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

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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