BSI Teradata: The Shocking Case of Home Electronics Planet
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Transcript of BSI Teradata: The Shocking Case of Home Electronics Planet
BSI TERADATA EPISODE 12:HOW WE DID IT THE SHOCKING CASE OF HOME ELECTRONICS PLANET
WATCH THE EPISODE AT HTTP://BIT.LY/1EOQRMF
2 © Teradata BSI Studios 2014
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 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
BSI Teradata
DIRECTOR
JODICEBLINCO
3 © Teradata BSI Studios 2014
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.
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 look-alike capabilities to convert visitors to buyers
• Turnaround in sales
Home Electronics Planet:•Chief Marketing Officer: Katie Hullman•VP IT: Lincoln Duckett
BSI:•Jodice Blinco•Mercedes Marple•Frazier MacDonald
CAST OF CHARACTERS
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• 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.
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.
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• Overall head of BSI, helping companies become Data-Driven Businesses
• Very interested in digital marketing uses of data, hates spam• Longtime Teradata expert
Jodice Blinco – Head of BSI
BSI Teradata
DIRECTOR
JODICEBLINCO
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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!
Mercedes Marple – Principal Investigator
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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!
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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
Scene Synopsis
At Home Electronics Planet HQ
Problem: Katie shows Jodice the KPIs and explains her current marketing efforts. Jodice accepts the assignment to help.
SCENE 1
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The Problem – Digital Marketing Not Working
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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 HEP.com 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
Scene 1: Problem
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Jodice Recommends
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Katie’s Current Approach for Keyword Buys
Despite 10,000 keyword purchases, driving 21.7M visitors, only 3% of these visitors buy anything
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Current Email Effectiveness is Dropping
Things are getting worse: Let’s take a look at the details ...
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• 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 best-selling products for newbies/non-purchasers, from 6 previous product buy categories for existing customers
Typical Email
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Open Rates OK, Click Through Rates Dropping
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Problem: 50% drop in Average Sale per Email(only 50 cents / email)
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Bad Trend: Unsubscribes have TRIPLED
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
SCENE 2
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• 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”. These are called “nPath diagrams”.
• Mercedes starts by looking at people who enter the HEP.com website based on various keywords – but do NOT buy.
They Break up the Work
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Keyword to Product to Browsing but NO SALE! ID (Twice)
nPath Diagram – Keywords That Did NOT Drive Purchases
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nPath Diagram: Keywords That Lead to a Purchase (over one or more sessions)
TIP: Sometimes a purchase might not occur in the firstsession, so you need to aggregate all of a person’s sessions to see if a particular keyword had an impact on the sale of theproduct. These keywords are the “keepers”.
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• 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.
Impact of the Keyword Investigation
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• 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 HEP.com 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 HEP.com in tweets
Acquisition Aside – Many Other Investigations on Sourcing Traffic Are Possible
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• 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:
Frazier Looks First at Browsing Affinities
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• 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.
Frazier Can Also Look at Purchase Affinities
As you might expect, people who buy a Sony laptopoften buy an HP OfficeJet printer at the same time
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• “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
Frazier Looks At the Sequence of Product Purchases, Creates “Personas”
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The “Photo Family” Sequence of Purchases
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The Idea is to Match Partial Sequences to Full Sequences – Find the Best Recommendation
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• 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
“High Tech Trainer”
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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
More Analytical Options
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• Though not shown in the BSI Video, Mercedes and Frazier can also analyze keyword effectiveness, web browsing and purchases by geography. For example:
Many Other Options: Geospatial Overlays
Do consumers in all countries behave the same on the website?
Do the same keywords (translated) work the same?
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Cart Abandonment (including Technical Issues)
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
SCENE 3
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• Jodice just finished covering some of the Aster visualizations and discoveries, and now starts to go over the needed tech components and architecture
Scene 3: Readout on Results
Lincoln Duckett is the VP of IT. They already use Teradata and just bought Hadoop from Hortonworks, a Teradata Partner.
37 © Teradata BSI Studios 2014
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.
Key Technology Points
Math and Stats
DataMining
BusinessIntelligence
Applications
Languages
Marketing
ANALYTIC TOOLS & APPS
USERS
INTEGRATED DISCOVERY PLATFORM
INTEGRATED DATA WAREHOUSE
ERP
SCM
CRM
Images
Audio and Video
Machine Logs
Text
Web and Social
SOURCES
DATA PLATFORM
ACCESSMANAGEMOVE
TERADATA UNIFIED DATA ARCHITECTURESystem Conceptual View
MarketingExecutives
OperationalSystems
FrontlineWorkers
CustomersPartners
Engineers
DataScientists
BusinessAnalysts
TERADATA DATABASE
HORTONWORKSHADOOP
TERADATA DATABASE
TERADATA ASTER DATABASE
39 © Teradata BSI Studios 2014
• Capture any kind of data
• In this case, web clicks
• Down the road, could be tweets
• Or even customer voices from Customer Care
• Cheap long-term storage of raw data
Data Platform – Lots of StorageTeradata Appliance for Hadoop
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• In this case, the next best offers are driven “out to the customer” using Teradata’s Integrated Marketing Management tool
Insights Need to Lead to Actions
LET’S WALK BACK THROUGH AND SEE “UDA IN ACTION”
RUN THESE SLIDES IN ANIMATION MODE TO SEE THE DOTS “FLOW”
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The Overall Flow: From Data and InsightsTo Campaign Planning and Execution
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• 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
Celebrus Technologies
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Web DataWeb Data
Email, SMSEmail, SMSResponsesResponses
Sales /Sales /POSPOS
InventoryInventory
Product / Product / SKUSKU
Loading Web Data from Celebrus into HadoopA Subset May Be Pulled into Aster for Investigation
Feedback
Unified Data ArchitectureTM
For Digital Marketing
TeradataTeradataCustomerCustomer
DataDataWarehouseWarehouse
TeradataTeradataAsterAster
DatabaseDatabase
Customer InteractionManager
Segmenting Leads
Digital Messaging Center
ReportingTrending
Campaigns
Real-Time InteractionManager
Content &Offers Rules
In-Store
CallCenter
Mobile
Web
Channels
Real-TimePersonalization
Data
Photo FamilyGroup
Responses
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Aster Discovery Based on All Kinds of Data
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Doing Analysis to Discover InsightsUsing Some Data from Hadoop and Some Loaded from Teradata
Feedback
Unified Data ArchitectureTM
For Digital Marketing
TeradataTeradataCustomerCustomer
DataDataWarehouseWarehouse
TeradataTeradataAsterAster
DatabaseDatabase
Customer InteractionManager
Segmenting Leads
Digital Messaging Center
ReportingTrending
Campaigns
Real-Time InteractionManager
Content &Offers Rules
In-Store
CallCenter
Mobile
Web
Channels
Real-TimePersonalization
Data
Photo FamilyGroup
Responses
Web DataWeb Data
Email, SMSEmail, SMSResponsesResponses
Sales /Sales /POSPOS
InventoryInventory
Product / Product / SKUSKU
47 © Teradata BSI Studios 2014
To “See” Patterns, Aster Provides Visualization
48 © Teradata BSI Studios 2014
• 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
Insights to ActionIntegrated Marketing Management
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Using Insights to Design CampaignsCustomer Interaction Manager
Feedback
Unified Data ArchitectureTM
For Digital Marketing
TeradataTeradataCustomerCustomer
DataDataWarehouseWarehouse
TeradataTeradataAsterAster
DatabaseDatabase
Customer InteractionManager
Segmenting Leads
Digital Messaging Center
ReportingTrending
Campaigns
Real-Time InteractionManager
Content &Offers Rules
In-Store
CallCenter
Mobile
Web
Channels
Real-TimePersonalization
Data
Photo FamilyGroup
Responses
Web DataWeb Data
Email, SMSEmail, SMSResponsesResponses
Sales /Sales /POSPOS
InventoryInventory
Product / Product / SKUSKU
50 © Teradata BSI Studios 2014
• 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)
E-Mail Campaigns use Digital Message Center
• 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
51 © Teradata BSI Studios 2014
Note in this email: 1.It’s personalized to Mrs. Smith2.It’s relevant – she was just browsing laptops3.It’s timely – maybe we program it to go out 30 minutes after she closes her session without buying4.It’s a good deal5.And if she doesn’t like that laptop, there are other options
Sample: Better Focused and Timely Emails
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Executing a Targeted E-Mail CampaignOne Way Email, Drives to Come to Store or Website
Feedback
Unified Data ArchitectureTM
For Digital Marketing
TeradataTeradataCustomerCustomer
DataDataWarehouseWarehouse
TeradataTeradataAsterAster
DatabaseDatabase
Customer InteractionManager
Segmenting Leads
Digital Messaging Center
ReportingTrending
Campaigns
Real-Time InteractionManager
Content &Offers Rules
In-Store
CallCenter
Mobile
Web
Channels
Real-TimePersonalization
Data
Photo FamilyGroup
Responses
Web DataWeb Data
Email, SMSEmail, SMSResponsesResponses
Sales /Sales /POSPOS
InventoryInventory
Product / Product / SKUSKU
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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
Web Personalization
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Executing Web Campaigns – Real-TimeTwo Way Interactions
Feedback
Unified Data ArchitectureTM
For Digital Marketing
TeradataTeradataCustomerCustomer
DataDataWarehouseWarehouse
TeradataTeradataAsterAster
DatabaseDatabase
Customer InteractionManager
Segmenting Leads
Digital Messaging Center
ReportingTrending
Campaigns
Real-Time InteractionManager
Content &Offers Rules
In-Store
CallCenter
Mobile
Web
Channels
Real-TimePersonalization
Data
Photo FamilyGroup
Responses
Web DataWeb Data
Email, SMSEmail, SMSResponsesResponses
Sales /Sales /POSPOS
InventoryInventory
Product / Product / SKUSKU
55 © Teradata BSI Studios 2014
Web DataWeb Data
Email, SMSEmail, SMSResponsesResponses
Sales /Sales /POSPOS
InventoryInventory
Product / Product / SKUSKU
Closed Loop – Feedback - Iterate Learn from What Did and Did Not Work – Refine Campaigns
Feedback
Unified Data ArchitectureTM
For Digital Marketing
TeradataTeradataCustomerCustomer
DataDataWarehouseWarehouse
TeradataTeradataAsterAster
DatabaseDatabase
Customer InteractionManager
Segmenting Leads
Digital Messaging Center
ReportingTrending
Campaigns
Real-Time InteractionManager
Content &Offers Rules
In-Store
CallCenter
Mobile
Web
Channels
Real-TimePersonalization
Data
Photo FamilyGroup
Responses
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Put it Together: Teradata UDA in Action !
Feedback
Unified Data ArchitectureTM
For Digital Marketing
TeradataTeradataCustomerCustomer
DataDataWarehouseWarehouse
TeradataTeradataAsterAster
DatabaseDatabase
Customer InteractionManager
Segmenting Leads
Digital Messaging Center
ReportingTrending
Campaigns
Real-Time InteractionManager
Content &Offers Rules
In-Store
CallCenter
Mobile
Web
Channels
Real-TimePersonalization
CelebrusCelebrusWeb DataWeb Data
Email, SMSEmail, SMSResponsesResponses
Sales /Sales /POSPOS
InventoryInventory
Product / Product / SKUSKU
Data
Photo FamilyGroup
MORE TECHNICAL DETAILS ABOUT TERADATA ASTER
58 © Teradata BSI Studios 2014
Discovery Approach: Faster ExperimentsMore 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.
Large Volumes Interaction Data Structured Unstructured Multi-structured Hadoop
1
Relational/SQL MapReduce Graph Statistics, R Pathing
2
Business Users Analysts Data Scientists
3
Fast Iterative Investigative Easy
4
59 © Teradata BSI Studios 2014
Teradata Aster Discovery Platform
Industry’s First Visual SQL-MapReduce ® Functions
AFFINITY VISUALIZERVisualize clusters & groups
HIERARCHY VISUALIZERVisualize hierarchical relationships
FLOW VISUALIZERVisualize paths & patterns
Complementary Value•BI: Batch Visualizations Outside the Database, General & Generic•Aster: Rapid Visualizations, in-Database, for Specialized Analytics
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Sample Analytics Modules in Aster
Fastest path to big data analytics
PATHING ANALYSISDiscover Patterns in Rows of Sequential Data
TEXT ANALYSISDerive Patterns and Extract Features in Textual Data
STATISTICAL ANALYSISHigh-Performance Processing of Common Statistical Calculations
GRAPH ANALYSISDiscover Natural Relationships of Entities
SQL ANALYSISReport & Analyze Relational Data
MAPREDUCE ANALYTICSCustom-built, domain-specific analysis
MORE DETAILS ABOUT TERADATA APPLICATIONS
INTEGRATED MARKETING MANAGEMENT
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Teradata Application: Integrated Marketing ManagerComponents Used To Create and Run Recall Workflows
62
A few months later , Katie and Jodice get together to see how things are working ...
SCENE 4
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Keyword mix drives same traffic and sales but with reduced cost
Daily Impact of Changing Mix of Keywords
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• 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.
Options – Keyword Purchasing
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Impact Overall, Projected Annual Changes
42,000more
shoppersper month
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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
How She Did It
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• 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 pepper-crusted 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!!!
Katie’s Happy With the Improvements
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Katie Rewards Jodice with Some “Affinity Art”
WRAPUP
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For more information – UDA
• Teradata UDA> http://www.teradata.com/products-and-services/unified-data-arc
hitecture/
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For More Information
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For More Information - Aster
Teradata Aster: http://www.teradata.com/Aster-Big-Analytics-Appliance/
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For more information – Teradata Applicationshttp://www.teradata.com/teradata-applications/
75 © Teradata BSI Studios 2014
• www.teradata.com
For more information: Teradata
76 © Teradata BSI Studios 2014
• http://www.celebrus.com/
For more Information: our partner Celebrus
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• This episode appears at: http://bit.ly/1eOQrMf
• You can see all our episodes at www.bsi-teradata.com on Facebook: links to 11 other Videos and other “How We Did It” Powerpoints
Thanks for watching!
78 © Teradata BSI Studios 2014
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