Ready to Retire? Multigenerational Financial Insights with JMP (2025-US-30MP-2431)

Well, hello. Good morning and good afternoon. I am excited to be sharing with you a case study on that analysis on what I really hope is a very relatable subject to all of us.

Before I start, I want to make a disclaimer. I am not a financial advisor, and the information I am sharing with you is for educational purposes only. I hope you are all working with qualified financial professionals to understand how the topics I'm sharing with you today affect your personal financial situation.

For more information on the subject, I have listed a few resources that I use to gather data and some hypotheses. The resources are the Pew Research Institute, the Trans-American Institute, and obviously, the US Census Bureau.

Let me just share a little bit about why I started on this topic. I just celebrated my 30th anniversary working at Procter & Gamble this past January. Obviously, recent news had made me more aware of the closer I'm getting to my life after Proctor.

I also have two young sons, and they are actually just starting their careers. One just started his career, one has just graduated from college, and I'm mentoring a lot of really new hires in Proctor.

As I have conversations with them about money and finances, I start coming with a couple of articles that just sparked my interest. I thought this would be a really neat topic to investigate and understand from the perspective of people across different ages.

Let me I'll tell you who I am. I'm Giselle Baker. As I said, I've been working in my current company for about 30 years. In my day-to-day job, I use JMP statistical software to investigate and analyze data from market research. That's basically what I did.

However, this time, I use JMP just to help my curious mind and continue my conversation, not only with my children, but with people at work I mentor. When I share this topic with Tracy, and I told her that I was going to submit the paper, she jumped into the JMP forum because I think this is a very relevant topic for her as well. Why don't you introduce yourself, Tracy?

Great. My name is Tracy Desch. I just celebrated my 35th anniversary with Proctor & Gamble. I have a very similar role to Giselle in product research, but my current focus is primarily understanding the quantitative data that comes from assessing North America hair care, their product usage, their experiences with shampoos, conditioners, treatments, and those insights drive our initiative design.

Tracy and I have known each other for many, many years, so we have become really good friends at Proctor, and we also share many things in common outside. One of them is, as I said, we are getting ready to retire pretty soon. Not soon enough, but we'll eventually do it.

Let's start with some background. You probably are aware that in the US, people are living longer. The fact that we are living longer requires, obviously, that we need sufficient funds to help us live for life. Interestingly, the increased years in retirement can translate to higher overall healthcare cost, living expenses, and potentially the need for additional long-term care. It was very interesting.

I read an article saying that people, they're retiring in the 65 now, today is expected to live until they are about 90 years old. That means that they will be living almost 30 years post-retirement, almost as long as they've been working, many of them. That is a lot of years.

For us, particularly, for Tracy and I, we know that women on average live longer than men, which then can affect our financial planning needs, regardless of having a partner or not. Many of us, early on our years, stay home to take care of our children, and we then came back to the workforce until later.

This actually is a very relevant topic. As I was reading through different articles, and I was talking with my children and some colleagues, I just start noticing some common things that people were hoping for, but then also worry about.

Then that inspired my curiosity, and I'm a researcher, I'm a scientist. This is a topic that we get a lot of research. What I did was actually launch a couple of surveys. One was a qualitative survey where I asked people, professional people, between the ages 18 to 55, just to tell me about their dreams and hopes for the future for retirement. That was an open-ended. It was very qualitative.

Then I also did a survey where I asked more pointed questions. In fact, if you are interested in taking the survey, the QR code in the slide is there. It's an open-ended. You can complete the survey. It's quite anonymously. I don't promise you that I use that data because I already have some data.

But the survey that I asked was questions about the confidence in retirement, some demographics, some attitudes and beliefs about life, and then how prepared people felt like they were for retirement.

During the rest of our time today, I wanted to divide in. Tracy and I will give you an overview of the things that we learned using many of the platforms at JMP. JMP is an awesome statistical tool for discovery.

For the rest of our time together with you, I hope we can cover a few things in the JMP platforms. We'll be showing you Text Explorer, Graph Builders, some categorical platforms, but then also we are going to do a little more of the multivariate platforms and a little more in-depth predictive modeling.

At the end of our time today, the three things that I hope that we all know is the overview of the retirement dreams and fears across generations, how they are same and different, what are the life priorities and retirement and readiness difference and similarities between generations, but more importantly, understanding the key factors influencing readiness for retirement between generations and then things that we can actually do. With that, I'll go ahead and pass it with Tracy.

Great. Thank you. I am going to focus on the first three, Text Explorer, Graph Builder, and Categorical. Text Explorer is a wonderful platform that we can use to actually then go further in other platforms. First of all, just to run it, running a basic Text Explorer. This is the question, what do you… Describe your ideal age of life. You can play with these down here. I just like to go to three.

I am sharing, aren't I? Am I not sharing?

Now you're sharing.

I'm going to cover the first three: Text Explorer, Graph Builder, and Categorical. Text Explorer is a great platform to understand qualitative data, but it can also help you really dig out insights and use them in other platforms further on in your analysis.

The first thing we're going to do is just create a Text Explorer, and you'll put the open-ended question here. You can play around with these based on what works in your category or your project are.

Then you have this. One of the most important things is to clean up the data. Not going to do that today, but for example, money and financial, or those words that you want to group together, or their spelling issues, et cetera.

Another thing, after you play with this, you can manage your stop words. Retirement, probably everybody used that word. That's probably not a very useful word to look at. So you can add it as a stop word, and it will disappear.

Then you come, and you start seeing a word cloud, really all the words based on size, on the number of times they were used. You can really start seeing what words are people using about their retirement, what words they describe as their dreams and fears.

One thing that you might want to do is that you can use for other analysis, either as a filter or as a…

[inaudible 00:10:46] so can save as an indicator.

What are you using for? Filters and…

You can save words as indicators to use as filters, to use as other variables, et cetera. It's very easy to just save as indicators. I've actually already saved some of these for time purposes.

Another great thing that you can also use for future analysis is looking at sentiment analysis, where it takes each word, and it assigns a sentiment based on what JMP has told it to do or if you want to manage those numbers.

They also have intensifiers that some words get double the score, or get a little bit less, because of the words that they use. You can manage all that in the sentiment platform. But once you have a sentiment, you can actually save the score, and then you can use that.

If you go back up to the word cloud, this is where the fun begins, because you can turn… You can actually order it different. You can color it. I prefer looking at it like this. People have their preferences. But you can also start coloring this. We'll do here.

We'll say, "We'll switch the colors. We'll make this linear." You can start seeing. You can actually color by a column value. This is where sentiment that you had come up with in looking at sentiment is really helpful.

Look at how do people think of the words. Just changing some of these, and we'll look at reverse and discrete. Once you have those set up, now you can see, overall, there's a lot of green, but there's some words that are… Would is a big one.

You can also look at the text. What is it? I would be able to. That's probably a stop word. Live, there's a little bit. But when you start seeing the power is when you can bring up the local data filter and say, "Let's look at it by generation." You see a lot of more in the middle. A little bit more not as positive, not as negative.

Here, if you go to Gen Z, you're seeing a lot more of green. They seem to be more optimistic about retirement. That makes sense. They're closer, they can see the shorter path forward versus some of the Gen Z, young millennials, so you can really see how that changes.

Then you have words, you've made indicator columns, you have your overall sentiments for her respondent. That's where then you can start having fun. You can plot it. Here we're plotting generation by female. If you want to change this, you can say, "I want to put labels on by value."

Here you start seeing with your younger generations, Gen Z and young millennials, you see that men tend to be a little bit more optimistic than women, but when they get older, you don't really see that gender difference. Then it can be really interesting and powerful to see how this plays out.

Another thing that you can look at is, remember, we saved the indicator columns. We saved them in for time purposes, we actually renamed them to make it easy that there's these positive indicators and there's these negative indicators. We made them indicator columns and then made them multiple response.

Let's look at their retirement dreams by generation. What does that show us? I'm going to take away the share of responses. What do we see? It looks like dreams for the younger generation is family, community, home or business ownership. That's really what their dreams are. When you look older consumers in the Gen X, they're more concerned about health and well-being.

They've hit 40 or 45, and they start seeing that decline in their knees and other body parts. They've already seen the… Most, if they wanted, have seen the family, seen homeownership. It's very interesting. The dreams for the different generations are very different.

If you want to then look at fear, just replace it and look. The older generations start having financial insecurity. They started purchasing things, and they are more concerned with money.

Whereas the younger generation is worried about isolation and loneliness, loss of purpose and boredom, dependency on others, old age, and long-term care. They have a whole lifetime ahead of them to get there. You don't just know in this day and age what that future holds.

One other great thing that you can do with the sentiment score is you can make a map graphical. You can plot it by the overall sentiment, and you start seeing what places are happier, more optimistic versus less optimistic.

Here we have Montana. They're really happy. They have an overall sentiment average of 90. Whereas if you look at Arkansas, they're down to negative 22. If you bring generations and look at it by generation, Louisiana is negative for Gen Z, whereas the older millennials are driving the Arizona negatives. Sentiment and text analysis can lead to greater analysis. Giselle will continue to talk about how we can use these in the other platforms.

Perfect. Let me go ahead and then go back and share my screen again. Hopefully, you can see my screen now. Maybe? Hold on. Yeah, share. Perfect.

As Tracy said, one of the things that we discover, as she did the Text Explorer and the Graphic Builder, those are wonderful platforms to really bring the unstructured data into the structured data.

It was very interesting for me as she was doing the analysis to understand that dreams for retirement, in general, a topic that everybody talks about it. However, the younger generations really are more about relationships building, about homeownerships, where the older generations, really, we just hope to stay healthy and active as we get older.

However, when you look at what fear bothers us, for us, as we get closer to our retirement, it's really more about that financial stability, about having enough money that will last us for the many years that we are going to hopefully live. Whereas the younger generations really are talking about being lonely and not having somebody to share things with.

The next thing we're going to do is taking all those hypotheses, and as I said, one of the things I am really fascinated is the Text Explorer, because I use it often on my everyday job. There is a lot of discovery you can do from this platform.

Another fantastic JMP platform is the Categorical, as Tracy shared. It helps you analyze a variety of data. I do like Categorical because you can put any data. You can put nominal, ordinal, or continuous data and start getting some insights with this platform.

We use a lot of the multiple responses because obviously, as you think about retirement, there is not the only one thing that is important to you. As you think about your everyday life, there is only one thing.

Categorical is a good platform that helps you analyze multiple responses type of data. In the survey that I'm hoping that you guys scan, and then if you're curious to know what type of questions, and we empathize that would be important for people as they think about retirement across all ages, I asked a few questions.

I asked a few agreement questions about life attitudes. I asked a few questions about understanding of what is available for us to start planning for retirement. Then I also asked a few questions about what concerns people have. I wanted to understand what things were similar and what things were different just because we understood that there seemed to be some difference across the generations.

I wanted to understand what things are common for me, like generations, for them, like myself, and for younger generations, like my children, because then with that information, I can then empathize, and I can become a better parent, but then also a better mentor for those people that I'm a mentor and proctor.

Let me move into journal now, and I will show you the next thing that I wanted to do. With the survey, what I did was actually took all the responses on the survey, and I did a few analyses. Let me show you one of the things that I discovered on the multi-response analysis on Categorical. I'm not going to go through the whole details in how to build it, but I'm rather going to focus on the insights that we have.

One of the questions that I asked in the survey was, "What are the top priorities of your life at this moment?" Then what I did was use the Categorical platform where I put on the top of the structure platform, the generation breakdown. But then on the side, I put the multiple response. You can actually see some of the responses.

Obviously, typically, most of us read from top to bottom. I thought that the insights in here were on the bottom. What I did, you know that in JMP, anything that you can do, you can it from the red triangle. I'm actually going to change it. I don't want to see it by order, but rather by alphabetical order.

You can see that for the younger generations, what is important and this priority on their life is career advancement and professional advancement. That makes sense. When I talk with my children and when I talk with young people in the company, it's very ambitious people. They wanted to make a name for themselves. That totally makes sense.

However, when you look down into the older generations, things that had top priorities for us, it really was creating pursuits and hobbies. I am at the time where I love what I do, I love my job, but I'm getting to really prioritize my hiking and my traveling. This actually was a pretty cool insight that we got. Interestingly, I am a very visual person, so not everybody likes to read charts. What I did, I actually brought some of the same questions using Graphical Builder.

What I like about Graph Builder, as Tracy said, it's a very flexible tool. In this graph, for those who are visually inclined, like myself, you can actually plot your data in a way that it brings a little more impact into the insights that you have.

What I did was plot the interest of career and professional growth because I already knew that there was some differences. When you look in the totality of the percentage, you can totally see that the Gen Zs and the Millennials by far said, when I asked them, "What's the priority on your life?" "It's my career, my job."

Again, those are two things that the Categorical platform and the graphical builder can help you. Let me just jump back into the slides, and then I can show you the insights that you have. As we age, our priorities in life change. That is, I think, super important because when you talk with people of different ages, you can actually relate to them and understand where they are. Interestingly, for the Millennials and Gen X, really having more balance was one of the biggest priorities from the Categorical-platform.

Having said that, though, there are other things that you can actually start understanding because you see the difference between the platforms. But then the question is, how does these priorities there are impacting the retirement savings, the income needs they have, or they expect to have, and then, how confident they are for retirement?

Tracy show us already that there are some difference on the dreams and fears they have about retirement. But then if we just are a little more curious and trying to understand, what is the relationship between how prepared people are, and then what do they expect on life? We can use more JMP tools to understand the financial stability and retirement savings difference or similarities between the generations.

Two questions that I asked in the survey was, "Tell me how much you are contributing to your retirement? Tell me how much do you have on your retirement?" Then more importantly, "How much do you expect to need for when you retire?" I also asked, "How confident are you that you will retire when you plan it?"

Let's jump into JMP, and again, to show you what were the insights. There we go. Two things I did. I did first plot the retirement savings across the generations. I do like bar plot on the jump because they are very intuitive and easy to read. The bar platforms, the icon that you can actually see, and it shows you basically the summarize by categories.

On the X-axis, you have the generations, the Gen Z generations, the Millennials, the Gen X, and the Baby Boomers. This is basically what the average of the respondents told me that they have been saving for retirement. Interestingly, Millennials seems to be saving a little less than Generation Zs.

As I was reading some articles, I thought that this was interesting because a lot of the Millennials, especially people that I mentor in Procter, are really worried about all the student loans that they have. Obviously, the priorities on life for them is probably paying those student loans and that they are not able to save enough for retirement. Interestingly, the Baby Boomers are set up pretty much very well, and they have saved quite a bit of money, which is a great thing.

The other thing that I also asked them was, "How much do you expect to save for retirement or need it for when you retire?" The next one, I just actually the box plot for that retirement. It was super interesting to see that pretty much everybody thought, "I'm going to need a lot of money to retire."

What was interesting, if you guys remember, the Millennials actually are the ones who are saving a little less money. However, they are the ones that are probably feeling that they are going to need a little more. If you think about it, with all the chatter that is happening around us about social security, nothing there for us, about the need that a lot of employers don't have pensions anymore, that actually starts building a little bit of that anxiety.

One thing that I liked in JMP, in case you guys had seen it, is that you can actually combine windows. I went ahead and combined the two windows because what I thought that was pretty interesting, you can actually have your crafts represented and you can start seeing some differences.

What I thought was super interesting is that the older consumers or the older generation have not only several more money, but they are actually expecting to need typically less for the rest of their lives.

That actually makes sense because if you think about it, if I'm at 60 years old, and I'm retired, maybe I feel like I don't need that much more to maintain my lifestyle. However, when you look at the Millennials, there is pretty much a huge gap. They think they need about eight times more of what they are going to do, they have saved it for retirement.

Just have actually seen some difference of, yes, retirement seems to be a topic that is important for everybody. Having said that, though, there are some startling differences in where people are in their lives.

The other thing that you can actually do, and I particularly like about the platform of the ANOVA is that you can plot your data, and depending on your data, you can do many different analyses. If you have two continuous variables, you can do a regression analysis. But since I have continuous variables or categorical variables, and then a continuous variables to understand what's the confidence, my next question was, is there a difference on confidence?

With this data, my hypothesis was that the Millennials were going to be the least confident about retirement. Let's see what happens. When you plot your data, how confident you are, this is a very simple X-by-Y plot. On the Xs, you put your generations, and then on the Y, you put the confidence. The analysis of variants actually plus your data and gives you the means of the confidence across the generations. Here you can see, I wanted to make the graph a little bigger, so you just can see it.

You can see that the Millennials are actually somehow confident. Typically, this was what's expected, the Baby Boomers were a lot more confident than the other generations. In fact, they were significantly more optimistic or have a higher confidence than the younger generations.

What was interesting to me was that the Generation X seems to be the least confident of all the generations. I start thinking, why would that be? I am part of the Generation X. I can relate to this because if you think about it, our generation is the sandwich generation.

We are taking care of not only ourselves and our children, but many of us are also taking care of our parents. All those life situations are going to have an impact on how do we feel about stop working, stop having an income, and then feel ready for what comes in the future in our life.

Again, those are topics that I think are very relevant for everybody. Let me go back into this one. With a simple Bivariate analysis and with a simple exploration, there is a lot of things that you can learn from your data analysis. I am not going to go into many details in a little more in-depth analysis. One of the things I said, "Well, people are not very confident." There are different things that affect how confident we are.

The question that I have is, "How can every situation that we have in our life actually is going to impact the confidence that we feel about retirement?" The good thing is that JMP is the Swiss Army knife of curious minds. I wanted to share with you two more platforms that can help you in your quest for understanding. It doesn't matter what topic it is. In this case, we're going to be in the retirement just because it's a topic of interest for us. But you can apply that any topic that you wanted to understand.

Let me share with you two very cool tools that I like in JMP. I am going to do another disclaimer: I am not a statistician, so sometimes reading the statistics, I just go with my statistical friends, and then they can help me understand how to read the data that comes from JMP. What I like about JMP is that even the layman person like myself can understand and start drawing some conclusions from the output.

Let me show you the Predictor Screening tool and the Multivariate methods that I use, because these tools help me answer what are the returning confidence drivers important for each generation, and then what is the number one topic that might help me better understand the best ways to start my journey personally on retirement and things that I can do personally, but then also things that I can empathize with those that feel confident enough with me to talk about financial situations and retirement readiness.

Let me go back and then jump again into our JMP. It is my journal again. Two of the things that I wanted to share with you is the Predictor Screening tool. The predictor screener, you can find it in Analyze, and there is the screening platform, and there is many predictors depending on the data that you have.

I like the predictor screening tool because this is a platform that you can put many type of inputs like nominal, ordinal, and continuous variables. I use that just to understand, of everything that I ask about priorities in life, fears, understanding, amount of money that you have saved, amount of money that you have contributed, what of everything is actually most likely to drive your confidence level?

Let me launch the platform. I already have saved a script. It takes a little bit to run, not very much. But with that, like Tracy said. You have all the questions that you have about how confident you are in retirement, for example, how confident, what's your age. Do you have an emergency savings? Pretty much all the variables that you wanted to investigate at that predicting your readiness score.

But then, like Tracy shared, I think filters are your friends. Using filters, then you can actually start seeing if for Generation Z, like the Zoomers, like my children, is the same thing than for other generations. For example, in the Generation Z, it looks like the concerns about being health care, as Tracy

May learn, and then how much they are contributing to retirement, seem to be the predictors of how confident they are in retirement. Let's go and look into my generation and see if it's the same, or it's different. For Generation Z, it's really more about our hopes. How comfortable I feel like I'm going to be in my old age, and then how much I agree that I feel like I'm in track to retire.

But then also the contribution to retirement seems to be another important factor, meaning that the more that we can actually contribute and prepare, seems to be a universal thing that we all can do to start helping us get into and increase our confidence on retirement.

Again, this is a lot of variables. When you look at all the variables, I do like to look at the contribution of this variable, but then also at the portion of the variable, because that basically tells me an indication of how important these variables are. Having said that, though, I think that there is some concerns at play in how well do we feel that we are ready to retire. For that, I actually like to use the multivariate analysis.

The multivariate analysis, let me show you in the table, you can find it on the multivariate methods, and there is many tools in JMP that you can use. I am going to demonstrate to you the multivariate analysis, which is basically a tool that helps you understand the relationship between different variables. Then I am also going to show you the factor analysis. The factor analysis is a very useful tool that allows you to reduce the number of variables to start doing some driver's analysis.

I personally like these two tools, but there is also many tools in this platform of the multivariate methods that allows you to continue doing further analysis of your data for insights.

Let me jump into the multivariate analysis now. What I did is put the variables that, based on the predictor screening tool, most likely were affecting the confidence. You can actually see, let me put it in here. What the multivariate tool gives you is basically a very neat table that organize your data into the correlation between the variables of interest. The higher or the closer to one, the stronger the relationship.

As I said earlier, I like numbers, but I'm more a visual person. What I like about the multivariate analysis is the color maps on the correlations, because with that, you can very easily see what is the relationship of everything. Just visually, you can see that there seems to be a pattern of strong relationships where they're returning confidence.

But within the question that I asked, there's some relationships into how confident into the concern about your financial security, for example, and then how concerned about your long-term care. If you think about it, if I'm worried about my health, I am worried about the cost of living, and that's going to have an impact in how concerned I am about my financial stability.

On the other hand, if you feel like you are overwhelmed, you're going to have less confidence on enjoyment in the life, and that's going to be an inverse relationship. You actually can start seeing some patterns in there, and that's when you actually start looking into the factor analysis, because I know there was some relationship.

On the factor analysis discovery, I actually find out that there are very interesting themes. There is retirement concerns. There is a theme about how prepared you are in terms of contribution, in terms of money that you have saved.

But then, also, there are some relationship between the positive attitudes that you have in life and then what are your concerns at first. But then also, I would say, knowledge is harder. The more that we know about retirement and then the opportunities that we have and different ways to save, I think the more confident that we will be. Then also hope for the future.

Let me show you how I got to these seven factors. You launched the factor analysis tool, again, from the analyze, multivariate methods, and factor analysis. Factor analysis then gives you a really nice starting graph that basically gives you an indication of how many factors do you have and the eigenvalue.

As I said, I'm not a statistician. Rule of thumb is the eigenvalue is basically, a cumulative way to understand how each one of your factors are going to explain your variable response. In this case, it looks like, obviously, the more factors you have, the more that you're going to have explanation for your values. But then why do I want to do that? I wanted to understand the minimum number of factors that will help me explain.

For me, because this is a humanistic matter, humans are no machines, I don't expect to have 80% of everything explained. I would say between 60%, it's okay. Again, value for about one gives me about seven factors. But For me, the thing that I really like to look into is, can I explain those factors?

JMP gives you the rotated factor loading. With the rotated factor loading, you can actually start seeing how the variables are actually being combined or putting together based on the responses and based on the variability of your data. Factor one seems to be about the fear. It's because it includes things about the concerns on long term and health care costs, on the lifestyle and living standards that I maintain. That actually makes sense.

Factor two, it seems to be about more of that readiness and about the repartments, about the actions people are taking to get ready for retirement. For example, do you have a retirement savings account? Are you having an emergency savings? How much do you have? How long do you are planning for retirement? These The first factor seems to be more about how prepared or how ready you are.

The third factor is more about your attitudes in life. Do you feel like you have a purpose in life? Are you enjoying? Are you a happy person? Do you feel like you can open a talk about finances with others? The fourth factor is more about how anxious do we feel about. It's not so much about the concerns, but it's more about how anxious you are, how worried you are. This is more about fear.

The fifth factor is understanding of things that contribute or impact on retirement. For example, do you have an understanding of how social security works? Do you understand how catch-up contributions work? This is more about knowledge empowered.

The factor six and seven are more about hopes for the future. I feel like I'm ready, I feel like I will have a good lifestyle when I stop working. The last factor is really more about your network. How much money do you have?

Why do you want to do these factors? When you do these factors, then it actually helps you understand what is the most important predictor of how ready you feel like you're going to be retiring. Let's just go ahead and then jump into regression analysis and understand what of these factors are going to be more impactful across the generations.

The linear regression is another really awesome tool in JMP that helps you understand pretty much your data. Again, when you look at the multiple regression analysis on the Y variable, you put your readiness for retirement and then on the X factors is the things that you are trying to explain. I input all the seven factors that we just discovered, and then on the Y, you can actually see the retirement confidence.

JMP gives you a lot of information in here. Again, typically what I look into is my R-Square and my BICs because it just tells me how good my model is. In this case, it's super low, so I'm not going to worry about it because this is just a basic qualitative understanding.

Again, don't call me accountable on this. These are just insights that I'm sharing with each one of you. It looks for in general, the number one predictor for your confidence, is how prepared you are. I do like the prediction Profiler because it gives you a very visual way of looking at your data and understanding more impactful factors that affect your continuous variable.

The other thing that I like about the Predictive Profiler is that you can actually understand what is the most important tool and then the weight that each one of those variables has into your explanatory variable. In this case, it looks like the term in practice is by far the most important factor. If I were to have this, it's like, well, if I wanted to feel confident about returning, I better start doing something about it, which is contributing to my 401k.

But let's see then if this actually holds across different generations. Again, using the filters as your friends, you can look into what is the number one factor for Generation Zs. Interestingly for generations, for the younger ones, is really more about the hope they have for the future. It's like how confident they are but then also the impact about how worried they are.

In a sense, it clicks for me because when I was looking at this data, I was thinking, wait a minute. If you are worried, that's going to increase your confidence or if I think about it, when I talk with my children, if they are worried, they're going to start saving.

That actually is perhaps something that prompts and call to action to do something about in the future. Let's just look into the old generation now. How does the baby boomers actually feel, and what is driving the confidence and factors? For them, it's retirement preparedness and also the network. That totally makes sense to me. Because if by my 65, I have not accumulated enough to feel confident, that's going to have a bigger impact on how ready I am.

I'm not here to tell you a number, because the number, if you go to any financial planner, they are going to tell you, is very independent. It depends on your lifestyle, it depends on what you wanted to do, and it depends also on different sources of income that you might have. Not everybody has an inheritance, but there is a lot of people who are going to be having a lot of inheritance, as you guys see in the news. The younger generations are going to have a boom into money from the inheritance of their parents.

With that, let me go back into the predictor screening and multivariate platforms. I already show you there is so many things that you can do with JMP and understanding your financial future. Let me go back and then see if I can just do this one. Maybe not. I hope I can see you in Austin, so we can talk more about it. There is so much richness to understand about the topic.

I wanted to leave you with the following last words. Retirement readiness is not only about the numbers. There is a lot of social and emotional things that come into factors. Nevertheless, approaching this emotional issue with a curious mindset can actually help you and start conversations with people around you.

Really, the statistical platforms that we have in that job are useful not only for professional growth and insights for an everyday job, but actually you can use it for personal discovery. With that, I hope to see you all in Austin. We can talk more about it. I can show you details in how I generated some of the things that I did.

I wanted to leave you with a tip that a friend of mine at work gave me when we were talking about getting ready to retire from Procter. She told me, "Giselle, you will know that you are ready for retirement when you answer yes to the following three questions." Here they are. Have you made enough? Have you done enough? Have you had enough?

Thank you very much for your attention. We are looking forward to seeing you in Austin. Yes, come and visit us pretty soon. Looking forward to seeing you.