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Good Fortune in 2018…19

What made 2018 a banner year for you? Here’s why it was a great one for us. (Hint: It involved a magic kingdom.) Plus, our resolutions for 2019.

2018 was a year that Ingram Market Analytics really took off… and 2019 seems like it will be equally exciting.

Website launched – Whoo Hoo! This was a long haul, but we didn’t take any shortcuts and I’m proud of the site. Now, to keep up with blog posts like this one. That’s my resolution for 2019. If you have a topic you’d like to hear about let me know. I’m also on the lookout for opportunities to be a guest blogger – to share how data analytics applies to your industry.

Grants cafe presentation. I loved hearing from non-profit professionals about using data to attract donations, recruit members and get elementary school children to visit museums. I’m sponsoring Grant’s Cafe in 2019 and hope to meet with them again.

 

NCES Data Institute. OK, maybe you have to be a data enthusiast (nerd) to get a thrill out of this one. But in 2019 I’ll be working with the best of the best to study my passion – the Student Debt Crisis – and maybe even find some solutions to help colleges and students. It doesn’t get better than that.

Disneyworld – can you believe this was my first trip?

 

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Big Data — It’s NOT for everyone.
But is it for you?

Initiating a big data project can be overwhelming. It’s not just one more thing on your plate — it’s more like three plates spinning at once. Take step one and see how you can use big data to solve a problem by locating hidden patterns in large volumes of data.

First, let’s figure out if big data will work for you — in three simple steps — without the mess of broken dishes.

Step 1 — Define the problem

Step 2 — Figure out how much data you need

Step 3 — Measure the problem against the data

Hint – If you don’t know your dilemma, what data you have, or where you want to end up then you need to focus on these three steps before diving into a data analysis project.

What’s your headache?

Do you have a headache? By a headache, I mean a pressing business need that needs to be addressed. Sam does.

Sam is a director of Admissions as a mid-size university, in charge of retaining students.

For many years the university has focused on the overall freshman year experience – and enjoyed a record number of freshman returning year two.

Recently, however, there has been a decline in the number of freshmen returning their sophomore year. Sam needs to know why, and how to reverse the decline. He’s hoping big data can help.

Is a big data project right for Sam?

Yes, he has a pressing business need — one that would apply to any business seeking to keep existing customers.

If his first-year retention was down by 13% last year, and if that trend continues for two more years, enrollment will be only 66% of its previous level — a critical business need!

Sam needs to know what’s causing this trend. What if the college’s rapid growth, or lack of new programming that caused students to look elsewhere after their freshman year? Which types of students left in droves, and which types remained?

Let’s see if data mining can identify the answers. Please read the next blog post to learn how.

 

 

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Have you got the data you need?

To use data analysis you need to acquire sufficient data that is relevant to the business need. Simply put, you need data on what works to promote your business, increase sales or reach other goals, and what stops it from growing.

Sam, director of Admission at a mid-size university, is in charge of retaining students — has plenty of data. His university regularly collects a great deal of data on all their students. Some of it is clearly relevant to understanding and improving future retention. It’s part of running the institution.

He just needs to pin down the factors that have the most influence in determining if freshman return for their sophomore year. Students may drop out because the work is too hard, or their grades haven’t measured up. Maybe their high school academics didn’t prepare them for the rigors of college, or they just aren’t smart enough. Perhaps it is a financial situation and they are unable to afford to return.

Sam has decided to measure these four factors:

  • Net price (tuition and fees, after financial aid)
  • Date of their application
  • Program of study
  • High School GPA

It was easy for Sam to figure this out, but that’s not always the case. Ingram Market Analytics can help you review your issues and identify the factors that need to get the answers you want.

With four key factors to consider, he can estimate how much data he needs:

Number of records needed must be greater than 60 x 2 Number of factors

With 4 key factors, the above formula estimates that he needs 960 records. Since Sam has thousands of records, the answer is YES, he has relevant and sufficient data for data mining.

Sam has it easy. Your customers may not share this much data with you. Generally, in considering your situation, it is important to have data that represents:

  • Stimulus and response (what was done and what happened)
  • Positive and negative outcomes

Simply put, you need data on both what works and what doesn’t. Both on products sold and products returned, people who ordered your service a second or third time, and those who never call again.

But before you do this analysis, let’s look at what you want to accomplish, that is the end results in terms of performance you need.

Please read the next blog post to learn how.