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iPad Results for Trentino Handbag

• Based on the good buzz for Trentino Handbags, Matt increased the price starting in week 2

• This provided additional margin• By using social media insights, Matt

added $72,000 in revenues

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iPad Results for Abruzzo Cosmetics

• Based on the bad buzz and sales for Abruzzo Cosmetics, Matt dropped the price starting in week 2

• This “cleared” the bad product

• By not re-ordering replenishments that wouldn’t sell, Matt saved Brizio $27,000

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iPad Results for Florentino Perfume

• Cody’s campaign to the encourage FashionFluencers to buy and tweet/email their followers

Extra Florentino Perfume Units Sold Social Media Campaign Targeting Fashionfluencers (FF) - by Week

0

2,000

4,000

6,000

8,000

1 2 3 4 5 6 7 8

Week

Un

its

FF CumulativeImpact

FF Weekly Impact

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CMO Takes the Results Back to the CEO

Giorgio is happy With the Results – and he’s keeping the iPad for himself!

But Martina doesn’t carebecause her intuitions were Right!

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Brizio is On To Phase 2

You can do much more with social media analytics...

Deeper insight about targeted customers

A different-than-expected-audience was identified: • Target audience was 45-54 professional women, • Early buzz shows that the 25-30 younger

demographic is buying instead – women who need to look smart in today’s competitive job market

Actionable insight: • Better bundling of offers e.g., a professional scarf or other accessory for the work environment

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SummaryThe Case of the Retail Tweeters

CASE CLOSED

Brizio Fashion wanted to see if Social Media could make a difference in their sales and forecasting processes

BSI explored 3 hypotheses:

1. Hot buzz – keep pricing high

2. Cold buzz – discontinue product early

3. Fashionfluencers – can drive sales

All 3 hypotheses by BSI paid off!

how we did it: the case of the retail tweeters

Technology

hot buzz

cold buzz

social media real

social media teams

social media feeds new

platform google buzz

theiractive data warehouse

tb active data warehouse