How Data Analytics Can Improve Interchange Income

Non-interest income is an oft-neglected part of a credit union’s total income. And it makes sense, too—revenue from loan interest is generally lower, and it flies under the radar.

But what if you could increase interchange income? Would non-interest income still play second fiddle? Or would you pursue a program that could increase your profits from each member card transaction?

Continue reading “How Data Analytics Can Improve Interchange Income”

CU 2.0’s Credit Union Data Analytics Provider Guide for 2020

Credit Unions are at a unique crossroads. We are behind on a handful of key trends. We must balance our technological needs with our focus on service, growth, and profit. At the same time, we have outside factors impacting us including fintechs, compliance, IT security, and ever-changing regulations.

One advantage shared by credit unions is their access to data. Individually, they generate quite a bit of data. Collectively, they generate tremendous sums. Even with all of that, we remain behind woefully in leveraging our data.

Continue reading “CU 2.0’s Credit Union Data Analytics Provider Guide for 2020”

Credit Union Data Analytics: Who are your best members? :)

Our “A” members are the ones who know the difference between a credit union and a bank, believe it, feel it, and espouse it. They bring us all of their financial services business and tell all their friends. If you are interested in what their data tells you about them then this post in Credit Union 2.0’s “Almost 99 Small Data Credit Union Hacks” series will be helpful.

This blog is the ninth and final part in Credit Union 2.0’s “Almost 99 Small Data Credit Union hacks” series and is based on the book Credit Union 2.0 – A Guide for Helping Credit Unions Compete in the Digital Age which covers in depth both big and small data for credit unions. There are six types of data that your Credit Union should be aware of:

  1. Digital Analytics – Desire
  2. Profitability – Fit
  3. Wallet Share – Depth
  4. Transaction – Triggers
  5. Design Data – Predictive
  6. Execution – IFTT (if this than that)

The key here is patterns. Once you sniff out the predictive behavior, you can be proactive with how you interact with the member.

Here are some ideas on where to look for your “A” member patterns:

Data What to do with it?
Favorite Grocery Store Special promotion with that store
Favorite Vehicle Special promotion with the local auto dealer
Favorite Coffee Shop Hang out there too – send them a cup on you!
Favorite Online Retailer Special Promotion
Favorite Local Small Merchant Hang out there too
Favorite Gym Special Credit Union deal
Who They Follow on Facebook Could this be a potential guest blogger on your site? Maybe there is an opportunity for you to update your content to be more relevant.
Where They Work Follow their leadership online
When They go to Branches Better service hours
Who Their Favorite Tellers/Call Center Reps Are Understand why
What Life Stage They are at What is next for them and how can you help?

 

Knowing your “A” members inside and out and further tailoring your services will help those members bring their friends. You want your “A” members fiercely loyal and believing in the credit union difference. Show those members the difference every day and hang out with them and their friends.

Want to learn more about how your fellow Credit Union leaders are using data? We invite you to join our Credit Union 2.0 Strategist Group where over one thousand industry leaders comment on new news and trends while sharing and learning from one another.

This is the final post in a nine part series. If you want the full “Almost 99 Credit Union Small Data Hacks Guide” click here!

In case you missed it:

Click here for part one of the data analytics series.

Click here for part two of the data analytics series.

Click here for part three of the data analytics series.

Click here for part four of the data analytics series.

Click here for part five of the data analytics series.

Click here for part six of the data analytics series. 

Click here for part seven of the data analytics series. 

Click here for part eight of the data analytics series. 

Credit Union Data Analytics: Who are your best members?

Our “A” members are the ones who know the difference between a credit union and a bank, believe it, feel it, and espouse it. They bring us all of their financial services business and tell all their friends. If you are interested in what credit union data analytics tells you about them then this post in Credit Union 2.0’s “Almost 99 Small Data Credit Union Hacks” series will be helpful.

This blog is the ninth and final part in Credit Union 2.0’s “Almost 99 Small Data Credit Union hacks” series and is based on the book Credit Union 2.0 – A Guide for Helping Credit Unions Compete in the Digital Age which covers in depth both big and small data for credit unions. There are six types of data that your Credit Union should be aware of:

  1. Digital Analytics – Desire
  2. Profitability – Fit
  3. Wallet Share – Depth
  4. Transaction – Triggers
  5. Design Data – Predictive
  6. Execution – IFTT (if this than that)

The key here is patterns. Once you sniff out the predictive behavior, you can be proactive with how you interact with the member.

Here are some ideas on where to look for your “A” member patterns:

Credit Union Data Analytics Patterns

Data What to do with it?
Favorite Grocery Store Special promotion with that store
Favorite Vehicle Special promotion with the local auto dealer
Favorite Coffee Shop Hang out there too – send them a cup on you!
Favorite Online Retailer Special Promotion
Favorite Local Small Merchant Hang out there too
Favorite Gym Special Credit Union deal
Who They Follow on Facebook Could this be a potential guest blogger on your site? Maybe there is an opportunity for you to update your content to be more relevant.
Where They Work Follow their leadership online
When They go to Branches Better service hours
Who Their Favorite Tellers/Call Center Reps Are Understand why
What Life Stage They are at What is next for them and how can you help?

 Credit Union Data Analytics: A Members

Knowing your “A” members inside and out and further tailoring your services will help those members bring their friends. You want your “A” members fiercely loyal and believing in the credit union difference. Show those members the difference every day and hang out with them and their friends.

Want to learn more about how your fellow Credit Union leaders are using data? We invite you to join our Credit Union 2.0 Strategist Group where over one thousand industry leaders comment on new news and trends while sharing and learning from one another.

This is the final post in a nine part series. If you can’t wait for next week and want the full “Almost 99 Credit Union Small Data Hacks Guide” click here!

In case you missed it:

Click here for part one of the data analytics series.

Click here for part two of the data analytics series.

Click here for part three of the data analytics series.

Click here for part four of the data analytics series.

Click here for part five of the data analytics series.

Click here for part six of the data analytics series. 

Click here for part seven of the data analytics series. 

Click here for part eight of the data analytics series. 

Credit Union Data Analytics: How do you know your member is about to leave you? :(

If you work for a credit union and are looking for ideas on how to stem attrition or member loss, then this post is for you.  This blog is part eight in Credit Union 2.0’s “Almost 99 Small Data Credit Union hacks” series and is based on the book Credit Union 2.0 – A Guide for Helping Credit Unions Compete in the Digital Age which covers in depth both big and small data for credit unions. There are six types of data that your Credit Union should be aware of:

  1. Digital Analytics – Desire
  2. Profitability – Fit
  3. Wallet Share – Depth
  4. Transaction – Triggers
  5. Design Data – Predictive
  6. Execution – IFTT (if this than that)

Sometimes members give us very subtle clues that they are moving on. Here are a few key actions to be watching for:

What the member does? What it means?
Reduce bill pay items by more than 25% Moving over to somewhere else
Credit Card activity stops one month New Credit Card
Member stops logging into online banking No longer the PFI
Member doesn’t order new checks Moving soon and not planning on taking you along
Member doesn’t get a new car loan from you and pays off the old Found a better deal
Payroll declines or disappears entirely Switching accounts
Checking account activity declines in volume Switching accounts
Have more to add? Email [email protected] and help us improve this post!

 

If you pay attention to the warning signs, you may be able to save the membership and get the member engaged again. Credit Unions spend over $200 for each new member, however most problems are way less expensive to solve for a current member and require a lot less labor.

Want to learn more about how your fellow Credit Union leaders are using data? We invite you to join our Credit Union 2.0 Strategist Group where over one thousand industry leaders comment on new news and trends while sharing and learning from one another.

This is the eigth post in a nine part series. If you can’t wait for next week and want the full “Almost 99 Credit Union Small Data Hacks Guide” click here!

In case you missed it:

Click here for part one of the data analytics series.

Click here for part two of the data analytics series.

Click here for part three of the data analytics series.

Click here for part four of the data analytics series.

Click here for part five of the data analytics series.

Click here for part six of the data analytics series. 

Click here for part seven of the data analytics series.