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Martech Ecosystem > Lecture 1 > Flashcards

Lecture 1 Flashcards

1
Q

Data

A

Data is plural of “datum,” a Latin word

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

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2
Q

A “datum” is

A

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

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3
Q

Types of Data: Identity

A

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

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4
Q

Types of Data: Quantitative

A

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

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5
Q

Types of Data: Qualitative

A

Attitude, motivation & opinion data usually collected through a questionnaire

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6
Q

Types of Data:

A

Additional profile information covering family & lifestyle details

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7
Q

The world most valuable resource is no longer oil but

A

data

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8
Q

Smartphones and the internet have made data

A

abundant, ubiquitous and far more valuable

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9
Q

Every activity creates a digital

A

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

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10
Q

Data volume is also increasing with

A

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

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11
Q

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

A

180 zettabytes (180 followed by 21 zeros) in 2025

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12
Q

By collecting more data, a firm has more scope to

A

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

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13
Q

Access to data also protects companies from

A

rivals

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14
Q

Data is no longer simply a stocks of

A

digital information

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15
Q

The new economy is more about analyzing

A

rapid real- time flows of often unstructured data

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16
Q

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

A

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

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17
Q

Analytics Are Deployed Across Four Areas

A

Radically improve lead generation

Match the people

Maximize customer lifetime value

Get the right price

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18
Q

Radically improve lead generation:

Analytics Use Cases:

A

Lead generation

Lead scoring

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19
Q

Match the people

Analytics Use Cases:

A

Coverage planning

Field productivity

Talent and people management

Pipeline management and forecasting

20
Q

Maximize customer lifetime value

Analytics Use Cases:

A

Cross-sell/upsell

Churn reduction

21
Q

Get the right price

Analytics Use Cases:

A

Dynamic pricing

Dynamic deal scoring

A/B price testing

22
Q

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

A

the accuracy of lead generation and automate presales processes

23
Q

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

A

with lead generation

24
Q

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

A

generate a complete view of the customer

25
Q

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

A

30% lift in conversion

26
Q

Better Match People to Deals

Leveraging analytics to understand what drives

A

sales success and to inform coverage, hiring, and training

27
Q

Better Match People to Deals

More effective resource allocation with the

A

introduction of basic analytics to sales planning

28
Q

Better Match People to Deals

Integrating email, calendar, and CRM interaction data to

A

identify which actions in the field correlate with success

29
Q

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

A

5 to 10 percent

30
Q

Maximize CLV

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

A

what similar customers have bought

31
Q

Maximize CLV

Machine-learning algorithms can also identify patterns of

A

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

32
Q

Maximize CLV

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

A

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

33
Q

Get The Right Price

Deal analytics can provide

A

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

34
Q

Get The Right Price

Dynamic deal scoring re-levels the playing field by

A

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

35
Q

Get The Right Price

Using decision-tree analytics, reps can identify

A

similar purchases and comparable deal information to guide selling

36
Q

Get The Right Price

Companies are implementing dynamic- pricing engines that integrate

A

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

37
Q

Insights Value Chain

A

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

38
Q

The insights value chain is multiplicative, meaning…

A

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

39
Q

Technical Foundations

A

Data
Analytics
IT

40
Q

Business Foundations

A

People

Processes

41
Q

How to Translate Data Insights into Value

A
  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
42
Q
  1. Generating and collecting data
A

Data extraction, transformation, and loading

Appending of external data

Creation of an analytic sandbox

43
Q
  1. Refining data
A

Data mining

Predictive analytics to support decisions

Prescriptive analytics to drive value creation

44
Q
  1. Turning insights into action
A

Process redesign

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

45
Q
  1. Driving adoption
A

Build frontline and management capabilities

Proactive change management

Scale up road map

46
Q
  1. Mastering tasks concerning technology and infrastructure as well as organization and governance
A

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

Martech Ecosystem (12 decks)

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