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How We Did It: The Case of the Retail Tweeters


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How We Did It: The Case of the Retail Tweeters

  1. 1. How We Did The Investigations The Case of the Retail Tweeters Brought to you by And Our Partners
  2. 2. 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
  3. 3. Brizio Fashion Debate Between Giorgio (CEO) and Martina (CMO) ...Yes, it’s real! And I need some budget to prove that to you! Is social media real or a fad? And what does it mean to our company?
  4. 4. 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)?
  5. 5. 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
  6. 6. 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 from Teradata 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)
  7. 7. Brizio’s System Architecture CRM Corp LAN Teradata Production 4 Nodes 5600H Dev – 1 Node 5550H Test – 1 Node 5550H Reporting 24TB Sentiment Analytics Teradata Relationship Manager
  8. 8. Social Media Data Model Segment of Teradata’s Retail Data Model
  9. 9. Buzz-Based Marketing Experiments – Matt
  10. 10. Pricing Experiments Based on tweets and blogs, can we pick out new products with “hot buzz” and “cold or no buzz”? Possible Actions: Do not drop price as fast (or increase it) on “hot buzz” drop faster than normal for “cold buzz”
  11. 11. “ Sentement 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
  12. 12. Sample Tweets for New Product Introductions
  13. 13. 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
  14. 14. Sentiment Scores from Attensity
  15. 15. Base Price Analysis: Week 4
  16. 16. Differential Pricing Analysis: Week 4
  17. 17. Matt’s Work after 8 Weeks Increased price created extra margin on Trentino Decreased price to clear the Abruzzo Cosmetic line
  18. 18. 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 profit COLD BUZZ 12,000 units of Abruzzo not moving Dropped price to clear units within 8 weeks Stopped replenishment order Impact: $27K savings NET IMPACT + $72,000 + $27,000 + $99,000
  19. 19. Viral Marketing Experiment “FashionFluencer Study” – CODY
  20. 20. The Fashionfluencer Experiments Viral Marketing Analytics Idea: based on emails, tweets, and 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 this group and let them market to their followers Don’t market to the followers – save $$$ Social Media Network
  21. 21. Chi and Cody Studied Influencers Used Tableau to see “Twinfluencers” Some people have huge influence – the Tweeting Influencers
  22. 22. 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
  23. 23. Cody Finds the “Queen” Fashionfluencer Who is this mystery woman who drives the Universe of Fashion? Hint: It’s not …
  24. 24. Sample tweets on the Florentino Perfume of the Month Offer
  25. 25. 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 Communications Suppressions 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
  26. 26. Email Campaign and Response Architecture Cody Sets Up The Campaign 2. Sends Email Offer 3. Influencers Get Email, Buy/Try Product 4. Tweets, Blogs, Emails 5. Attensity monitors Tweet data 6. ETL key data 7. Cody analyzes whether Followers buy Inner Firewall Teradata Database T C I M C l u s t e r TCIM Load Balancer TRM and TRMi TRM / TCIM Administrator Outer Firewall DMZ Corporate Intranet TXS IWI Reporting User TRM Marketing User Emarketing Server Social Media Influencers Social Feed Social Feed Email Offer Send Email Offer Process email campaign using social attributes P o s t M e s s a g e s Post Social Data
  27. 27. How He Did It: Create A Fashionfluencer Segment
  28. 28. Create and Monitor Communications Relevant, Personalized Dialogues
  29. 29. Other Results from Cody – Geospatial Analysis Florentino Perfume – Locations of Top Influencers
  30. 30. 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?
  31. 31. 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
  32. 32. 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!” Dashboards Apple iPAD
  33. 33. Martina Takes the Results Back to Giorgio Martina is Happy – intuitions are confirmed - social media analytics will pay off for Brizio Fashion
  34. 34. 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
  35. 35. 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
  36. 36. Results for Florentino Perfume Cody’s campaign to the encourage FashionFluencers to buy and tweet/email their followers
  37. 37. Twitter Feeds for Brizio Twitter Feed monitoring of subjects “Trentino”, “Abruzzo”, etc.
  38. 38. 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 care because her intuitions were right!
  39. 39. 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 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
  40. 40. Summary The Case of the Retail Tweeters 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 or increase prices 2. Cold buzz – discontinue product early 3. Fashionfluencers – can drive sales All 3 hypotheses by BSI paid off! CASE CLOSED