HOW WE DID THE INVESTIGATIONS The Case of

HOW WE DID THE INVESTIGATIONS The Case of the Retail Turnaround

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 also many other episodes available that showcase the use of business analytics to solve real-world problems. 2

SCENE 1: TAYLOR & SWIFT HAS A PROBLEM

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. 4

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 5

Key Performance Indicator: Same Store Results Revenue by month per store is dropping Seasonal Promotions: 6 Spring Fling Back to School Holiday Season

Becky sees better results for # of Visits on the Digital Channels (Web. Store, Mobile) However, this is misleading –number of purchases/visits is up but size of purchase/market basket has dropped. 7

Overall Results – 210 Stores Plus Digital Average store revenue continues to decline, while digital channel (web and mobile) sales are flat. Problem! 8

The Job for BSI 1. Specifically, analyze customer Taylor & Swift consumer 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 around 9

SCENE 2: BSI INVESTIGATORS CHI TYLANA AND FRAZIER MCDONALD ANALYZE THE DATA

Chi and Frazier load T&S data into Teradata and Aster sandbox systems • Sandbox systems are great for discovery of trends • They begin by segmenting customers by browse vs. buy channels • The focus of the work will be on those who are on the digital channels – can we get them to go into the stores, too? For more technical information about Sandbox technologies and Agile Analytics, click here 11

Venn Diagram - Browse/Buy Analytics Behavior Across Channels 12

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. 13

Customer Value Depends on (# of Visits) Multiplied by (Average Market Basket $ Size) This demonstrates that multi-channel visitors are more valuable 14

Brainstorming: How To Get More People to Become Multi-channel Shoppers? Idea #1: CLICK AND COLLECT If customer is near a store and store is in stock for all items in the market basket, offer local store pickup option 15

Frazier is an expert at doing Data Discovery using Aster • Aster Data was acquired by Teradata in 2011 and is used by numerous customers to analyze “nontraditional” data that doesn’t fit nicely into traditional relational tables and rows • Graph pattern matching is an example that we show in this episode • Teradata Aster and Tableau can help you visualize all the patterns , click 16

Teradata Aster Analytics Endpoint: Digital Paths Ending in a Purchase 17

Teradata Aster • 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. • Frazier takes several looks at pathways – an area that Aster calls “n. Path” because there can be 1, 2, … n steps on the way to purchase. 18

Teradata Aster Analytics Endpoint: Digital Paths Dropping Out at the Shipping Page 19

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 stores 20

Teradata Aster Analytics Endpoint: Bailouts When Out of Stock – Split Shipments 21 T&S loses more customers if they make it past the Shipping Charge page, but then find that the order will be split because some items are not in stock

Teradata Aster Analytics Endpoint: Bailouts When Out of Stock – Split Shipments 22 T&S loses more customers if they make it past the Shipping Charge page, but then find that the order will be split because some items are not in stock

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 to be shipped because Taylor & Swift is out of stock. going • Frazier could 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 sale 23

Teradata Aster Analytics Discovery: If “First in Basket” ships first, Purchase is salvaged 24 Deeper discovery – who does NOT bailout despite a split shipment? Answer: in many cases, if the First in Basket makes it into the First Shipment

Teradata Aster Analytics First in Basket Items are Very Important • 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 stock 25

SCENE 3: CHI AND FRAZIER SHOW THEIR 5 OMNI-CHANNEL RETAILING IDEAS TO TAYLOR & SWIFT EXECS

Mark likes the “Recover Dropped Demand” Send Emails when back in stock at stores 27

The Emails can be personalized and also feature other browsed-but-not-bought products Clock countdown feature may help 28

The Email Campaign can be run automatically using Aprimo Relationship Manager with Real-Time Messaging • 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 29

Workflow for Driving the Automatic E-Mails Click to see the sequence of events that Aprimo will automatically monitor – driving emails for Dropped Demand items 30

Mark also wants to try Click and Collect (featuring the First in Basket item) 31

SCENE 4: OMNI-CHANNEL RETAILING EXPERIMENTAL RESULTS

8 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 stores 33

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Financial Impact – Click and Collect • 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. 9 M in revenue • Scaling up from 20 stores in 8 weeks to 210 stores annually, this could be $120 M of added revenue 35

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Financial Impact – Dropped Demand Recovery 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 $835 K on top of the $2. 2 M in dropped demand merchandise – total $3. 0 M Scaling up nationwide, annually, this could yield $189 M of revenue to Taylor & Swift. 37

Mark and Becky are happy with the BSI analytics and experiments … 38

Thanks for viewing these slides And thanks to our Partners … 39