TL;DR

• Using Kogenta footfall volumes to analyse where commuters are coming from.
• Bringing context to this data by enriching it with Kogenta Contextual Indices (KCI’s).
• Profiling TfL stations based on footfall trends across the week and using those insights to plan for the traffic they can expect.
• Specific use case analysis, with the case study of a Fulham matchday, allowing us to understand where fans are coming from.

Coming and Going! – Understanding commuter patterns across London

The idea of ‘working from home’ was arguably quite a foreign notion a few years ago. In the post Covid world however, it is now almost an expectation within employment. With it, we have seen many changes to the working calendar. Here in the Kogenta UK office, based in London, we have a flexible, hybrid working schedule where we are in the office typically three days a week. We thought it would be interesting to use the footfall data we have at our disposal to attempt to analyse how these ‘working from home’ trends vary across the city. This set us along the path of analysing footfall trends across the week, providing us with some fascinating results.

Not only can our mobility footfall data inform us of footfall totals across the city, but we can also start to analyse patterns of where these commuters, or any visitor type, are coming from. As you can imagine, this comes with lots of applicable use cases, not least for London’s leading transport provider; Transport for London (TfL). The value of footfall data cannot be overstated, especially in the upcoming cookieless world; Kogenta’s aggregated and anonymised data provides an actionable solution to understanding, and bringing context, to footfall patterns. At Kogenta, privacy always comes first.

In this blog, we dissect the movement patterns into TfL stations across the week, discovering and profiling stations based on their footfall volumes. The movement patterns are proprietary geo-contextual data layers that leverage aggregated carrier grade mobility data from our partner CKDelta. In this example, we only ever work with non PII data. From this data, we create aggregated ‘multiplier scores’ with a baseline of 1, indicating the typical volume of footfall into a location. Any score above 1 highlights more than expected footfall, and less than 1 lower volumes.

Commuter Patterns

The initial catalyst for this blog was understanding the typical commuter patterns into London Bridge, where our Kogenta UK office is based. We wanted to understand the best days for us all to commute in, when the public transport was less busy – nobody likes being stuck on an overcrowded train in the morning!

Using the Three UK data, we set a Point of Interest (POI) at every TfL station to understand the amount of traffic passing through. With this, we can also obtain the aggregated Output Areas (a geographical location more granular than Postcode Sector) that people are travelling in from. The graphic below shows the patterns of footfall into London Bridge for our sample week.

We enlisted a traffic light system to highlight typical footfall into the London Bridge station, where red indicated up to the average 1x footfall, amber 1x to 5x and green 5x plus! This enabled us to create hotspots around where people were visiting from. As expected, with London Bridge being a central station with connections in all directions, we have footfall from all over. With these hotspots we can then overlay any of the 400+ Kogenta audience segments to understand what type of demographic is travelling into the station.

Overlaying Kogenta’s Residential Demographic data, we find an over indexing of 164% for 20-34 year olds coming into London Bridge, highlighting the number of Young Professionals that work in the area. Additionally, we see over indexes on Social Grade AB and Household Income, indicating those working here are likely paid well for it. To provide even more context to this demographic profile, we join Kogenta’s Household Expenditure data on top of this and see an over index of 63% for both eating and drinking out – this makes sense with all of the post-work drinks and lunches out! The enrichment of this Kogenta data with the mobility data brings context to the footfall, bringing the data to life. Out of Home Advertising providers love this kind of detail as it gives them great confidence when determining the best locations to target a campaign at.

Furthermore, the line graph on the dashboard highlights the sum of footfall across the week, with the seven points indicating Monday through to Sunday (left to right). As we expected, we see peaks on Tuesdays and Thursdays, arguably the most prominent office working days. There’s even an acronym for those of us whose office days are Tuesdays, Wednesdays And Thursdays! Best that I not disclose that here, though. There are clear troughs on the other working days, but most importantly the weekend, where we obviously see significantly less commuting office workers coming into the city. We can support this at Kogenta, as I write this in the office on a Friday, and you can almost hear a pin drop!

Leisure Patterns

To see a contrasted TfL station, we next analysed Covent Garden. We saw Covent Garden as a leisure and entertainment stop, rather than a commuter station like London Bridge! This was seen as a good control station as we would expect to see a lot less office workers, and potentially more impact on the weekend; the graphic below shows our findings.

Unsurprisingly, we see less of the commuter trend in the footfall totals here, but interestingly enough, a peak on Friday and Saturday and no significant drop on Sunday. As Covent Garden hosts various leisure activities, food and drink spots and tourist attractions, increased footfall over the weekend makes complete sense! After a long work week, people from all over come into Covent Garden to enjoy all the activities that come with it. Additionally, we see a weekly low on a Monday, indicating that travelling into the city first thing after the weekend is not top of everyone’s priority list!

Bespoke Patterns

As a company working in the advertising space, we get asked all the time whether we can profile audiences travelling into venues, whether this be sporting events, music festivals, retail areas etc. In the sample week, we saw that Fulham FC played a home game on the Friday night and we wondered if this saw spikes in the footfall across relevant stations surrounding the stadium.

This graphic shows the expected trends, with weekly highs for footfall at Putney Bridge and Fulham Broadway stations, alongside a Friday plateau in Hammersmith – something, as shown from our previous analyses, we wouldn’t expect for a typical TfL stop. It is worth highlighting that this is not tracking footfall into Craven Cottage (the Fulham FC stadium) but only the surrounding stations, hypothetically though we could do exactly the same analysis but with the stadium as the POI.

As you can clearly see, footfall data when enriched with geo-contextual data provide powerful insights that support all sorts of interesting use cases for the Advertising industry and also great inputs into planning and route optimisation for Transport operators. Better decisions through data. That’s what Kogenta is all about.

TL;DR

• How one can use a data-driven approach to solve issues in identifying a target audience.
• Considering how the Age UK charity may use such an approach to best allocate their limited resources.
• Highlighting how the Kogenta Contextual Indices (KCIs) can be used to maximise the efficiency in which this task is completed and promoting the brilliant work charities do for our community across the UK.

Can data help eradicate the Loneliness Epidemic?

How data can be used to support charities and their deployment of resources

Kogenta’s wide range of demographic resources are usually used to obtain insights on audiences for advertising purposes. In this blog, we show how that same data can be applied to support charities in identifying their target audience, allowing them to better allocate their resources.

The COVID-19 global pandemic promoted isolation more than ever before, and since then statistically, people have become lonelier than ever before. Unfortunately, this is especially true for those later in life, with the UK charity Campaign to End Loneliness saying that it estimates over two million over-50s in the UK will be living with extreme loneliness by 2026. This ever-growing phenomenon has been termed – the ‘Loneliness Epidemic.’

ONS data indicates that those most likely to feel loneliness more often are people in poor health or who have conditions they describe as “limiting,” as well as those that are living alone due to the fact they are either single or widowed. The mapping above demonstrates hotspots in the spread of people over the age of 90 in the UK, statistically the people most likely to be lonely.

Patterns emerge in the data, where larger cities are more sparsely populated with elderly people, and generally areas where this populus over indexes are by the coast. The district of Eastbourne is a notable example of this, falling in the highest category of the subgroup of resident demographics, as shown below.

Adding the additional layer of Age UK data, we can see that they have successfully highlighted this risk of loneliness. Using the Age UK API, we can see that Eastbourne has multiple areas where the calculated net risk of loneliness is very high and very consistent.

The town is noticeably over indexed (in other words, hosts a higher proportion of this demographic compared to the national average) in its most central postcode areas. We can assume that the organisation has clearly taken a data-led approach to dispersion of their resources, striving to focus their philanthropic efforts into these over indexing areas.

Our research shows that Eastbourne OAPs, and indeed any in greater East Sussex, have access to a host of accessibility resources; telephone befriending, home support for those with cancer, house clearance and decluttering and assistance bringing pensioners home from the hospital. There are additionally two physical centres that residents of Eastbourne town can visit; a clothes vendor and a furniture warehouse and donation centre. This is a superb example of effectively matching the services to where the need exists most.

This can be taken one step further where we can use Kogenta Contextual Indices (KCIs) to help predict and plan the future allocation of charities’ resources; we can help identify areas where there may be high demand for support but an under-allocation of support resources.

We can see here that the village of Bucknall in Lincolnshire, situated just West of Horncastle, significantly over indexes for those households with members over the age of 90, alongside over indexing for those in fair to very bad health. We can then use the Age UK API again to identify their current activity in the same region.

Whilst the Age UK data acknowledged the need for elderly help profiled in Eastbourne, there is no recognition of the anomaly in Bucknall, despite it having tell-tale signs of an aging population – just one small primary school and a large nursing house. Age UK’s services within Lincolnshire seem to focus primarily on the city of Lincoln and the town of Boston – where their primary outposts are.

Here we have used the Kogenta Explorer platform to analyse geographic regions, cross referencing it with third party data resources, to create real-world solutions and spot important oversights.

Charities do fantastic work supporting our communities with much needed services. Having a data driven approach such as the one described here enables new and more efficient placement of these vital, life-changing services, resulting in best possible outcomes.

TL;DR 

• Kogenta datasets can help Advertisers identify audiences and contextualise points of interest (POI).
• For Westfield White City, we highlight the use of our movement data to identify footfall patterns around a POI. Overlaying relevant demographics and interests of those taking these journeys helps drive invaluable marketing insights.
• This case study demonstrates the robustness of Kogenta data, with young adults, shoppers, restaurant lovers all over indexing when identifying the demographic that travel into Westfield.

How Kogenta creates insights by harnessing real world behaviour

A Westfield Case Study – investigating footfall patterns, bringing context to geography 

Kogenta has revolutionised the utilisation of a wide range of unsystematic location, movement, and demographic data by converting it into easily accessible geo-contextual intelligence. This invaluable information empowers organisations to optimise business operations, enhance customer service, minimise potential risks, and boost profitability. Through the transformation of intricate data into actionable insights, we equip our clients with the knowledge to make informed decisions that drive their businesses forward.

The Kogenta taxonomy is vast and has been amalgamated through numerous data sources from location based collections. This helps us create Kogenta Contextual Indices (KCIs), which are proprietary indices created using a wide range of geo-contextual data uniquely combined with other verified data sources. They allow advertisers to gain valuable insights into user intent, interests, and demographics.

A unique addition to the taxonomy comes from the mobility data we receive from our partner, CKDelta. This data comes directly from the Three UK network, creating anonymised and aggregated data based off of cell tower trilateration. The data can be broken down into three separate offerings:

• Point of Interest (POI) based footfall; movement into a POI
• Origin-Destination footfall; journey patterns
• Grid-based footfall; aggregated heatmaps

This blog focuses on Kogenta’s unique process to aggregate and use this movement data, alongside the KCIs, to create a new dynamic data offering providing valuable movement based insights – Kogenta’s Movement Enhanced Attributes (MEAs).

MEA: The Movement Enhanced Attributes fuse Telco grade mobility data from CKDelta/ThreeUK, with Kogenta’s best in class geo-contextual attributes, covering Demographic and Household Expenditure segments; all uniquely indexed against the national average.

The Westfield fan base

A case study is perhaps the most illuminating way to demonstrate the breadth, granularity and applicability of our MEAs. The extent to which we can examine this is emphasised through Kogenta’s bespoke polygon mapping layers, to the point where we can analyse specific POIs. Taking, for example, Westfield White City, a popular retail centre in West London, the largest in both the UK and Europe.

Well located in London, the centre is surrounded by an advanced public transport network. Although we would usually consider the local area when profiling the audience of a retail centre, when in London it is necessary to consider the fact that customers can venture from all over to commute to the centre. The map below uses the Three UK movement network, mapping the areas that show the highest footfall into the White City Westfield, highlighting the output areas that experience the most journeys into the Westfield.

Using these output areas we can then overlay the demographic attributes associated with the local postcodes. This leaves us with a general picture of the typical demographic that travels into Westfield.

Examining this, we see some interesting socio-demographic attributes that over index for this audience. We see this targets a population of 110,262 people across 44,420 households. As expected, customers are typically quite well off, over indexing by 54% for household income – in essence over half of the households visiting Westfield take home over the national average. We also see an 82% over index for the Social Grade AB employees. The number of students and young professionals was also very high, with both the 18+ age group and the 20-44 age group over indexing heavily.

Kogenta’s MEA dataset provides the ability to profile visitors into postcode sectors, allowing us to examine the people that move into the area within a 500m radius of Westfield. Thus we can examine the typical, most average visitor into that area – a sample of these attributes and by how much they over index nicely depict this:

• 62% for 25 to 44 year olds
• 37% for ‘Restaurant Lovers’
• 64% for ‘Cafe Culture’
• 66% for ‘Fast Food Lovers’
• 37% for ‘Bus Users’
• 20% for ‘Beer Drinkers’

As similar to the previous portrait, the 25-44 age bracket significantly over indexes, as well as the Social Grade AB demographic. It’s also possible to see with our data that Westfield is a popular day out for coffee lovers, parents, pub goers, restaurant fans, veggies, techies, fashion enthusiasts and flat-dwellers. Phew! It’s definitely a good thing the shopping centre caters for all these people.

Overall, it is clear the benefits that come with looking at the habits of people that travel, or ‘move’, into specific areas; with our retail area example here highlighting this.

A dive into the cost of living crisis’ impact on the general buying power of the average household across the UK.

In this blog, we show how geo-contextual indices can be used to demonstrate where the impacts on buying power have been greatest.

The impact the cost of living crisis has had on the price of chicken

Here at Kogenta, we believe in location based, geo-contextual solutions for Advertisers – the future of a cookieless world. However, we also see that the same geo-contextual data can have real life ramifications and even offer incisive, amusing and distinct insights on the everyday lives of the people of the United Kingdom.

Kogenta’s data portfolio hosts a wide range of granular attributes: KCI’s, or Kogenta’s Contextual Indices. The granularity of these attributes goes as far as considering Household Expenditure on particular food groups, for example – Chicken.

Many popular fast food chains have been through chicken-induced financial jeopardy in recent years, suffering with a lack of accessibility to poultry produce and therefore sustaining mass financial loss due to their economic dependence on the product. In 2021, popular restaurants such as KFC and Nandos, struggled significantly to keep their restaurants full of chicken. Zinger burger and wings without the zing or the wing anyone? Richard Griffiths, the chief executive of the British Poultry Council, stated that the members of his association were forced to “cut back weekly chicken production by 5-10%, year-round turkey production by 10% and Christmas turkey production to be cut by 20%.” The ramifications of the major delays in food processing were caused by a plethora of issues; such as Brexit, Covid and labour shortages. Griffiths went on to say that these issues leave the British public stranded and ‘being forced to rely on more imported food… this can only lead to a two-tier food system where high-quality British food may be out of reach for struggling consumers.’

Kogenta can see how these shortages have affected the country on a very real level. Our Kogenta Explorer Platform allows users to gain geo-contextual insights through the KCI data, leaving a clear picture of a certainly fractured society:

This image demonstrates an indexed view at the gross household expenditure on poultry products – where the national average is represented by a score of 100. Households in the lightest blue section spend up to 68% less than the national average. Similarly, the areas in the highest range indicate up to to 75% higher than the national average.

A first glance of the data evidently depicts a large disparity between the North and South. The regions with the highest household expenditure are, as suspected, predominated by London, the Home Counties, and the generally regarded as wealthier areas of the West Midlands – Warwickshire and Chesire, for example. Chicken may seem an odd choice to indicate the disparity of the poverty line and its very real effects, but upon further examination of chicken as a category of produce. Chicken historically connotes cheap, healthy, easy produce, and indeed is the main protein in the UK’s National dish – the humble Chicken Tikka Masala. Additionally, the commercial success of restaurants like the aforementioned KFC and Nandos delegates chicken as a fundamental staple of every UK household.

Using Kogenta’s data and geo-contextual mapping, it is also possible to view households in the United Kingdom exclusively by Household Income (HHI), of which we can see a similar pattern:

Here we have a correlation between the propensity to spend on chicken and areas with a lower HHI index, depicting a very similar heat map. So using the data, we can come to the probable and, unfortunately, predictable assumption that the cost of living crisis is undoubtedly hitting the poorest households the hardest.

The data we collect at Kogenta allows us to draw such specific comparisons, and even though these general assumptions may be predictable, the granularity of the data permits for a more specific analysis, picking out areas that under/over index, the extremes. This allows us to analyse and identify any surprising outliers that stand out from the national average.

Glastonbury is a world-renowned annual music and arts festival held here in the United Kingdom, attracting thousands of attendees from around the globe. It features a diverse mix of live performances from top-tier musicians, colourful artistic displays, and a vibrant camping culture, making it a celebrated cultural event and one of the largest festivals of its kind worldwide.

Glasto-goers are famed nationally for being an eclectic bunch of festival lovers, bringing in elderly ravers, infant revellers, yurt-dwelling yummy mummies and, of course, the music snobs nodding along in the indie music tents. Using our funky fresh data, we can have a detailed look at the two most recent years of Glastonbury lovers, well –  the lucky few that actually managed to get tickets!

This graphic shows the spread of Glasto goers in the regions geographically closest to the festival itself – the pink indicates areas with higher volume in 2022, and the blue from 2023

Here, we used our unique Kogenta Contextual Indices (we’ve fondly called them KCIs) to bring the vivid tapestry of these Glasto-goers alive, allowing us to understand the demographics of people who visited the festival in each year.

Despite the headliners in 2022 being teen queen Olivia Rodrigo and Glasto’s youngest ever headliner, American pop princess Billie Eilish, the demographic breakdown tells us a different story. 2022 sees a significant over indexing for those age groups over 55. Based off of the set list, it would’ve been fair to first anticipate the very opposite when first looking at the data! In 2023, Glastonbury hosted significantly more nostalgic artists, Guns N Roses, Blondie and was even honoured by Elton John’s final farewell to live performance. Your classic Mum and Dad music. Similarly, we saw the converse in those attending. The age groups for 25-29 and 18-19, significantly increased – by 5% and 2.5% respectively – compared to 2022. Are these younger Glasto fans just a fan of unironically listening to their parents’ vinyls or is there a different reason behind these unexpected stats?

In actuality, it’s likely these younger 2023 ticket holders were actually inspired by the 2022 line up and bought tickets in advance of the announcement of the artists attending. In fact, the first batch of Glasto tickets that went out months before there were even whispers of the line up sold out in under 19 minutes. It speaks mostly to the unfailing calibre of the festival and the artists it is able to recruit for Glastonbury and the faith people consistently have when buying a ticket that the line up will meet their expectations. Using this same model, we would predict that next year’s attendance generally hit the older age brackets based on the year’s just gone lineup of the all-star golden oldies – but who can be sure?

In both years, people of all social classes attend. Unsurprisingly, given a general admission ticket will set you back at least 340 quid, the social class that is most able to snap up those tickets, and over indexes by 30%, is the Social Grade AB. Whilst the average household income of these same ticket holders is at least a staggering 25% above the national average. It’s the weekend for these music-lovers almost as expensive as one week’s rent of their flats in Brixton.

Looking at all this data, it’s clear that what we can say about Glastonbury is that it’s pretty age-less and gender-less in its appeal. The qualifiers to be able to attend seem to be a spell of annual leave as well as a large amount of spare change – as well as a love for good music, of course. Festival goers evidently know that the organisers will continually secure billboard topping, show stopping, beat dropping headline acts – even despite the recent articles revealing that a lot of these headline acts are surprisingly paid much less than at other big name festivals. For attendees and artists alike, Glasto has become a power that everyone wants to be attached to; a brand name anyone who is anyone wants to buy.