Unlocking Customer Value: The Game-Changing Power of Income Data in Service Provision

In this episode, we explore the challenges and opportunities in utilizing modeled income data to drive impact and support vulnerable populations. Learn how this data helps identify and target underserved communities while overcoming its inherent limitations. Plus, stay tuned for upcoming episodes where we’ll dive into breaking down barriers for low-income customers, personalized outreach strategies, and predictive tools like the Energy Burden Index to forecast payment challenges. Don’t miss out on this insightful journey into data-driven solutions for financial inclusion!

Visit our website at https://blastpoint.com/ to learn more about what BlastPoint can do for your business.
If you want to learn more and speak with our specialist, please contact us to schedule a demo today!

What is BlastPoint Deep Dive Podcast?

BlastPoint Deep Dive Podcast explores the intersection of data, AI, and customer insights. Each episode dives into how AI-driven solutions help businesses understand and engage their customers, improve satisfaction, and reduce churn. Join us for expert insights, real-world examples, and practical tips on leveraging AI to transform the customer experience.
Whether you're a business leader or a data enthusiast, this podcast will give you the tools to stay ahead in today’s competitive landscape.

If you want to learn more about what we do and offer, contact us today to schedule a demo at https://blastpoint.com/about/contact/.

Tom: 00:00

Welcome to the Blastpoint deep dive.

Anna: 00:02

A podcast exploring the power of data and AI driven solutions.

Tom: 00:07

I'm your host for this deep dive. And today, I'm joined by our AI expert.

Anna: 00:11

Hey, everybody. I'm Anna, and we're diving into the world of income data.

Tom: 00:15

So just sit back and relax, and let's discover how income data is changing the game for financial inclusion and support for vulnerable populations. Okay. So let's unpack this whole income data thing. Where does it all come from? It's not magic, is it?

Anna: 00:47

You're spot on. It's definitely not magic. It's actually a mix of different sources. So we have self reported data, you know, like when you fill out a survey or an application.

Tom: 00:56

Right. But people aren't always the best at remembering things, are they? I mean, sometimes I can't even remember what I had for breakfast.

Anna: 01:01

You're telling me so, yeah, there's definitely room for error with self reported data. That's where third party data comes in. This comes from sources like credit bureaus and public records.

Tom: 01:10

Oh, so it's more like a bigger picture view, but maybe not as up to date.

Anna: 01:14

Exactly. And then there's the really cool stuff, model data.

Tom: 01:18

Oh, AI magic. Tell me more.

Anna: 01:21

This is where things get really interesting. So model data uses algorithms to actually predict income based on all sorts of factors.

Tom: 01:29

So it's like having a data crystal ball.

Anna: 01:31

Kind of. But instead of predicting the future, it helps us understand the present, you know, like filling in those missing pieces of the puzzle.

Tom: 01:38

This is really cool, but how is it actually helping people?

Anna: 01:41

Well, imagine a utility company trying to reach low income customers who qualify for assistance. They could use income data to target their outreach efforts instead of sending generic mailers to everyone.

Tom: 01:52

Wow. So it's making sure that those who need help the most are getting it.

Anna: 01:56

Precisely. And we've seen this approach lead to a 300% increase in engagement with those customers.

Tom: 02:01

That's a huge impact. What other real world examples come to mind?

Anna: 02:04

Another great one is the Low Income Home Energy Assistance Program or LIHEAP. They used income data to optimize their aid disbursements and actually doubled the amount of support they provided.

Tom: 02:15

Wow. It seems like income data is really key in making these programs work.

Anna: 02:18

It's all about making sure that resources are allocated where they are most needed and making a difference in people's lives.

Tom: 02:25

It all sounds so complex. How do we even go from raw data to something we can use?

Anna: 02:31

It's actually more straightforward than you think. First, you have to clean and validate the data, making sure it's accurate.

Tom: 02:37

So getting rid of the junk?

Anna: 02:38

Exactly. Then we have to normalize it for things like cost of living differences. $50,000 goes a lot further in some parts of the country than others.

Tom: 02:47

Makes sense. So you're leveling the playing field.

Anna: 02:49

Right. Then there's trend analysis and forecasting, which is basically using historical data to predict future income trajectories.

Tom: 02:56

Oh, so it's like connecting the dots.

Anna: 02:58

Exactly. And finally, we have segmentation and persona development, which combines income data with other information to create profiles of different customer groups.

Tom: 03:07

So you're going from raw numbers to understanding the people behind them.

Anna: 03:11

Yeah. And that allows us to serve those people better. So, yeah, it's really all about making data work for people.

Tom: 03:16

I totally agree. And that's what makes it so fascinating. But, you know, we focus a lot on the positives. Are there any downsides to using this kind of data?

Anna: 03:28

That's such an important question because we need to make sure income data is used responsibly and ethically, like data privacy and security, for example.

Tom: 03:36

Yeah. That makes sense. People's financial information is super sensitive.

Anna: 03:39

Exactly. We can't be careless with that data. Strong safeguards are absolutely essential to prevent misuse.

Tom: 03:45

So things like encryption and anonymization are really important.

Anna: 03:48

For sure. And it's not just about protecting the data itself. It's also making sure it's used to benefit people and not harm them.

Tom: 03:56

So avoiding any kind of discrimination or bias is crucial.

Anna: 03:59

Absolutely. We always need to be on the lookout for potential bias in the data and then take steps to mitigate it.

Tom: 04:05

It sounds like ethical use of this data is just as important as the technical side of things.

Anna: 04:10

I'd argue it's even more important. Technology is just a tool. And like any tool, it can be used for good or bad. It's up to us to use it responsibly.

Tom: 04:18

That's a great point. Now I'm really curious to hear about how Blast Point has helped its clients use income data effectively.

Anna: 04:26

Mhmm.

Tom: 04:26

Do you have any good success stories?

Anna: 04:28

Of course. One that comes to mind is a utility company that was struggling to get people enrolled in their assistance programs. They were sending out generic mailers to everyone, but participation was really low.

Tom: 04:40

So they weren't really reaching the right people.

Anna: 04:42

Exactly. They needed to be more targeted. So we helped them analyze income data with other information to identify customers who are likely eligible.

Tom: 04:50

So they could focus on those who actually needed the help.

Anna: 04:53

Right. We help them send personalized communications to those people explaining the programs and making it easier to apply.

Tom: 05:00

Did it work?

Anna: 05:01

Oh, yeah. They saw a 300% increase in engagement with low income customers. Thousands more any other stories come to

Tom: 05:08

mind? Well, there was this client working to optimize their LIHEAP disbursements. They were having

Anna: 05:11

trouble getting the funds to the right households. So they had the resources, but they just couldn't get them to the right people? Exactly. We helped them use income data to predict which households were most likely to qualify for LIHEAP, and then they could target their outreach and streamline the applications.

Tom: 05:33

Sounds like they were able to cut through the noise and get the aid where it was needed.

Anna: 05:36

And the results were great. They actually doubled their LIHIP disbursements and helped more families with their energy bills.

Tom: 05:42

Those are powerful examples. It really shows how income data can make a difference. But what about the future? What trends are shaping this field?

Anna: 05:51

One of the most exciting trends is the advancement of AI and machine learning. You know, these technologies are becoming so sophisticated.

Tom: 05:59

So we're gonna see even more powerful models.

Anna: 06:01

For sure. These models can predict income more accurately and identify patterns we might not see on our own. And real time data is becoming more available, so we'll be able to make faster decisions with the most up to date information.

Tom: 06:13

So it's gonna be a more responsive system.

Anna: 06:15

Exactly. Imagine being able to adjust, like, your marketing campaigns or your credit decisions in real time based on someone's current financial situation.

Tom: 06:25

That's really interesting. It seems like we're moving toward a much more personalized and efficient system.

Anna: 06:29

And it's not just about efficiency. It's about fairness too. As AI gets more advanced, we need to make sure it doesn't perpetuate existing biases. You know?

Tom: 06:38

So ethical considerations become even more important.

Anna: 06:41

Absolutely. We need to make sure that everyone benefits from these advancements.

Tom: 06:45

That makes sense. So the future of income data seems really bright.

Anna: 06:49

It is. It has the potential to change how we think about financial inclusion and support vulnerable populations.

Tom: 06:56

I agree. Mhmm. And I really appreciate the work that Blast Point is doing to make that happen.

Anna: 07:00

Well, our mission is to use data for good, and we're committed to building a more equitable and inclusive world. It really does feel like we're at the beginning of something big here.

Tom: 07:10

Yeah. There's so much potential for positive change.

Anna: 07:12

It's not just about the technology itself.

Tom: 07:14

Yeah.

Anna: 07:15

It's about using it and make a real difference.

Tom: 07:17

And that's what's so inspiring about this. It's solving problems and helping people. Yeah. You know?

Anna: 07:22

Yeah. That's why I love doing this work. Knowing it can actually help people is so rewarding.

Tom: 07:26

We've talked a lot about the big picture, but can you share a specific story that highlights the power of this data?

Anna: 07:33

Sure. There was this project with a financial institution that wanted to make their services more accessible to low income communities. They had these great products, but they weren't reaching the people who needed them most.

Tom: 07:45

So they had the right solutions, but the wrong approach.

Anna: 07:48

Exactly. Their traditional marketing wasn't working. So we helped them create a data driven strategy using income data to find and target potential customers in those communities.

Tom: 07:58

Oh, so they could use the data to figure out where to focus their efforts.

Anna: 08:02

Yeah. We helped them segment their audience based on income and location and things like that. And then they created targeted marketing campaigns that actually resonated with those communities.

Tom: 08:12

That's a smart strategy. Did it work?

Anna: 08:14

It did. They saw a big increase in engagement and new accounts from those communities. It was great to see how data could connect people with the financial services they needed.

Tom: 08:24

That's awesome. It shows how data can make things fairer for everyone.

Anna: 08:28

And that's what we're all about at Blast Point, using data to empower people and make a positive impact.

Tom: 08:34

I think we've covered a lot today. From the basics of income data to its potential to make a real difference and the importance of ethical AI.

Anna: 08:42

It's been a great conversation.

Tom: 08:44

I've learned so much, and I hope our listeners have too. If you're interested in learning more about income data, check out the Blast Point website. You'll find tons of information and resources.

Anna: 08:53

Yeah. Come check us out.

Tom: 08:54

And don't forget to subscribe to the blast point deep dive podcast for more deep dives into the world of data and AI.

Anna: 09:01

We'll be back soon with another episode.

Tom: 09:03

Until then, stay curious everyone.