Lecture 1

Question 1

Data

Answer 1

Data is plural of “datum,” a Latin word

Data represents a collection of data points (discrete unit of information)

Question 2

A “datum” is

Answer 2

a single factual, or point of matter and is most often called data point

Question 3

Types of Data: Identity

Answer 3

Any info which enables an individual to be uniquely identified
(i.e. demographics, postal address, telephone #, email address, etc…)

Question 4

Types of Data: Quantitative

Answer 4

Measurable operational data of customer interactions with your business
(i.e. transactional, communication, online activity, customer service, social network)

Question 5

Types of Data: Qualitative

Answer 5

Attitude, motivation & opinion data usually collected through a questionnaire

Question 6

Types of Data:

Answer 6

Additional profile information covering family & lifestyle details

Question 7

The world most valuable resource is no longer oil but

Answer 7

data

Question 8

Smartphones and the internet have made data

Answer 8

abundant, ubiquitous and far more valuable

Question 9

Every activity creates a digital

Answer 9

trace (i.e. going for a run, watching TV)

Question 10

Data volume is also increasing with

Answer 10

IoT (self-driving car will generate 100 gigabytes per second)

Question 11

IDC predicts that the “digital universe” (the data created and copied every year) will reach

Answer 11

180 zettabytes (180 followed by 21 zeros) in 2025

Question 12

By collecting more data, a firm has more scope to

Answer 12

improve its products, which attracts more users, generating even more data

Question 13

Access to data also protects companies from

Answer 13

rivals

Question 14

Data is no longer simply a stocks of

Answer 14

digital information

Question 15

The new economy is more about analyzing

Answer 15

rapid real- time flows of often unstructured data

Question 16

Facebook and Google initially used the data they collected from users to target advertising better
Now…

Answer 16

…they turned the data into any number of AI or “cognitive” services and extracting more value from it
(i.e. translation, visual recognition, etc…)

Question 17

Analytics Are Deployed Across Four Areas

Answer 17

Radically improve lead generation

Match the people

Maximize customer lifetime value

Get the right price

Question 18

Radically improve lead generation:

Analytics Use Cases:

Answer 18

Lead generation

Lead scoring

Question 19

Match the people

Analytics Use Cases:

Answer 19

Coverage planning

Field productivity

Talent and people management

Pipeline management and forecasting

Question 20

Maximize customer lifetime value

Analytics Use Cases:

Answer 20

Cross-sell/upsell

Churn reduction

Question 21

Get the right price

Analytics Use Cases:

Answer 21

Dynamic pricing

Dynamic deal scoring

A/B price testing

Question 22

By using rich data sets to identify the right customer at the right time, companies can improve

Answer 22

the accuracy of lead generation and automate presales processes

Question 23

Introduction of lead-scoring algorithms based on detailed and granular data sets can help

Answer 23

with lead generation

Question 24

Improve lead generation by combining customer’s history with external data to

Answer 24

generate a complete view of the customer

Question 25

i.e. An IT services company used big-data analytics to predict which leads were most likely to close, resulting in a

Answer 25

30% lift in conversion

Question 26

Better Match People to Deals Leveraging analytics to understand what drives

Answer 26

sales success and to inform coverage, hiring, and training

Question 27

Better Match People to Deals More effective resource allocation with the

Answer 27

introduction of basic analytics to sales planning

Question 28

Better Match People to Deals Integrating email, calendar, and CRM interaction data to

Answer 28

identify which actions in the field correlate with success

Question 29

Better Match People to Deals A high-tech company used a granular account and product-level approach to realign its US coverage model, increasing sales productivity by

Answer 29

5 to 10 percent

Question 30

Maximize CLV Implementing next-product-to-buy algorithms that draw on data about

Answer 30

what similar customers have bought

Question 31

Maximize CLV Machine-learning algorithms can also identify patterns of

Answer 31

customer discontent and the associated risk of losing a customer, helping increase retention

Question 32

Maximize CLV i.e. A logistics company mined historical ordering patterns to

Answer 32

identify cross-sell opportunities within its customer base and then built tailored micro-campaigns around them

Question 33

Get The Right Price Deal analytics can provide

Answer 33

price transparency and allow sellers to make complex trade-offs during negotiations

Question 34

Get The Right Price Dynamic deal scoring re-levels the playing field by

Answer 34

placing relevant deal information in the hands of sales reps during the negotiation

Question 35

Get The Right Price Using decision-tree analytics, reps can identify

Answer 35

similar purchases and comparable deal information to guide selling

Question 36

Get The Right Price Companies are implementing dynamic- pricing engines that integrate

Answer 36

real-time competitive and market data with sales strategies to generate optimal quotes

Question 37

Insights Value Chain

Answer 37

Data * Analytics * IT * People * Processes = Value Captured

Question 38

The insights value chain is multiplicative, meaning...

Answer 38

...you are only as good as the weakest link in the chain.

Question 39

Technical Foundations

Answer 39

Data Analytics IT

Question 40

Business Foundations

Answer 40

People | Processes

Question 41

How to Translate Data Insights into Value

Answer 41

1. Generating and collecting data 2. Refining data 3. Turning insights into action 4. Driving adoption 5. Mastering tasks concerning technology and infrastructure as well as organization and governance

Question 42

1. Generating and collecting data

Answer 42

Data extraction, transformation, and loading Appending of external data Creation of an analytic sandbox

Question 43

2. Refining data

Answer 43

Data mining Predictive analytics to support decisions Prescriptive analytics to drive value creation

Question 44

3. Turning insights into action

Answer 44

Process redesign Integrated and automated execution; tools for real-time decision making

Question 45

4. Driving adoption

Answer 45

Build frontline and management capabilities Proactive change management Scale up road map

Question 46

5. Mastering tasks concerning technology and infrastructure as well as organization and governance

Answer 46

Develop the building, buying, licensing, or partner strategy for supporting/enabling technologies and software Define central and business-unit roles needed; attract and train talent Create tracking and visibility of ongoing impact