ReThink Productivity Podcast
In this exciting podcast, Simon Hedaux from ReThink Productivity shares his insights and strategies for improving productivity and efficiency in the retail and hospitality industries. With the help of clients, partners, and the ReThink team, Simon covers everything from measuring and tracking productivity to developing and implementing effective strategies.
Whether you're a business owner, manager, or employee, this podcast is a must-listen for anyone who wants to learn how to get more done and improve their bottom line.
Here's what you can expect to learn:
- How to measure and track productivity
- Proven strategies for improving efficiency and reducing waste
- How to create a culture of productivity and innovation
- Tips for motivating and engaging your team
- Real-world examples of how other businesses have used ReThink Productivity to achieve success
Don't miss out on this opportunity to learn from the experts and get ahead of the curve with your own business.
ReThink Productivity Podcast
ReThink Revealed - Ep.5. The Data Deep Dive: Productivity Lessons from the Numbers - James
James Bolle, Rethink Productivity's Head of Insight Development, reveals how businesses can transform raw productivity data into meaningful insights that drive tangible improvements. His career-spanning mission has focused on helping organisations make better decisions through data analysis, quality processes, and contextualisation.
- Rethink's rigorous three-layer quality control ensures trustworthy data analysis
- Standardised templates and visualisations make analysis more efficient and consistent
- Productivity data provides valuable benchmarking across industries
- Partnerships bring new methodologies, including video analysis
- AI offers significant potential but requires focus on specific, high-value applications
- Real examples show how small data insights created major productivity improvements
- Three key tips: know what you need to know, identify data gaps, structure data properly
- Even small businesses have valuable data that needs proper organisation
If you've enjoyed this episode, visit the Rethink Productivity website for more podcasts and resources to help your organisation improve efficiency and productivity.
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Welcome to Rethink Revealed a podcast series from Rethink Productivity that will delve into the minds of our productivity specialists to ask the deepest of productivity questions. And I'm your host, james Bradbury-Willis, head of Business Development at Rethink. I'm a marketing and sales professional and I'm keen to get the inside story from the people powering productivity. And I'm keen to get the inside story from the people powering productivity. I know how excited you all are that I'm back for another podcast or the fan mail is causing issues at home but I won't let that stop me. Today we're joined by Bolle.
Speaker 1:James Bolle, as well as having a fabulous first name, he is also our Head of Insight Development here at Rethink. He's the guy who takes raw data and turns it into something meaningful, spotting trends, uncovering opportunities and helping our clients make smart decisions. With a background in consumer insights, client services and now leading our innovation and insight, james is also behind many of the ways we capture and analyze data, including partnering with the University of Portsmouth and exploring how video and AI can unlock even more value Outside the world of productivity. James plays in a rock band and is currently training a puppy which, depending on the day, might be the tougher challenge. In this episode, we'll explore the arts of data analysis and how businesses can transform numbers into action. We'll dive into the quality control, the tools we use, including AI, and what the future of Insight might look like. If you've ever wondered how we make sense of the thousands of data points we collect and turn them into something that actually drives change, this episode is for you, so let's get into it. Hey James, how are you doing?
Speaker 2:Yeah, great, thank you, james. Very well today. I've just had my oven fixed so I can cook dinner again. I'm very happy you could feed the family, which is always a bonus.
Speaker 1:So, james, let's get cracking, based on a bit of a little introduction, so we can get to know you a little bit better. Can you tell us a little bit about your background and what led you to the epicentre of data and productivity that is ReThink?
Speaker 2:Well, you summed up my background pretty well, james. I think my entire career has been a quest to help people make better decisions based on data, and the thing that really has inspired me throughout that is the idea that I'm helping people understand their situation a bit better and find their own way. So that started in market research. Long story short, I actually chose to get into market research because I did a statistics degree and I knew I wanted a job where I could use my skills to help people make interesting decisions. That led me into customer experience, where I worked for 10 years again with data, helping to understand why people behave the way they do. And that led me into employee experience, where I was again running surveys to help people understand their culture and how people felt about working in their businesses.
Speaker 2:And it was when I was working in customer experience that I met Sue and Simon Hedo from Rethink Productivity. Sue was a client of mine when she worked at Boots and then became a colleague, and when a role became available at Rethink kind of in 2023, it was a chance that I jumped at because I'd done some freelance work with Sue and Simon previously. Lots of interesting data in an interesting area, and we really can turn on lights for our clients so they can find their way. It's that motivating factor of helping people understand what's going on is a big win for me. So yeah, jumped at the chance then, and the thread that's gone through everything is how do you use data to make those decisions and help people understand what to do? So that's what led me here.
Speaker 1:And I suppose, from that respect, what is the most rewarding part of your role?
Speaker 2:Well, it's when we really do help people find their way.
Speaker 2:So a few examples recently we were working with a business where their managers were doing lots and lots of admin and lots of emails.
Speaker 2:So, using AI, large language models to analyze the notes that our productivity consultants had taken on the admin work and the emails that were being sent to help understand what's creating that traffic, to help them maybe reduce the admin load on their managers the chart that shows the issue for an organization. So, thinking of another client we worked with recently, showing that some of their locations were almost twice as efficient as others, and really getting to the heart of the variability in that business and helping them understand what the next step to take was or you know, when you're in a meeting with a client, the question that prompts a new way of thinking about their business those are the things that really reward me, where working with Rethink gives people a new perspective and helps them be better, and that really aligns, I think, with with with rethink's purpose of surfacing great insights so people can make positive change so, james, moving on to our quickfire questions, nice and nice and rapid answers good luck.
Speaker 2:Good luck with that. I'm never, never one to use one, one word when ten will do, okay well we'll see how we get on.
Speaker 1:We got ai guitar solo, or coffee, which one saves your day more often um, well, I don't drink caffeine, so it can't be coffee.
Speaker 2:Um, guitar solos. Honestly, I'm a bassist and I spend my life trying to stop guitars, guitarists solo, so we can get on with the actual meat of the song. So I'm going to have to say AI. I think in this case probably something that I do use day to day. Often it's a solution looking for a problem, but I think there are lots of really neat ways that you can use AI and, yeah, I'm going to say AI, thank you.
Speaker 1:What's your guilty pleasure in excel? Color coding cells, building formulas or hiding columns?
Speaker 2:uh, it's as simple as vlookups. I love them good vlookup.
Speaker 1:Is quality actually um? Which is harder to train ai models or puppies?
Speaker 2:um, well, I mean, unfortunately it turns out you can't just tell a puppy something once and then it understands it. You have to repeat it over and over. So probably puppies. But the truth is, um, I don't know, uh, about training ai models, that there are so many costs associated with ai on top of training the models. I feel like actually training AI is the least of your problems on AI. So I'm going to say puppies.
Speaker 1:I agree with you on that. Training is very difficult. So, moving on to the general discussion then, james so you lead Rethink's insight development, can you walk us through what that actually involves from a day-to-day and from a cross-project perspective?
Speaker 2:Yeah, of course. I mean. The real heart of what Rethink Productivity do is based around collecting data. Wherever that data is, wherever people are doing processes, we can go and collect the data, and we have a team of superstar productivity consultants who collect that data using our retime apps. And that's the heart of the business always has been and always will be. But then you need to figure out what are you going to do with that data.
Speaker 2:And something that Rethink is really strong on is the level of quality control, kind of the number of checks that go in on top of that data. This isn't people just kind of bashing a clock and then we send that raw data to our clients. It will be checked by the productivity consultants themselves, it will be checked by the project manager and then somebody from the analysis team will check it a third time, so we can be absolutely sure that when we've got data to analyze, it is of the highest quality. Part of my my role is participating in in that, but also because I'm head of insight development and not just head of insight, it's also thinking about how modern technology such as ai could complete some of the the narrow and routine checks instead of instead of people having to do it. So once the data has been checked, it needs to be organized and analyzed, and that's the heart of the role really.
Speaker 2:So, day to day, I will be doing analysis on client data with Jamie and Sue. I'll be working on the processes that we use to do that client analysis. So think about how to simplify the analysis routines and maybe productize it so that it gets easier and easier. And then I'll be doing client work as well. So, throughout my career, while I've worked with data, one of my skills is facilitation of meetings. So when clients need workshops whether it's process mapping, whether it's customer experience mapping, whether it's just working through the data and coming up with action plans I also facilitate that. So you know, the majority of my time is analysing data and thinking about our processes, with a smattering of client work on top of it. So, yeah, that's what I do day to day and it's you know, on a project, I'll be involved at the beginning in terms of helping understand how we're going to organise the data once it's collected and making sure the project's set up right, and then right at the very end in terms of cleaning, organising and analysing that data.
Speaker 1:Yeah, great. So I mean, I think you've already answered this question is like once, obviously, once you've captured the data, what are the? You've already answered this question is like once, obviously, once you've captured the data, what you've mentioned the key steps you take to transform it into something meaningful and actual for clients. But is there a way you and your team, is there any ways that you and your team have developed ways of analyzing data? Can you share how things like video analysis and partnerships with university of portsmouth are helping shape the future of what we do? Yeah, absolutely.
Speaker 2:I mean, you know we talked about organising and charting the data when I began with Rethink. That was done ad hoc, per project. We now have a set of templates that we use and, you know, fixed visualisations and set ways of getting the data so that we can fulfil those visualisations, which has made us more productive. But we're also thinking about, you know, how do we build and again using modern technology how do we build databases using the right programming languages, python scripts built on SQL databases and BI tools to automate all that kind of stuff. And you know we are I'm standing on the shoulder of giants here because we've been doing this for 13 years and Sue and Simon know the type of analysis you need to get to the heart of the productivity issues that the businesses are facing. So it's taking that knowledge and that contextualization that they can provide and trying to simplify and productize that so it's easier for people to get to. I mean, there may be a world some years hence where people don't need a presentation from Rethink because we've built a system that can take all of our retail knowledge, all of our productivity knowledge, and automate it alongside the data. We're obviously some way from that right now, but that's the world we're thinking about is, once it's organized, it's not just about visualizing it, it's about interpreting it and contextualizing it, and we've got 13 years of data that helps us to do that, and that's where partner organizations have been really useful. So we've run a knowledge transfer partnership with the University of Portsmouth, who you mentioned, and our colleague Rishan was our associate on that project. He's now a full-time employee at Rethink and his project was looking at how can we use some modern techniques in order to yield value from that historic data. So you could argue maybe a study from 13 years ago isn't relevant, given modern technology and given what's happened since the pandemic. But if you are a business thinking about how efficient you are or your productivity, it is useful to know what the Rethink database shows you from the last five years in terms of how other similar businesses are more or less productive and what techniques and tools they use that make them so, because it's not just knowing that you spend 10% of your time with your customers. That's useful. It's knowing how that compares with your competitors, how they free up more time to work with customers and how you can do it as well. That kind of tells the real story, so the focus is always on that.
Speaker 2:Working with external organizations gives us new ideas from academia, new ideas from other industries that we haven't thought about. And we're actually about to embark on our second KTP with the University of Portsmouth where we're looking at can we use computer vision to analyse videos of productivity processes? So we're not looking to replace our amazing productivity consultants, but how can we enhance what they do? Or how can we use video to do things that they can't do, like watch a till for the whole time the store is open, count the customers and how long they're waiting, and stuff like that. We're looking at how we can do that, and we're a relatively small business. We don't have that expertise in our organization, and partnering with people like the University of Portsmouth enables us to take huge leaps forward in those types of projects that we wouldn't be able to make on our own.
Speaker 1:And I suppose with AI advancing so rapidly, what opportunities and challenges do you see when it comes to using AI and productivity insights?
Speaker 2:Oh, yeah, I mean AI will revolutionize what we do, but it hasn't done that yet. Like, we work in quite a specific niche in terms of the types of data we collect and the types of interpretation we want to make on it, and so you know, there are, there are ways that you can train large language models to with retrieval, augmented generation, to to to build chatbots to interrogate our data, but it's going to take a bit of work to get there because some of the stuff out of the box I don't think it's going to do do the job for us because it's too too generic for what we're trying to solve. Having said that, there are lots of narrow and routine tasks within our business that we could automate using AI, and that's always on the table. And, yeah, always scanning the market for tools that we can buy and import that are going to make us more efficient. But I think the journey to full AI integration is going to be a long one for us and you know there's still huge amounts of value outside of AI.
Speaker 2:For example, you know, can we find links with our clients between how their customers feel about their experiences, how their employees feel about working there, and productivity? I don't think an organisation has effectively brought those three things together yet, and it could be huge, like if you think about investing in employee experience, if one of the outcomes of employee experience is that you save 10 seconds every time you stack a shelf in your supermarket. It doesn't feel very sexy or exciting, but that could be millions of pounds your organisation is saving every year and people just aren't looking at these types of things. So you know, we're not just thinking about AI, we're also thinking about how we can use existing data in different insightful ways Do you have, without obviously naming names?
Speaker 1:do you have any examples that you can think of at the top of your head, that you have projects where you know it seems like a small little change or a small something that we've recorded has made such a big impact? And by going through the data, all of a sudden there's this picture emerges of a quick win or low flight, low hanging fruit that can be plucked. Can you think of anything?
Speaker 2:Well, I'll give you I mean this kind of off the top of my head. I'll give you a couple of examples. One is not about quick wins, but it's about the link between customer experience and productivity. Like, we were working with a well-known quick service restaurant and we discovered that the average pace, the average effectiveness with which people worked in their stores was correlated with their NPS score, their net promoter score, so the rating of how likely their customers were in those stores to recommend going there. And we've put that down. Our hypothesis for explaining that is because better managers get their teams working more efficiently and make their customers happier, rather than there being a direct link between you work more efficiently, your customers will be happier. But that type of insight then enables different thinking in that organization in terms of, okay, well, where do we focus our time and our training? And if you're freeing up, if you're spending less time on a process and freeing up some budget for something, actually investing in manager training is not necessarily how you would think to spend that money, but it could have a huge impact on your productivity rather than on a new gadget or gizmo, I mean.
Speaker 2:I think in terms of quick wins and small changes.
Speaker 2:There are quick wins in every presentation that we do, every presentation that we do.
Speaker 2:Thinking about a sushi restaurant we worked with recently, it was taking them I can't remember the exact numbers, but it was taking them longer to make their sushi rolls than other sushi restaurants we've worked with in the past, and they didn't use any sushi making machinery in their restaurants. They discounted it because in trials, it turned out, the sushi making machinery wasn't any faster than their best sushi chefs. But guess what? Not all of their sushi chefs work at the same pace as their best ones and actually, if they could invest a small amount of money in a sushi cutting machine for all of their restaurants, it would save them tons of time. That adds up to huge amounts of money over the course of a year and pays for itself in a few months. So that's a really, really good example where going in, getting robust data on how long processes take and actually thinking about the variability you're seeing and why it takes so long, can yield quick wins really, really quickly. Obviously, quick wins yielded quickly, but it's not rocket science.
Speaker 1:It's something you can do straight away. I always find it interesting when, when you see latest technologies coming out or uh, quite often, uh solutions finding problems particularly if you go to big shows just using the data that we can capture and obviously then you analyze it james is then actually building that business case from the data, which and the not just the data, but from the, the analysis work that we do on it then all of a sudden can make a huge impact on a business. So I always find that really interesting. What you do with the team is fascinating oh, yeah, it's.
Speaker 2:And, like the you know, I would say the same thing to our clients in terms of you know, are you looking to get more from data? Like they're probably. They're probably looking at the latest AI tools and thinking, oh, that's a bit sexy, could be quite exciting. But, like, what you need to think about is where do your people spend money most time and how can you make that quicker? If you're going to think about how to get more from your data, think about what the business needs to know and define the questions and the data needs correctly. You might not need AI or new tools to do that at all. Actually, it might be that you just need to think about the data you've got slightly differently because you've not framed the questions properly.
Speaker 2:So, yeah, that's always kind of my advice for people looking to get more from their data, and we say it all the time, like in this sushi example yeah, there's a piece of technology that could help cut the sushi quicker, but this isn't a groundbreaking technology. This is something that's been around for a while. Just, you know, people didn't have the data or, if they did have the data, hadn't analyzed it in in the right way to make the right decision. So, yeah, let's.
Speaker 1:Let's focus on what you've got so without giving too many tips away, james, because we're now moving into the top three tips. So, starting at three and counting down to one, what three pieces of advice would you give to ensure the data is turned into meaningful insights?
Speaker 2:can I support the question, james? Because I don't think I. I think I need to start with number one, because I went number two.
Speaker 1:Number three might not make sense, so I mean we, just you, totally revolutionize the way we do this podcast. Go for it it, James. Start at one and go to three.
Speaker 2:You know I'm not one to criticize a format, but I just it's all good.
Speaker 1:It's all good. I'm flexible enough to change the numbers around.
Speaker 2:Like I don't want to sound like Donald Rumsfeld, but you need to know what you know and know what you don't know. That's the number one tip. I mean it kind of links to what I was just saying before, Like if you don't know the types of information you need, the types of questions you need to answer for your business, then you can do all kinds of analysis on your data but you're not actually making any difference to the organization. It's all a bit pointless. It's a bit like a five-minute guitar solo in a pop song. It might be nice to play and a few people might get it, but it's not really adding any value.
Speaker 2:So, yeah, figure out what you know, what you don't know, what you need to know, and then figure out what data gaps you've got would be tips one and two. And tip three is, once you know those things, really focus on how your data is organized and how it's structured for analysis. It's not sexy, it's not exciting, but you but if your data is all over the place in different databases, not properly integrated, it's difficult to find, then it's valueless because you're not ever going to be able to analyse it effectively. So, yeah, counting back down, structure and organise your data in light of the gaps that you have and what you need to know. Those are my top three tips Nice.
Speaker 1:I think that last one particularly is people probably don't realize that they've gathered lots of useful data already. It's just how it's been organized and where it is just makes it impossible to then analyze it.
Speaker 2:Yeah.
Speaker 1:And even in.
Speaker 2:You know, in every business, if you're listening to this and you you work for Starbucks, like, clearly you've got loads of data and it will be all over the place. But if you work in a, if you run your own coffee shop on on the on the high street in your town, you've got one branch, you've still got loads of data and it's still probably all over the place and you can't make the most of it unless you've thought about what you need to know and how over the place, and you can't make the most of it unless you've thought about what you need to know and how. How can you structure it to answer those questions? So that would, yeah, that's my advice for everyone no thanks, james.
Speaker 1:That's some, some useful and really interesting points you've come across there and that that wraps up rethink revealed. Can you think how emotional are you feeling right now?
Speaker 2:I'm very emotional. I mean, I'm a bit worried that I've done my normal, which is to give five minute answers instead of one minute answers, but I've enjoyed it very much.
Speaker 1:No, it's all good man, it's nice to speak to you and, yeah, we'll catch up with you very soon, take care. Thank you, james, all the best, bye-bye. Well, that's it for Rethink Revealed. I hope you found, like me, you learn something new. You can find great podcasts from rethink productivity on our website, which I'll link in the show description along with the music we use today. I'll hopefully catch you again soon for the next episode of rethink revealed. Until then, bye, bye.
