Lecture 1 Flashcards
Data
Data is plural of “datum,” a Latin word
Data represents a collection of data points (discrete unit of information)
A “datum” is
a single factual, or point of matter and is most often called data point
Types of Data: Identity
Any info which enables an individual to be uniquely identified
(i.e. demographics, postal address, telephone #, email address, etc…)
Types of Data: Quantitative
Measurable operational data of customer interactions with your business
(i.e. transactional, communication, online activity, customer service, social network)
Types of Data: Qualitative
Attitude, motivation & opinion data usually collected through a questionnaire
Types of Data:
Additional profile information covering family & lifestyle details
The world most valuable resource is no longer oil but
data
Smartphones and the internet have made data
abundant, ubiquitous and far more valuable
Every activity creates a digital
trace (i.e. going for a run, watching TV)
Data volume is also increasing with
IoT (self-driving car will generate 100 gigabytes per second)
IDC predicts that the “digital universe” (the data created and copied every year) will reach
180 zettabytes (180 followed by 21 zeros) in 2025
By collecting more data, a firm has more scope to
improve its products, which attracts more users, generating even more data
Access to data also protects companies from
rivals
Data is no longer simply a stocks of
digital information
The new economy is more about analyzing
rapid real- time flows of often unstructured data
Facebook and Google initially used the data they collected from users to target advertising better
Now…
…they turned the data into any number of AI or “cognitive” services and extracting more value from it
(i.e. translation, visual recognition, etc…)
Analytics Are Deployed Across Four Areas
Radically improve lead generation
Match the people
Maximize customer lifetime value
Get the right price
Radically improve lead generation:
Analytics Use Cases:
Lead generation
Lead scoring
Match the people
Analytics Use Cases:
Coverage planning
Field productivity
Talent and people management
Pipeline management and forecasting
Maximize customer lifetime value
Analytics Use Cases:
Cross-sell/upsell
Churn reduction
Get the right price
Analytics Use Cases:
Dynamic pricing
Dynamic deal scoring
A/B price testing
By using rich data sets to identify the right customer at the right time, companies can improve
the accuracy of lead generation and automate presales processes
Introduction of lead-scoring algorithms based on detailed and granular data sets can help
with lead generation
Improve lead generation by combining customer’s history with external data to
generate a complete view of the customer
i.e. An IT services company used big-data analytics to predict which leads were most likely to close, resulting in a
30% lift in conversion
Better Match People to Deals
Leveraging analytics to understand what drives
sales success and to inform coverage, hiring, and training
Better Match People to Deals
More effective resource allocation with the
introduction of basic analytics to sales planning
Better Match People to Deals
Integrating email, calendar, and CRM interaction data to
identify which actions in the field correlate with success
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
5 to 10 percent
Maximize CLV
Implementing next-product-to-buy algorithms that draw on data about
what similar customers have bought
Maximize CLV
Machine-learning algorithms can also identify patterns of
customer discontent and the associated risk of losing a customer, helping increase retention
Maximize CLV
i.e. A logistics company mined historical ordering patterns to
identify cross-sell opportunities within its customer base and then built tailored micro-campaigns around them
Get The Right Price
Deal analytics can provide
price transparency and allow sellers to make complex trade-offs during negotiations
Get The Right Price
Dynamic deal scoring re-levels the playing field by
placing relevant deal information in the hands of sales reps during the negotiation
Get The Right Price
Using decision-tree analytics, reps can identify
similar purchases and comparable deal information to guide selling
Get The Right Price
Companies are implementing dynamic- pricing engines that integrate
real-time competitive and market data with sales strategies to generate optimal quotes
Insights Value Chain
Data * Analytics * IT * People * Processes = Value Captured
The insights value chain is multiplicative, meaning…
…you are only as good as the weakest link in the chain.
Technical Foundations
Data
Analytics
IT
Business Foundations
People
Processes
How to Translate Data Insights into Value
- Generating and collecting data
- Refining data
- Turning insights into action
- Driving adoption
- Mastering tasks concerning technology and infrastructure as well as organization and governance
- Generating and collecting data
Data extraction, transformation, and loading
Appending of external data
Creation of an analytic sandbox
- Refining data
Data mining
Predictive analytics to support decisions
Prescriptive analytics to drive value creation
- Turning insights into action
Process redesign
Integrated and automated execution; tools for real-time decision making
- Driving adoption
Build frontline and management capabilities
Proactive change management
Scale up road map
- Mastering tasks concerning technology and infrastructure as well as organization and governance
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
