- 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.
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!
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!
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.