One Page Summary Data Analytics Intermediate Module 1 28sep
Transcript of One Page Summary Data Analytics Intermediate Module 1 28sep
The Value of Data
Data and AI Technologies are Creating Huge Value for Businesses
The Value of DataDeal Making
Example: Pedestrian Accident DataData is the world’s
most valuable resource
Conclusion Data is a new fundamental resource that can be converted into business value in various styles.Data can be used endlessly, meet business needs and can be analyzed for business decision making.
The Value of Data: Data Driven Decisions
Real Cases: 7-Eleven Thailand
Issue: American Express needs a data model to predict customer churn.
Introducingfacial recognition and AI technologyacross 11,000 stores in Thailand to :
Identify loyalty membersAnalyze in-store trafficMonitor product levelsSuggest products to customersMeasure the emotions of customers as they walk around
How do we know which customers are important, and what will indicate which are likely to leave?In just one quarter, T-Mobile USA managed to reduce customer churn rates by 50%!How did they do it? What questions did they ask?
Goals & OutcomesPreserve 100M global cards, $1T annual charges.Biz model: Targets affluent customers ($150/charge vs. Visa $50/charge).Outcomes: Flagged 24% of accounts to churn next qtr → retention marketing. Reduced personalization model from three days to 20 minutes. Targeted-ads increased online customer acquisition by 40%.
Case Studies
Data Framework Part 1: Identifying Objectivesand Questions
Part 2:Data preparationand analytics
Real Cases:T-Mobile
Billing Analysis
Drop Call Analysis
Sentiment Science
Used data science and data analyticsto help answer :
Draft objective: Reduce the churn rate of credit card customers.SMART objective: Reduce the churn rate for Super Prime credit card customers by 15% compared to the same Q last year by looking for factors that affect churn and finding measures to reduce those factors by 1 quarter.
EXAMPLE: SMART ObjectiveWhere do data come from?
4 V's of Big Data
Shows how often, where, and how long a user calls with whom
If a customer moves and data showshe/she gets limited coverage in the new area, a customer rep is alerted to offer a new phone to prevent the customer from switching.
Predicts triggers and indicators of future customer actions and their perception of T-Mobile. This helps T-Mobile to proactively respond to any complaints.
Everyone working towards and objectives understands the who, what, how, and the why of the objective.
The objective is measurable with data collection available.
It’s feasible based on historic data and budget.
It supports overall business goals; ladders up to a goal above it
Clear start and end date.
Example: Bangkok Credit Service (BCS) would like to know why Super Prime credit card customers cancel their usage and switch to credit cards from competitors
Reduce time spent onadministrative tasks inservice center
Why do some administrativetasks require more time than others ?
How are we going touse this information ?Always a good starting
point to clarify the objective further
Five why’s
Find the root cause of a problem or objective and
identify the full picture
What else do youthink I should know ?Analysts should always end on this question to
surface unexpected insights
Which ‘call to action’ is drivingthe hightest conversion?
Which products have the highest profit margin?
Increase reach of Facebookadvertising, whilst maintaining conversion rate.
Increase number of hight-profit products sold
EXAMPLE: Objectives Guides Questions
Let’s Get SMART with Our Objectives!
When solving data problem:aim for Insights which leads to action
Actionable !
Organizedand analyzed
Raw
Insights
Information
Data
Does the spending amount from the last 3 billing cycles and the card's validity affect the churn?Does the amount of customer tax payment in the past 3 years affect the churn?
Question
Specific
Measurable
Attainable
Relevant
Time-bound
Conclusion
From objective to question: Three Tactics for Getting to a Good Question
Setting SMART objectives and Hypothesis-Driven questions is an important step to start a Data Analytics Project.
Data Analytics Workflow
FrameDevelop
hypothesis-drivenquestions foryour analysis
Select, import,& clean
relevant data
Structue, visualize& complete your
analysis
Create insightsand business
decisions fromyour analysis
Present data-drivenfindings and
recommendationsto your audience
Prepare Analyze Interpret Communicate
Department
Operations
Marketing
Sales
Objective Question
VOLUME VELOCITY
VARIETY VERACITY
2021 © TRUE DIGITAL ACADEMY Data Analytics Intermediate I Module1: Intro to Analytics
Data Analytics Intermediate 1Intro to Analytics
Module
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Hypothesis-Driven Questions/Insights and Actions
Ask great questions to reveal key findings
Presentation Canvas
Story Map
The good Hypothesis-Driven Questions is thatwe can try to guess the answer and
can be wrong (falsifiable) such as
Does the spending amount from the last 3 billing cycles and card validity affect churn?If yes, is there any particular group of customers with low spending?
A story map can be used to diagram your presentations ahead of time.This can help you consider all of the relevant elements you may want to discuss, including
Setting / Time / Place1
2
Situation: This is the current state; define the problem(s).Complication: Contextualize the problem with details.Question: Given these barriers, what should be done?Answer: Your call to action or methods, framed as the solution.
The specific business context: Location of problem Where was data collected? What locations are involved?
Plot/Events4 Process, considerations, actions taken: What did you do to solve these problems? What was the timeline of your approach? Explain your process in terms your audience can easily understand
Presentation Strategies
The traditional narrative arc is a linear story, consistion of four elements :
Point: Presentations = stories. Stories are a cultural framework that most audiences are already familiar with: setting, characters, problem, solution.
Planning presentations in this manner can help you remember to describe and focus on the people, problems, and goals involved.
Finally, the GA data workflow is not just a framework for solving problems, but can also be used to help you clearly organize your data presentations
Problem3 The issues and opportunities at-hand: Motivations? Pain points? Opportunities? Hypothesis?
CharactersPeople and data involved: Data dictionary? Business unit owner? Metadata description? Issues of data governance?Audience stakeholder(s) involved: Who is the presentation for? Identify all key decision makers.
5 Resolution What should be done? What are your recommendations? Include your assumptions. Is any additional data needed?Communicate your results Use visuals. Customize this for your audience. Make your data the focus.
Story Map
Setting
Plot/Events
Problem
Characters
Resolution
PlaceTime
Presentation ObjectivesWhat does your presentation need to accomplish ?
Audience SegmentsWhat describes your audience & their enrollment ?
Audience ObjectivesWhat does your audience needfrom your presentation ?
Explains where we are now.
Creates tension in the storyyou’re telling; triggers the Question you will ask
Asks what weshould do now giventhe Complication.
The Answer to the Question isthe substance of your presentation.
Presentation ContentHow will your presentationfit both needs ?
SITUATION
SITUATIONCOMPLICATION
COMPLICATION
QUESTION
QUESTIONANSWER
ANSWER
2021 © TRUE DIGITAL ACADEMY Data Analytics Intermediate I Module1: Intro to Analytics
Data Analytics Intermediate 1Intro to Analytics
Module
1