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Think Like a Data Scientist
How do data scientists utilize predictive and prescriptive analytics to create business value?
Identify Key Business Initiatives
Learn more about identifying key business initiativesThese should be:
Critical to immediate-term performance
Documented (communicated internally/publicly)
Cross-Functional (involving multiple business functions)
Championed by a senior business executive
Measurable against clear financial goals
Time Bound that is well-defined
Advantageous (deliver financial or competitive advantage)
Make It Happen
+Key Technologies
+Develop Stakeholder Personas
Identify the key business stakeholders who either impact or are impacted by the targeted business initiative.
Learn more about building stakeholder personasMake It Happen
+Key Technologies
+Identify Strategic Nouns
What are the key business entities that either impact or are impacted by the organization's key business initiative?
Learn more about identifying strategic nounsCustomers
Patients
Stores
Wind Turbines
Trucks
Products
Students
Medication
Employees
Make It Happen
+Key Technologies
+Capture Business Decisions
Document business stakeholder key decisions and write brief descriptions.
Learn more about capturing business decisionsHow much stuff do I need?
How many staff should be working?
How much of product X should I stock?
When is the best time to order more product?
Make It Happen
+Key Technologies
+Brainstorm Business Questions
This is perhaps the hardest part of the "thinking like a data scientist" exercise, which involves examining your strategic nouns from 3 perspectives...
Learn more about how to brainstorm business questionsDescriptive Analytics:
Understanding what happened
How many widgets did I sell last month?
Predictive Analytics:
Predicting what will happen
How many widgets will I sell next month?
Prescriptive Analytics:
Recommending what to do next
How much of component Z should I order?
Make It Happen
+Key Technologies
+Leverage "By" Analysis.
This is an exploratory technique of examining a strategic entity by its data attributes. This can uncover:
- Additional data sources
- Additional dimensional entity characteristics
- Additional areas for analytics exploration
"Show me Customer
habits by..."- Category
- Remodel Date
- Store
- Day of Week
- Customer demo
Make It Happen
+Key Technologies
+Create Actionable Scores
Look for groupings of strategic noun dimensions and attributes that can be combined to create a more predictive and actionable score.
Learn more about
scoring techniquesFICO Score
Make It Happen
+Key Technologies
+Put Analytics Into Action
Deliver analytics-driven scores and recommendations to the key business stakeholders.
Learn more about putting analytics into actionMake It Happen
+Key Technologies
+Step 1: How do I identify my Key Business Initiatives?
Key business initiatives include what the organization plans to achieve with their business strategy over the next 9-12 months; usually includes business objectives, financial targets, metrics and timeframe.
A Business Initiative supports the business strategy and has the following characteristics:
- Critical to the immediate-term business and/or financial performance (usually 9 to 12 month timeframe)
- Documented (communicated either internally or publicly)
- Cross-functional (involves more than one business function)
- Owned or championed by a senior business executive
- Has a measurable financial goal
- Has a well-defined delivery timeframe
- Delivers compelling financial or competitive advantage
What's Important to Sports Shop?
Their Key Business Initiatives could be:
- Improve merchandising effectiveness
- Develop a compelling apparel assortment
- Make our stores and internet sites exciting places to shop and buy
- Increase the productivity of employees
Step 2: Develop Stakeholder Personas
We want to develop personas for each of the business stakeholders to understand better their work characteristics and job characteristics. Understanding this helps to capture the decisions and questions that these stakeholders must address with respect to the targeted business initiative.
A persona is a 1-2 page “day in the life” description that makes the key business stakeholder “come to life” for the data science and User Experience (UEX) development teams. Personas are useful in understanding the goals, tasks, key decisions, and pain points of the key business stakeholders. The persona helps the data science team to identify the most appropriate data sources and analytic techniques to support the decisions that the business users are trying to make and the questions that they are trying to answer. Personas are created for each type of business stakeholders affected by the given business initiative.
Stakeholder Persona for Sports Shop
Step 3: How To Identify Strategic Nouns
Strategic nouns are critical to data scientists' thinking process because these are the entities from which to gain new, actionable insights, that ultimately help build analytic profiles.
Examples of strategic nouns include:
- Customers
- Patients
- Students
- Employees
- Stores
- Products
- Medication
- Trucks
- Wind Turbines
Strategic Nouns for Sports Shop
For the "Improve Merchandising Effectiveness" business initiative, the strategic nouns could be:
- Customers
- Products
- Campaigns
- Stores
Step 4: How Do I Capture Key Business Decisions?
What decisions do the business stakeholders need to make about the strategic nouns, in support of the targeted business initiative. What data insight would support those decisions? These help to form the basis for generating an actionable analytics recommendation that can accelerate a targeted key business initiative.
Capturing and validating these decisions is critical to the "Thinking like a data scientist" process. Leading organizations like Uber and Netflix are disruptive because they build a business model that seeks to simplify their targeted customers' key "decisions.” For Uber, one of the customer decisions that they address is "How do I easily get from Point A to Point B?" For Netflix, one of the customer decisions that they address is "What content (movie, TV show) can I easily watch tonight?"
Key Business Decisions for Sports Shop
We want to capture the decisions (where decision is defined as a conclusion or resolution reached after consideration) especially in light of the entity’s business initiatives. For our sports shop's "Improve Merchandising Effectiveness" Business Initiative, we are likely going to make decisions around product placement, special offers, and promotions.
Examples of Business Decisions for Sports Shop:
- Which products should be featured prominently?
- How should I bundle products to drive revenue per transaction?
- How many employees should be on the floor versus behind the registers?
- How can I sell more gift cards
- How should I inform rewards card members of special offers?
- Are Buy 1 Get 1 Free offers more attractive to customers than 50% off?
- When should I start Back to School and Black Friday promotions?
- What is the right balance of men’s versus women’s items?
- What is the right balance of clothing versus sporting goods?
Step 5: How Do I Brainstorm Business Questions?
Brainstorm with each of the different stakeholders the decisions they need to make with respect to each strategic noun or key business entity in support of the targeted business initiative.
The Evolution of Analytic Questions
What Happened?
Descriptive (BI)
- How many widgets did I sell last month?
- What were sales by zip code for Christmas last year?
- How many of Product X were returned last month?
- What were company revenues and profits for the past quarter?
- How many employees did I hire last year?
What Will Happen?
Predictive
- How many widgets will I sell next month?
- What will be sales by zip code over this Christmas season?
- How many of Product X will be returned next month?
- What are projected company revenues and profits for next quarter?
- How many employees will I need to hire next year?
What Should I Do?
Prescriptive
- Order [5,000] component Z to support widget sales for next month
- Hire [Y] new sales reps by these zip codes to handle projected Christmas sales
- Set aside [$125K] in financial reserve to cover Product X returns
- Sell the following product mix to achieve quarterly revenue and margin goals
- Increase hiring pipeline by 35% to achieve hiring goals
Key Business Questions for Sports Shop
For their "Improve Merchandising Effectiveness" Business Initiative, we want to brainstorm the "Customer"strategic noun questions as such:
Descriptive Analytics (Understanding what happened)
- What customers are most receptive to what types of merchandising campaigns?
- What are the characteristics of customers (e.g.,age, gender, customer tenure, life stage, favorite sports) who are most responsive to merchandising offers?
- Are there certain times of year where certain customers are more responsive?
Predictive Analytics (Predicting what will happen)
- Which customers are most likely to respond to a Back to School event?
- Which customers are most likely to respond to a BOGOF offer?
- Which customers are most likely to respond to a 50% off in-store markdown?
Prescriptive Analytics (Recommending what to do next)
- What personalized offers (recommendations) should I deliver to Anne Smith to get her to come into the store?
Step 6: What is "By" Analysis?
The “By” analysis technique exploits a business user’s natural “question and answer” enquiry process to identify new data sources, dimensional characteristics, variables and metrics that could be leveraged by the data science team in building the predictive and prescriptive analytic models to help predict business performance. The “By” analysis leverages a business stakeholder’s natural curiosity to brainstorm new:
- Metrics, measures and key performance indicators
- Dimensions (e.g., strategic nouns) and the attributes and characteristics associated with those dimensions or strategic nouns
- Areas for potential analytics exploration
The “By” analysis uses a simple “I want to [verb] [metric] by [dimensional attribute]” format to capture the business stakeholder brainstorming process and uncover new data and analytic requirements. The “By” analysis format looks like such as:
“I want to”
- Verb such as [see, know, report, compare, trend, plot, predict, test score]
- Metric such as [sales, margin, profits, social media posts, comments, physician notes, vibration levels, sensor codes]
“By”
- Dimension or dimensional attribute such as [city, state, zip code, date, time, seasonality, product category, remodel date, store manager demographics]
Here is a “By” analysis example:
- I want to [report] [sales and product margin] by… [product category, store, store remodel date, day of week, store demographics, and customer demographics]
"By" Analysis for Sports Shop
Here is an example of "By" analysis for hypothetical merchandising, using customer questions to improve merchandising effectiveness:
What customers are most receptive to what types of merchandising campaigns by:
- Age
- Gender
- Marital Status
- Number of children
- Length of marriage
- Income level
- Education level
- Loyalty card member
- Own or rent residence
- Tenure in current home
- Value of current home
- Favorite sports
- Favorite teams
- High school sports
- College sports
- Weekend sports
- Active athlete?
- Exercise minutes/week
- Types of exercise
- Level of athletic effort
- …
The significant number and variety of “By” dimensions and attributes that can surface in a brainstorming session can lead to incredible insight. And remember as you go through this process, all ideas are worthy of consideration; this is not the point to try to filter the creative ideas or handcuff the creative thinking process!
Step 7: What is Score Technique?
The purpose of the “Score” technique is to look for groupings of strategic noun dimensions and attributes that can be combined to create a more predictive and actionable score. These scores are critical components of our “thinking like a data scientist” process by supporting the decisions that we are trying to make, and/or what actions or outcomes we are trying to predict with respect to our targeted business initiative. Scores are very important constructs in the world of data science, and can help to cement the business stakeholders’ buy-in to the data science process. The best familiar score example might be the FICO score, which combines multiple questions and dimensions about a loan applicant’s finance history to create a single score that lenders use to predict a borrower’s ability to repay a loan.
Financial Services
- FICO
- Retirement Readiness
- Investment Risk
Credit Cards
- Attrition Risk
- Fraud Risk
- Product Preferences
Manufacturing
- Equipment Maintenance
- Supplier Reliability
- Supplier Quality
Gaming/Hospitality
- Customer LTV
- Gaming Preferences
Education
- Graduation Readiness
- Cohorts Influence
Healthcare
- Wellness Condition
- Stress Risk
Utilities
- Energy Efficiency
- Conservation Effectiveness
Pro Sports
- Fatigue Factor
- Motivation Factor
Scores for Sports Shop
Here are some examples of scoring opportunities for Sports Shop and variables that would contribute to them:
Step 8: Put Analytics Into Action
Facilitate the development of a compelling and actionable user experience by starting with a simple “Recommendations Worksheet.” The “Recommendations Worksheet” ties the decisions that our business stakeholders need to make (captured in Step 4) to the predictive analytics or scores that that the data science team is going to need to build. The “Recommendations Worksheet” starts with the decisions captured in Step 4, and then identifies the potential recommendations that could be delivered to the business users (or consumers) in support of those decisions. Finally, the worksheet captures the potential scores (and the supporting variables and metrics) that can be used to power the recommendations.
Analytics Into Action for Sports Shop
For our Sports Shop "Improve Merchandising Effectiveness" business initiative, the resulting Recommendations Worksheet could look like: