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

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

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


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

CAST OF CHARACTERS
Home Electronics Planet:
•Chief Marketing Officer: Katie Hullman
•VP IT: Lincoln Duckett
BSI:
•Jodice Blinco
•Mercedes Marple
•Frazier MacDonald

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


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


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


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


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


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.

The Problem – Digital Marketing Not Working

11


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 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

12


Jodice Recommends

13


Katie’s Current Approach for Keyword Buys

Despite 10,000 keyword purchases, driving 21.7M
visitors, only 3% of these visitors buy anything
14


Current Email Effectiveness is Dropping

Things are getting worse:
Let’s take a look at the details ...
15


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


Open Rates OK, Click Through Rates Dropping

17


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

18


Bad Trend: Unsubscribes have TRIPLED

19


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

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 HEP.com
website based on various keywords – but do NOT buy.

21


nPath Diagram – Keywords That Did NOT
Drive Purchases

Keyword
22

to

Product to
Browsing
ID © Teradata BSI Studios (Twice)
2014

but NO SALE!

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


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


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 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

25


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


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


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


The “Photo Family” Sequence of Purchases

29


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

30


“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


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


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


Cart Abandonment (including Technical Issues)

34


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

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


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 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

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


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


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 Insights
To Campaign Planning and Execution

42


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


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


Responses

Aster Discovery Based on All Kinds of Data

45


Doing Analysis to Discover Insights

Using Some Data from Hadoop and Some Loaded from Teradata
Manager

Data

Product /
SKU

Teradata
Customer
Data
Warehouse

Sales /
POS
Email, SMS
Responses
Web Data

In-Store
Segmenting

Leads


Inventory

Teradata
Aster
Database

Reporting
Trending

Campaigns

Manager

Content &
Offers

Call
Center
Photo Family
Group

Email

Mobile
Real-Time
Personalization

Web
Rules

Feedback
46

Channels


Responses

To “See” Patterns, Aster Provides Visualization

47


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


Using Insights to Design Campaigns
Customer Interaction Manager

Manager

Data

Product /
SKU

Teradata
Customer
Data
Warehouse

Sales /
POS
Email, SMS
Responses
Web Data

In-Store
Segmenting

Leads


Inventory

Teradata
Aster
Database

Reporting
Trending

Campaigns

Manager

Content &
Offers

Call
Center
Photo Family
Group

Email

Mobile
Real-Time
Personalization

Web
Rules

Feedback
49

Channels


Responses

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


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


Executing a Targeted E-Mail Campaign
One Way Email, Drives to Come to Store or Website
Manager

Data

Product /
SKU

Teradata
Customer
Data
Warehouse

Sales /
POS
Email, SMS
Responses
Web Data

In-Store
Segmenting

Leads


Inventory

Teradata
Aster
Database

Reporting
Trending

Campaigns

Manager

Content &
Offers

Call
Center
Photo Family
Group

Email

Mobile
Real-Time
Personalization

Web
Rules

Feedback
52

Channels


Responses

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


Executing Web Campaigns – Real-Time
Two Way Interactions

Manager

Data

Product /
SKU

Teradata
Customer
Data
Warehouse

Sales /
POS
Email, SMS
Responses
Web Data

In-Store
Segmenting

Leads


Inventory

Teradata
Aster
Database

Reporting
Trending

Campaigns

Manager

Content &
Offers

Call
Center
Photo Family
Group

Email

Mobile
Real-Time
Personalization

Web
Rules

Feedback
54

Channels


Responses

Closed Loop – Feedback - Iterate

Learn from What Did and Did Not Work – Refine Campaigns
Manager

Data

Product /
SKU

Teradata
Customer
Data
Warehouse

Sales /
POS
Email, SMS
Responses
Web Data

In-Store
Segmenting

Leads


Inventory

Teradata
Aster
Database

Reporting
Trending

Campaigns

Manager

Content &
Offers

Call
Center
Photo Family
Group

Email

Mobile
Real-Time
Personalization

Web
Rules

Feedback
55

Channels


Responses

Put it Together: Teradata UDA in Action !
Manager

Data

Product /
SKU

Teradata
Customer
Data
Warehouse

Sales /
POS
Email, SMS
Responses

In-Store
Segmenting

Leads


Inventory

Teradata
Aster
Database

Reporting
Trending

Campaigns

Manager

Celebrus
Web Data
Content &
Offers

Call
Center
Photo Family
Group

Email

Mobile
Real-Time
Personalization

Web
Rules

Feedback
56

Channels


MORE TECHNICAL DETAILS
ABOUT TERADATA ASTER

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







Fast
Iterative
Investigative
Easy

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


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


Derive Patterns and Extract
Features in Textual Data

Discover Natural
Relationships of Entities

MAPREDUCE
ANALYTICS

Custom-built, domainspecific analysis

MORE DETAILS ABOUT
TERADATA APPLICATIONS
INTEGRATED MARKETING
MANAGEMENT

Teradata Application: Integrated Marketing Manager
Components Used To Create and Run Recall Workflows

62

62


SCENE 4
A few months later , Katie and Jodice get together to see how
things are working ...

Daily Impact of Changing Mix of Keywords

Keyword mix drives same traffic and sales but with reduced cost
64


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


Impact Overall, Projected Annual Changes

42,000
more
shoppers
per month

66


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


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


Katie Rewards Jodice with Some “Affinity Art”

69


For more information – UDA
• Teradata UDA

> http://www.teradata.com/products-and-services/unified-data-architec

71


For More Information

72


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

73


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

74


For more information: Teradata
• www.teradata.com

75


For more Information: our partner Celebrus
• http://www.celebrus.com/

76


Thanks for watching!
• 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

77


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


BSI Teradata: The Shocking Case of Home Electronics Planet

In this document