How to Talk to your Boss about Analytics

Presenter: James ParrySr. Systems EngineerSPSS Inc.

Are these your senior executives speaking?

“There are many methods for predicting the future. For example, you can read horoscopes, tea leaves, tarot

cards, or crystal balls.

Collectively, these methods are known as "nutty methods."

Or you can put well-researched facts into sophisticated computer models, more commonly referred to as "a

complete waste of time."

Scott Adams, The Dilbert Future

“There are many methods for predicting the future. For example, you can read horoscopes, tea leaves, tarot

cards, or crystal balls.

Collectively, these methods are known as "nutty methods."

Or you can put well-researched facts into sophisticated computer models, more commonly referred to as "a

complete waste of time."

Scott Adams, The Dilbert Future

Why predictive analytics is not used in many organizations?

“The entry barrier is no longer technology, but whether you

have executives who understand this”

Thomas Davenport, “Competing on Analytics”

“The entry barrier is no longer technology, but whether you

have executives who understand this”

Thomas Davenport, “Competing on Analytics”

Agenda

Why data mine: Demystifying and myth busting

Four steps to planning and presenting your data mining project plan

Reporting: Conveying the strength of a data mining model

What is lift? Considerations for efficient reporting

Tips for when talking to your boss about data mining

Q & A

Close

Demystifying and myth busting

Myth # 1: It’s not for me

“Predictive analytics is rocket science– it’s way above and beyond what I need to do.”

Analytics is now a “hit” in the Top 50 Best-Selling Business Books

And is catching on in institutional fundraising as well…

Predictive analytics becomes mainstream

Myth # 2: I don’t understand it.

“The idea of predictive analytics sounds good, but I really don’t understand what it does, and I couldn’t possibly explain it to anyone else to get their buy-in.”

Predictive Analytics: Defined

Data driven approach to problem solving

Focused on business objectives

Leverages organizational data

Uncovers patterns using predictive and descriptive techniques

Uses results to help improve organizational performance

What Does Predictive Analytics Do?

Predictive Analytics uses existing data to: Predict Group Associate Find outliers

Predictive Analytics: What it isn’t

A product, a particular piece of software, or a given algorithm

It is a business process that is enabled by technology

A model, segmentation scheme or business rules Those are some outputs from the Predictive Analytics process It is a method of discovery that yields information and insight

leading to some action

An end product in and of itself It is a means of harnessing the insight often trapped in large

masses of data It is an iterative, ever improving, feedback cycle

A SQL query, an OLAP hub, or a BI Dashboard

Statistics per se

Predictive Analytics is Part of CRISP-DM, the Industry Standard

Phases Business

Understanding Data Understanding Data Preparation Modeling Evaluation Deployment

Myth #3: I’ve got one already!

“We already do analytics through our business intelligence tools and corporate dashboards.”

Key Differences between BI and Predictive Analytics (PA) BI supplies the core facts of an organization:

Core business metrics KPI’s Factual reporting

PA helps you to interpret these facts as actionable information Predictive associations Optimized models Causal reporting Key Performance Predictors

Strategic Viewpoint Differences between BI and PA Typical BI applications provide a great picture

of what has happened… a rear view perspective

Dashboards in real time show current conditions and metrics… a clear windshield view

Predictive analytics enables future views and forecasting… a peek around the approaching corner and can create new metrics for closing the feedback

loop into the BI system

Myth #4: It won’t pay off

“Our organization is under constant pressure to lower the amount spent to raise a dollar. Predictive analytics will never pay back in time to make a real impact on our campaigns.”

Predictive analytics is important because it delivers value

“The median ROI for the projects that incorporated predictive technologies was 145%, compared with a median ROI of 89% for those projects that did not.”

Source: IDC, “Predictive Analytics and ROI: Lessons from IDC’s Financial Impact Study”

Nucleus Research . . .

Nucleus Research: The Real ROI from SPSS Inc.

•94% of customers achieved a positive ROI, with an average payback period of 10.7 months

•Key benefits achieved include reduced costs, increased productivity, improved customer & employee satisfaction, and greater visibility into operations

•81% of projects deployed on time, 75% on or under

budget

Nucleus Research: The Real ROI from SPSS Inc.

•94% of customers achieved a positive ROI, with an average payback period of 10.7 months

•Key benefits achieved include reduced costs, increased productivity, improved customer & employee satisfaction, and greater visibility into operations

•81% of projects deployed on time, 75% on or under

budget

“This is one of the highest ROI scores Nucleus has ever seen

in its Real ROI series of research reports.”

Rebecca Wettemann, Vice President of Research, Nucleus Research

Why is Predictive Analytics so critical to business decisions?

Performance of analytics targeted to certain consumers cross-industry and channel, research from Forrester, Jupiter, Amazon.com and Ovum (DM Review, Feb 11, 2003)

Beforeanalytics

Banner ad click through rates 0.3%Mail response rates 0.5%Conversion rates (post-response) 0.9%Buyer repeat rates 2.0%

Afteranalytics

21%18%10%60%

Four steps to planning and presenting your data mining project plan

Step 1: Determine Business Objectives

Thoroughly understand what you want to accomplish

Describe the criteria for a successful or useful outcome to the project from a business point of view EG: Increase the number of transfers from low to

medium donation groups.

Step 2: Assess Your Situation

Create an inventory of your available resources, including:

Personnel Data ComputingResources

Software

Step 3: Determine Data Mining Goals

Describe the intended project outputs and how you will arrive at them

Business goals vs. Data Mining Goals Example business goal: Increase the average gift

amount among annual fund donors by X%. Corresponding data mining goal: Predict the

propensity of annual fund donors to give more than they gave last year, using their giving history, demographic information, and stated level of satisfaction with your advancement program.

Step 4: Prepare and Present Your Project Plan List and describe each project stage, including:

Who’s involved? What other resources are required? What is the outcome or objective? When will it be completed?

Remember to include in your plan specific points in time to regroup and review progress and make updates as necessary

Create and follow a strategic plan to secure executive buy-in- Recap

Determine Business Objectives

Assess your Situation

Determine Data Mining Goals

Present your Project Plan

1

2

3

4

Data Mining and Reporting

29

Generated Models

The gold nuggets.

Reporting Considerations

Visually Explaining Competing Models Model lift

Eliminating Tedious, Repetitive, Time-Consuming Edits (3 D’s . . .) Design reusable graphs and graph templates

Getting the Right Information into the Right Hands, Securely Socializing/Publishing results - quickly

Self-Service Reporting Portal Create secure, online reporting environment Place the onus on the end-user, not the analyst

Automate!!

Data Mining: Who’s Involved?

The Power User More hands-on Understand how to connect to the data Understands data preparation Creates Report Templates

Ad-Hoc Reporter/Analyst Runs graphs and tables upon request (many, many) Socializes/Publishes Results

Consumer Usually stake-holder or C-level Does not license desktop application Relies on thin client

After you run some models . . . then what?

Measuring Lift

% of people 100%0%

% o

f re

turn

100%

20%

20%

50%

50%

20%

70%

ROI

34

The Perfect Model Doesn’t Exist, But …

The perfect model

35

Further Comparison – Business Rules

Business rules

36

Picking Our Model

Compare the C5.1 decision tree model to the others at the 40th percentile engagement point.

Presenting the Results

PASW Statistics Base

PASW Modeler

PASW Collaboration &Deployment Services(Predictive Enterprise

Browser)

Design a Template (Analyst/IT)

Pre-Template Chart

Post-Template Chart

Post-Template Chart

SPSS User Publishes to Web

Consumer Log-in

Predictive Enterprise Browser

Predictive Enterprise Browser

Results Rendered in Browser

Reporting Recap

Model Lift – conveys in $$ why using a predictive algorithm makes sense.

Graph Templates – decrease busy work, save $$$ in efficiency

Publishing to the Web Self-Service Reporting Platform – takes the burden

off the IR office thus making it more efficient $$$

Additional tips for talking to your boss about data mining

Laying the communication groundwork There is a communication gap between the

analyst (the maker) and the executive (user) Consumer of analytics is usually non-technical

prefers simple answers to complex explanations Analyst methods are treated like a black box of

information or voodoo but now more than ever, analysts are being called upon to explain how they arrived at an answer

Important first steps

Set proper expectation levels as soon as possible Bosses can have expectations which are too high –

“It’s magic” and will work perfectly They need to be brought down to earth before they

get disappointed and it reflects negatively on you

Bosses can also have mistakenly low expectations They don’t realize the potential of powerful analytics

and set their sights to low to demonstrate significant impact

Remember the audience at all times

Make all output relevant to the consumer Use business terms, not math, tech, stat verbiage Use graphs not words Turn everything into prospects or dollars Place everything into a problem-solving context Consider the price of inaction or not knowing

Words to avoid at all costs

Logistical regression

Hierarchical clustering

Algorithm

Coefficient

R-squared

Neural networks

Words to use frequently

ROI

Prospects

Stewardship

YIELD

Affinity

Growth

Capacity Ranking

Efficiency

Cost reduction

You are not alone in the struggle

Look beyond your own domain Other departments within your institution may

already be employing predictive analytics and/or using SPSS solutions.

List-servs and professional groups such as Prospect DMM, APRA, and CASE, AACRAO, AIR.

Befriend the IT organization Bridge the gap between data expertise and domain

expertise Involve IT to align goals and communicate needs

Over-arching principles

Demystify

Others are doing it

It has been proven

You can do it in small bites

Have a strong plan in place before you start!

Seek help

Questions?

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Key Take-aways

Remove the jargon and rocket science

Stay focused on the goal or business objective

Use external sources as support

Automate insight

Identify internal allies

Contact Information

James ParrySr. Systems Engineer

SPSS Inc.P. 800.543.2185 extension 2092

e-mail: jparry@spss.comwebsite: www.spss.com