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


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


• 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.


• 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


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


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!


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


The Problem – Digital Marketing Not Working


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


Jodice Recommends


Katie’s Current Approach for Keyword Buys

Despite 10,000 keyword purchases, driving 21.7M visitors, only 3% of these visitors buy anything


Current Email Effectiveness is Dropping

Things are getting worse: Let’s take a look at the details ...


• 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


Open Rates OK, Click Through Rates Dropping


Problem: 50% drop in Average Sale per Email(only 50 cents / email)


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


• 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


Keyword to Product to Browsing but NO SALE! ID (Twice)

nPath Diagram – Keywords That Did NOT Drive Purchases


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”.


• 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


• 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


• 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


• 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


• “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”


The “Photo Family” Sequence of Purchases


The Idea is to Match Partial Sequences to Full Sequences – Find the Best Recommendation


• 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”


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


• 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?


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


• 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.


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


• 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


• 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”


The Overall Flow: From Data and InsightsTo Campaign Planning and Execution


• 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


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

Email

Mobile

Web

Channels

Real-TimePersonalization

Data

Photo FamilyGroup

Responses


Aster Discovery Based on All Kinds of Data


Doing Analysis to Discover InsightsUsing Some Data from Hadoop and Some Loaded from Teradata

Feedback






DatabaseDatabase


Segmenting Leads


ReportingTrending

Campaigns



In-Store

CallCenter

Email

Mobile

Web

Channels


Data

Photo FamilyGroup

Responses

Web DataWeb Data



InventoryInventory



To “See” Patterns, Aster Provides Visualization


• 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


Using Insights to Design CampaignsCustomer Interaction Manager

Feedback






DatabaseDatabase


Segmenting Leads


ReportingTrending

Campaigns



In-Store

CallCenter

Email

Mobile

Web

Channels


Data

Photo FamilyGroup

Responses

Web DataWeb Data



InventoryInventory



• 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


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


Executing a Targeted E-Mail CampaignOne Way Email, Drives to Come to Store or Website

Feedback






DatabaseDatabase


Segmenting Leads


ReportingTrending

Campaigns



In-Store

CallCenter

Email

Mobile

Web

Channels


Data

Photo FamilyGroup

Responses

Web DataWeb Data



InventoryInventory



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


Executing Web Campaigns – Real-TimeTwo Way Interactions

Feedback






DatabaseDatabase


Segmenting Leads


ReportingTrending

Campaigns



In-Store

CallCenter

Email

Mobile

Web

Channels


Data

Photo FamilyGroup

Responses

Web DataWeb Data



InventoryInventory



Web DataWeb Data



InventoryInventory


Closed Loop – Feedback - Iterate Learn from What Did and Did Not Work – Refine Campaigns

Feedback






DatabaseDatabase


Segmenting Leads


ReportingTrending

Campaigns



In-Store

CallCenter

Email

Mobile

Web

Channels


Data

Photo FamilyGroup

Responses


Put it Together: Teradata UDA in Action !

Feedback






DatabaseDatabase


Segmenting Leads


ReportingTrending

Campaigns



In-Store

CallCenter

Email

Mobile

Web

Channels


CelebrusCelebrusWeb DataWeb Data



InventoryInventory


Data

Photo FamilyGroup

MORE TECHNICAL DETAILS ABOUT TERADATA ASTER


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


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


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


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


Keyword mix drives same traffic and sales but with reduced cost

Daily Impact of Changing Mix of Keywords


• 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


Impact Overall, Projected Annual Changes

42,000more

shoppersper month


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


• 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


Katie Rewards Jodice with Some “Affinity Art”

WRAPUP


For more information – UDA

• Teradata UDA> http://www.teradata.com/products-and-services/unified-data-arc

hitecture/


For More Information


For More Information - Aster

Teradata Aster: http://www.teradata.com/Aster-Big-Analytics-Appliance/


For more information – Teradata Applicationshttp://www.teradata.com/teradata-applications/


• www.teradata.com

For more information: Teradata


• http://www.celebrus.com/

For more Information: our partner Celebrus


• 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!


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 analytics3. 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 BREACHA 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 CALLSA major Telco digs into real-time dropped call data to understand high-value and high-influence 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 STAGNOBANKCustomer 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 TURNAROUNDA 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 LASAGNAA 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.

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