398. Exploring Behavioral Insights with Michael Hallsworth: Lessons from Traffic to Health (Refreshed Episode)

Welcome to episode 398 of the Brainy Business Understanding the Psychology of why People Buy. Today's episode is a conversation with Dr. Michael Hallsworth of the Behavioral Insights Team. Ready? Let's get started.

You are listening to the Brainy Business podcast where we dig into the psychology of why people buy and help you incorporate behavioral economics into your business, making it more brain friendly. Now, here's your host, Melina Palmer.

Hello. Hello everyone. My name is Melina Palmer and I want to welcome you to the Brainy Business Podcast. Today's episode is a refresh of my conversation with Dr. Michael Hallsworth, managing Director in the Americas for the Behavioral Insights Team. In this conversation, we specifically are talking about his book Behavioral Insights, which he co authored with Elspeth Kirkman. It is a fantastic outline for anyone looking to get started in the field who may be considering the best methods to start with as you look to begin applying behavioral science into your work. Why am I refreshing this one today, you might ask? Well, it's because of my conversation this upcoming Friday with Max Maube, who was previously at the Behavioral Insights Team. We talk about his work in history and the East Model which is created by Behavioral Insights Team in that conversation. And it felt like a great time to refresh this conversation with Michael. Don't forget that there are links for everything including my top related past episodes, books and articles all waiting for you in the show notes for this episode which are found within the app you're listening to and at the brainy business.com 398. All right, let's talk about behavioral insights. Dr. Michael Hallsworth, welcome to the Brainy Business podcast.

Hi, thanks for having me today.

Absolutely. I'm so excited to be able to chat with you. You have a new book out that's called Behavioral Insights that's available and you are working in the Behavioral Insights team here in North America. If you can just introduce talk a little bit about what got you interested in behavioral science, how you found the field. Would just love to learn more about that.

Absolutely. So about 10 or so years ago I was working in an organization called the Institute for Government in the UK and that was all about how you apply evidence to improve the way government works. And this was around the time that nudge had come out and there was interest from the UK government about what is this stuff and how could you apply it in practice? And the Institute was asked to draw together evidence from behavioural science to fulfill that goal, to say, you know, what could you do practically? What's an easy way of using it because much of the attempts to do that previously had kind of been a list of models and then policymakers or people doing practical things were expected to choose a model and then fit everything into the model. And we figured that that probably wasn't working very well because having some experience of government, it was not really fitted to people's needs in the day to day. So we tried to do something a bit different. We drew up this framework called Mindspace, which was basically around a mnemonic. Can you help people bear certain things in mind that may be important when they're forming policy which they otherwise wouldn't think about? We put that together that had something of an impact and people started using it. And not long after that the Pavel Insights team was set up. My former boss at the Institute of Government went to run the pavement science team and I transitioned to working in that team in the government and from there on I've kind of been there since and we've changed a lot. Obviously we've spun out of government, become the government bigger in many different countries. And two years ago I came to run our work in North America.

Yeah, that's awesome. So for anyone who's not yet familiar with Behavioral Insights Team, can you talk a little bit about. I know you have obviously UK and North America and more as I know. So what can you tell about the group and the type of work you guys are doing these days?

Yes, of course. So we're about I think 200 people across the world and our offices around different places including Sydney, Paris, New York, Toronto. We basically try to take behavioral science and improve the way that policies are designed, services are designed, the way government works. And we also try to do that by testing different approaches. So it's not simply that we would go up and say here's is the solution based on available evidence, go off and implement it. We would work with people implementing things in practice and saying can we introduce this kind of new variation and then can we test the impacts? Because we know the behavior is complex and you may not always get the result that you expect and we need to know if you don't. So we've also had a real strand of trying to help organizations build in low cost evaluations that give them rapid results but also retain rigor and that's quite powerful because often in the kind of public sector you don't get that kind of feedback quickly and it's proved quite welcome. So yeah, we basically arrange across a variety of different policy areas and we try to also kind of advance the field keep putting out new ideas that other organizations can use if they want to. For example, a couple of years ago, there's a piece of work called Behavioral Government, which is all about how can you apply behavioral science to the way government itself works. Kind of biases or barriers in the policy making process. So turning it back on government rather than just seeing government as some body that influences other people, because there are plenty of things it could do to improve the way it works itself.

Yeah. And so if you were, do you have any studies or projects or things that, knowing that you have many that you have worked on over the years, is there one that's maybe a 4 flagship? If you were to explain the type of work you really just love to do, or some cool findings that you've had, what, what would those be?

One that I am quite proud of, I suppose, is the one relating to antibiotic prescribing in the uk. So this was a situation where the number of antibiotics, the rate of antibiotic prescribing had increased quite a bit in the uk, as it had done in other countries. And that's. That can be a problem because if you use antibiotics when they're not needed, resistance can build up. And everyone's been very concerned about antibiotic resistance. Now the, I suppose, traditional way of dealing with that problem is to pay doctors to prescribe differently. And, you know, that works, right, that has an impact. But we were interested to see if you could take an alternative approach. And at the time we knew that the data on who was prescribing what had recently been made publicly available free to download. And so we took that data and we looked to see if we could identify the prescribers, you know, the doctors who were outliers, who were prescribing much more than people in their local area. And we did a few things to make sure it was a fair comparison because you may have regional variations. And then what we did was we basically wrote to those prescribers and told them where they were. So we chose the top 20%. We didn't just write to everybody. And this is where the testing part comes in. So in most kind of traditional communications, and this was coming actually from England's chief medical officer. You send it to everybody here, we split the group in half. We didn't send letters to half the top 20%. And so we could see the impact. It was a randomized control trial and we found that there was a substantial decline in prescribing of antibiotics. By the end it was equivalent to around 1% of England's prescribing of antibiotics just by sending this letter, giving information based on social norms. And the specific wording was around 80% of providers in your local area prescribe fewer antibiotics per head than you have. Social norm. And then one thing just to say is that this had a comparable effect to this massive financial incentive program, showing that there are alternatives that can work. And we published that also in, in the Lancet Journal. So it was all together a project that hit a few different points throughout the implementation. Yeah.

And so knowing that you have some space in Australia, so I know that the beta, or beta, the behavioral nudge, the unit there in Australia, they did a similar project. So Beck Weeks, who is in the States now, but originally from Australia, and I interviewed her about her app peak, she was talking about that project, a similar one that was done down there in Australia.

Yeah. So basically we did ours and it's been taken up in a few different countries now. Australia, yes, beta did a nice job because they took the original work and they added some bits on, did things differently, which is exactly what you want. But I've also seen it done in Italy, I think, and a couple of other countries. So now it's been kind of absorbed into the standard way that countries do, like what they call antimicrobial stewardship, which is. Which is really what you want to see, right?

Yeah, absolutely. And so one of the things I was talking with her about on that episode when. So the insights that you get just knowing that one of the big things that I found in applying behavioral lessons and things that we learn from these research, whether they're done in field or in a lab, is people like to think that what you learn is going to be completely generalizable across every aspect. And this was antibiotics, which means it should also work for, you know, this other, you know, getting people to invest in their 401ks or whatever it happens to be. And because it worked in uk, it will be exactly the same in the US or in Australia. And that's just not the way that it works, which is why, you know, you want to do those other tests. What do you have any thoughts on, on how that comes into play, about how things just don't generalize in general? That's a weird phrasing, but I'm going to go with it.

Yeah. So, I mean, it's a complicated question. What I definitely don't think we should be doing is going around saying it worked in this one place. No need to do any more thinking about it. Just implement it in exactly the same way. We have seen instances where things have surprisingly had very similar results. One that comes to mind is work around missed appointments, wording of reminders to encourage people to turn up to their appointments. This has now been replicated in a few different countries with very different health systems. I've seen it in Israel, in Singapore and in Australia. And some really quite similar messages have similar results. Equally, you have ones that simply don't work. And it's been seen as a big issue, particularly if you get to the point where there's a pilot that shows promising results and then you get to large scale and you see little effect or no effect. I know this has been an example in the book around helping students access financial aid. I think you have to separate out two things here. One is, was the original result just noise? Was it a fluke? Was it not real? And that's a possibility. The other one is there is some part of the way that the positive result, if you like, was implemented that was working and we don't necessarily know what, and it wasn't present in the adaptation. So the key here is trying to have that tension, the productive tension between retaining what seems to be the active ingredient that worked, but then adapting it so it'll continue to work elsewhere. And I don't think we know enough. The last chapter of the book, we talk about the use of qualitative research to try and get at some of the why questions, which, you know, you can do through randomized control trials. But it really helps to get that kind of understanding of what were the smaller or the less clear things that were actually really crucial to making this one right.

Yes, I, I'm a big fan of mixing the qual and quantitative stuff in together and knowing it's, you know, you do, you do one, and then you learn enough to do the quantitative, and then once you get findings from that, you have more qualitative questions and it becomes this really nice circle of, of learning. But I think just being able to. The point I was trying to make with that question, I guess, is just showing just the importance of testing and knowing that we see something. And it very well might work and be exactly the same across the board over here, and, you know, what that happens to be. But not to just take everything we read as, you know, the gospel truth that this will hold no matter what, and just to still be questioning what we see and look for, you know, the hidden what's different in this scenario than that one and how that happens to all come together. I know so in the book you have kind of a ten step process, I believe that goes through applying the, you know, the information and everything. Do you want to talk a little bit about the way that you approach problems? And you know, maybe you have a, there's an example in the book, but maybe you have a different one that you would want to talk through. Kind of, you don't have to dig into each and every one, but just, you know, how you think about problems and if someone was to come to you, how you were going to work on solving that.

Absolutely. So what we wanted to do in the book was avoid trying to promote one method or another because there are, there are many out there. Behavioral Insights Team has one which is kind of around the mnemonic tests. You know, it's force five stage process. We didn't want to say that this was the right one. The OECD has the basic framework. There are, there are many others. Most of these frameworks have some core elements in common around, you know, identifying the problem up front. Or maybe problem is the wrong words, you know, the goal, the behavior in question, the context, what is, what is the, why are we here? What is going on here? And then going deeper and saying, can we understand what is happening? What is driving behavior or impeding it? And that generally uses the application of some kind of theory or evidence from either the policy area, for example, if there is evidence around specifically why people don't turn up to hospital, plus these more generic principles from behavioral science, you use these to try and diagnose a bit more what's going on on the basis of that diagnosis. You then try to come up with interventions, sometimes called solutions, which attempt to address the factors you've seen. And then most people say you should do some kind of implementation and, or testing of that solution or those interventions that you've developed. And then you may or may not at the end have this kind of idea of scaling it, of saying rather than just stopping at the we've got a result phase, saying, but it's about something that's bigger than that. How do you take this resultant and help it to be used elsewhere? And that can be much harder actually than the initial kind of focused work because you're dealing with influencing people in different systems. They may not have the incentives to take on what you've developed and, and so on. So at a very high level, those stages seem to be quite common across all of the different frameworks that you see. And yeah, we talk about it in terms of an example of redesigning invitations to kind of job fair, to make it more likely people turn up, but you see it in many different ones. And yeah, we just want, we say 10 stage thing, but the reason it's 10 stages is we really try to take it back to the start because we didn't want to be saying, oh, you've just turned up and you jump straight to a kind of classic, if you like, nudge intervention. We wanted to make sure people could work through what is the overall context here? What's the nature of the problem? What are you trying to achieve at a high level? And then you go down to more specific, what is the intervention? And one distinction we make actually in the book, which I think is fairly helpful, is between this kind of strategic and tactical use of behavioral science. And often we talk about the more tactical use. So in other words, like changing the wording of the letter or you know, these more applied changes to the way things are implemented. But you can also go in a much higher level and say, how do we influence policy or the overall structure, the rules of the game at a very high level, the fundamental drivers of this situation. And sometimes we don't talk about that use as much because it doesn't provide as clear examples.

Right.

If you have a trial, it can be very clear that you've done this and have this result. Whereas if you're talking about messy kind of strategy, policy field, it can be much less clear what impact you had or the route of influences is actually much more indirect. Yeah.

Do you have any policy type projects that you have examples of that you've worked on and it's okay if they don't have numbers associated with them like you're saying. But just to show, I guess the, you know, that in that depth and breadth of the type of work where behavioral science, behavioral economics can be applied, I think that'd be great to hear a little bit about.

Absolutely. So in the book, in the first chapter, we kind of go through an extended example around food consumption and obesity and how behavioral science can be applied in various different ways to kind of change the way you think about information incentives and regulations. And so one of the things we talk about is that the behavioral effects of the sugar drinks tax in the uk, that's a very kind of high level thing. But it turns out the way you design a sugar drinks tax is important. So many of these taxes certainly in the US have been based around a kind of flat rate of volume. So a certain volume of drink is taxed more. Whereas the kind of, I suppose the insight around the sugar drinks tax in the UK is that most of the heavy lifting can actually be done not by trying to shift people's purchases, but by changing the content of what people purchase. So reformulating drinks so people don't have to actually change their behavior, but their sugar content is kind of reduced. And that's been done in many other areas. And it can be done slower or faster. But the point of the tax was to influence the reformulation, not to get people to move away from a sugar drink because it's more expensive. Instead, you're trying to influence the behavior of the retailers and the producers to reformulate. And that's why there are different kind of tiers of the tax. More sugar in your drink, the more tax you pay to try and encourage people to change their products, they move under that threshold and get taxed less. And that's exactly what happens. So the reason I give that example is there's good empirical evidence that that happened, that a lot of sugar was removed from the market. And the reason that happened was by thinking differently about behavior, shifting from consumers to producers. Right.

I love that example because it just goes to show the importance again of really understanding the problem and the making sure you're asking the right question before you go in and start implementing things. And that's where I find, and I think a lot of those that work in this, you know, applied space have the same that when people, when we're left to our own devices because our brains like to think that we know the answer and we've seen everything and we're, we're smarter than everybody else, you know, then you think, well, this is definitely the problem, so I'm just going to go solve it. And we need, people need to be taxed individually because they, they should know better, whatever that is. But when you don't take the time to step back and say, but what if we did something else? Or why, how, how about if we were to look at it this way and would it taste almost the same? Would people even notice that the sugar goes down? So it's, you know, helping them through the process, but, you know, then we are able to bring it back, like you said to the producers or wherever that happens to be, that you can have a much better impact without actually having to be felt by those individual consumers in a way that it would have been really negative otherwise. So I think that is a really great example. I also appreciate from reading in the book and you were talking about the food stuff, it kind of jogged my memory about the, I think it was servings of macaroni and cheese that you were giving certain, you know, when you gave people larger helpings, everybody said they ate the same amount every day. But really when you gave them more, they ate more and, but they felt like they were eating the, you know, regular amount. Is that a study that you had done or was that just one that was a, an example from the world?

No, that's a study by Barbara Rawls who's done a lot of work on food consumption over the last 30 years. So that's actually a study from like I think 2002 and this portion size effect is pretty robust. There are some pretty good meta analyses out there that show it's the case. Everyone was quite concerned about this kind of area because as you know, Brian Wansink did a lot of the research and everyone's very concerned that of course many, many problems that have been uncovered this research. However, he wasn't the only person working in this field and there are other people who have done it the right way and have had similar results. So that's why we feature that example because we wanted to go and say that despite the confusion that introduced, there is also rigorous studies in that field.

Yeah, absolutely. That's great. Good to have other examples because those studies like you said by Brian Wansink, you read them and you know it, it's right, you know, and it doesn't have the information behind it. But then the same like really glad knowing that there are these other studies that prove the same thing that don't have the questions behind how the studies were conducted to. Because you know that that's how we all do things. We all eat in that way and, or consume. And so it's great to see some of those other examples. If you are going to be looking at moving forward the types of studies that you are excited about or what's on the agenda. I will say, I guess I'll do a quick caveat of have you done anything around masks and other stuff in the coronavirus space? I'm guessing there's maybe quite a bit that you've been doing in that space. If there's anything you're able to talk about that's worked or, or not?

Yeah. So in the US we've done quite a few kind of rapid trials with cities. And you know, this is, this was earlier in the year where cities were kind of confronted with a challenge of communicating messages really urgently to their residents. And there were problems obviously with doing tests in real world context. So we could instead run rapid Online tests for messaging using a platform called Predictive, which we kind of created. And there we are looking at people's recall and understanding and stated intent around behaviors and sentiments. Now we know that people's intentions can have a weak relationship to their behavior. So we try to get kind of multiple indicators and be very clear about the caveats. But we do think that it was useful because we could look at things like messages around staying home, around maintaining social distance while shopping, face mask use. There's also stuff around contact tracing and how people might react. And then these results could be gained really quickly through online testing and then put into practice really rapidly. There were some interesting and sometimes counterintuitive results. You know, we. We tested various forms of posters encouraging maintaining 6 foot distance in grocery stores. And we found that there's a message around duty. You know, duty to protect staff and other choppers was the most effective. Then we then could see what kind of image is most effective. Is it an image with two shoppers and you have to stay away from another shopper, or is it image with a staff member and a shopper and it turns out that it was the staff member and the shopper that was the most effective. We also, in terms of face masks, looked at what was the best way of communicating the right kind of mask, if you're creating your own one. And we saw quite a big effect in terms of the way you showed the picture of mask on people's stated kind of recall of getting questions right about what you should be doing. So 14 different cities we did those trials for, and I think it made a real difference. I think obviously the challenge of COVID is evolving rapidly and some other things we would like to do around the way in which you arrange urban environments. I think this is a really, really interesting and crucial area. We've done stuff in the past around, for example, redesigning traffic intersections. But I think there's a lot that could be done around making it as easy as possible to kind of maintain distance in crowded environments through the way that the choice architecture is set out. You'll be familiar with these famous studies around the canteen and making things easier to reach for. Same principle could be applied or make it easier for people to remain distant from each other. Right. So I think that's another area we.

Can look at and so can you. So you brought up, you know, something with a traffic intersection. Can you. Do you have any examples, whether it's that specific one that had come to mind when you were just talking right there or some projects that you've worked on to help with things like traffic or other human behavior.

Yeah, so I mean we have done. The thing about the paper Insights team now is that we've worked across a lot of different areas and I can't even remember now everything that we've done. I bet certainly in the traffic area we've looked at things around what would encourage people to take carpooling to Heathrow Airport. That was one project that was one that where it was very difficult to influence people. And this is obviously in a pre Covid world worldwide, people more willing to do that. That was one I remember where there was no effect. And we do talk about these things in our reports. So we tend to publish an annual report which has got a lot of stuff in it, a lot of different trials, but we're very open about the ones that didn't work. If you go back and look at any of these annual reports, you'll find multiple examples of things where we didn't get a result and that's fine. And we're always very upfront about that and saying there's benefit from learning what didn't work. And part of the reason for that is because it may point you towards the fact you need a bigger intervention, you need a bigger structural change. We've seen that sometimes where I suppose a classic nudge is not enough and we're fine with that as well because we're not the nudge unit. We call ourselves a papillon sense team because if nudges say certain things are off the table, you can't do big changes in incentives in legislation. Well, we say that paper science can complement the way which you make a law in which you design incentives. So you should go there as well. And so yeah, we may have instances where this light search intervention didn't work and you need to do something more intensive. But just make sure that you're drawing on the best available evidence when you design that.

Yeah, I love that. And like you said, I think it's so important. I did a recent episode on survivorship bias and to where if all we ever see is what goes really well and what has been done, what was successful and we don't look at anything that wasn't. It just creates a completely different perspective on the field and then you end up with a bunch of tests or, you know, things that people think should work and if they aren't replicating or, you know, whatever it is, I think it just sets things up so differently if we aren't seeing the things that didn't work along with the things that do. So I think that is such an important thing that you're doing to be sharing that information with the world. It's really important. So thank you for doing that and for letting us know that that's something that you all do there at the Behavioral Insights Team. That's great. So if you. I know you talked a little bit about, you know, some of the projects that you've worked on, you know, back for, you know, years and years, and some of what you've been doing recently with Coronavirus and some of what you're looking to be doing. But is there anything else that you really wish that I would have asked? I know we talked a little bit about the book and the flow of it, but what else would you want to talk about? Of cool stuff that you're, you know, working on or that you're excited about right now?

Yeah, so I. I think one of the things we talk about the end of the book is bigger policy issues. One of the criticisms, and we haven't talked so much about the criticisms of this approach, which there are quite a few, and we give an overview of them and try to explain which are the most kind of problematic. And we don't try and rebut them all, we just try and explain them and give ways forward. But I think one thing we talk about is this idea that behavioural insights is only applied to kind of downstream issues when the big decisions have already been made. And I think that as part of our evolution as an organization as well, certainly in the us, we're looking to go more upstream and look at the bigger policy issues. In the uk, it's slightly different because of our work with the central government there. And as part of that, we've been looking at things like economic mobility. We've done a big project funded by the Gates foundation, the Balmer Group and Bloomberg Philanthropies on how do you increase economic mobility in the us, which has been declining. And it's a major driver of growth, if you get it right, and also social justice. And so that's been really great. Look at this big, meaty issue and show how the approaches from behavioral science can really make a difference to it. And I think that's something I'm looking forward to doing more. And it's a kind of common, I suppose, critique that, you know, you don't get to those bigger issues. And we kind of say, well, maybe we just talk about it less. But also, it's not like a technical question. I've had to Go to those upstream places to get involved in the strategic conversations. It's more like a political one. You've got to understand how you present yourself as well. And at the start of the book, we talk about three pillars of behavioral insights. One is the Evans Spur behavior, drawing on a dual process theory model. One is commitment to practical issues, being pragmatic and applied. And the third one is evaluation. And what you can see sometimes as you get further upstream is this tension between the evidence and the pragmatic need, because you want to be really, really in tune with the evidence. But also if you're always insisting that everything has to be evaluated through an rct, eventually you don't get involved in the big conversations because people don't see that you can make those kind of judgments about when to push something, when not to. And I think that's the real question for people who are applying this stuff. How do you get that really productive balance between being the person who knows the evidence and knowing how to communicate it, but also not being so rigid that you're not in the room at all? And I think that's something that people will need to get their answers to if behavioural insights is to fulfill its. Its promise of really transforming the way we do things.

Wonderful, I feel, I mean, put a bow on it. I think we really like, summed all that up a really great. Lots of important little tidbits in there about the way to be, like you said, approaching problems and exciting things on the horizon. I just really love the way that you said all of that. So thank you for sharing that here with the group for anyone or for everyone who wants to get in touch with you, because I'm sure there are many or with Behavioral Insights team in general. And we'll have a link for the book. Behavioral Insights will be in the show notes as well. But what's the best way for someone to reach out if they want to learn more about you and your work?

I felt that I finally had to create a website which, you know, everyone's kind of like, oh God, should I actually do this? But I decided it's probably worth it. So if you Google me, there's a website and you can contact me there quite quickly.

All right, well, we'll make sure to put it in the show notes and so people will be able to go directly to find you and links to all the great stuff that you're working, working on. So thank you again so much for joining me today, Michael. It was great to speak with you and learn from you. So thank you.

It was great. Thank you so much.

So what got your brain buzzing as you learned about behavioral insights with Dr. Michael Hallsworth today? For me, I always love to learn the frameworks others use and how they tackle problems and projects. Human behavior is complex even when you're just talking about one person and one single situation. As you look at many people in lots of scenarios, there's so much going on that it can be easy to get lost in the idea of where to begin and never actually start. That's why having some proven frameworks and methodologies is great and I love sharing those with you here on the podcast and in our other work. Michael and I talked about some today and I've linked to some other past episodes in the show. Notes for you that break down other frameworks like combi and more. My advice is to learn a little about each know what each of them is trying to do, what it's been used for in the past, and how it might relate to whatever work you're doing now. As you look at multiple models, you'll start to see some overlaps and maybe even some gaps for whatever you're specifically trying to do. Maybe you want to take a couple of models and use them each time or try them all out to see what fits. I always encourage people to start with whatever feels like a fit for you. If it's intuitive and you can remember the acronym, there's almost always an acronym and you think you could get into a rhythm with it as you build a testing and experiment habit. I say start there. You can modify and try other things down the line, but in general, any of these frameworks is going to be better than not doing anything or just going with your gut and intuition. So jump in, try it out and come share your experience with me on social media. You'll find me as the brainy biz pretty much everywhere and as Melina Palmer on LinkedIn, I can't wait to hear from you. And to make it easy, there are links in the show notes along with links to my top related past episodes and books, a timestamped summary of the episodes, ways to connect with Michael and tag him and get his book, and more. It's all waiting for you in the app you're listening to and at the brainy business.com 398 and just like that, episode 398 with Michael Hallsworth is done. Join me Friday for a brand new episode with Max Maube of thinks, insight and Strategy. It's going to be a lot of fun. You don't want to miss it. Until then, thanks again for listening and learning with me, and remember to be thoughtful.

Thank you for listening to the Brainy Business Podcast. Molina offers virtual strategy sessions, workshops and other services services to help businesses be more brain friendly. For more free resources, visit thebrainybusiness.com.