Teradata BSI: Case of the Retail Turnaround


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This set of Powerpoint slides describes the analytics work of Teradata: Business Scenario Investigation employees who help move Taylor & Swift, a big-box retailer, from a silo’d stores vs. web approach to an integrated Omni-Channel Retailing approach to customers, marketing, and sales. The team comes up with 5 ideas, 2 of which are tried out. The story illustrates the use Teradata, Aster, Aprimo, and Tableau as tools to glean faster and deeper analytical insights on Big Data, specifically web walks.

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  • Lot of useful information here! Good storyline that highlights the business value of this set of technologies. Teaches several best practices in retail and in many other industries. Way to go, Dave!
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  • This is the 10th BSI episode. You can find the video on YouTube with search terms 'BSI Case Retail Turnaround' for this one, or 'BSI Teradata Case' for all 10. You will also find the How We Did It Powerpoints here on Slideshare.net for the other episodes.
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  • 48% of customers: Further analysis (not shown) can be done to see whether people then buy a substitute that is in stock as a substitute (e.g., they want a printer but the brand they prefer is not available in their price range, so they pick a different brand)
  • Teradata BSI: Case of the Retail Turnaround

    1. 1. HOW WE DID THE The Case of theINVESTIGATIONS Retail Turnaround
    2. 2. Prelude – Case of the Retail Turnaround This video appears on www.youtube.com. You can find it by searching using keywords: “BSI Teradata Case Retail Turnaround.” This accompanying deck is designed to answer questions about the Teradata and partner technologies shown in the story. For best effect, run it in Powerpoint animation mode. This is our second BSI episode for the Retail Industry. For another episode, see “BSI Teradata Case of the Retail Tweeters.” There are also many other episodes available that showcase the use of business analytics to solve real-world problems.2
    3. 3. Note from the Investigators Hi everyone, We’re the brains behind the scenes and wanted to answer your questions about “how did we do our analytics to help Taylor & Swift with Omni-Channel Retailing?” 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, send them to us at the www.bsi-teradata.com FB page! Yours truly, Chi Tylana and Frazier McDonald BSI Investigators3
    4. 4. Scene Synopsis Scene 1: Taylor & Swift has a problem Scene 2: BSI Investigators Chi Tylana and Frazier McDonald analyze the data Scene 3: Chi and Frazier show Five Omni-Channel Retailing ideas to Taylor & Swift execs Scene 4: Omni-Channel Retailing experimental results4
    6. 6. Taylor & Swift has a problem with overall revenues dropping. Their COO Mark Woolfolk calls a meeting with his VP of Digital Stores, Becky Swenson, plus two investigators from BSI Teradata he’s brought in to provide a fresh set of ideas for turning things around.6
    7. 7. Senior Leadership at Mark Woolfolk is the Chief Operating Officer for T&S. He knows operating results have been just so-so, stagnant, and has a hunch they need some new ideas to get people into the stores, which will improve financial results. Becky Swenson is the VP for the Digital Store. Her results are definitely better than the physical stores but she cannot compete against the pure Webs on price alone (T&S cost structure issue), and wants to help with possible synergies, but isn’t sure what to do.7
    8. 8. Meeting at T&S Headquarters Becky Frazier Mark Chi8
    9. 9. Key Performance Indicator: Same Store Results Revenue by month per store is dropping Seasonal Spring Back to Holiday Promotions: Fling School Season9
    10. 10. Becky sees better results for # of visits on the digital channels (WebStore, Mobile) However, this is misleading – number of purchases/visits is up but size of purchase/market basket has dropped.10
    11. 11. Overall Results – 210 Stores Plus Digital Average store revenue continues to decline, while digital channel (Web and mobile) sales are flat. Problem!11
    12. 12. The Job for BSI 1. Analyze customer segments based on behavior for visiting and buying 2. Because multi-channel visitors purchase more, figure out how to use insights from the digital channels to drive more people into stores 3. Use Taylor & Swift’s investments in “active” near-real-time technology and sandboxing for fast data discovery 4. Come back with some recommendations for turning the financial results around12
    14. 14. Chi and Frazier load T&S data into Teradata and Aster sandbox systems • Sandbox systems are great for discovery of trends > They load 18 months of purchase data from all channels, plus web click data into their sandbox systems > They use Tableau to do quick visualizations • They begin by segmenting customers by browse vs. buy channels > Some people stick to one channel (e.g., browse on the Web, and buy on the Web) > Others switch channels (e.g., browse on mobile or Web, then go to the store to buy) > A simple Venn diagram can show the relative numbers of people • The focus of the work will be on those who are on the digital channels – can we get them into the stores, too? For more technical information about Sandbox technologies and Agile Analytics, click here14
    15. 15. Venn Diagram - Browse/Buy Analytics Behavior Across Channels15
    16. 16. Geospatial Analytics Geocoding customer street/city addresses provides customer “dots” on the page. Chi then uses Teradata geospatial capabilities to find only those customers within a 20-mile drive of a physical store.16
    17. 17. Customer Value Depends on (# of Visits) Multiplied by (Average Market Basket $ Size) This is the “proof” that multi-channel visitors are more valuable.17
    18. 18. Brainstorming: How To Get More People to Become Multi-channel Shoppers? Idea #1: CLICK AND COLLECT If customer is near a store and all items in the market basket are in stock, offer local store pickup option18
    19. 19. Frazier is an expert at doing Data Discovery using Aster Analytics • Aster Data (now Teradata Aster) was acquired by Teradata in 2011 and is used by numerous customers to analyze “non- traditional” data that doesn’t fit nicely into traditional relational tables and rows • Graph pattern matching is an example that we show in this episode > Specifically, the page-by-page views that a customer looks at and which items are put in a market basket is of high interest • Teradata Aster and Tableau can help you visualize all patterns • For more information about click here • For more information about , click here19
    20. 20. Teradata Aster Analytics Endpoint: Digital Paths Ending in a Purchase20
    21. 21. Teradata Aster – nPath Analysis • The highlighted path shows one shopper who put Labels in the shopping cart, then Envelopes, then an Office Machine, and finally an Electronics item. • These digital pathways provide more information than traditional POS (point-of-sale) information from the store system: not just WHAT you bought, but IN WHAT ORDER. • Aster can also be used to monitor non-purchase behavior. • Specifically: Bail-out Analytics can be quite useful to see where people “X-out” of sessions before purchasing. This can be helpful in redesigning Web sites to decrease confusion and increase conversions, and in making decisions about whether shipping charges are a problem area, etc. • Frazier takes several looks at pathways – an area that Aster calls “nPath” because there can be 1, 2, … n steps on the way to purchase.21
    22. 22. Teradata Aster Analytics Endpoint: Digital Paths Dropping Out at the Shipping Page22
    23. 23. How to Reduce Dropouts on the Shipping Page 28% of customers who initiate the purchase sequence after shopping are dropping out at the Shipping Charges page Idea #2: Coupons for In-Store Pickup vs. Shipping Charges If the products are all in stock, then offering a modest amount of money ($5) to customers to drive to pick up the items might drive them into the stores23
    24. 24. Teradata Aster Analytics Endpoint: Bail Outs When Out of Stock – Split Shipments T&S loses more customers if they make it past the Shipping Charge page, but then find that the order will be split because24 some items are not in stock.
    25. 25. Dropped Demand Recovery • Frazier finds that another 48% of the customers bail out when they find that something in the market basket isn’t going going to be shipped because Taylor & Swift is out of stock. • Frazier could also also analyze whether they come back – after 1 day, after 3 days, after a week. • If neither of these happen, then we have “Dropped Demand” and can assume we lost the sale (to competition) or the customer is going to wait longer • If we act quickly, we might be able to recover the Dropped Demand, which leads to Idea #3: send an email when the local store is back in stock • They could come to the store to buy, or buy on the digital store – in either case, we get the sale25
    26. 26. Teradata Aster Analytics Discovery: If “First in Basket” ships first, Purchase is salvaged Deeper discovery – who does NOT bail out despite a split shipment? Answer: in many cases, if the First in Basket26 makes it into the First Shipment.
    27. 27. Teradata Aster Analytics First in Basket Items are Very Important • Frazier’s final discovery in this story is that sometimes with a split shipment, customers still go to the Purchase page > A study of those customers illuminates a new discovery – that if the item they put first in their basket makes it into the first shipment, then they proceed • As a consequence, it’s important for Taylor & Swift to pay close attention to all First in Basket items since those are the “drivers” for purchases • Chi suggests Idea #4: they use First in Basket visuals in store circulars • And Frazier comes up with Idea #5: adjust “safety stock” levels (at the digital store as well as physical stores) to ensure that it’s likely that these leading products are always in stock27
    29. 29. Mark likes the “Recover Dropped Demand” Send Emails when back in stock at stores29
    30. 30. The Emails can be personalized and also feature other browsed-but-not-bought products Clock countdown feature may help30
    31. 31. The Email Campaign can be run automatically using Aprimo Relationship Manager with Real-Time Messaging • Taylor & Swift bought Teradata’s Aprimo Relationship Manager tool two years ago to help design and execute marketing campaigns For more information about click here • It’s not difficult to add “events” with workflows to describe what to do when Taylor & Swift notices various activities by customers • In the case of Dropped Demand, Chi and Becky set up a workflow to automatically detect when out-of-stock order bailouts occur by Web-only customers who live near stores. When the item is back in stock, an email goes out automatically using the Real-Time Messaging module.31
    32. 32. Workflow for Driving the Automatic E-MailsClick to see the sequence of events that Aprimo will automaticallymonitor – driving emails for Dropped Demand items.32
    33. 33. Mark also wants to try Click and Collect (featuring the First in Basket item)33
    35. 35. Eight Weeks Later, Experimental Results Are In Experiment 1: Click and Collect Experiment 2: Recover Dropped Demand Experiments were tried at 20 of T&S’s 210 stores35
    36. 36. 36
    37. 37. Financial Impact – Click and Collect • The Cascade visual shows: > the number of Web sessions > what number of offers were made for store pickups (when every item is in stock) > the number of pick up offers accepted (so items were held) > the number of actual pickups • This campaign drove 59,000 people into the stores that otherwise probably would not have gone there • They bought what they ordered • But we also measured incremental (impulse) purchases, which was $32.08 • An additional $1.9M revenue • Scaling up from 20 stores in 8 weeks to 210 stores annually, this could be $120M of added revenue37
    38. 38. 38
    39. 39. Financial Impact – Dropped Demand Recovery • The Cascade visual shows: > the number of dropped demand sessions > the number of emails sent when back in stock for that item > the number of pick up offers accepted (so items were held) > the number of favorable responses to re-order > which channel they used – digital or in-store The results were split 50-50, with half the people re-ordering on the digital channels and half going to stores But a key finding was that those who went back to the digital channels ordered an incremental $12 of merchandise beyond the dropped demand items, whereas in-store purchases made $22 of additional purchase. Total incremental revenue of $835K on top of the $2.2M in dropped demand merchandise – total $3.0M Scaling up nationwide, annually, this could yield $189M of revenue to Taylor & Swift39
    40. 40. Mark and Becky are happy with the BSI analytics and experiments…40
    41. 41. Omni-Channel Retailing is a Very Hot Topic For more information • PODCAST: “Trending in Retail Consumer Insight” • Best in Class: Cabelas > RIS News article “Why Cabelas Has Emerged as the Top Omni- Channel Retailer” > Baylor Business School, Prof. Jeff Tanner, “Decoding Path to Purchase” • Best in Class: DSW -41
    42. 42. Thanks for viewing these slides And thanks to our Teradata divisions and Partners for making it all possible!42