0
Magnifying Glasses
and Crystal Balls
Use your data to raise more today and
predict the future
1/2/2014

@BI_JCA ~ bi@jcain...
YOUR PANEL

Bob Dillane, Director, Enterprise Information Systems
Lebanon Valley College

Cari Maslow, Senior Director, Do...
SESSION AGENDA AND GOALS
• What is Business Intelligence?

• Case Studies
- Lebanon Valley College: The Power of a Data Wa...
WHAT IS BUSINESS INTELLIGENCE?

1/2/2014

@BI_JCA ~ bi@jcainc.com

#bbcon

4
BI IS…

Business intelligence is a set of theories,
methodologies, processes, architectures, and
technologies that transfo...
BI IS…

Business intelligence is a set of
theories, methodologies, processes, architect
ures, and technologies that transf...
NON-TECH BI ARCHITECTURE

Warehouse

Data

1/2/2014

Information

@BI_JCA ~ bi@jcainc.com

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7

Knowledge
DATA WAREHOUSE

Sales
PK

id_product
id_client
id_client group
id_shop

ETL

id_supplier
id_date

id_country
id_supplierna...
ANALYTIC CUBES

1/2/2014

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9
VISUALIZATION

1/2/2014

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TYPES OF VISUALIZATION
TABLES

HEAT MAPS

TREE

1/2/2014

CHARTS & GRAPHS

GAUGES

TIME SERIES

@BI_JCA ~ bi@jcainc.com

#...
BI AND NON-PROFITS
• The Challenge
- The data landscape has changed – there’s a lot more of it now
- Constant struggle to ...
CASE STUDY: THE POWER OF A DATA WAREHOUSE

1/2/2014

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13
LEBANON VALLEY COLLEGE
Lebanon Valley College in Annville, Pa.,

FAST FACTS

welcomes 1,600 full-time undergraduates

• Gu...
TRANSACTION DATABASE VS. DATA
WAREHOUSE
• A transaction database is designed for efficient data entry
• A data warehouse i...
DO YOU NEED A DATA WAREHOUSE?
• Technical Concerns
- Unless you have a large database, technology is not a big concern

• ...
COMPLEXITY OF THE RAISER’S EDGE
• SQL code to query event participant attributes directly from the
Raiser’s Edge database:...
SIMPLICITY OF A DATA WAREHOUSE
• Same query against the warehouse:

1/2/2014

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GENERAL USES FOR OUR WAREHOUSE
• Reports we can’t do, or can’t do easily in RE
• Reports that need data from outside RE
• ...
FUND REPORTING
• This is on fund categories, not funds. Our goals are by category
instead of individual funds.

1/2/2014

...
DONOR GIVING SUMMARY

1/2/2014

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21
USING GIFT ANALYSIS WAREHOUSE
• The Gift Analysis table has information about gifts, not donors.
• ConstitGifts table has ...
OTHER WAREHOUSE USE IN SUPPORT OF
RE DATA
• We also use the warehouse to calculate the alumni/parent year code
that is use...
REPORTING TOOLS USED AT LEBANON
VALLEY COLLEGE

• SQL Server Reporting Services is our formatted report writer
• Entrinsik...
CASE STUDY FROM APPALACHIAN MOUNTAIN CLUB:
TRACKING NEW MEMBERS BY SOURCE USING
BUSINESS INTELLIGENCE

1/2/2014

@BI_JCA ~...
ABOUT APPALACHIAN MOUNTAIN CLUB
• Helps people protect, enjoy, and understand the mountains,
forests, waters, and trails f...
BUSINESS INTELLIGENCE AT AMC
• Implemented in 2010
• Reporting
-

Appeals
Member number
Member retention
New members
To Fi...
THE “OLDEN DAYS”
• Standard 2010 new member report by source
Revenue

Avg. Dues
Payment

Total Cost
to Acquire

Cost/
Memb...
THE UNKNOWNS
•
•
•
•
•

1/2/2014

What is new member retention by source?
What is retention rate, added giving, cost to re...
2011 RAW BI DATA

1st Year
Retention Pool

1st Year
Retained

1st Year
Retained $

Avg. Dues
Payment

277

74

27%

$2,150...
ADDING CONTEXT WITH BI

2011 #
Retained

M’brshp +
Annual Fund $

2012 #
Retained

M’brshp +
Annual Fund $

Avg. Dues
Paym...
FILLING IN THE DETAILS

Year 3
Retention

Source

M’brshp
Revenue

Total
Cost

Total Ann.
Fund
Giving

Net/New
Member

Yea...
IN THE MEANTIME…
• 2013
- Dropped Groupon/Living Social
- Reduced cost of direct mail acquisition
- Hired staff to boost n...
PLAN FOR 2014
• Cut direct mail acquisition by 20%
• Use Google Grant (awarded 8/2013)
• Develop plan to boost retention a...
CASE STUDY: THE POWER OF CUBES

1/2/2014

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35
CARNEGIE MUSEUMS OF PITTSBURGH
• Founded in 1895
• 4 Distinct Museums
–
–
–
–

Carnegie Museum of Art
Carnegie Museum of N...
THE NEED FOR BUSINESS INTELLIGENCE
• Pressure to increase revenue with shrinking resources
• Mountains of data in Raiser’s...
THE QUESTION OF RETENTION RATE
• Projecting revenue was the impetus for taking what seemed to be a
simple question further...
WHY DOES IT MATTER?
The retention rate is 63%
SO:
# of members x average gift x 63% =
Overall Renewal Revenue Projection
B...
BEFORE JCA ANSWERS: THE EVOLVING
RETENTION SPREADSHEET
Multi vs. First Year Members
• Use different renewal strategies
• R...
BEFORE JCA ANSWERS: THE EVOLVING
RETENTION SPREADSHEET
Timing
Next we started running multiple queries and plugging the co...
BEFORE JCA ANSWERS: THE EVOLVING
RETENTION SPREADSHEET
The retention rate at each time point then calculated into summary
...
SO…
•
•
•
•
•

1/2/2014

It worked but…
Labor Intensive
Risk of human error high
Not an easily transferable task
Technical...
BUT…
• No clarity around the month to
month variations
- Large Variances
- Not Consistent Year over Year

• Multi vs. Firs...
MORE QUESTIONS
•
•
•
•
•

How many times do I have to get a member in the door?
Does renewal rate change if I have an acti...
WITH JCA ANSWERS WE COULD DATA MINE
Is there a
difference
between joins
& rejoins?

What’s going
on with the
Individuals?
...
DELVING INTO THE MONTH OF EXPIRE
By month:

SENR
5%

Membership category:

January

INDL
3%
PREM
19%
DUAL
23%

FMLY
50%

S...
DELVING INTO THE MONTH OF EXPIRE
• Greater Detail on Family Level
• Renewed vs. Upgraded:

1/2/2014

@BI_JCA ~ bi@jcainc.c...
IDENTIFICATION OF SIGNIFICANT FACTOR
January
• Purchase Method:
DM - Direct
Mail
52%

March

DM - Direct
Mail
38%

1/2/201...
WITH JCA ANSWERS
• Ability to identify which factors determine the larger variances in
retention
• Role out testing that f...
IS THERE A MAGIC NUMBER OF YEARS?
• Nugget: Getting
them into their 3rd
year is key

Why is the pattern
changing in 2012?
...
JOIN VS. REJOIN
• Nugget: Depends on the
level of membership
• Strategy Idea: Switch
telemarketing acquisition of
lapsed m...
ACQUISITION LIST PURCHASE
• Nugget: Zip codes with strong purchase
data don’t always have strong retention
rates
• New Str...
OTHER CASE STUDIES

1/2/2014

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#bbcon

54
AQUARIUM
• Implemented JCA Answers for The Raiser’s Edge and Gateway
Galaxy about 4 years ago
• Have an integrated BI envi...
ART MUSEUM
• Converted to The Raiser’s Edge about 4 years ago
• Came to JCA initially for a data warehouse
• Now have an i...
PREDICTIVE ANALYTICS

1/2/2014

@BI_JCA ~ bi@jcainc.com

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57
LOOKING INTO THE CRYSTAL BALL

1/2/2014

@BI_JCA ~ bi@jcainc.com

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58
PREDICTIVE ANALYTICS IS HERE

• Rise in Google Searches of ―Predictive Analytics‖

1/2/2014

@BI_JCA ~ bi@jcainc.com

#bbc...
PREDICTIVE ANALYTICS IS HERE
• Rise in Google Searches of ―Predictive Analytics‖ versus ―Business
Intelligence‖

1/2/2014
...
WHAT IS PREDICTIVE ANALYTICS?

Predictive analytics is business intelligence
technology that uses predictive models built ...
WHERE DO WE BEGIN?
• Start with a business question

How do I raise more money?
versus
How can I increase the number of me...
PREPARATION IS KEY
• Do we have all the data we need?
• Is our data usable?

1/2/2014

@BI_JCA ~ bi@jcainc.com

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63
LET’S EXPLORE
• Explore your data
• Keep your business question in mind

1/2/2014

@BI_JCA ~ bi@jcainc.com

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64
PREDICTIVE MODELS
• Models use patterns found in your data to identify risks and
opportunities.
• Models apply scores to y...
PREDICTIVE MODELS
• Naïve Baise
- Used for classification

1/2/2014

@BI_JCA ~ bi@jcainc.com

#bbcon

66
PREDICTIVE MODELS
• Decision Trees
- Used for classification, regression and association

1/2/2014

@BI_JCA ~ bi@jcainc.co...
PREDICTIVE MODELS
• Clustering
- Used for segmentation

1/2/2014

@BI_JCA ~ bi@jcainc.com

#bbcon

68
IMPROVE YOUR MODEL
• Models are a work in progress.
• Test before deploying your model.

1/2/2014

@BI_JCA ~ bi@jcainc.com...
THE THREE R’S OF PREDICTIVE ANALYSIS
• Reliable – Your predictive model must be accurate.
• Repeatable – You need to be ab...
QUESTIONS?

1/2/2014

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#bbcon

71
FOR A COPY OF THIS PRESENTATION

• Leave us your card
• Send us an email at bi@jcainc.com
• Stop by our booth: 107/109
• T...
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Magnifying Glasses and Crystal Balls

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Use your data to raise more today and predict the future
(Presented at BBCON 2013)

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  • Magnifying Glasses: looking at yesterday to see what you can learnCrystal Balls: peering into the future - these are accomplished using BI…we are going to talk about what it is and how it can help youThe tools and technology that enable us to do that are called Business Intelligence. We spend a little time talking about the parts of BI (try not to be too boring, but its important to understand what “lies underneath”)Then we will move into the case studies, hear how about BI in the real-world, spend most of our time herePredictive analytics – crystal ball part. starting to get into the cutting-edge stuff here…goal is to introduce you to it so you can take the next stepLast, we will leave some time for questions, but really okay to answer any as we go along.
  • I don’t want to spend a lot of on the definition of BI, but I think its important that we are all thinking of the same thing.Here is the definition from Wikipedia – that about covers it…but there is a lot going on in that sentenceTheories – like what?Methodologies – which ones exactly?Processes – like entering a gift? Running a report?Technologies – oh yeah…enough to make your head spinDoes that include collecting data? Coding it? Queries? Reports? Visualizations? Analytics? Systems integrations? Yes. It is so general that it can mean a lot or not much, depending on your view point. There is one word that I think is important and we will focus on today: useful.BI must help you make more and spend using the less data in your database.We are going to hear about different ways that organizations are using BI to get results.That being said, there are a few things that we will need to understand before we jump into that. Let’s discuss a few key terms that we should all understand. These terms go hand-in-hand when we talk about BI and they will be things that you’ll hear much more about from our presenters today. This is the stuff that underlies BI.
  • These are the bones, the basics., that will form the core of BI. There is a lot to know about each step, but what we want to communicate here is that it is about optimizing the data to make it useable, to make it BI-ready. Taking it from data to knowledge.It starts with RE (or any OLTP system – where you enter the data).As we move left to right, the tools optimize the data for usability.I will talk a little more about each part, but wanted you get understand that BI, from a technology standpoint, isn’t that complicated in terms of what we need to understand.Put your data warehouse, cube it, and then make it graphic. Simple as that! (now doing it, well, it gets more complex)You need all parts of this. You can go out an buy really cool dashboard software, but its not worth a lot without the stuff in the middle. It’s like when my daughters brought home a goldfish from the fair…that was great, it was easy and it was visible. But I needed a lot more to make them viable.Let’s look a little closer at each part.
  • The data warehouse is a foundational pieces of a BI infrastructure. Start with RE – transactions, data entry. 700 tables, not very usable in that state.The magic of ETL happens. Take it out, transform it/denormalize it (not a term you need to know, it means make it a lot more usable, optimized for reporting and analytics) and then load into the warehouse.It goes from 700 tables to 100, or 150.The image on the screen is a very simplified view of what happensOn the left we have a snapshot of what tables might look like in a production database, such as Raiser’s Edge.On the right is the view of what that data would be transformed into in a data warehouse. We go from a huge jumbled-looking mess to a single, consolidated table that’s going to make it easier for us to pull data for reporting and analysis.You have achieved real BI power at this point, if you do nothing else. A warehouse is a beautiful thing.We don’t show it here, but the warehouse can and should be a central repository that brings together multiple sources. This puts your warehouse on steroids.I won’t talk too much more about warehouses as this is something that Bob will be talking about in his section.
  • The Data Cube. Now we are getting into the cool stuff. You will also here these called OLAP or OLAP cubes. We will call them cubes.Cubes are often pre-summarized across multiple dimensions to drastically improve query time. It’s hard to conceptualize what “multi-dimensional” means and you don’t need to, any more than you need to conceptualize an internal combustion engine to drive a carThe image on the screen shows you how you have multiple dimensions: constituent, gifts and events. The cube creates tremendous amount of data by intersecting those three perspective.What it means to us is speed and depth at the same time. You can report on millions of records at a time and then decide to change it in the it takes you to think about it.Pulling this sort of information directly from a system like Raiser’s Edge can be exponentially more time consuming.
  • At the beginning I talked about how BI is all about being useful, getting results. When we talk about results we often get into conversations about KPIs, key performance indicators. The usually leads to someone asking for a dashboard.Data visualization is the visible, flashy aspect of BI. Its extremely valuable. Communicating information is as important as the information itself.Some people can look at a large table and get what they need. Others, prefer basic charts, while some need dashboards that look like they belong on a spaceship.Don’t start with the tool, start with what you need to measure, what is the business question, what data do you need to see, and what is the simplest way to share it (without being too simple).The list of data visualization tools is as long as your arm and they range from really-not-free. Start simple.If you have a warehouse and cubes, that may be all you need.You may need only Excel (we are considering this at JCA).Or, you may need a powerful visualization tool.Let the need drive the choice, don’t choose the tool first (do push-ups at home before you buy the gym membership).But dashboards don’t have to be complicated nor expensive. You can get started with basic dashboards with Excel. And you can create some pretty sophisticated dashboards with Excel with a little effort.
  • Here are some examples of visualizations you can use. There are a sea of other options out there, but most are variations on these themes.
  • CHALLENGEMore data: we have access to more data now than ever before, and that is only going to continue in that directionThis is on a spectrum, whether it’s getting to the data you have, in one system, bringing in data from other systems (finance, programs, ticketing, the web), or adding data from third parties; or moving into the world of “big data” and seeing what your constituents are doing on facebook, twitter, linkedinIt’s not enough to just collect it allThe struggle to earn more, spend less. Most nonprofits are under tremendous pressure to take steps to make more, but aren’t always able or willing to invest.The unfortunate upshot form that is that nonprofits seem to lag behind because BI and technology can be expensive or intimidating or resource-intensive. OPPORTUNITYLeveraging BI, when done smartly, is the ultimate win/win. You are able to solve the riddle of “how do I unleash the power of my data” that will result in creating anew asset at your organization.“It’s too expensive” doesn’t have to apply to BI. It can, but it doesn’t have to.You can do all of the things that we have talked about today, and more, with tools that are reasonably priced.It can be the ultimate win/win. More power, better tools, and it pays for itselfWe are in the early stages of some very cool BI offerings at JCA. We are coming it at it with mindset: if they don’t pay for themselves, you shouldn’t have to pay for it.Enough with the preamble…One of the goals of this session is to talk about taking advantage of concepts that are leading edge and make them applicable and realistic for non-profits. Bob, Teri and Cari are going to share examples of what they’re doing at their organizations. They come with different experience and work in different areas and are at different points in their “BI journey”. Through each of their case studies we’re going hear about how they’re using BI to get results. Let’s start with Bob Dillane from Lebanon Valley College.
  • Section header
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  • * Where NPO BI is headed* How to build them* What they tell you
  • Now we’re getting into the “Crystal Ball” portion of our presentation. There are a lot of organizations out there that make business decisions based on assumption or gut feeling. BI is the direct opposite of that and we’ve heard about how some organizations are using BI to make fact-based decisions. The final area we’ll talk about today is also throwing that gut-based decision making to the side. We’re going to talk about “Predictive Analytics.”
  • Predictive analytics isn’t a new concept but it’s definitely on the rise right now. For-profits have been doing predictive analytics for years and some larger non-profits are already doing it. Some of you may already be looking at this. If we look at Google Trends we’d find that there has been a 300 percent spike in searches for predictive analytics over the last few years. The image here on the screen shows that spike. This image starts out in 2005 when not too many of us were thinking about predictive analytics. It ends where we are today and you can see there’s been a substantial increase.
  • I thought this image offered an interesting, slightly different perspective. This plots the number of searches for predictive analytics, in blue, against the number of searches for business intelligence, in red. As you can see, there are a lot more searches for business intelligence but what’s interesting is where the two are trending. We see that searches for Predictive Analytics are on the rise but business intelligence is starting to go down.So this is great, lots of people are interested in predictive analytics but what the heck is it? Before we go any further, let’s talk about what predictive analytics means.
  • Well, as you see from the definition on the screen here, predictive analytics is all about using data to make informed decisions about the future. No more relying on your gut alone!I thought this image did a nice job of summing up how BI leads us to this place where we’re talking about predictive analytics. Up to now everything we’ve been talking about is really focused on what’s happened in your business already and what’s happening now. This next level is anticipating what is about to happen. You can monitor and track real-time data and trends over time. That’s great but, again, the entire point of BI is about results. It’s about making smarter decisions going forward before its too late. Looking into that crystal ball!For example, if your analysis shows you that your direct mail solicitation did not go well because you included too large a segment of constituents who don’t have a history of responding to direct mail appeals, there isn’t much that you can do at that point if you’ve already paid for printing and postage. Predictive analytics helps you see what kind of challenges and opportunities you will face before you face them so that you have a chance to act on it proactively. Business intelligence just doesn't get more actionable than that.As Steve stated when we first started out, we wanted you to leave today’s session much more informed about predictive analytics. If you’re looking for results and you want to turn to predictive analytics to help you get there, I want to make sure you’ve heard more about what goes into it. As we saw when we talked about BI, there is a ton of technology out there to help you start down the path of predictive analytics: IBM, SAP, Oracle, Rapid Insight, Microsoft. Regardless of the tool you use or if you’re doing it yourself or hiring someone to do it for you, you need to be an informed consumer and understand more about what goes into predictive analytics. Let’s walk through this a little bit more.
  • The one place that absolutely every predictive model needs to start at is a business question. What is the business question you’re trying to answer? We have to define this question before we can begin. In order for any of this to be meaningful for your organization, your question must be specific to your business. It must take into account the specifics of your organization otherwise its not going to hold much value for you. So let’s use a hypothetical situation to guide us through this. Let’s say I have a client who comes up to me and says, my business question is: “How do I raise more money?”Well, good question but this is a big question. We need to break this down even further to get to something that we can work with. So I might tell my client, “Okay, how do you make money?”Let’s say my client is a zoo and they say: “I sell memberships.”So maybe we’d talk about selling memberships and determine that membership renewals is a huge revenue area. So one way to make more money would be to renew more members. Now we’re getting to a slightly different question. Now we’re asking, “How can I increase the number of members who renew?” which, as a result, is going to help me make more money.
  • So we have our question and we know that we’re going to be looking more closely at member renewals. If you think back a few slides when we had our definition of predictive analytics up on the screen, you remember that predictive analytics uses models built on your data to make predictions about the future. So before we can get too far, we’ve got to make sure that 1) we have the data we need and 2) that it’s usable. One great analogy I’ve heard is that you have to think about this step as the Zamboni machine that goes over the ice before a hockey game. It’s got to make the ice nice and smooth before they can play. This data preparation step is doing just that.If you’re data is a mess or you’ve got it spread across multiple, disconnected databases, it’s going to be hard to make sense of it. Going back to our membership renewals example, if you want to figure out how visitation impacts member renewals but you have no way of knowing when a member visits, it’s going to be impossible to include this factor in your model.Another thing to consider in this stage is getting the data to a place where it’s ready to be explored. Often we can’t do this directly from a source database. For example, it’d be much harder to prepare your data going directly from Raiser’s Edge. So this is when a lot of people turn to a data warehouse. Sound familiar? We already heard about data warehouses and what they can do for an organization. They absolutely come into play here as well.
  • Okay, so we’ve got our question, our data is in a good place. Now what?Next we want to explore our data. For many of us, we’ve spent years collecting a tremendous amount of collecting tons of valuable information about our constituents but we may not have spent a lot of time digging into the data and trying to understand what’s there. In this step, we want to get our hands a little dirty and see what’s in our database. What’s one of the ways you can explore your data? Well, you can use the data cubes you’ve also heard about.One of the objectives of this step is to make sure that you’re exploring your data all the while keeping your business question in mind. If we’re wanting to increase member renewals, we need to think about this as we’re exploring our data to make sure that our data is going to allow us to answer this question.
  • Now we’re ready to build a model. As I said earlier, predictive analytics is built on models. These models use patterns found in historical and transactional data to identify risks and opportunities. The model will apply predictive scores to your constituents. Each constituent's predictive score should inform the strategic actions that you’ll take so you can improve outcomes, in other words, get results.It would be so nice if there was just one model out there and it automatically did what we wanted it to do. Unfortunately that’s not the case. There are several different types of models so before we can even begin to build one, we have to choose which one we want to use.
  • JCA has a Business Intelligence Group where we’re doing a lot of work these days with predictive modeling so we’ve spent time thinking about these different types of models. I’ll share a couple today that are options as part of Microsoft‘s Analysis Services predictive models. I’m not going to cover all of them but I wanted to share a few so that you’d be familiar if you ever heard these come up again.The first is Naïve Bayes. This model is based on theorems that I’m not even going to attempt to go into.This model is primarily used for classification.So for instance, let’s say that we’re going to send out a direct mail solicitation. We want to reduce costs so we only want to send it to constituents who are likely to respond. Well, if we look in Raiser’s Edge, we’ve got a ton of demographic data and information about what a constituent has responded to in the past. We want to use this data to see how demographics such as age and location can help predict response to a mailing, by comparing potential donors to donors who have similar characteristics and who have given to us in the past. Specifically, we want to see the differences between those constituents who gave and those who haven’t. By using the Naive Bayes model, we can quickly predict an outcome for a particular donor profile, and can therefore determine which constituents are most likely to respond.
  • Another type of model that some of you might have heard of is called Decision Trees. This model is primarily used for classification, regression and association.Let’s say we’re throwing a gala. Our Events team is trying to identify the characteristics of previous event ticket buyers that might indicate whether those constituents are likely to buy gala tickets in the future. Again, we have our database where we store a wealth of demographic information that describes previous gala ticket buyers. By using the Decision Trees model to analyze this information, our events team can build a model that predicts whether a particular constituent will purchase gala tickets, based on what we know about the constituent, such as demographics or past buying patterns. As the model works through each characteristic of a constituent, we get closer and closer to seeing exactly the type of person who will buy a ticket.
  • The last model type I’ll bring up today is called Clustering. This model is primarily used for segmentation and grouping, as the name implies.This model will group constituents who share similar demographic information and who share similar profiles, such as donors who consistently give by mail versus those that don’t. This group of people represents a cluster of data. Several such clusters may exist in a database. By observing the characteristics that make up a cluster, you can more clearly see how records in a dataset are related to one another.
  • Predictive models are living things. They are approximations that are going to start out okay but get better and better as you continue to work on it. You keep tweaking the model until you can’t get it any better. You’ll often hear people refer to training the model. You need to keep testing your model so that if the type of model you chose or the data you have isn’t giving you good results, you can tweak a previous step in the process or consider using a different type of model.
  • Predictive models can be great to help you predict the most likely outcome, but what's the best that could happen.  We couldn’t get into the nitty gritty details of predictive analytics in our one session alone but hopefully this brief introduction is helpful. Throughout your predictive analytics journey there are a few guiding principles you want to keep in mind. I thought these three R’s were a nice way to remember it all.Reliable – Your predictive model must be accurate. This means that you’ve got go through that data preparation step and make sure you’re building your model off good data.Repeatable – You shouldn’t be going through this modeling exercise one time and then walking away from it. You need to be able to replicate results across multiple time periods. You should have a framework that allows you to apply the model time and time again so you go beyond a one-time project.Relatable – This applies in several different ways. You need to make sure that you’re starting with a business question that means something for your organization. Once you get to seeing the results of your model, you need to understand the results. Seeing a bunch of impressive looking statistical terms or fancy looking models does you no good if you don’t know what they mean.
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  • Transcript of "Magnifying Glasses and Crystal Balls"

    1. 1. Magnifying Glasses and Crystal Balls Use your data to raise more today and predict the future 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 1
    2. 2. YOUR PANEL Bob Dillane, Director, Enterprise Information Systems Lebanon Valley College Cari Maslow, Senior Director, Donor Relations and Membership Carnegie Museums of Pittsburgh Teri Morrow, Membership Director Appalachian Mountain Club Stephanie Reyes, Manager, Business Intelligence Group JCA Steve Beshuk, Director, Business Intelligence Group JCA 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 2
    3. 3. SESSION AGENDA AND GOALS • What is Business Intelligence? • Case Studies - Lebanon Valley College: The Power of a Data Warehouse - Appalachian Mountain Club: Tracking Members Source Using BI - Carnegie Museums of Pittsburgh: The Power of Cubes • Predictive Analytics • Q&A 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 3
    4. 4. WHAT IS BUSINESS INTELLIGENCE? 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 4
    5. 5. BI IS… Business intelligence is a set of theories, methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information for business purposes. 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 5
    6. 6. BI IS… Business intelligence is a set of theories, methodologies, processes, architect ures, and technologies that transform raw data into meaningful and useful information for business purposes. 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 6
    7. 7. NON-TECH BI ARCHITECTURE Warehouse Data 1/2/2014 Information @BI_JCA ~ bi@jcainc.com #bbcon 7 Knowledge
    8. 8. DATA WAREHOUSE Sales PK id_product id_client id_client group id_shop ETL id_supplier id_date id_country id_suppliername id_supplieraddress id_shop 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 8
    9. 9. ANALYTIC CUBES 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 9
    10. 10. VISUALIZATION 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 10
    11. 11. TYPES OF VISUALIZATION TABLES HEAT MAPS TREE 1/2/2014 CHARTS & GRAPHS GAUGES TIME SERIES @BI_JCA ~ bi@jcainc.com #bbcon 11
    12. 12. BI AND NON-PROFITS • The Challenge - The data landscape has changed – there’s a lot more of it now - Constant struggle to grow revenue and keep costs in check - Today’s donors are demand results - Unfortunately, many nonprofits are behind the curve • The Opportunity - Today’s BI gives you the tools to leverage your untapped ―data asset‖ - Sophisticated BI does not have to mean expensive - BI can pay for itself – results! 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 12
    13. 13. CASE STUDY: THE POWER OF A DATA WAREHOUSE 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 13
    14. 14. LEBANON VALLEY COLLEGE Lebanon Valley College in Annville, Pa., FAST FACTS welcomes 1,600 full-time undergraduates • Guaranteed degree completion in 4 years studying more than 30 majors, as well as self- designed majors. Founded in 1866, LVC has graduate programs in physical therapy, business, music education, and science education. Annville is 15 minutes east of Hershey and 35 minutes east of Harrisburg; Philadelphia, Washington, D.C., and • 3 out of 4 LVC students choose to live in excellent, safe, guaranteed campus housing • Low student-to-faculty ratio of 13 students to each professor allows for personal interactions and a customizable education • High-achieving students, 1/2 of whom were in the top 20% of their high school class, continually teach each other while learning together Baltimore are within two hours. • 98 % of students receive some form of financial assistance with an average aid package of more than $24,500 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 14
    15. 15. TRANSACTION DATABASE VS. DATA WAREHOUSE • A transaction database is designed for efficient data entry • A data warehouse is designed for ease and speed of reporting • These are two very different design goals 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 15
    16. 16. DO YOU NEED A DATA WAREHOUSE? • Technical Concerns - Unless you have a large database, technology is not a big concern • Practical Concerns - Can I get answers with a data warehouse that I can’t (efficiently) get without one? (the answer is virtually always ―yes‖) - Will those answers improve our ability to perform our mission? 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 16
    17. 17. COMPLEXITY OF THE RAISER’S EDGE • SQL code to query event participant attributes directly from the Raiser’s Edge database: 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 17
    18. 18. SIMPLICITY OF A DATA WAREHOUSE • Same query against the warehouse: 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 18
    19. 19. GENERAL USES FOR OUR WAREHOUSE • Reports we can’t do, or can’t do easily in RE • Reports that need data from outside RE • Perform calculations and import data back to RE • Create custom gift analysis warehouse generated from JCA Answers warehouse 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 19
    20. 20. FUND REPORTING • This is on fund categories, not funds. Our goals are by category instead of individual funds. 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 20
    21. 21. DONOR GIVING SUMMARY 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 21
    22. 22. USING GIFT ANALYSIS WAREHOUSE • The Gift Analysis table has information about gifts, not donors. • ConstitGifts table has links donors and gifts, with a flag for the type of linkage (hard credit, soft credit, match credit, etc.) • Using these tables, we can quickly answer questions such as: - ―How much did each donor give to the Mund Buidling Fund, the Annual Fund and any endowment funds (3 separate totals) between July 17th and September 12th?‖ • For frequently asked amounts (annual fund, etc.), we generate totals nightly and import them to RE as constituent attributes. This extends the capability of query and export in RE. 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 22
    23. 23. OTHER WAREHOUSE USE IN SUPPORT OF RE DATA • We also use the warehouse to calculate the alumni/parent year code that is used with constituent names. • We analyze: - What degrees and graduation years the individual has - What undergraduate degrees and graduation years the person’s children have - We calculate an alumni/parent code and import it back to RE as an Add/Sal 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 23
    24. 24. REPORTING TOOLS USED AT LEBANON VALLEY COLLEGE • SQL Server Reporting Services is our formatted report writer • Entrinsik Informer as our primary add hoc query tool • Crystal Reports for some formatted reports, but we are phasing this out • Excel with Microsoft Query to access the warehouse • JCA Answers as our primary data warehouse - 1/2/2014 Warehouse rebuild runs at 8:00 p.m. Various calculations are run and exported from the warehouse Calculations are imported into The Raisers Edge Warehouse is rebuilt again at 7:00 a.m. @BI_JCA ~ bi@jcainc.com #bbcon 24
    25. 25. CASE STUDY FROM APPALACHIAN MOUNTAIN CLUB: TRACKING NEW MEMBERS BY SOURCE USING BUSINESS INTELLIGENCE 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 25
    26. 26. ABOUT APPALACHIAN MOUNTAIN CLUB • Helps people protect, enjoy, and understand the mountains, forests, waters, and trails from Maine to Washington, DC. • • • • • 1/2/2014 86,000 members; 16,000 volunteers; 20,000 advocates 12 chapters Backcountry huts, camps & campsites, and front country lodges Maintain over 1,800 miles of trail; 350+ miles of the AT 8,000 volunteer- and staff-led activities @BI_JCA ~ bi@jcainc.com #bbcon 26
    27. 27. BUSINESS INTELLIGENCE AT AMC • Implemented in 2010 • Reporting - Appeals Member number Member retention New members To Finance Leaving Madison Spring Hut Photo by Chris Lawrie, AMC New Hampshire Chapter 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 27
    28. 28. THE “OLDEN DAYS” • Standard 2010 new member report by source Revenue Avg. Dues Payment Total Cost to Acquire Cost/ Member Net/ Member 351 $10,118 $28.83 $15,515 $44.20 ($15.38) Direct Mail 7,742 $250,936 $32.41 $619,509 $80.02 ($47.61) Email/Web 4,641 $242,555 $52.26 $21,693 $4.67 $47.59 Gift Memberships 659 $35,048 $53.18 $5,374 $8.15 $45.03 Miscellaneous 988 $31146 $31.52 $27,897 $28.24 $3.29 Reservations 852 $52,277 $61.36 $10,651 $12.50 $48.86 1,374 $65,841 $47.92 $66,906 $48.69 ($0.78) 16,607 $687,921 $41.42 $767,544 $46.22 ($4.79) Source New M’brshps Collective Buying Telemarketing to Formers Total 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 28
    29. 29. THE UNKNOWNS • • • • • 1/2/2014 What is new member retention by source? What is retention rate, added giving, cost to renew? Incorporated BI data into long-term reporting Appeals coded with type and channel (AQDM, RNWB) Type and channel split in BI—track at the channel level @BI_JCA ~ bi@jcainc.com #bbcon 29
    30. 30. 2011 RAW BI DATA 1st Year Retention Pool 1st Year Retained 1st Year Retained $ Avg. Dues Payment 277 74 27% $2,150 $29.05 7,742 3,367 47% $113,566 $31.23 Email 174 49 28% $2,331 $47.57 Miscellaneous 646 236 37% $9,851 $41.74 Reservations 979 324 33% $19,166 $59.15 TM to Formers 1,900 602 32% $28,865 $47.95 Web 4,459 1,828 41% $87,939 $48.11 Total 16,177 6,750 42% $263,866 $39.09 Source Collective Buying Direct Mail 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 30 % Retained
    31. 31. ADDING CONTEXT WITH BI 2011 # Retained M’brshp + Annual Fund $ 2012 # Retained M’brshp + Annual Fund $ Avg. Dues Payment 74 $2,328 33 $1,542 $37.24 Direct Mail 3,367 $135,693 2,443 $124,894 $39.82 Email/Web 1,877 $106,114 1,120 $79,875 $53.49 Gift Memberships 100 $5,254 85 $5,291 $50.51 Miscellaneous 236 $13,684 141 $11,541 $46.27 Reservations 324 $22,766 207 $15,981 $62.14 TM to Formers 602 $41,651 269 $22,496 $53.93 6,850 $327,489 4,298 $261,620 $45.74 Source Collective Buying Total 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 31
    32. 32. FILLING IN THE DETAILS Year 3 Retention Source M’brshp Revenue Total Cost Total Ann. Fund Giving Net/New Member Year 3 M’brshp ROI % Ann Fund Giving Collective Buying 9% $17,825 $13,497 $491 ($10.93) $0.78 4% Direct Mail 32% $657,151 $461,779 $49,744 ($18.81) $0.78 11% Email/Web 24% $46,681 $392,731 $35,813 $82.28 $9.18 9% Gift Memberships 13% $8,225 $43,670 $1,923 $56.70 $5.54 4% Miscellaneous 14% $33,212 $47,521 $8,850 $23.44 $1.70 19% Reservations 24% $15,039 $84,305 $6,718 $89.18 $6.05 8% Telemarketing • Re-calculated 20% $75,938of retention as to Formers 2012 and re-calculated$39.34 ROI. $109,212 $20,776 $1.71 19% Total $1,152,714 $1.50 11% 1/2/2014 26% @BI_JCA ~ bi@jcainc.com $854,071 #bbcon 32 $124,315 $25.47
    33. 33. IN THE MEANTIME… • 2013 - Dropped Groupon/Living Social - Reduced cost of direct mail acquisition - Hired staff to boost non-direct mail/non-telemarketing efforts • Growth in web/email sales slow • New members from guest reservations up nearly 300% • Memberships purchased by visitors at AMC huts, camps, and lodges fewer than 100 in 2010 now up to nearly 900 in 2013. • Tracking 2011 and 2012 new members • Guest stay data not available • No volunteer data in database 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 33
    34. 34. PLAN FOR 2014 • Cut direct mail acquisition by 20% • Use Google Grant (awarded 8/2013) • Develop plan to boost retention and Annual Fund support from gift memberships • Assess non-Membership department sales for 2014 growth potential. Crisp Colors Photograph by Nicholas Gagnon, AMC New Hampshire Chapter 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 34
    35. 35. CASE STUDY: THE POWER OF CUBES 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 35
    36. 36. CARNEGIE MUSEUMS OF PITTSBURGH • Founded in 1895 • 4 Distinct Museums – – – – Carnegie Museum of Art Carnegie Museum of Natural History Carnegie Science Center The Andy Warhol Museum • Serve 1.3 million people annually 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 36
    37. 37. THE NEED FOR BUSINESS INTELLIGENCE • Pressure to increase revenue with shrinking resources • Mountains of data in Raiser’s Edge but no easy way to get answers to questions • Answers we had weren’t actionable and instead just led to more questions • No easy way to measure strategy performance which led to a culture of adding initiatives but not discontinuing any already implemented 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 37
    38. 38. THE QUESTION OF RETENTION RATE • Projecting revenue was the impetus for taking what seemed to be a simple question further • With an on-going, multi-hit renewal series, we needed to estimate not only what percentage would renew but when they would renew • Knew the makeup of the population would impact the results but had no way of knowing which characteristics were most impactful 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 38
    39. 39. WHY DOES IT MATTER? The retention rate is 63% SO: # of members x average gift x 63% = Overall Renewal Revenue Projection BUT: • Makeup of population impact both the monthly and the year-to-year results • Needed to predict when in the solicitation cycle renewals would happen 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 39
    40. 40. BEFORE JCA ANSWERS: THE EVOLVING RETENTION SPREADSHEET Multi vs. First Year Members • Use different renewal strategies • Run monthly query off an attribute set for segmentation of the first direct mail hit • Didn’t answer the ―when‖ question • Confirmed theory that the makeup of the monthly populations mattered 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 40
    41. 41. BEFORE JCA ANSWERS: THE EVOLVING RETENTION SPREADSHEET Timing Next we started running multiple queries and plugging the counts into this spreadsheet data table 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 41
    42. 42. BEFORE JCA ANSWERS: THE EVOLVING RETENTION SPREADSHEET The retention rate at each time point then calculated into summary tables 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 42
    43. 43. SO… • • • • • 1/2/2014 It worked but… Labor Intensive Risk of human error high Not an easily transferable task Technical: had to make friends in IT @BI_JCA ~ bi@jcainc.com #bbcon 43
    44. 44. BUT… • No clarity around the month to month variations - Large Variances - Not Consistent Year over Year • Multi vs. First was not enough population segmentation • Still left us without answers when asked ―Why…‖ • AND then we changed our upgrade solicitation method which further skewed the model 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 44
    45. 45. MORE QUESTIONS • • • • • How many times do I have to get a member in the door? Does renewal rate change if I have an active email address? Does it matter whether they visit one, two or all three museum sites? Are the increasing number of member events making a difference? Retention gains in one year don’t appear to hold, why not? Everything we did to try to impact retention was still just an educated stab in the dark. 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 45
    46. 46. WITH JCA ANSWERS WE COULD DATA MINE Is there a difference between joins & rejoins? What’s going on with the Individuals? True of all multi years or does upgrading impact? Does it matter how many years? Do the rates by category stay consistent month to month? Does the purchase method impact the rate? 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 46 What about timing?
    47. 47. DELVING INTO THE MONTH OF EXPIRE By month: SENR 5% Membership category: January INDL 3% PREM 19% DUAL 23% FMLY 50% SENR 4% INDL 2% DUAL 18% March PREM 22% FMLY 54% 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 47
    48. 48. DELVING INTO THE MONTH OF EXPIRE • Greater Detail on Family Level • Renewed vs. Upgraded: 1/2/2014 @BI_JCA ~ bi@jcainc.com • Number of Gifts: #bbcon 48
    49. 49. IDENTIFICATION OF SIGNIFICANT FACTOR January • Purchase Method: DM - Direct Mail 52% March DM - Direct Mail 38% 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 49
    50. 50. WITH JCA ANSWERS • Ability to identify which factors determine the larger variances in retention • Role out testing that focuses on chosen factors and monitor results - Years of Membership - Join vs. Rejoin - Direct Mail Retention by Zip Code • Ability to target specific groups of people with different offers & communication - Non-visiting members 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 50
    51. 51. IS THERE A MAGIC NUMBER OF YEARS? • Nugget: Getting them into their 3rd year is key Why is the pattern changing in 2012? • New Strategy: Extend 1st year discounting to 2nd year members 2012 2010 1/2/2014 @BI_JCA ~ bi@jcainc.com 2011 #bbcon 51
    52. 52. JOIN VS. REJOIN • Nugget: Depends on the level of membership • Strategy Idea: Switch telemarketing acquisition of lapsed members from Family & Premium to Dual, Individual and Senior levels 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 52
    53. 53. ACQUISITION LIST PURCHASE • Nugget: Zip codes with strong purchase data don’t always have strong retention rates • New Strategy: Work with direct mail firm to use data from Answers when making list buys 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 53
    54. 54. OTHER CASE STUDIES 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 54
    55. 55. AQUARIUM • Implemented JCA Answers for The Raiser’s Edge and Gateway Galaxy about 4 years ago • Have an integrated BI environment • Incorporated budget information into data warehouse • Have been able to better understand deferred revenue reporting • One department alone saw savings of 500 hours in staff time a year due to efficiencies made possible by JCA Answers. 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 55
    56. 56. ART MUSEUM • Converted to The Raiser’s Edge about 4 years ago • Came to JCA initially for a data warehouse • Now have an integrated BI infrastructure that combines their Raiser’s Edge and Ticketmaster VISTA ticketing data • Starting to do more work in Predictive Analytics – initial savings: $20,000 annually 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 56
    57. 57. PREDICTIVE ANALYTICS 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 57
    58. 58. LOOKING INTO THE CRYSTAL BALL 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 58
    59. 59. PREDICTIVE ANALYTICS IS HERE • Rise in Google Searches of ―Predictive Analytics‖ 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 59
    60. 60. PREDICTIVE ANALYTICS IS HERE • Rise in Google Searches of ―Predictive Analytics‖ versus ―Business Intelligence‖ 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 60
    61. 61. WHAT IS PREDICTIVE ANALYTICS? Predictive analytics is business intelligence technology that uses predictive models built from your data to make predictions about the future. 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 61
    62. 62. WHERE DO WE BEGIN? • Start with a business question How do I raise more money? versus How can I increase the number of membership renewals? 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 62
    63. 63. PREPARATION IS KEY • Do we have all the data we need? • Is our data usable? 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 63
    64. 64. LET’S EXPLORE • Explore your data • Keep your business question in mind 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 64
    65. 65. PREDICTIVE MODELS • Models use patterns found in your data to identify risks and opportunities. • Models apply scores to your constituents, which can help guide your strategy for improving outcomes 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 65
    66. 66. PREDICTIVE MODELS • Naïve Baise - Used for classification 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 66
    67. 67. PREDICTIVE MODELS • Decision Trees - Used for classification, regression and association 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 67
    68. 68. PREDICTIVE MODELS • Clustering - Used for segmentation 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 68
    69. 69. IMPROVE YOUR MODEL • Models are a work in progress. • Test before deploying your model. 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 69
    70. 70. THE THREE R’S OF PREDICTIVE ANALYSIS • Reliable – Your predictive model must be accurate. • Repeatable – You need to be able to use your model more than once. • Relatable – You need to understand the results. 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 70
    71. 71. QUESTIONS? 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 71
    72. 72. FOR A COPY OF THIS PRESENTATION • Leave us your card • Send us an email at bi@jcainc.com • Stop by our booth: 107/109 • Tweet us @BI_JCA • Go to our website: www.jca-answers.com 1/2/2014 @BI_JCA ~ bi@jcainc.com #bbcon 72
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