The counties with the most dangerous roads in Ireland ahead of the bank holiday weekend

Country Road with a mountain background

On Bank Holiday weekends we’re used to reading about people being killed on Irish roads. But which counties have the most dangerous roads?

Although Dublin and Cork have had the highest number of fatalities, does that mean they have the most dangerous roads in the country or do other factors need to be taken into account?

As with most bank holiday weekends, there is a heightened risk of driving over the next few days. This can mainly be attributed to higher volumes of traffic as many people visit family and friends and in doing so, undertake long road journeys.

The Road Safety Authority (RSA) have issued statements about taking extra care on the roads this weekend. And by looking at the numbers over the last 20 years, it’s clear the RSA are succeeding in their goal to make our roads safer. Even though the number of fatalities this year are higher than the same time period in 2015, the overall trend is that our roads are becoming safer.

In the interest of improving the safety of Irish roads, we here at Idiro Analytics wanted to shed some light on some of the details of road safety statistics that can usually be overlooked or misinterpreted, leading to the wrong conclusions.

In 2014 and 2015 the number of road fatalities in Ireland were 193 and 166 respectively. By studying the charts below, it’s easy to see how the assumptions can be made that the two most dangerous counties for road accidents are Dublin and Cork. However, these figures don’t show the full story, because there are a lot of other variables to take into consideration.

Other details that need to be taken into account are:

  • The length of road in each county
  • The number of vehicles on the roads
  • The average distance traveled
  • The total population sizes

Below you can see details from each of these different variables (note: summarized tables – not all information is contained):


When analysing all the information, we can see a clear picture can starting to form. Although Dublin and Cork may at first glance seem to have the most hazardous roads and will be noticed more in the national press, it is Longford and Monaghan that rank 1 and 2 respectively with the most dangerous roads.

Both Longford and Monaghan have low populations, low road lengths, a low amount of vehicles on the road and low average distance travelled, but it was found that they have a high proportional fatality rate averaged over 2014 and 2015.

  • Longford and Monaghan: 2 fatalities per 10,000 vehicles
  • Longford and Monaghan: 3 fatalities per 300 million km travelled
  • Dublin: 0 fatalities per 10,000 vehicles
  • Dublin: 1 fatality per 300 million km travelled
  • Cork: 1 fatality per 10,000 vehicles
  • Cork: 1 fatality per 300 million km travelled

Looking for a cause

With this in mind, we can now try to work out some of the possible causes and determine areas that may need further investigation.

Access to public transport could be one possible factor. Both Longford and Monaghan have a low number of public service vehicles (buses and taxis) per km per head of population.

Another factor leading to these insights could be found in a recent road surface survey carried out by the Department of Transport, Tourism and Sport (DTTAS) and the National Roads Authority (NRA) in 2011/2012.

The survey found that although Longford and Monaghan rank low on counties needing ‘routine maintenance’, ‘surface restoration’, ‘road reconstruction’, both counties are ranked number 1 and 2 for needing resealing & restoration of skid resistance.

We all know, from the information given to us from the RSA, that on a bank holiday weekend we need to be extra careful when travelling. And we also know that a lot of lives have been lost on the roads in both Dublin and Cork, a higher number than any other county.

But, one thing we need to be aware of is that although the number of road deaths in those two counties is high, they would not have the most dangerous roads in the country. Per km the roads in both Co Longford & Co Monaghan pose a greater risk and extra care needs to be taken.

Therefore, be careful out on the roads this weekend & especially so in Cos Longford & Monaghan.

In order to make this article more accessible, we’ve only included summaries of the overall data that we analysed. But, we invite anybody who finds an interest in these figures to contact us if you have questions or would like to discuss any part in detail. We’ll be happy to discuss the findings with the hope that the information can lead to safer Irish roads.

About Idiro
Based in Dublin, Ireland, Idiro Analytics is an award-winning provider of analytics to businesses around the world.

For an overview of Idiro’s analytics services, see our homepage

Media contact information
Simon Rees, Clients & Marketing Director, Idiro Analytics.
+353 1534 30 34 –

Mayo still to beat Dublin by one point in the All-Ireland replay

An article on predicting who will win the GAA Football match between Dublin & Mayo

In the two weeks following an All-Ireland final containing 2 own goals and a draw that nobody predicted, a trend seems to be forming among GAA supporters; Dublin underperformed and Mayo may have missed their chance. The consensus being that for the replay, Dublin will ‘click into gear’ and play ‘their’ game and come out on top.

But is this really the case?

Most seemed to think the 2016 title belonged to Dublin before they even stepped into Croke Park on that rainy Sunday afternoon, but by looking at the numbers, we found that the pundits’ confidence was unfounded.

If you read our last article, you’ll have seen that Dublin were not the predicted winners of the final, we had Mayo to win by one point. And although we admit predicting the exact score of a game isn’t really possible, we ended up being pretty close.

We had set ourselves the challenge of working out a model to predict who would be the 2016 champions (and get an edge on the bookmakers). We did this by looking at the information available to us on both the Dublin and Mayo teams’ performances over time. With key areas being goal difference between Mayo/Dublin, point differences between them, regular differences in finals, average goals and points that season, differences between average goals/points that season and the finals etc.

We came up with the prediction of Mayo to win by just one point.

Now, even if we were to analyse every single data point and statistic since the GAA was formed in 1884, we still wouldn’t have been able to predict two own goals in an All-Ireland final. But the fact that our prediction seemed to go against the general opinion of Dublin being favourites and the game ending up being so close, we thought it might be worth trying again.

The difference, this time, is that we now have more data to work with. Not only have both teams played another game that we can factor into our original predictive model, but we now have more data on how each team performs against each other.

Results of previous fixtures

Over the past four years, Dublin and Mayo have now played each other four times. The particular details of those matches play a key role in predicting the outcome of this Saturday’s match, with a higher weighting on the most recent games as they are the most relevant to each team’s current form.

GAA Football All-Ireland Senior Championship final 2016

With these extra details in mind, we were able to refine our original prediction and develop a new one.

The Idiro Analytics official prediction for the All-Ireland final replay

Mayo 1:13 – 1:12 Dublin

Mayo to win by just one point

Now there’s no doubt that the weather did have a major effect on the performance of both teams on that error-filled Sunday two weeks ago. But with the weather forecasted to be a lot milder this weekend, we should see a much-improved display by both teams. Although looking at the numbers, we still stand by our original analysis that these two teams are more evenly matched than people seem to think.

About Idiro
Based in Dublin, Ireland, Idiro Analytics is an award-winning provider of analytics to businesses around the world.

For an overview of Idiro’s analytics services, see our homepage

Media contact information
Simon Rees, Clients & Marketing Director, Idiro Analytics.
+353 1534 30 34 –

Analysis performed by Eduards Vanags

Mayo to beat Dublin by one point

An article on predicting who will win the match between Dublin & Mayo

All the Dubs and Mayo people will give you an answer, of course, but can data analytics predict the outcome of this weekend’s All-Ireland final, or more impressive yet, the score?

Predicting the outcome of a single game is a difficult task, predicting a winner of a league competition would be a much safer bet.

In a league competition, teams would play a lot of games, diminishing the impact of losses on their overall performance. And although they may falter a few times, underperform, fail to capitalise on chances etc. over the course of a season, it is usually the best teams who come out on top. But in a knockout competition, anything can happen. Which is an argument for why Leicester City winning the English Premier League is a bigger achievement than Portugal winning the Euros. In the knockout competition, Portugal were crowned champions by only winning four games, only one of those in 90 minutes and one on penalties. How much of that success was down to luck, and if it was a league style competition, would they have still won?

But let’s say we want to work out who’s going to win the All-Ireland final this weekend, Dublin or Mayo, is it even possible? The short answer, no! But let’s give it a shot anyway.

Now, some other sports (e.g. soccer) have the luxury of huge pools of data and statistics. With such sports, we can base the predictions for who will win games in the Euros and the World Cup with huge weightings on player performance rankings, and comparing performances when they’ve played against the same teams. But the GAA isn’t quite there yet in terms of individual player data. Also, the way the league is structured means that rarely do both Mayo and Dublin come up against the same teams on a regular basis (over the last four years, Dublin have played Kerry just twice and Mayo have also played Kerry twice).

What we do have to work with is the performance of both teams over time. Our data analysts broke this down and looked into key areas such as goal difference between Mayo/Dublin, point differences between them, regular differences in finals, average goals and points that season, differences between average goals/points that season and the finals etc.

By excluding any emotional bias and purely looking at the history and current form of both teams, Idiro Analytics have calculated a prediction of:

Mayo 1:15 – 2:11 Dublin

Mayo to beat Dublin by one point

Again, predicting the result of a single game is definitely not an exact science. That’s especially true with such a fast paced high scoring sport, where one misplaced pass or slip could sway the game one way or the other. But interestingly, by only focusing on the numbers and not the emotional elements of the game, our prediction seems to go against the general consensus of Dublin having the edge on Mayo.

If you were to base your opinion on who would win by just looking at the odds set by the bookmakers, you may be led to believe that Dublin are 8 times more likely to win. But the thing to keep in mind here is the relative number of people making the bets. The population of Dublin is roughly ten times more than the population of Mayo – and with matches like this, many punters bet with their hearts, not their heads – meaning the odds may look disproportionate. Another thing to remember here is that bookmakers set the odds solely with the intention of making a profit no matter who wins. So although Dublin may look like they have this all wrapped up, that might not be the case.


Our predictive model has Mayo to win by a margin of one point, which at first glance may not seem like such a big deal considering how evenly matched these two counties are (by looking at their results over the last number of years). But for Mayo to be so close to Dublin really is a major achievement, again when we take into consideration the relative populations of each county. According to the most recent Irish Sports Council’s monitor report, the percentage of people actively playing Gaelic football in Connacht is 3.7%, whereas in Dublin county it’s just 0.6%. But adjusting for population size, the number of active players the Dublin team could potentially choose from is roughly 8070 with Mayo only having 4014 players.


Now, if Mayo had the same population as Dublin (1 345 000 people), with an active player percentage of 3.7%, they would have a pool of players to choose from of 50 000, compared to Dublin’s 8070.

The Mayo players will know that looking at the history it’s too close to call, but looking at how well they’ve played given the disproportionate advantage Dublin have in terms of population, they may just feel they deserve it more.

Dublin supporters might not want to be too confident.

About Idiro
Based in Dublin, Ireland, Idiro Analytics is an award-winning provider of analytics to businesses around the world.

For an overview of Idiro’s analytics services, see our homepage

Media contact information
Simon Rees, Clients & Marketing Director, Idiro Analytics.

The benefits of playing Pokemon Go

Pokeball on a background with overlay

There are plenty of articles about Pokemon Go around the internet, some outlining the frustrations of trying to go about your day without having to sidestep someone staring at their phone, others showing the “mass hysteria” caused by the sighting of a Charizard, and many more about the accidents people have gotten themselves into while playing.

But few people are discussing why this game has become such a huge success and embraced it for what it is, a fun trending topic which has potential benefits.

So to understand this rise in popularity let’s cast our minds back to January 2014, and the sudden rise of another game, Flappy Bird. If you’re unaware, Flappy Bird was a side-scroller game where the player tries to control a small bird through obstacles using very limited controls.

If you’re unaware, Flappy Bird was a side-scroller game where the player tries to control a small bird through obstacles using very limited controls.


The idea of Flappy Bird becoming one of the most successful mobile games of that time defied all logic to professional game designers. It had little to no original game mechanics or design, it was even openly criticised for plagiarism from other game designers, and yet it took off to become the most downloaded free game in the IOS App Store when it was released.

Others have written about the addictive nature of Flappy Bird and that people liked how it was fresh and simple when so many games were becoming overly complicated, but simply, it became popular because everyone’s friends were playing it.

And now it’s the same story with Pokemon Go.

Pokemon Go is essentially a simplified version of the game Ingress, (both games were created by Niantic), but created to appeal to diehard Pokemon fans. Pokemon Go and Ingress have a lot of the same features and use the same game mechanics, but one just has some Pokemon thrown into the mix. But, for Pokemon Go things have escalated dramatically.

Pokemon Go started to build momentum, a few people started playing and then more, it got noticed and talked about on sites like Reddit, and the more it was talked about, the more successful it became.

Every once in a while something like this happens, and it doesn’t necessarily need to be a game, think of the rise of Tinder (100 million downloads as of March 2016). If people were interested in dating apps, there were plenty of options available, but it became so popular because everyone knew someone who was talking about it.

It becomes the thing to be a part of, to stay in the loop, to stay cool. If everyone playing Pokemon Go was a huge Pokemon fan, they would have already been playing one of the many other Pokemon games out there.

What makes Pokemon Go interesting, compared to other mobile games/apps that have had their moment in the sun, is that non-players can’t avoid interacting with players. Step outside and look around and you’ll most likely spot someone playing the game.

People may complain, but then again, people tend to complain about everything. The game brings people out to interact with real world places, making it more difficult for others to ignore. But anything that encourages exercise and gets people of all ages to get on their feet and move around is a great thing.

So, being data analysts, (and nerds), we wanted to encourage the exercising aspect of Pokemon Go by working out specific numbers to help players understand the physical benefits of playing. We wanted to help players justify their Pokemon hunting habit by having solid data to back up the ‘it’s good for you to keep playing’ argument.

So let’s break it down and look at what you would need to do in order to get to level 20 in the game, of course, people can go higher than level 20, but we’ll just set that as a nice target for now.

First, let’s break down some of the numbers:


To reach level 20, you would need to gain 210,000  experience points overall.


To reach that goal by only focusing on catching Pokemon, would involve one of the following:

Catching 1909 Pokemon with a curveball bonus / Catching 1909 Pokemon with a nice! throw bonus / Catching 1400 Pokemon with a great! throw bonus / Catching 1050 Pokemon with Excellent! throw bonus  


But from the chart above, we know that catching Pokemon isn’t the only way to gain experience points (XP). 

Another way is to incubate eggs in order to build up your XP. If you have an egg and place it into an incubator, you can hatch that egg and earn XP points. The egg will hatch after you travel 5km and the speed at which you travel that 5km will determine the amount of XP points you earn.


Walking 1 egg(5km) – you can earn 8 XP per min / Jogging 1 egg(5km) – you can earn 15 XP per min / Running 1 egg(5km) – you can earn 24 XP per min

We decided to run and experiment in order to get a benchmark. We started our experiment at a beginner level 5 and played Pokemon Go for 94 minutes in a city centre. 

These were our results:

  So if we take these numbers as the base, in order to reach level 20 we would need to play Pokemon Go for a total of 67 hours, travelling a distance of 202 km,  along the way catching 562 Pokemon. In other words, playing Pokemon Go for just less than three days straight without stopping, and travelling the distance of the London marathon almost 5 times, that’s not so bad, right? In terms of calories burned while walking that distance (we’ll assume we won’t be running those three days), we would burn 27259  calories**The number of calories burned here is calculated based on our particular weight and average speed walking.  


Other less fun activities we would need to do in order to burn that many calories would be*:


Now, our numbers here are based on our particular experiment, we would need a larger dataset in order to get more solid results. But, there is no reason why you can’t use this a baseline reference when arguing with friends and family about whether playing Pokemon Go is a waste of time.

We’ve also run a more complex experiment using the data analytics tool Red Sqirl. We used advanced predictive data analytics techniques to work out where Pokemon will appear in the game. You can read more about this experiment here on hack.guides()

When it comes to buying houses, people in Dublin are clearly superstitious

Streets of Dublin on an article about Irish Housing Market

Who would have thought that in this day and age, the Irish people would still be suffering from this ancient affliction? The terrible problem of Triskaidekaphobia, or the fear of the number thirteen.

The Irish people, as a nation have achieved many great things, we’ve become one of the biggest technology capitals in Europe, we’ve produced some of the world’s greatest athletes and sports stars, we’ve lead the way in giving equal rights to every citizen, not to mention the musicians and actors that other countries would love to claim as their own, but we know they’re Irish in their hearts.

But alas, we still have trouble shaking the quaint “luck of the Irish” image that American tourists hope to see when they step foot in temple bar. The image of a superstitious nation who base decisions on old wives tales and mythology.
We may say to ourselves that this isn’t the case, that it’s just how the Irish people are portrayed on tea towels found in Carroll’s. But like everything in life, you can only really find the truth by looking at the data.

So that’s what we here at Idiro Analytics did. We are experts in data analytics for business. We looked at the data, to prove how far we’ve come as a nation, that we base our decisions on reason and logic and not on whether or not our palm itches (so we know we’ll be coming into some money). But unfortunately, the data showed us our true colours.

We looked at the price of houses in Ireland over the past six years. We took the data from the Property Services Regulatory Authority, showing every house sold in the Republic of Ireland since January 1st, 2010. We analysed housing data from every corner of Ireland, looking at the values, the locations, the house names etc.

And we found that when it comes to a large decision, such as buying a house, a lot of our nation are still as superstitious as ever. The value of properties sold in counties such as Dublin, Cork, Kildare, Cavan and Longford is significantly lower if the house is a number thirteen.

When analysing the average prices of houses we can see the drop in value for houses numbered 13 compared to their neighbours 12 and 14. It seems the Dublin population are slightly more superstitious 4.01% than the people from Cork 3,46%. In Longford, this drop in value is as much as 23.8%.

So all that hard work done by Brian O’Driscoll, all of those times he put his body on the line to dispel the unlucky nature of the number 13, it appears, have all been in vain.
This isn’t the case for the entire country though, the west of Ireland can be proud that they have bucked the trend. With counties Galway having an 8.67% increase in value for houses numbered 13 over their 12 and 14 neighbours, Mayo having a 3.28% increase, and Sligo having a massive 20.22% increase.

Some other insights we’ve pulled from the data are, that houses with particular words in the names have a higher average value. If you’re looking to buy a house with “Mara” in the name (refers to the sea) in Dublin, you might have to be willing to pay up to 115.18% on average more than houses named “An Tigin” (The Cottage).

The two most popular saints in Ireland to name a house after are St. Patrick and St. Mary, although we probably could have guessed that one. With the choice of over 10,000 named saints (it’s difficult to get a definitive ‘headcount’), the Irish people prefer to keep it traditional.

Idiro Analytics provide Big Data Analytics solutions to businesses across Ireland. We help businesses gain a better return on investment by helping them understand and use the data they already have.

About Idiro
Based in Dublin, Ireland, Idiro Analytics is an award-winning provider of analytics to businesses around the world. For an overview of Idiro’s analytics services, see our homepage

Media contact information
Simon Rees, Clients & Marketing Director, Idiro Analytics.
087 240 5999 –

Three and a half degrees of separation?

Three and a half degrees of separation?


Last month a team of researchers at Facebook posted an article where they update the “mean degree of separation” of Facebook users.  You have most probably heard of the “Six Degrees of Separation” legend: between you and me, as between anyone in the world, there is a chain of acquaintances that connect us; this chain is at most 6 steps long. In other words, you know somebody, who knows somebody, …, who knows me! Apparently, this idea dates back to Frigyes Karinthy, a Hungarian writer from the first half of the 20th century, but it was then investigated by social scientists and, with the arrival of social networks and Big Data, people have started using online social networks to test it experimentally.
In 2011, researchers at Cornell, the Università Degli Studi di Milano, and Facebook computed the mean degree of separation across the 721 million people using Facebook at the time and found that it was 3.74. Here the separation is defined in terms of intermediate individuals between a given pair, instead of the number of steps. The news is that Facebook users grew to 1.59 billion and the mean degree of separation shrank to 3.57. If you visit the page, a fast algorithm calculates your own mean degree of separation.
However, one may ask how representative is Facebook of real-world social acquaintances.  Maintaining a real-world social relationship is expensive in terms of time and energies while Facebook “friendship” comes almost for free.  Therefore, one can argue that Facebook connectedness overestimates the real connectedness of individuals.
It is reasonable to believe that some of those links are so weak to be non-meaningful. Also, social contacts change with time, while one may expect that most Facebook users wouldn’t regularly prune their inactive links.
Finally, albeit large, the Facebook world is still a sample of the whole humankind, and it is certainly not a random sample.  Just to mention a few reasons, access to the Internet in Africa is still much more difficult than in the other continents (even if things are changing fast), the population on Facebook is less represented for elderly people, etc.
As a result, it is possible that the Facebook sample has a lower mean degree of separation than the world population as a whole.  But it is still a very large sample.
All these remarks are quite intuitive, but the network scientist Duncan Watts has criticized both the work and the approach, putting in evidence the counter-intuitive behaviour of the so-called “small-world networks”.  In a famous paper written with Steven Strogatz in 1998, they proposed a very simple model that showed how adding a few “shortcuts” in a network (links connecting random pairs of individuals) quickly reduces the shortest path length (i.e. the degree of separation) and, quite interestingly, that doesn’t get much shorter if you keep adding random links after this initial drop.  The argument, therefore, is that the world has already become small decades ago, and it’s quite unlikely to shrink much further.
This, in my opinion, opens the question if a mathematical model could quantitatively fit the measured reduction in Facebook’s mean degree of separation from 2011 to 2016. It could help us in understanding better which topological features of the network are relevant in the process.
Anyway, there are many more details in a real-world social network that still need to be understood besides the degrees of separation.  In a recent paper, the sociologist Robin Dunbar has investigated the relationship between the number of “friends” reported in Facebook and the number of the ones personally perceived by individuals.  In some earlier papers, he and his co-workers had identified progressively self-contained layers of closeness in human acquaintances.  They showed that, typically, human layers of social closeness approximately contain 5, 15, 50 and 150 individuals, plus two external layers of 500 and 1500 alters.  In his recent paper and references wherein, Dunbar shows that online social networks can realistically approximate not only the 150-friends layer but also the two most internal layers.  He also claims that the number of online contacts is usually not larger than the one of offline contacts.
Indeed, there is a minority of users who report a larger set of online friends with respect to the offline world, but it is hypothesised that whose extra online connections are weak acquaintances, that in online social networks cannot be typically distinguished from close friends.  This can only be seen by investigating the traffic among individuals by counting the direct posts on Facebook or replies on Twitter.
Besides online social networks, mobile phone networks are a valid alternative to measure social acquaintances.  Phones are still diffused in areas with low Internet connectivity, communication often carries a cost and it is possible to measure the traffic between individuals.
In Idiro we investigate these problems every day and are able to detect layers of social acquaintance to improve digital marketing strategies and provide value for our customers.

Run for the Wicklow Hospice Foundation

Dublin Night Run

On the 1st of March, five Idiro staff members braved the wind and rain to take part in the Dublin Night Run in Sandymount.

Wicklow Hospice

Idiro are extremely proud to have staff take part in events such as these, to raise money for local charities. The money raised by Idiro’s runners from this event will be donated to the Wicklow Hospice Foundation. After a long fundraising campaign by the Wicklow community, construction will soon begin on a new hospice facility at Magheramore in Brittas Bay. The hospice will provide 15 new beds for those in need and will cater to the people of Co. Wicklow and north Wexford.

The project is expected to be fully completed and open for the first patients by the end of 2018.

Well done to Aidan, Simon, Paddy, Brian and Ann-Marie on compleating the run. And congratulations to Brian for coming 5th overall in the 5KM distance, and Simon for beating his PB by 13 seconds in the 10KM distance.



Idiro’s LorcanTreanor and the Wicklow Hospice Chairperson Dr. Brendan Cuddihy

More information about the Wicklow Hospice Foundation can be found here.

Idiro and host breakfast seminar on analytics

Assessing key issues in your analytics journey and how you can solve them

Idiro and senior management team poses after the Data Analytics Seminar

On the 2nd of February Idiro Analytics, along with our partners Alternatives, hosted a breakfast seminar for over 70 senior executives in the Irish market.

The seminar “Where are you on the data analytics journey”, was very successful in highlighting key issues surrounding data analytics in business, and informing the attendees about the potential benefits of data analytics and processes involved in getting started on the analytics journey.      

The event was facilitated by Charley Stoney, Managing Director of Alternatives and the panel of speakers consisted of:

Richard Harris, Head of Online Marketing & Customer Intelligence, Paddy Power/Betfair,
Olivier Van Parys, Head of Analytics, Bank of Ireland,
Ronan Brennan, Insights Manager, LinkedIn and
Aidan Connolly, CEO, Idiro Analytics

Below are images from the event and video highlights.

If you would like to be kept informed of future events hosted by Idiro, please contact us at


On the 17th of February 2016, the analytics breakfast seminar held by Idiro and Alternatives was featured in the Irish Herald by Michael Cullen.

Newspaper article about an event hosted by Idiro Analytics

Idiro researcher holds seminar on Social Network Analysis in telecommunications

Academic research has always been important to Idiro – that’s how we became world leaders in the application of Social Network Analysis (SNA) techniques to telecommunications business problems.  It is also why our CEO is on the board of CeADAR, the Irish Centre for Applied Data Analytics Research.

So we are happy to report that our colleague Davide Cellai, who has been working on advanced uses of SNA in solving telecommunications business problems, this week gave a seminar at the University of Aalto, Finland on his advanced SNA churn research.  Davide says:

“Last Thursday I was invited to give a seminar at the University of Aalto, near Helsinki. This week, I have been hosted by the group of Jari Saramaki and Kimmo Kaski, who were possibly the first researchers to focus a research group in social network analysis applied to telecommunications, several years ago. I presented our work on port-out churn, plus some percolation models of robustness of infrastructures I have been involved with in recent years.  Here are some examples:

Social Network Analysis in telecommunications - research by Idiro Analytics
Distribution of fraction of time two subscribers spend speaking to each others in an interval of 12 weeks

In order to profile the type of connection between pairs of subscribers, we calculate the amount of time two individuals spend at peak (i.e. during working hours) and off-peak time of the day. For each pair, we can evaluate if conversations occur mainly at peak or off-peak time and give a score accordingly. In the first figure, we show the distribution of this score over all the phone calls that occurred in a period of 12 weeks. We can see two strong peaks, representing calls occurring exclusively off-peak and at peak times, respectively. We also show an enlargement of the central zone, with a broad hill representing pairs of subscribers who speak both at peak and off-peak time. This polarization suggested us to identify three layers of acquaintances: peak, off-peak, and mixed peak/off-peak.

Social Network Analysis in telecommunications - research by Idiro Analytics
Probability that a subscriber churns as a function of churning subscribers in her network of social acquaintances
This plot shows the probability that a subscriber churns after m_out of her friends have churned in a recent time interval. Generally speaking, we can see that a subscriber with more churning friends is more likely to churn, as the red crosses tend to grow with m_out. Moreover, we can see that the probability of churning is higher for the mixed peak/off-peak layer (purple boxes), meaning that at this level of relationships, churning propagates more easily.
The seminar participants were quite interested in the way we could identify types of social acquaintance based on the time of calls. They also suggested that exposure to churn in terms of duration, instead of number of churners, would improve the sensitivity of the method. In particular, Jari Saramaki, who has also experience in the data analytics industry, envisions that machine learning methods should be fed with this kind of insightful social network information to produce best lift.

The week I have spent here has been very useful. For example, researchers have shown me a method to identify families that doesn’t use any community finding algorithm, and a way to map a temporal network into a network of events, that can be treated with a known formalism. Another post-doc is working with psychiatrists to detect the onset of a mental disorder in the pattern of social activities of a person. Finally, it has been also interesting to hear that a few students or post-docs are starting a company. Best of luck, and thank you Aalto!”

Idiro gratefully acknowledges the support of Science Foundation Ireland in this work.  Idiro works with telecommunications companies across the world, helping them with customer retention, customer acquisition etc.  To learn more about Idiro’s work on Social Network Analysis in telecommunications, or to find out how Idiro helps telcos to get better marketing results through our SNA models, contact our experts.

Idiro Analytics – proud to announce the launch of our new website

Idiro Analytics Logo


Here at Idiro Analytics we’re very excited to announce the launch of our new re-designed website, which has gone live today at

Following our recent name change earlier this year, we wanted to provide our customers, and potential new customers, with a new website that more accurately represents Idiro Analytics, what we do, and how we have grown from where we began back in 2003.

We have carefully designed a new website layout, that now has a better user experience, and will help our customers find all the information they need about Idiro, and answer any questions they may have.

We are also extremely proud of the experience and expertise of our staff and we wanted to showcase this. Our company page now shows more information about our company, and the people in charge, who are always driving Idiro Analytics forward.   

Our CEO, Aidan Connolly said:

“I am very pleased to introduce our new Idiro Analytics website. This new website reflects where Idiro is today and our company’s evolution over the past twelve years. We work hard to always stay ahead in an ever-changing technology world,  and this new website is another representation of that work. On behalf of everyone here in Idiro Analytics, I’d like to take this opportunity to thank you for your continued support.”