BSI Teradata: The Shocking Case of Home Electronics Planet


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Home Electronics Planet, a big-box retailer, has digital marketing campaigns that are failing. Their Chief Marketing Officer gets some analytics and data science help from Business Scenario Investigators who recommend changing their search keywords mix, creating tighter customer segments based on product purchase sequencing coupled with real-time web page personalizations, and revising their e-mail marketing to improve business results.

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  • Start outside the UDA
    This is a “system view”, for introductions. There are more layers behind this.
    Ultimate goal of supporting every user/application with the right data
    Support any analytic technique – seamless to end user
    Data – ALL data, is on the left hand side – from traditional to newer kinds of data
    End users and their tools are on the right.
    Shift inside the UDA
    3 general categories of data-based tools
    Why data flows left to right
    Data can be loaded directly into any of the systems
    The value of Data Platform – integrated for storage
    The value of Discovery Platform – integrated for analytics
    The value of IDW and its evolution – point of value integration/realization
    Arrows/connectors in between are important glue
    Management and governance tools across the top –value-added software to make the system easy to manage.
  • Many web tools provide aggregated behavior of web customers. By contrast, Celebrus collects extremely granular data about each and every customer’s every action on the website
    CRM – Customer Relationship Management, coming up in a couple of pages. The real-time component is called “RTIM”.
    Tagging-free – many web monitoring systems require substantial manual effort to “tag” information elements on web pages; Celebrus avoids this effort
  • SAX - Enables Machine Data Analysis, such as analysis of sensor data in Manufacturing. Identify anomalies in manufacturing production process or performance of devices.
    Sequential Pattern - Automatically identify frequent patterns in sequential data.
    Attensity ASAS - Entity/event extraction, classification, sentiment analysis.
    Confusion Matrix - Used in machine learning for quantifying the performance of an algorithm and helps improve predictive models. Returns true/false positives/negatives.
    Single Decision Tree - Build and apply a single decision tree for classification. Identify important variables (and disregard irrelevant) that play role in making a decision.
    Distribution Matching – Test the hypothesis that the data is distributed from a certain distribution and estimate the parameters of several distributions that may fit the data.
    LARS – Selects a set of variables that are the most important in the context of a linear regression analysis. Can be used as LARS or Lasso.
    Fpgrowth – An association mining algorithm for recommendation engines. Discover elements that co-occur frequently in large datasets.
    Histogram –Counts the number of observations that fall into each of the disjoint intervals.
    IP Geo/Mapping - Identify the location using IP address
    New Slide: Synergistic multi-genre analytics
    Mfg Yield Management = SQL + Statistical + Time Series + …
    Location analytics = geospatial + time series + sql
    Digital Mktg analytics = sql +time series + statistics + Text + Graph..
    Social Media Analytics = SQL + Graph +Text (Attensity) +Statistics
    Churn Analytics = SQL + Time series + statistics + text
    Recommendation/Affinity engines = SQL + Statistics + Graph + Time Series
    Fraud Analytics = SQL + Statistical + Graph
  •  This slide represents the high-level Aprimo vision for Integrated Marketing Management. We have been in this space for 13 plus years and we recognize the value and the viewpoint around a vision to create integrated marketing for organizations so they can successfully communicate across multiple channels to reach their customers efficiently and effectively.
    Looking at the center circle of this slide – we realize there are many functions within marketing - and we have to support those various levels within each organization. From the corporate marketing levels to the field and regional managers, to brand managers and business units. You also have to be able to reach and collaborate with the external suppliers and other external functions within the company yet outside of marketing (c-levels).
    And, after 13 years in the IMM space, we have also recognized the ability to utilize the number of channels continues to grow and the ability to communicate and how you communicate has evolved over the years.
  • BSI Teradata: The Shocking Case of Home Electronics Planet

    2. 2. We’re Getting A Lot of Questions … BSI Teradata Hi Everybody, We’re the brains behind the scenes and wanted to answer your questions about “how we helped Home Electronics Planet.” This write-up explains our client’s architecture and some details about the investigation. Take a look, and if you still have questions, shoot them to us! Yours, Jodice, Mercedes, and Frazier 2 © Teradata BSI Studios 2014 JODICE BLINCO DIRECTOR
    3. 3. One Page Story Synopsis Shocking Case of Home Electronics Planet Situation A worldwide retailer of electronics and home electrical products is in trouble because their limited digital marketing efforts are failing. Sales are dropping and conversion rates are poor. Their CMO needs fresh ideas. Problem Current approach – offers are not well targeted or timed. BSI investigates their data, discovering better keywords, market segments, and more relevant offers. Solution Used Teradata, Aster, Hadoop and Teradata Applications components of the Teradata Unified Architecture™, plus Celebrus Technologies software, to re-engineer the Home Electronics Planet’s email and web marketing efforts, adding personalization. 3 © Teradata BSI Studios 2014 Impacts • Much more granular customer data in real-time • Faster discovery of consumer needs and behaviors • More targeted set of keywords that drive buyers not lookers • Flexible platform for better marketing on digital channels • New effort to try out lookalike capabilities to convert visitors to buyers • Turnaround in sales
    4. 4. CAST OF CHARACTERS Home Electronics Planet: •Chief Marketing Officer: Katie Hullman •VP IT: Lincoln Duckett BSI: •Jodice Blinco •Mercedes Marple •Frazier MacDonald
    5. 5. Home Electronics Planet • Katie Hulman – Chief Marketing Officer – very worried about her digital marketing initiatives. Needs fresh ideas from BSI on how to improve acquisition and conversion efforts. • Lincoln Duckett – VP of IT – Experienced with Teradata, experimenting with Hadoop but needs use cases. Eager to try out Aster for quicker discoveries and Celebrus for real-time customer web data capture. 5 © Teradata BSI Studios 2014
    6. 6. Jodice Blinco – Head of BSI • Overall head of BSI, helping companies become Data-Driven Businesses • Very interested in digital marketing uses of data, hates spam • Longtime Teradata expert BSI Teradata JODICE BLINCO DIRECTOR 6 © Teradata BSI Studios 2014
    7. 7. Mercedes Marple – Principal Investigator Professional: Level 2 BSI, Deep expertise in Retail Supply Chain, Logistics, Web. Excellent skills in search keyword optimization and product demand forecasting, 8 years industry experience, MS-Industrial Engineering, Univ of Cal, San Diego Personal: Lives at the beach. Beach volleyball, rollerblading, outrigger canoeing, ballroom dancing. Describes self as: “competitive, motivated, happy, organized, talkative”. Huge extended family in Southern California. Everybody’s favorite aunt! 7 © Teradata BSI Studios 2014
    8. 8. Frazier McDonald– Principal Investigator Professional: Level 3 BSI, polymath / generalist, trend-spotter. BS in psychology / economics, MS in history / genomics. Occasionally lectures at Cal Tech on Data Science, is writing a textbook based on BSI cases. Personal: Surfing, sailing, volleyball, crossword puzzles, remodeling. Reads history, poetry. Self description: ”disciplined, hard-working, traditional, resourceful, visionary”. Even appeared on Jeopardy! 8 © Teradata BSI Studios 2014
    9. 9. Scene Synopsis 1: Home Electronics Planet HQ – CMO Katie describes Planet’s problems and shows Jodice some discouraging KPIs 2: BSI Labs – Frazier and Mercedes dig into the data, do discovery, and build visualizations to improve acquisition and conversion strategies so sales will improve 3: BSI HQ – Jodice does a readout for Katie and IT VP Lincoln about the technologies ae needed to help Planet, and how the components work together within a Unified Data Architecture 4: Home Electronics Planet HQ – Katie shows Jodice what impact the changes made to their business results 9 © Teradata BSI Studios 2014
    10. 10. SCENE 1 At Home Electronics Planet HQ Problem: Katie shows Jodice the KPIs and explains her current marketing efforts. Jodice accepts the assignment to help.
    11. 11. The Problem – Digital Marketing Not Working 11 © Teradata BSI Studios 2014
    12. 12. Scene 1: Problem The good news is that Home Electronics Planet has executives that do want to manage “by the numbers”. They have good KPIs in place for Sales and put in place their website before many of their competitors. However, there are multiple issues: •Sales: > Are dropping. The numbers are down about 3% over the past two years > Katie had hoped that her digital marketing would take off •Digital Channels: > Buying keywords has helped Visit counts go up but ... > Sadly, conversions are down, so sales are also down 12 © Teradata BSI Studios 2014
    13. 13. Jodice Recommends 13 © Teradata BSI Studios 2014
    14. 14. Katie’s Current Approach for Keyword Buys Despite 10,000 keyword purchases, driving 21.7M visitors, only 3% of these visitors buy anything 14 © Teradata BSI Studios 2014
    15. 15. Current Email Effectiveness is Dropping Things are getting worse: Let’s take a look at the details ... 15 © Teradata BSI Studios 2014
    16. 16. Typical Email • Home Electronics Planet used to send out physical mail and also bought inserts for weekly newspapers • Two years ago Katie shifted the mix and put more money into email campaigns • Typically an email would show 6 products on sale • These 6 are based on bestselling products for newbies/non-purchasers, from 6 previous product buy categories for existing customers 16 © Teradata BSI Studios 2014
    17. 17. Open Rates OK, Click Through Rates Dropping 17 © Teradata BSI Studios 2014
    18. 18. Problem: 50% drop in Average Sale per Email (only 50 cents / email) 18 © Teradata BSI Studios 2014
    19. 19. Bad Trend: Unsubscribes have TRIPLED 19 © Teradata BSI Studios 2014
    20. 20. SCENE 2 Back at BSI Labs, Mercedes and Frazier tap into Planet’s data stored on Teradata, and use a new discovery tool called Aster to discover what’s really happening with the customers
    21. 21. They Break up the Work • Mercedes tackles the problem of acquisition: > exploring the keyword buys to see why they are attracting visitors but not business (“lookers” vs. “bookers”). • Frazier tackles the problem of conversion > figuring out what offers might result in higher “take rates”. • To do their investigations, they use an analysis and visualization tool called Teradata Aster. > Often, the work involves looking at each consumer’s behaviors over time that results in an “interesting end event”. called “nPath diagrams”. These are • Mercedes starts by looking at people who enter the website based on various keywords – but do NOT buy. 21 © Teradata BSI Studios 2014
    22. 22. nPath Diagram – Keywords That Did NOT Drive Purchases Keyword 22 to Product to Browsing ID © Teradata BSI Studios (Twice) 2014 but NO SALE!
    23. 23. nPath Diagram: Keywords That Lead to a Purchase (over one or more sessions) TIP: Sometimes a purchase might not occur in the first session, so you need to aggregate all of a person’s sessions to see if a particular keyword had an impact on the sale of the product. These keywords are the “keepers”. 23 © Teradata BSI Studios 2014
    24. 24. Impact of the Keyword Investigation • Using the detailed data, merging keyword-based traffic analytics with the true customer purchase (or non-purchase) patterns, can help companies optimize their spending on the right keywords. In this case, Mercedes thinks they can chop back 15% on the keywords that are unproductive. 24 © Teradata BSI Studios 2014
    25. 25. Acquisition Aside – Many Other Investigations on Sourcing Traffic Are Possible • Paid keyword searches are just one avenue for driving traffic. > Mercedes can explore other data to make recommendations on the optimal mix of digital spending • In general, on retail sites, traffic can ‘Arrive’ from any of these 7 sources > Show-ups - have the site bookmarked, or typed in the URL > Organic Search – searches within the website > Unpaid Referrals – hyperlink to site from blogs/articles > Display Ads placed on other locations (Facebook, Google) > Affiliates – the network of HEP’s affiliates’ websites > Email – consumer clicked on link within an email > Social- mentioned of in tweets 25 © Teradata BSI Studios 2014
    26. 26. Frazier Looks First at Browsing Affinities • Browser affinity diagrams show what products are browsed during the same session. Dots show products, and lines show the connections between products, e.g., they were viewed “together”. Here’s an example: 26 © Teradata BSI Studios 2014
    27. 27. Frazier Can Also Look at Purchase Affinities • For conversion, he wants to know what products are not just browsed but bought together. He can use web purchases (market basket analytics) as well as store purchases. As you might expect, people who buy a Sony laptop often buy an HP OfficeJet printer at the same time 27 © Teradata BSI Studios 2014
    28. 28. Frazier Looks At the Sequence of Product Purchases, Creates “Personas” • “Personas” represent customer clusters of people who buy the same category of products in roughly the same sequence • Here a “Movie Buff” Persona – goes from buying a TV to adding the sound system and movies 28 © Teradata BSI Studios 2014
    29. 29. The “Photo Family” Sequence of Purchases 29 © Teradata BSI Studios 2014
    30. 30. The Idea is to Match Partial Sequences to Full Sequences – Find the Best Recommendation 30 © Teradata BSI Studios 2014
    31. 31. “High Tech Trainer” • Demographic: Male, 30s, income around $45-50K • Past Product Sequence: bought a laptop or a PC, then an exercise band • We look at the top purchase sequence matches for people in his demographic • Last year, they bought ellipticals to continue inside training, but mostly bought only in the fall • SO: we will target ellipticals to this person starting in September • We “know” him because he has bought before, but can also use this technique for “Lookalikes” – people who are browsing laptops and exercise bands, even though we know nothing more about them 31 © Teradata BSI Studios 2014
    32. 32. More Analytical Options The video just scratches the surface on analytics. More in-depth analytics might include data like: •a customer’s geography, •demographic information (like age or gender) if they are in an existing customer in the Teradata database •product sales history (by geography, by customer demographic) •the devices a customer uses to browse and/or purchase •on-site and referring search terms vs. successful conversion rates The following visuals from our partner Tableau illustrate some of these additional analytics / visualization options 32 © Teradata BSI Studios 2014
    33. 33. Many Other Options: Geospatial Overlays • Though not shown in the BSI Video, Mercedes and Frazier can also analyze keyword effectiveness, web browsing and purchases by geography. For example: Do consumers in all countries behave the same on the website? Do the same keywords (translated) work the same? 33 © Teradata BSI Studios 2014
    34. 34. Cart Abandonment (including Technical Issues) 34 © Teradata BSI Studios 2014
    35. 35. SCENE 3 Jodice provides a Readout and Architecture Recommendations for Katie and the VP of IT, Lincoln Duckett You’ll find details in this section on the •Teradata Unified Data Architecture™, •Celebrus Technologies •Teradata Aster and •Teradata Applications, specifically Integrated Marketing Management
    36. 36. Scene 3: Readout on Results • Jodice just finished covering some of the Aster visualizations and discoveries, and now starts to go over the needed tech components and architecture Lincoln Duckett is the VP of IT. They already use Teradata and just bought Hadoop from Hortonworks, a Teradata Partner. 36 © Teradata BSI Studios 2014
    37. 37. Key Technology Points Data Capture and Discovery : Teradata Unified Data Architecture™ > Teradata is the core repository of enterprise data – historical context, any structured data, e.g., information from ERP systems, product data, production data, sales data, retailer data > Aster - fast hypothesis testing for multi-structured data, e.g., pathing analysis, bailout analytics, product sequencing insights. > Hadoop is an optional component for fast, cheap capture of any kind of data, The Unified Data Architecture™ ties all the platforms together. Experimental results and data from discoveries in Aster or Hadoop flow into Teradata. 37 © Teradata BSI Studios 2014
    38. 38. TERADATA UNIFIED DATA ARCHITECTURE System Conceptual View ERP MOVE MANAGE ACCESS Marketing Marketing Executives Applications Operational Systems Business Intelligence Customers Partners SCM INTEGRATED DATA WAREHOUSE CRM Images DATA PLATFORM TERADATA DATABASE Audio and Video Machine Logs Data Mining TERADATA DATABASE HORTONWORKS HADOOP INTEGRATED DISCOVERY PLATFORM Frontline Workers Business Analysts Math and Stats Data Scientists Text Languages Web and Social Engineers TERADATA ASTER DATABASE USERS SOURCES ANALYTIC TOOLS & APPS
    39. 39. Data Platform – Lots of Storage Teradata Appliance for Hadoop • Capture any kind of data • In this case, web clicks • Down the road, could be tweets • Or even customer voices from Customer Care • Cheap longterm storage of raw data 39 © Teradata BSI Studios 2014
    40. 40. Insights Need to Lead to Actions • In this case, the next best offers are driven “out to the customer” using Teradata’s Integrated Marketing Management tool 40 © Teradata BSI Studios 2014
    42. 42. The Overall Flow: From Data and Insights To Campaign Planning and Execution 42 © Teradata BSI Studios 2014
    43. 43. Celebrus Technologies • Captures individual customer behavior data • Real-time granular data collection and contextualization • Streams into Data Platform and CRM personalization engine • Tagging-free, fast and easy deployment 43 © Teradata BSI Studios 2014
    44. 44. Loading Web Data from Celebrus into Hadoop A Subset May Be Pulled into Aster for Investigation Unified Data ArchitectureTM For Digital Marketing Customer Interaction Manager Data Product / SKU Teradata Customer Data Warehouse Sales / POS Email, SMS Responses Web Data In-Store Segmenting Leads Digital Messaging Center Inventory Teradata Aster Database Reporting Trending Campaigns Real-Time Interaction Manager Content & Offers Call Center Photo Family Group Email Mobile Real-Time Personalization Web Rules Feedback 44 Channels © Teradata BSI Studios 2014 Responses
    45. 45. Aster Discovery Based on All Kinds of Data 45 © Teradata BSI Studios 2014
    46. 46. Doing Analysis to Discover Insights Using Some Data from Hadoop and Some Loaded from Teradata Unified Data ArchitectureTM For Digital Marketing Customer Interaction Manager Data Product / SKU Teradata Customer Data Warehouse Sales / POS Email, SMS Responses Web Data In-Store Segmenting Leads Digital Messaging Center Inventory Teradata Aster Database Reporting Trending Campaigns Real-Time Interaction Manager Content & Offers Call Center Photo Family Group Email Mobile Real-Time Personalization Web Rules Feedback 46 Channels © Teradata BSI Studios 2014 Responses
    47. 47. To “See” Patterns, Aster Provides Visualization 47 © Teradata BSI Studios 2014
    48. 48. Insights to Action Integrated Marketing Management • Insights from Aster are put into Teradata • These “new” insights together with all kinds of other information in Teradata is turned into Campaigns by the Customer Interaction Manager • CIM orchestrates the “conversation with the consumer” across all the channels • Two components are key for Katie’s digital marketing efforts: > DMC for emails > RTIM for the web 48 © Teradata BSI Studios 2014
    49. 49. Using Insights to Design Campaigns Customer Interaction Manager Unified Data ArchitectureTM For Digital Marketing Customer Interaction Manager Data Product / SKU Teradata Customer Data Warehouse Sales / POS Email, SMS Responses Web Data In-Store Segmenting Leads Digital Messaging Center Inventory Teradata Aster Database Reporting Trending Campaigns Real-Time Interaction Manager Content & Offers Call Center Photo Family Group Email Mobile Real-Time Personalization Web Rules Feedback 49 Channels © Teradata BSI Studios 2014 Responses
    50. 50. E-Mail Campaigns use Digital Message Center • At periodic intervals, DMC will create customized emails and send them out, measuring results • Results can include opens, click-throughs, bounces, unsubscribe requests (as shown at the beginning of the video) • It’s the use of customized email offers in the emails that is critical to success • With the right data (from Celebrus), offers (from Frazier's insights) and timing (sequencing and time of year), HEP can achieve much better results 50 © Teradata BSI Studios 2014
    51. 51. Sample: Better Focused and Timely Emails Note in this email: 1.It’s personalized to Mrs. Smith 2.It’s relevant – she was just browsing laptops 3.It’s timely – maybe we program it to go out 30 minutes after she closes her session without buying 4.It’s a good deal 5.And if she doesn’t like that laptop, there are other options 51 © Teradata BSI Studios 2014
    52. 52. Executing a Targeted E-Mail Campaign One Way Email, Drives to Come to Store or Website Unified Data ArchitectureTM For Digital Marketing Customer Interaction Manager Data Product / SKU Teradata Customer Data Warehouse Sales / POS Email, SMS Responses Web Data In-Store Segmenting Leads Digital Messaging Center Inventory Teradata Aster Database Reporting Trending Campaigns Real-Time Interaction Manager Content & Offers Call Center Photo Family Group Email Mobile Real-Time Personalization Web Rules Feedback 52 Channels © Teradata BSI Studios 2014 Responses
    53. 53. Web Personalization The website provides an opportunity for dynamic personalization of offers/content The Real-Time Interaction Manager decides what content to show on each page, adapting to the customer’s interests and using the insights from the Discovery phase 53 © Teradata BSI Studios 2014
    54. 54. Executing Web Campaigns – Real-Time Two Way Interactions Unified Data ArchitectureTM For Digital Marketing Customer Interaction Manager Data Product / SKU Teradata Customer Data Warehouse Sales / POS Email, SMS Responses Web Data In-Store Segmenting Leads Digital Messaging Center Inventory Teradata Aster Database Reporting Trending Campaigns Real-Time Interaction Manager Content & Offers Call Center Photo Family Group Email Mobile Real-Time Personalization Web Rules Feedback 54 Channels © Teradata BSI Studios 2014 Responses
    55. 55. Closed Loop – Feedback - Iterate Learn from What Did and Did Not Work – Refine Campaigns Unified Data ArchitectureTM For Digital Marketing Customer Interaction Manager Data Product / SKU Teradata Customer Data Warehouse Sales / POS Email, SMS Responses Web Data In-Store Segmenting Leads Digital Messaging Center Inventory Teradata Aster Database Reporting Trending Campaigns Real-Time Interaction Manager Content & Offers Call Center Photo Family Group Email Mobile Real-Time Personalization Web Rules Feedback 55 Channels © Teradata BSI Studios 2014 Responses
    56. 56. Put it Together: Teradata UDA in Action ! Unified Data ArchitectureTM For Digital Marketing Customer Interaction Manager Data Product / SKU Teradata Customer Data Warehouse Sales / POS Email, SMS Responses In-Store Segmenting Leads Digital Messaging Center Inventory Teradata Aster Database Reporting Trending Campaigns Real-Time Interaction Manager Celebrus Web Data Content & Offers Call Center Photo Family Group Email Mobile Real-Time Personalization Web Rules Feedback 56 Channels © Teradata BSI Studios 2014
    58. 58. Discovery Approach: Faster Experiments More Details on the Aster Discovery Platform New business insights from all kinds of data with all types of analytics for all types of enterprise users with rapid exploration. Iterative hypothesis testing. 1       58 Large Volumes Interaction Data Structured Unstructured Multi-structured Hadoop 2      Relational/SQL MapReduce Graph Statistics, R Pathing 4 3  Business Users  Analysts  Data Scientists © Teradata BSI Studios 2014     Fast Iterative Investigative Easy
    59. 59. Teradata Aster Discovery Platform Industry’s First Visual SQL-MapReduce ® Functions FLOW VISUALIZER Visualize paths & patterns AFFINITY VISUALIZER Visualize clusters & groups HIERARCHY VISUALIZER Visualize hierarchical relationships 59 Complementary Value •BI: Batch Visualizations Outside the Database, General & Generic •Aster: Rapid Visualizations, in-Database, for Specialized Analytics © Teradata BSI Studios 2014
    60. 60. Sample Analytics Modules in Aster Fastest path to big data analytics PATHING ANALYSIS TEXT ANALYSIS STATISTICAL ANALYSIS GRAPH ANALYSIS Discover Patterns in Rows of Sequential Data High-Performance Processing of Common Statistical Calculations SQL ANALYSIS Report & Analyze Relational Data 60 © Teradata BSI Studios 2014 Derive Patterns and Extract Features in Textual Data Discover Natural Relationships of Entities MAPREDUCE ANALYTICS Custom-built, domainspecific analysis
    62. 62. Teradata Application: Integrated Marketing Manager Components Used To Create and Run Recall Workflows 62 62 © Teradata BSI Studios 2014
    63. 63. SCENE 4 A few months later , Katie and Jodice get together to see how things are working ...
    64. 64. Daily Impact of Changing Mix of Keywords Keyword mix drives same traffic and sales but with reduced cost 64 © Teradata BSI Studios 2014
    65. 65. Options – Keyword Purchasing • Katie can cut her spending by purchasing fewer but more effective keywords – the ones that the “real purchasers” use to find websites with the products they want > The impact of this can be lower marketing spend but the same traffic and the same or higher spending • Another option is to spend the same $ but change the mix so she gets not only better keywords like the previous case, but additional new keywords > The impact could be the same marketing spend, but higher traffic and higher sales • These “levers” are the options that Marketing people need to better understand – by capturing and analyzing the detailed data that associate keywords to visitors and customers, and their spending patterns. Not all visitors are created equal. 65 © Teradata BSI Studios 2014
    66. 66. Impact Overall, Projected Annual Changes 42,000 more shoppers per month 66 © Teradata BSI Studios 2014
    67. 67. How She Did It BETTER ACQUISITION and CONVERSION •Katie opted to cut some keywords and her spending •This didn’t create much more traffic – only 1.4% •But the quality of the traffic was better and conversions from “lookers” to “buyers” went up •In addition, once she acquired traffic, her customized emails and dynamic personalizations on the web site paid off: > An additional 42,000 people buying – per month > Annualized projection – 504K more buyers! > These more serious buyers also have – on average - $10 larger market basket sizes •Katie estimates the net annualized impact of her changes will be an increase of sales by year’s end of 10% - $234M 67 © Teradata BSI Studios 2014
    68. 68. Katie’s Happy With the Improvements • 1 year later – system has been used for more recall cases and we take a look at the impacts. Some key KPIs are: > Speed and Accuracy of Exploration, Root Cause Analysis > Speed, Precision, and Accuracy of Recalls • Jodice asks Wiley for an example recall they did with the new system and he shows her the results for a peppercrusted salami product. The problem was bad spices that were imported from overseas. International tracing can also be included in the system. Overall result: much more in control and reduced risk!!! 68 © Teradata BSI Studios 2014
    69. 69. Katie Rewards Jodice with Some “Affinity Art” 69 © Teradata BSI Studios 2014
    70. 70. WRAPUP
    71. 71. For more information – UDA • Teradata UDA > 71 © Teradata BSI Studios 2014
    72. 72. For More Information 72 © Teradata BSI Studios 2014
    73. 73. For More Information - Aster Teradata Aster: 73 © Teradata BSI Studios 2014
    74. 74. For more information – Teradata Applications 74 © Teradata BSI Studios 2014
    75. 75. For more information: Teradata • 75 © Teradata BSI Studios 2014
    76. 76. For more Information: our partner Celebrus • 76 © Teradata BSI Studios 2014
    77. 77. Thanks for watching! • This episode appears at: • You can see all our episodes at on Facebook: links to 11 other Videos and other “How We Did It” Powerpoints 77 © Teradata BSI Studios 2014
    78. 78. 11 Other BSI Episodes For You! 1. CASE OF THE DEFECTING TELCO CUSTOMERS A Telco uses analytics to see why they have a big customer retention problem 2. CASE OF THE MIS-CONNECTING PASSENGERS An Airline improves its customer rebooking engine using analytics 3. CASE OF THE RETAIL TWEETERS A Fashion Retailer uses social media tweets to get insights on “hot” and “cold” products and to find the FashionFluencers! 4. CASE OF THE CREDIT CARD BREACH A Bank and a Retailer collaborate to solve a stolen Credit Card case using analytics 5. CASE OF THE FRAGRANT SLEEPER HIT A Consumer Goods Manufacturer uses Social Media to recalibrate Manufacturing and Marketing plans 6. CASE OF DROPPED MOBILE CALLS A major Telco digs into real-time dropped call data to understand high-value and highinfluence customers, and where to place new towers. Create 5 campaigns to retain their most valuable and influential customers. 7, 8, 9: The SAD CASE OF STAGNOBANK Customer service is lousy, most marketing offers are rejected by customers, and the bank has lost its appeal to younger households. BSI is engaged to work on new ideas for Better Marketing, Better Customer Service, and New Mobile Apps – one episode on each topic. . 10. CASE OF THE RETAIL TURNAROUND A Big-Box Retailer learns how to use web path purchase and bailout analytics to create ways of driving shoppers into stores. 11. CASE OF THE TAINTED LASAGNA A Consumer Packaged Goods company uses the components of the Unified Data Architecture to figure out where contamination entered the supply chain, and does a targeted recall. 78 © Teradata BSI Studios 2014