Joel: The value and importance of transaction data is often underlooked and underscored.

It is an incredibly rich data set that helps us understand exactly how consumers do spend, but more importantly, how they go about those spending decisions.

When we think of the transaction data that’s derived from all of our Mastercard credit card transactions, it helps us truly understand not just how much a consumer might spend, but where they spend? How far did they come to make that purchase? Were they close to home, or was it more of a transient purchase they did on their travels? What types of merchants do they seem to have affinities for? Where do they spend more of their discretionary spend than not?

It can paint a complete customer journey and tell us an incredible amount of information about not just where people spend, not just how much they spend, but all of the other context around those decisions that help shed a lot of light on the types of questions and hypotheses that we know that the councils and the public sector stakeholders are looking for.

Conrad: Following on from Joel’s point, a single data set often only tells part of the story. So rich though we know our mobility data is and all of the insights that we can offer about how many people are visiting an environment and where they come from and how long they stay, that’s really just one lens and to really understand how a space is being used, what the real potential for that space is, you really need to combine it with other data sets.

Data quality is absolutely essential. We’re acutely aware that there are policies and initiatives being based on the products and services that we offer to our customers in the public sector and those have societal and economic impact. So it’s incredibly important that you can rely on the underlying data that’s providing those insights. So clearly, Mastercard is a fantastic partner for that. They have the quality of the data and the breadth of the data that we know is so essential to provide that. That consistency is what was critical when we decided to work together.

Joel: Just to add that, that combination of footfall and purchase data, it does help tell the whole story. You can imagine that if you just had one view versus the other that you might understand that people are visiting different locations, but they’re not making those spend decisions, or conversely, they’re making spend decisions but you don’t know what other types of journeys and visits that they’re making.

If you don’t combine those two things, you could put yourself on a path to making some very misguided decisions and really relying on work, as opposed to the veracity and strength of that data set.

Gary: Data is absolutely crucial because it drives evidence-based decisions which leads to the best possible outcomes. It allows us to document and learn from our actions, helping to minimise mistakes in the public sector, particularly where transparency and accountability are paramount. Data enables us to justify our decisions to the public that ultimately we serve.

It’s really key that we get that right.

Mark: Hopefully that’s demystified a little bit what footfall data is. The key bits that really stand out as being impactful and how that can start to help you in your sector and work towards a solution with you and your initiatives.

What we’ll quickly do is we’ll move on to start to think about the applications of it and where the value is. This section will cover how this data can be used to help secure funding, to justify investment and initiatives. Local councils and BIDs, often require clear, actionable insights that support those applications and good insight into the why, the problem, the issue.

It has to demonstrate the potential tangible impact of any public spending on local economies and urban development.

If the objective or the task at hand is about driving visitation and driving change in spend, then it naturally follows that the data that showcases this is clearly vital and that does feel to be a core component of much of the development initiatives that are happening out there.

However, what a lot of our attendees will feel today is understanding how residents and visitors interact with public spaces and retail centres is a challenge. It’s got a cost to it. It’s problematic; accuracy, timing, all of those things combined as well as constrained budgets. It’s a tough data point to acquire, but a really impactful one if we can.

Today we want to give you that intro into how it can be used and that footfall and consumer spend data when you can access it and you can apply it, it will give the most accurate picture of your communities and showcase real world behaviour. It’s those near real-time insights that give you the targeted validation that you’ll need for your change led initiatives.

Joel: It always comes down to the quality and simplicity of the use cases that you’ve defined. I’ll give you a couple of real world examples. One is around Wembley Stadium in the United Kingdom, where the hypothesis was that the traffic going to some of the events at Wembley were not putting as much of a burden on the public transit system as was expected.

We used Mastercard data to actually look at all of the activity that event goers who are off to Wembley to watch the football game were partaking in. Were they coming in the night before and staying in a hotel? Were they spending in restaurants before the match or were they spending in the pubs after the match? Were they in fact coming in public transit or were they perhaps travelling and taking private parking?

When we looked at all of those elements of the way people were moving, the way people were spending, it did highlight a few gaps on the public transport, infrastructure, where larger pockets of consumers were coming from for that particular event and provided for some additional investment of planning longer term to support that. On an economic development front a similar situation has arisen where Mastercard and footfall data plays a role.

This happens to be not specific to the UK, but through the World Economic Forum we’re looking at a project today we’re working on, where a football club has actually picked up and left town and they’ve gone. They’ve left a real hole, basically in the city centre.

The study is to very quickly leverage the data to identify what other merchant opportunities should we bring to that core that will bring back that same segment of customers, help stimulate similar amounts of spend and effectively create a bit of a new economy where the football economy has picked up and left. The data is telling us very clearly who those consumers are, what they’re looking to spend on, the times of days, the days of week and putting all that together is helping create a revitalisation plan for that downtown core.

Mark: There are some great examples there, thank you, Joel. It certainly touched on some of those bigger projects and experience that Mastercard have already, having dealt with and partnered with across the world. There’ll be some more examples as we go through this webinar.

Joel: That visibility word in the context of data provides for two related topics. One, the visibility of data should be a constant. You’ve pointed that out, as did Conrad. That’s a critical point because sometimes we don’t know what’s going to happen when it does happen.

We’ve done some studies with some of the councils in the United Kingdom, anticipating heat waves and understanding what impact the heat wave had on merchants and in different parts of the community.

The ability to visualise or have visibility down to more granular segments of the council’s more granular geographies, does become more critical. It also becomes much, much more difficult.

The ways in which we think about how to treat the data and how to provide the data at a very granular level, are carefully designed. They provide incredible power where I can now start at the state or region level, understand some of the dynamics that are going on from a spend, a footfall, a growth opportunity perspective. Then I can drill down to the council and I can drill down to the high street and look at what’s going on within a few blocks of the high street. When you start to analyse what’s possible at that level and truly understand it, the impact on your decision making, your council planning, all of those things are dramatically impacted in a positive way.

The only other comment I’d make around visibility, is really tied to the concept of visualising the data as well. We all have far too much data to consume today. That is a fact.

The only ways in which we can as human beings properly interpret the data, is if two things exist. Number one, some context around what it is I’m trying to extract from that data. That’s why I lean on the comment around use cases being well defined. The second is the opportunity to visualise that data so that I can interpret it effectively and typically in real-time.

That doesn’t necessarily mean a whole bunch of charts and graphs and dashboards all the time.

It can mean different ways in which that data can be visualised based on the best ways in which you can interpret that data. The whole visibility question is one about granularity, but it’s also one about how do you interpret the data at whatever level of granularity you may be at and the visual cues become absolutely a must.

Mark: That was a great point around data complexity and data volume as well. Lots of departments are juggling lots of data sets and the goal is to find that commonality and that buy in and that belief; something that is really linking into what is actually happening on the ground. As soon as you start to leverage that in the initiatives and strategies that you’re undertaking and implementing, that traction just builds and builds.

Conrad: The opportunities and building on Joel’s point, is to use the data to look at a much longer term view. Often the challenges that we see is that people come to us and say “Right, okay, I need this data to validate an idea that I’ve already formed or an initiative that we’ve already undertaken”. That’s completely valid, of course. But what tends to happen through the course of that exploration of the data, is that the data itself reveals where the opportunities are. The data itself reveals, these are the changes you could make, this is what can happen. In terms of that practical application of the data, we advocate that you start much earlier in the process because it plays a part in informing what you could or should do.

Secondly, it’s also the long term changes. The types of behaviours that we’re talking about measuring, sometimes there’s an impact and sometimes there’s an immediate impact of putting on an event or those kinds of things, but often the customers that we’re working with are trying to use those events to generate long-term change. So “I’m making a new audience aware of my space. I’m making them aware of the facilities in my town and all those things.”

Now, there might be a short term sugar hit of okay, I’ve drawn a few people in to do that. But really what you’re trying to understand in the long term is, are those people now returning, now that they know what’s here or now that they’re aware of what’s possible? The data types that we’re talking about today are absolutely ideal for measuring that.

It’s not just about the volume of the footfall, so when we talk about footfall data and mobility data, it’s also revealing where those journeys originated from. For instance, so you can identify these are postcode sectors which are now using our town but weren’t using it before we put these initiatives in place. In terms of using it to unlock funding, we’ve got some great examples.

The one I’ll pick on is a team in Manchester that had been trying for about six years to get a facility built in one of their parks and unsuccessfully for that period. By applying data to build the business case as to ‘we have the evidence to show that there is the right audience, the type of audience that generates the demand for this type of facility’, that enabled them to unlock a million pound plus funding for that facility. So a very modest investment in data enabled them to unlock that funding.

Great. Thanks for that, Conrad.

What I really liked in that is the hint towards this type of data being something to think about with those embryonic stages. It’s definitely a foundational component for a lot of these initiatives and projects that we’re seeing, especially talking to Gary in and around this, he talks that way, too.

If we think about where we’ve come so far in this webinar we’ve talked about an introduction to footfall and spend data, and we’ve talked about how it could be that almost semi-truth point to unlock funding.

We’re now moving into another benefit of having access to this data and using it to its fullest capacity, is that it almost becomes the thread, the common thread throughout an entire project, throughout an entire initiative from beginning to end. It can continue to be used to monitor, track any impacts of any actions, non action, as well as used to validate the success of those initiatives that are being implemented and put into place. Of course, that’s spend and footfall data, which are typically the key metrics of any initiative that is happening. Having this data provides total visibility throughout your project and ensures that your initiative is backed by real world evidence.

Joel: That visibility word in the context of data provides for two related topics. One, the visibility of data should be a constant. You’ve pointed that out, as did Conrad. That’s a critical point because sometimes we don’t know what’s going to happen when it does happen.

We’ve done some studies with some of the councils in the United Kingdom, anticipating heat waves and understanding what impact the heat wave had on merchants and in different parts of the community.

The ability to visualise or have visibility down to more granular segments of the council’s more granular geographies, does become more critical. It also becomes much, much more difficult.

The ways in which we think about how to treat the data and how to provide the data at a very granular level, are carefully designed. They provide incredible power where I can now start at the state or region level, understand some of the dynamics that are going on from a spend, a footfall, a growth opportunity perspective. Then I can drill down to the council and I can drill down to the high street and look at what’s going on within a few blocks of the high street. When you start to analyse what’s possible at that level and truly understand it, the impact on your decision making, your council planning, all of those things are dramatically impacted in a positive way.

The only other comment I’d make around visibility, is really tied to the concept of visualising the data as well. We all have far too much data to consume today. That is a fact.

The only ways in which we can as human beings properly interpret the data, is if two things exist. Number one, some context around what it is I’m trying to extract from that data. That’s why I lean on the comment around use cases being well defined. The second is the opportunity to visualise that data so that I can interpret it effectively and typically in real-time.

That doesn’t necessarily mean a whole bunch of charts and graphs and dashboards all the time.

It can mean different ways in which that data can be visualised based on the best ways in which you can interpret that data. The whole visibility question is one about granularity, but it’s also one about how do you interpret the data at whatever level of granularity you may be at and the visual cues become absolutely a must.

Mark: That was a great point around data complexity and data volume as well. Lots of departments are juggling lots of data sets and the goal is to find that commonality and that buy in and that belief; something that is really linking into what is actually happening on the ground. As soon as you start to leverage that in the initiatives and strategies that you’re undertaking and implementing, that traction just builds and builds.

Conrad: There’s two parts to that question. There are the individual metrics that you can measure with mobility. For us amongst the core ones of those are footfall itself, so the actual volume of people visiting an area. We talk about catchment areas, so that’s an analysis about where those journeys are originating from and what’s the home location of those visitors?

There was a question that came up in the chat actually about tourism and I’d like to point out that the power and the value of this data is that you can look beyond your own geography and understand what portion is domestic or coming from overseas. So if you’re in an area which is high in tourism, there’s a really fascinating lens about the behaviour of the tourist population that you can narrow down to. As well as catchment, you can also look at dwell times. So, how long are people spending in those spaces and how frequently are they returning? The power and the beauty of mobility data is it enables you to link to the demographic and socio-economic descriptions. Not only are you looking at the volume of visitors in the area, but are they the type of visitor and the type of user that you’re seeking to attract that area?

You can think about each of those metrics individually and certainly they each have tremendously powerful insight. As Joel was talking about, the combination of spend and mobility data provides you one lens, but indeed, the combination of multiple types of mobility metrics also reveal less than obvious insight. Some of the questions we often get is ‘I ran this event and it looked really busy and there were loads of people in that space’. Maybe that particular area had very high footfall, but did they stick around? Did they visit the rest of the town while they were there? Did they ever come back again? We often caution against just looking at footfall and most of our customers are seeing beyond this now and they’re seeing that footfall is the starting point. It’s a useful metric, an indicator to know where to start the conversation or start the analysis, but it certainly doesn’t stop there, it goes way beyond that.

And mobility data provides you the power to do that.

Joel: It’s not just understanding how that spend behaviour occurs within the council. It’s also making sure you’ve got some visibility and even the opportunity to benchmark data to some extent, as to how that spend is occurring, perhaps in other councils.

So if you use that tourism example, I may be delighted that we’re seeing 30% of our tourism dollar coming from Canada, where I happen to sit and that’s a wonderful thing that the council is celebrating.

But the council may also really need to know that the Canadian tourism dollar is actually being spent at a rate of three X over in Manchester, and I’m still leaving some of that Canadian tourism money on the table. This is an opportunity for me to rethink, ‘how do we continue to build that tourist presence, that tourist stay?’ The frequency, the length; all those good things. I may think I’m doing great but I’m actually leaving some money on the table as those tourism dollars go next door at a perhaps a greater rate.

Conrad: I’d just like to build on that point. That international angle to that but there’s also a very local lens that you can apply to that.

Understanding not only your own area, but also looking at your peers, your competitors, the region that you sit within. This benchmarking opportunity that is offered here is tremendous, because it’s about the context. You might think things are going badly or you might think things are going really well.

It’s very difficult to understand that in isolation. It may be relative to the past – good or bad, but what these data types are offering you the ability to do, is to see how that performance measures up in the context of what’s going on in the broader picture. So perhaps you’re doing better than you think, or perhaps you’re not maximising the opportunity that your neighbours are and you can look to those neighbouring areas to find clues about the strategies or the policies that they’ve put in place, which are enabling them to make the most of that. Benchmarking is a really, really important one.

Joel: We usually summarise it with a single question, which is the pursuit of ‘is it me or is it the market?’ As we see fluctuations up or down, it’s critical to know ‘how much of that is because of what’s going on in my own council, in my own backyard, as opposed to some more macro activity that might be further out of my control. It’s critical to know that.

Gary: It’s important that we look at control areas where we’re not having an intervention to compare and that might be within a local authority in itself. You’re comparing one area where you may not be implementing a measure against somewhere where you are and that’s absolutely key to benchmark and assess against control areas. You can use this data to do all of those things.

Mark: That’s a really good build just bringing in benchmarking at that point. The other bit I’d draw out in this conversation, it started with catchment, and then we moved to international, but the key thing there is connections; from the micro, to the local, to the regional, to the national, to the international. Having a bit of a data insight into all of those levels can prove really key because while some things rise, some things fall. But if you’re just looking at your particular zone, you may not have that full, eco system available to you. The bit I really relate to was match day around the Olympic Park. Obviously very busy with football coming in but as a resident that was not going to be a location I wanted to go towards at that particular time. So you can really see how understanding that wider catchment piece and historical trend can really start to pinpoint the true impacts and benefits of events, initiative and everything else that’s out there.

We’ll keep moving forward.

Gary: In Hertfordshire, we have a historic market town. It’s a traditional high street that faces the usual challenges that a lot of high streets face, with issues like congestion, pollution and ultimately it’s dominated by the car, which does affect active travel. In Hertfordshire we’ve got key policy objectives where we want to try and promote active travel, for obvious reasons such as, healthiness and well being and getting people out and about.

In addition to that and as part of our response to the covid pandemic and social distancing, it was an opportunity to apply this scheme in this town in Hertfordshire, that would also promote walking and cycling.

We closed a key high street to traffic on weekends and whilst this was mostly supported by residents, there were naturally some concerns about the economic impact of the scheme, particularly by certain groups. Data on footfall, dwell time and spend was absolutely crucial. Also talking about the importance of having lots of information, it was really beneficial to have data around journey time, traffic flows and public opinions as well. So, it all worked together to help us and it was crucial in understanding the impact of the intervention and also facilitating transparent discussions with those key stakeholders, including residents.

Although external factors like the economy and weather and various other things can influence results and that’s an important point, we were able to show that the high street and businesses were not adversely impacted directly by this particular scheme. That was a real key lesson that we learned from this exercise through the data and how we applied and understood that.

The evidence, especially when combining multiple data sets, helped reassure stakeholders that the scheme’s impact wasn’t as negative as initially feared. That was also very good and so we could actually apply that back to the residents and those stakeholders.

It’s also reassuring to see that this analysis that we did this exercise, corresponds with other pieces of evidence out there that support our findings.

The pedestrianisation does generally tend to improve spend in the local economy. That’s not to say that’s always going to be the case in every situation, but this data can help us understand and unpick that.

Mark: That’s been great to understand how you’ve seen success using this data. Now is probably a good time to probably draw on this a little bit more and elevate above that.

Gary: One of the biggest challenges for me and it may be similar to those in Hertfordshire, is understanding public opinion and perception, which can be really insightful. It’s really useful to tap into that knowledge, but it can also have certain biases and influences. Data helps by providing an objective view especially when multiple data sets align and start to tell that story. It’s important for analysts to remain impartial because we too can have inherent biases, particularly from local authorities, where we’re trying to achieve an objective and we also need to be objective ourselves.

Additionally, data shouldn’t be used against parties, but it should be used to gain buy in and help others. For example, businesses can use trend data to adjust their strategies and benefit from these insights because ultimately, we’re all here to try and achieve the same thing. So it’s about working together, sharing the knowledge, sharing this information and trying to achieve that common goal.

Mark: I love the phrase buy in and shared goals. That’s absolutely something that you’ve been able to gain a lot of traction and success with. If you can create that partnership approach by proving application and capabilities, it creates a really powerful team and gets you that momentum that you need to keep going. Great example there, thank you.

We’re going to move into our final section now and we’re going to make this more event focused. Whilst we’ve talked about long or mid to longer term initiatives, if there are large scale events, then clearly there’s an opportunity to be assessing the impact and the benefits that spend and footfall data bring. So, what we’ll do is we’ll jump over to Joel, he’s got a great case study here of a Taylor Swift concert and the impact of the Swift lift in boosting local spending.

Joel: It’s a fun example of the application of some of this data. Obviously there was a lot of speculation and consternation about how much financial impact a Taylor Swift concert might actually have on a given town or council and there was some debate as to whether it was really that big of a deal. We did do all of the data crunching and analysis around all of the venues the Taylor Swift that delivered worldwide and the data and the trends were unequivocal that she is moving economies.

Some of the interesting highlights to note, on average, the spend over the course of the two days before, the two days after, any given Taylor Swift concert at restaurants on average went up 68%. The highest was 172% in one particular market. Hotels saw a jump of between 42% if they were within three miles of the venue and a little bit less if they were further out at 32%.

Some real impact there. What we also looked at, though, was some of the indirect, economic impact, not just what’s going on in the obvious industry sectors that we could easily measure and identify the positive lift. For example, what was the actual economic impact relative to the various jobs that were created to host that venue or the impact and opportunity that was driven through the local shops that were able to sell tour memorabilia.

There were a lot of data points.Then what we had to do of course was to take away, what is the story? What is the point other than she has an incredibly huge impact on any given economy when she comes to town. There were a couple of interesting ones. One was making sure that we all understand the overall purchasing power of experiences, the demand consumers have and the amount that they will invest in an experience over perhaps large ticket durable goods, to the extent that in 2023 for the duration of the Taylor Swift concert, in all the markets she visited, the spend on large ticket durable goods, actually notably dropped.

We think that was due partly to inflation, but also partly due to the savings and upcoming opportunity to spend on that experience.

There were a lot of really interesting learnings that came out of the study. It did come across initially as a fun, interesting tidbit, but there’s some real lessons and learnings to uncover that can drive additional planning and thought around building experiences and creating those economic impacts around different experiences.

Mark: I thought that was a great example. Some of those numbers are eye watering on the Taylor Swift use case and again that just amplifies the power of this data. Looking at it outside of the core initiative event strategy, there’s this wider thread, this longer thread that runs through it. This data is that common theme that brings everything together.

What we’re going to do is bring Conrad in for just one last question on that point about benchmarking. Is there anything else you’d like to build on the benchmarking case, or if we want to take that into tracking consumer engagement before, during and after implementing initiatives.

Conrad: Certainly. Definitely to build on Joel’s point. Sometimes there is an obvious effect which is in your headline metric and sometimes there isn’t right. That’s also a fascinating area to explore. So, often we get customers that come to us and say, ‘well, I did this thing and I expected this, so can I have some data to demonstrate that?’ And sometimes the effect that has been felt is not in that metric right, but there’s nearly always another metric which has been affected. For instance, maybe it didn’t get the bump in volume of people, but maybe the people who did come spent a lot longer there than we expected, and maybe therefore spent more.

Again I come back to this point about combining these data sets in order to find that insight or finding this long term trend question.

‘I didn’t have the effect on that particular weekend or day or festival week that we expected. But I can see that it was actually the beginning of a change. It was the beginning of a change in opening it up to a new audience or a new type of behaviour. Benchmarking can be about ‘how do I compare to my peers?’, but it’s also ‘how does this compare to the past and what’s changed?’ You often need to be thinking about the data and how you explore what the data tells you over a much longer time period than you might initially think.

Mark: Maybe these are seasonal so they’re closing down outside of those summer months. Is there any way that football, spend data, benchmarks, catchment trends and flows could give them some confidence about staying open in winter months or extending that summer holiday period?

Conrad: The thing I come back to is two points there. One is the benchmarking, so maybe you’re closing down at the moment, but maybe there’s an area that you’ve observed that doesn’t close down that has a similar profile to you.

Maybe they’ve put some facility in there, or they have some attraction which is open all year round. So is that successful or not? Is that attraction having a positive or a negative impact? You can look to the data in other areas and it can give you clues as to if you were to do something similar, would it be worthwhile?

The second point, particularly for tourism, is that mobility data offers you the ability to see what the origin of those visitors are. For instance, say there is a large US contingent who visit a set of tourism sites in your area and you’re reliant on this US traffic, but your thought is, does that die down in the winter?

You can use the data to see if the surrounding regions are still seeing a meaningful proportion of that type of visitor that drives your traffic. This will give you clues as to whether, if you were to stay open, what your expected visitor rates might look like. You can use the mobility data to answer and understand those questions.

Joel: Benchmarking is great to see what else is working in other councils in other rural areas that’s addressing seasonality.

You could also look to see what other activities those consumers are doing during the off-peak seasons. For example, there may be rural areas that are popular for mountain biking, and by reviewing the footfall and spend data, you can see that those consumers also enjoy skiing or sledding.

We see each individual credit card holder and we track each and every one of their purchases and obviously their footfall activity, so we know that those same people have a higher propensity to come back if some of the activities they enjoy are introduced in that particular community.

You can see what else they do, in addition to what else might be going on in neighbouring councils.

Gary: From a local authority perspective, rural areas are absolutely key and they’re also very sensitive to uncertainty and to change. So it’s important that we really understand the impact of various different things that are affecting those rural areas. The beauty of this data, the footfall, the spend data, is that it’s easily transferable. It’s fair to say that generally you don’t have as much data in the smaller rural areas as a local authority that we do in some of the bigger towns and cities, so we’re more reliant on other sorts of data, to really understand and unpick exactly what the impact of those events are.

This data can really go into a granular level in a rural capacity and really help us understand those same trends that we can look at on those big events. It is very transferable to unpick those trends in rural areas as well.

Joel: You can also very quickly provide some segmentation insights and draw people based on further segmentation as well.

I was just thinking my wife and I often visit the Cotswolds, and our favourite spot is Bourton-on-the-Water. But why aren’t some of the other areas within the Cotswolds marketing to us to come in and stay with them? We consistently go back to the same place. That’s known data that’s available to councils and would help drive marketing and tourism initiatives that could introduce us to some of the other beautiful spots throughout the Cotswolds.

Mark: This data can help bring your audience to the table into your team. You may have a very successful approach to summer, but if you think about a strategy for those other months, you can use the data to see who you can pitch to and start investing your marketing spend in different locations and that can be domestic or international. Knowing who has the willingness and the propensity to visit those areas, those rural areas, is a great start. It derisks everything.

Gary: I suppose that’s the challenge in itself. You can never predict the future and what events are going to happen. We could never have understood or imagined that we would have had a pandemic a few years ago and what data we would require at that point. It’s answering the question in reverse. It is very difficult to position yourself to get the ideal bit of data, but it’s about getting prepared as early as possible. That’s absolutely key.

Having a plan and a strategy, to try as best as possible to understand what information you need in order to measure success. For me, that’s the absolute key, and that’s where I’ve been a big advocate around monitoring and evaluation. It’s really about planning, understanding and measuring what success looks like and having that agreed approach. In terms of biggest learnings, it’s that no single data set is perfect. Every single bit of data has its limitations and knowing that is the case and working with that is absolutely key.

In terms of footfall data, that means using it in the right context alongside other data sets, to get a comprehensive view. For example, you’d want to complement it with dwell time and spend data because it just gives you a much more rounded story in terms of understanding the overall impact.

It’s also very important to look at data in equal periods. The use case that I referred to earlier, I found the sweet spot being generally three months of data. So if you’re reviewing an initiative you look at that three months before, and then you compare it to the three months after, so you can remove that bias of external influences like seasonality. Ideally, you’d want to measure that against the same period in the future so you can really unpick it.

In terms of the future it is really difficult. So I don’t actually know what we’re going to need data for in what way in the future, because we’ll encounter that as and when those situations arise.

Mark: You could add to that, through your networks, colleagues and experience, you’d probably be happy to help people unpick anything, or where they see an opportunity. Certainly here at Huq, we’ve got a great team, and lots of collateral that explore potential use cases and how it would help. So you wouldn’t necessarily be operating in a very unknown space. A lot of the advice here is about having that conversation, isn’t it and unpick what the opportunity is.

Gary: It’s also about having a strategy. Going back to the monitoring and evaluation approach, really understanding from the onset, as early as possible, ‘what are the challenges?’ What is the key exam question that you’re trying to answer here and then you can move forward in terms of looking at key aims.

What is the ultimate vision and what does success look like? Then you can split it down into aims and objectives and then measurements of success KPIs. By doing that, it will help to determine what data you need to answer those questions. Having a structured strategy from the onset, is the best advice that I would give. That can then be translated into any situation or scenario.

Joel: This is a really simple one. We have only scratched the surface with the introduction of AI machine learning and it’s going to absolutely explode. So where does it go from here? We leverage AI machine learning to do two things really, really well. Number one, instead of understanding what did happen, how do we predict what’s going to happen? To do that with a level of accuracy has never been comprehended before.

The second is, if you can predict what is going to happen, then you should also have extremely tailored and specific recommendations on what to do around that particular prediction.

That is going to completely change the game.

I’m sure there’s many other things we can point to in terms of aggregation of data, and the ability to exchange data more freely. But from where I sit, the power of AI and what that means for prediction, prescription and really intelligent recommendations that are driven through those technologies, I don’t think anything comes close.

Conrad: I totally agree. The idea of looking into the future and the power of this data to look forward, that’s ultimately what all of our customers want to know. They can’t change what’s happened; they want to know what they should do next. That’s a big focus of ours certainly in terms of what mobility data can offer, as a lens looking forward. Picking up on Joel’s point on what AI brings to the world of data analytics, our customers shouldn’t have to be experts in the minutiae of how mobility or transaction data works. What they really want to get to is the ‘so what’. What should I do with my space and what does this mean for me?

We’re in a really exciting period of data companies beginning to apply those AI layers to help join the dots. We talk about the fact that no data set sits alone but what these AI layers are offering us the ability to do, is to see the unseen connection and the unseen implications of the relationship between one data set and another; the cause and effect. I’m really optimistic that we’re rapidly moving to a phase where we’re going to make life easier. As data providers we should be making life easier to local authorities, to BIDs and all of our customers, that we can do all of the heavy lifting and they can focus on making it happen.

Mark: That’s great, thank you so much, Conrad. Thank you to all the speakers. We’re going to call time here on the Q&A. We can clearly see that through the Huq and Mastercard partnership, we bring a lot of experience with local governments and BIDs across the UK. It’s great to see the power of the spend and footfall data and the difference that it’s making in the everyday world.