Net Promoter Score – Can you predict it?

Using NPS to add tangible results

Net Promoter Score. What a handy numerical way to understand customer sentiment. It’s so valuable, but also so challenging. We need to talk more about the value and the pitfalls of NPS, and learn how we can use this in conjunction with the myriad of our other customer data points.

Surveys are used by most brands to try and garner insight from their customers.

However:

  • Only a small proportion is surveyed (up to 3% of customer base)
  • Response rates are declining (10-30% response rate)
  • Surveys create customer effort
  • They can waste valuable customer contact time and risk opt-out!

 And, let’s think about it: in our personal lives you should never really have to ask somebody how they are when you should already know that there is something wrong. 

As Fred Reichheld, the creator of NPS,  famously said, “The instant we have a technology to minimize surveys, I’m the first one on that bandwagon.”

So your NPS scores are high – but if churn rates are high, who cares?

The concept of Predicted NPS uses advanced analytics to expose how customers already feel based on their past and current experience. It uses AI to understand and qualify past customer experience quality along with the survey data you collect to predict future customer sentiment. 

Will the customer churn? 

Or could there be an opportunity there for increasing their value? 

Are we risking damage to our brand?

A Commscope report predicted that by 2020, customer experience was expected to overtake both price and product as a key brand differentiator. Well, it’s 2021 now and as we’ve learned via research done by Bain & Co, “Sustained value creators have Net Promoter Scores two times higher than the average company.”

It’s not just what is said, it’s what is unsaid

You can zero in on the root causes of 

  • policy
  • process 
  • people 
  • systems 

And gain a better insight into what actually influences customer perception and customer sentiment. These naturally drive customer behaviour and experience.  

We know this…

To effectively increase campaign conversion, we need to engage with customers at the most appropriate time – and also stimulate customer lifetime value with more personalised and targeted campaigns. 

The dream is to reduce the cost of service and reduce customer effort. so the customer doesn’t need to seek help. At the same time, we can increase the number of promoters and turn customers into brand advocates – even promoters. 

Fringe benefit: this will also enable you to improve your service offering based on what your customers actually need.

It’s time to provide an incredible customer experience and positively improve your bottom line. Learn more about Predicted NPS here: https://idiro.com/predicted-nps/

Or join the dialogue as part of the Idiro Predicted NPS group here: https://idiro.com/predicted-nps-landing-page/

Tania O’Connor
Idiro CMO

We’ve predicted the 2020 Euros for you

UEFA euro 2020 cup with the flags of participating countries in the competition

Predicting the finalists and semi-finalists of the 2020 Euros - using the ELO rating system and random forest machine learning algorithm

After nearly a year of delay, the UEFA Euro 2020 tournament is finally upon us – and the excitement at Idiro is real. Twenty-four teams will battle it out to see who will be the best in Europe. Things are finally heating up not only for the competitors but for the analysts, bookmakers, and average punters making their picks. Don’t worry we’ve got you covered. We’ve predicted the finalists & semi-finalists for this year’s competition.

Everybody has their own techniques for formulating a prediction. Whether it be extensive research of a team and player’s performance over a multitude of games or consulting their local marine life (see Paul the Octopus!). We haven’t consulted any Pundits for this prediction instead, we let AI & machine learning (prescriptive analytics) do the heavy lifting for us. We created a model based on the historical ELO rating performance of teams in the Euros. 

The ELO rating system was originally devised as a method of measuring chess players’ ratings relative to each other. 

The basic principle is that every player (training set) is given a score and whenever one player beats another they take a certain amount of points from the loser. The bigger the gap in the ELO rating, the bigger the point gain/loss, the smaller the gap, the smaller the gain/loss. Eventually, over a large enough sample of games, the future events should be obvious. 

This rating system now applies to numerous games and sports, including soccer. Using the ELO rating, random forest learning model and several other factors such as rating changes over the past year, the number of home and away matches, and the number of goals scored, we’ve created a predictive model to find our picks for who’ll make it to the finals.

Our random forest model has predicted Spain and Portugal. Both teams are sitting at around 5th and 6th for odds of reaching the finals across bet making sites – which is not bad. 

The ELO system does have limitations when it comes to team sports predictions, as it can’t account for individual performances or changes to the line-up, but it still provides a solid statistical background for making value bets. 

So, tell us what you think in the comments below? Who are you backing for the finals and would you put the house on it? 

If you like this post, here is a link to some more of our work.

 

Maurice O’Neill 
Data Scientist

Analysing and predicting customer behaviour for Digicel

Multiple people walking on the street going to work

Digicel wanted to improve overall customer experience, reduce churn, and increase revenues. To do this, they wanted to implement targeted marketing campaigns to reach their customers on the right channel at the right time. Idiro provided the intelligence behind this targeting, analysing customer behaviour at various lifecycle stages.

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Evaluate your data asset through customer journey analytics

Corporate officials highlighted on a street explaining importance of real-time data

The secret to ongoing profitability are three little words “love your customer”. This is not just because of the purchases they make, but the behavioural data they leave behind. In the age of the data-driven business, this is where you will find insights that can be leveraged for acquiring new customers and maintaining existing ones.

You need to do whatever is necessary to keep existing customers on board. But when you have aggressive growth targets to meet, the only way to achieve meaningful uplift is by acquiring new customers. To succeed you need to be more creative in the way that you analyse your customer information and understand and utilise your data assets. Any one of your existing customers is a goldmine of information – if you know how to unlock and analyse the underlying data. Especially if you have the capability to analyse all of your customers’ behaviour.

Looking at the bigger picture, you can identify common trends and experiences that can be leveraged to attract new clients. By building cohorts of customers based on similar behaviours for instance, you can create marketing (and retention) strategies that are tailored to customer interests, preferences and behaviour profiles. Done properly, analytics can enable companies to reach that highly desired segment of one whereby each customer is understood and serviced as an individual.

Understanding your customer journey is critical to gaining insight into customer behaviour. In order to do this successfully you must understand the data footprints that illustrate customer journeys – only then will you be able to measure performance and success.

Marketers have long known that customer journeys are multi-stage affairs. But by performing advanced analytics on their data stores, the journey is shown to be made up of data footprints left by customers on their paths. Where the entire journey is digital, tools like Google Analytics make it very easy to identify and follow these footprints, tracking clicks and page navigation across your website.

But if your customer journey crosses multiple channels – online, phone, social media – it becomes more difficult to create an accurate, comprehensive oversight. Not least because each footprint will typically be recorded in a different system. You must have a way to query and aggregate each of these datasets to properly understand the various nuances of the journey.

 

What is customer journey mapping?

As we’ve already implied, customer journey mapping is the process by which customers go from brand new prospect, making a final purchase, ongoing consumption of the product/service, all the way through to the next buying cycle. In order to fully understand your customer’s journey you must also identify the data assets that document (or ‘record’) their experiences, decisions and behaviours.

Here at Idiro this is done by carrying out a deep-dive data asset discovery project to help identify what data assets are available within an organisation and how they might be used to drive value. Mining those data sets allows you to track customers across all of your channels, providing a granular view into every decision point and outcome. We then put these insights to work to understand which journeys are the most effective for achieving your commercial objectives; customer acquisition, customer value increase and customer retention.

Any business can perform a customer journey mapping exercise – even those still developing their analytics or customer management programs. All you need is access to skilled, experienced analytics experts, and their tried and tested methodologies.

Customer Journey Mapping diagram explaining the AIDA model


Moving beyond Post Its

The leap from journey map to actionable insight is not always so straight forward however. Sometimes your most valuable data asset is not the most obvious – in most cases it will be a correlation of multiple data sources.

All the data you need for behavioural analysis is available, but you may need specialist skills and tools to extract those insights and to perform data visualisation. Querying multiple data sets and collating results to piece together the fine details of the customer journey can be complicated – and potentially time-consuming.


Looking further afield

It may be that some of the data sets you identify exist outside your organisation. Examining these external sources of data can be difficult – especially when you don’t know exactly what you are looking for. Social media is a rich source of data relating to product/service experiences and referrals – but you need to know how to collect, aggregate and analyse relevant data.

The data asset audit of the journey map will also point you in the direction – you can then outsource the physical analytics tasks to experts like Idiro who have the tools and experience to analyse internal and external data sets.


Going social

Another source of data ripe for analysis is social media. With more than 2 billion active users who are sharing experiences, thoughts and glimpses of their everyday lives, Twitter is a great place to gain additional understanding of your target market because data is freely available to marketers for behavioural and intent analysis. And if you can begin matching social media profiles with contact names, you instantly gain a head-start on your sales leads.

Social media analysis also provides a way to gauge customer sentiment towards any subject of interest. This could be your brand, your products and services, or your competitors. Sentiment analysis provides another point on the customer journey map – and some insights on how to guide new prospects towards your brand.

Are people complaining about their current supplier for instance? Do they use negative language in their status updates? These are clear indicators of an unhappy customer – and an opportunity to poach them.

Once you have identified specific individuals (or similar groups of individuals) you can use your customer journey map to target messaging and draw them into your sales funnel.

Twitter, LinkedIn and Instagram offer similar opportunities – assuming you have the right social media analytics in place. Or a suitably experienced data mining partner.


A worthy investment

Never assume that the cost of predictive analytics and customer journey mapping is too high, or that you can simply “muddle” your way through. Because after all, you are entering a market that has incumbents – and you are going to have to entice most of your customers away from them.

To do this you will need to expand your data horizons to include third party information. Doing so will enrich your understanding of your marketplace and the potential customers that inhabit it. Not only will you better connect with new prospects, but the behavioural insights will provide another part of the puzzle for understanding existing clients, allowing you to further refine your customer retention strategies.

Businesses are quickly realising that advanced analytics is a crucial tool for managing the customer journey, and using their own behaviour to provide a better quality of service – and to maximise revenue earning opportunities. Making better use of the data you have is vital to love your existing customer, and to help find new ones.

To learn more about advanced analytics and using third party data to enhance the accuracy and quality of the insights you generate, please call us now on +353 1671 9036

In-house vs outsourced – building an analytics function that hits the ground running

a man standing in front of a wall, looking at his options to outsource data analytics for his company
We live in a data-driven economy and failure to build a data analytics competence of some kind leaves you at a competitive disadvantage. And we know that businesses need to become much smarter about how they use data to retain or attract customers.

One of the first choices you face is how to build out your analytics function – do you want to build a team in-house, or partner with external experts, or even choose a hybrid model? This decision will have wide-ranging consequences for your ability to exploit your data in the future.

DIY data analytics

In theory, building your data analytics capability in-house has one major advantage – you can begin analysing your data almost immediately. Obviously, you will still need to deploy predictive analytics tools, but you can save time that would be otherwise spent identifying potential partners and agreeing service contracts.

But this course of action assumes you already have data science and analytics skills in-house. If not, you will need to hire suitably-skilled staff. And that’s where you start to run into delays and risks.

Paradoxically, you need data science experience in order to hire your first data scientist – otherwise you cannot properly evaluate their technical skills. It is also incredibly important to realise that you cannot simply bolt data science onto existing operations – you must change your culture to be able to act on the insights being generated by your data science team. Too often, businesses make this mistake and never realise the full potential of their investment.

Expensive skills shortages

Data science skills are in very short supply helping to drive salaries up. According to Payscale.com, the average annual salary for a senior data scientist is currently €70,318 – and rising. And you’ll need a broad range of skills that are rare to find in one person – according to research from McKinsey, “Best-practice companies rarely cherry-pick one or two specialist profiles to address isolated challenges. Instead, they build departments at scale from the start.”

Although you will realise significant benefits, building an in-house team to turn your company’s data into money will involve a substantial initial outlay.

Instead of making new hires, you could retrain existing staff. But this will greatly increase the time to get your advanced analytics program up and running – and longer still until you see returns on your data analytics investments.

Why you should consider outsourcing

Keeping analytics in-house creates a huge burden on time and resources – at least during the initial stages of building your data analytics capability. Over time they will deliver value, but many CFOs will baulk at the time it takes to generate a return on investment.

Partnering with an external provider offers a much quicker return on investment because the entire process is shortened. And because your partner already has a suite of pre-configured analytics tools, they can begin unlocking value from your data almost immediately.

Outsourcing can be a transitional process too. One way to get the best of both worlds is to outsource all of your predictive analytics functions initially while you build an in-house data science team. As those capabilities come on stream, you can then start bringing functions back in-house.

Using third party consultancy also helps you avoid the staffing issues inherent in trying to maintain operations in-house. Your business doesn’t have to attract suitably skilled data scientists, or deal with rapidly increasing salary demands.

Outsourcing can be implemented in different ways too. Hybrid outsourcing allows you to split responsibilities with your analytics partner for instance. Under this model you retain responsibility for some elements – for example, the underlying database infrastructure, while the outsourcer provides others, such as the hard-to-come by modelling and analytics functions. The hybrid model is fully flexible because no two scenarios or deployments are ever the same.

This allows you to maximise the use of your own staff resources and minimise outsourcing costs without limiting your analytics projects and obtain the skills you really need for data-driven operations.

Speed is everything

When it comes to improving the customer experience, speed is incredibly important. Giving people what they want, when they want it is a key aspect of all customer retention strategies.

As you roll out your data analytics program, speed needs to be a factor at every point – including before you even begin analysing data. The faster you can get your predictive analytics capability in place and generating insights, the quicker you can begin to realise a return on your investment. McKinsey even put a figure on operating profit improvements, suggesting “first movers” account for around 25% of the gain Why? Because they have more time to integrate analytics with workflows than their competitors.

In reality, if your business has never used predictive analytics tools before, choosing to implement data-driven strategies in-house could be a mistake. Any initial cost saving will be quickly cancelled out by the extended time it takes to begin generating actionable insights. Far better to outsource the work to the experts initially, and have your outsource partner train and gradually hand over hand over responsibility for analytics as it comes up to speed.

For more help and advice on finding the optimum mix between in-sourcing and outsourcing for your data analytics team, please get in touch.

Advanced analytics, customer churn and the appliance of science

A crowded street with people marked, indicating the importance of analytics and data

In 2010 Eric Schmidt (former CEO of Google) said “Every two days now we create as much information as we did from the dawn of civilization up until 2003.” That’s something like five exabytes of data. According to IBM, the build out of the “internet of things” will lead to the doubling of knowledge every 12 hours. Let that sink in for a moment.

We take the digital era for granted these days, we’ve normalised its existence but when you step back and think about its impact, it’s as remarkable as it is overwhelming.

With the collective knowledge of the entire history of civilisation available for dissection, human behaviour has been documented in its entirely.

We’ll leave the philosophical ramifications of all of this to others – this is a B2B article on advanced analytics after all, but it’s worth taking in the bigger picture of just how much data is out there.

If we leave aside the focus on big data and the internet of things and apply advanced analytics on just a tiny speck of this information – your customer database – the insights gleaned from their behaviour will be decisive in the future success or failure of your company.

Customer Intimacy

To start with, let’s get the most obvious learning out of the way – retained customers are way more valuable than new ones, due to the costs of acquiring new customers. Adobe once found that it takes seven new shoppers to equal the revenue of a single repeat customer.

So if your focus is on retention campaigns, then your focus needs to be on your existing customer base. The development of programs to improve customer experience has been a direct result of this understanding. By delivering an exceptional experience, customers will not defect – or so the theory goes. But despite throwing millions of euros at “experiences”, customers continue to defect. If anything, they leave even more quickly and easily than ever before.

So what has gone wrong?

Net Promoter Score

Customer experience is a nebulous concept, but there has to be a way to assess its success. And so the famous “net promoter score” (NPS) was born. For a while marketers felt they had a good way of understanding satisfaction levels by simply asking customers what they thought.

Surveys were sacrosanct.

But there is a problem with surveys and the NPS regarding churn prediction – what customers say and do are two different things. According to a report published in Bloomberg Businessweek 60% of defecting customers describe themselves as ‘very satisfied’ just before they leave.

To make matters worse, the evidence of their impending defection has always been available – if you know where to look.

The Appliance of Science – Applied Analytics

Your existing customer database is a veritable goldmine of data for analysing customer behaviours. Every interaction between brand and consumer creates a digital footprint, an indication of intent – if you know how to read them.

Applied analytics provide a way to spot trends and patterns based on past behaviours. By classifying and categorising customers based on commonalities, you can drill down into those behaviours and better understand customers as individuals.

By following the behavioural trail you can identify indicators of intent. A customer may not say they are leaving, but their behaviour provides clues about what they are thinking. Has there been an increase in calls to customer support? A use of increasingly negative language in their emails? A reduction in their use of your service? All the behavioural indicators are there in plain sight – but only if you know what to look for and how to analyse it.

Taking these indicators and comparing them to the behaviours of other customers, you can predict their next move.

And here is the thing – you can identify, understand, and predict behaviour right down to the individual.

You can uncover how any one customer feels about your service and your offering and confidently predict how likely they are to leave, when are they likely to leave, why are they likely to leave, and what offer will make them happy to stay.

Act Early, Reduce Costs

With refinement your analytics will begin to identify these behaviours much more quickly, allowing you to act earlier. The sooner you act, the easier it is to recover the relationship – and the cheaper the incentive you need to offer. Your analytics will even reveal which retention incentives have had the greatest success for similar customers previously, further increasing your chances of a positive outcome for both parties.

Instead of issuing surveys that can be ignored, or which capture inaccurate sentiment data, analytics use the actual behaviours of your existing customers to make extremely accurate inferences and predictions. Statistical patterns provide actionable insights in a way that the nebulous NPS scoring system cannot, which means that your attempts to improve customer experience will always be more effective because you better understand each customer as an individual.

Fads come and go, but predictive behaviour modelling is just that…predictable. All the answers are there, but very few have the expertise or the tools to spot them, track them, report on them and recommend actions.

Speak to one of our analytics experts to see how you can use advanced analytics to improve your customer experience and reduce churn.

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 www.idiro.com.

Media contact information
Simon Rees, Clients & Marketing Director, Idiro Analytics.
087 240 5999 – simon.rees@idiro.com

Why you should not buy an Irish lottery ticket until the weekend

Irish lotto

Buying an Irish lottery ticket for the weekend draw? Hold on a minute.

Here’s why you should not buy an Irish lottery ticket until the weekend.  This month changes take effect in Ireland’s national lottery game – they are adding two numbers, so we now pick from 47 numbers. The odds of each row winning will now be just under one in eleven million. Expect more rollover draws, bigger wins and fewer winners.  And if you buy a Saturday ticket on the previous Sunday, you have a bigger chance of being murdered before the draw than you have of winning the big prize.

Bear with me.  There were 52 murders in Ireland last year. Therefore, the overall odds of being murdered in any given week is one in 4.5 million (one person per week, out of each of Ireland’s circa 4.5M population).  There were 196 road deaths last year, giving you average weekly odds of being killed on the roads of one in 1.2 million.  Let’s not talk about how many die of heart disease…

Let’s say you buy a 2-line ticket on a Sunday for the following weekend lottery draw.  You have a higher chance of being murdered (all things being equal) by the weekend than you have of your numbers coming up for the big prize on Saturday.  Furthermore, you have a much higher chance of dying on Ireland’s roads than of winning.

And worse, if you win the lottery, someone else may have the same numbers, meaning you have to share the prize money, reducing the benefit. If you die next week, however, it doesn’t matter how many others share your fate – there is no upside to dying in company.

Here’s the good news: your chance of being murdered or dying on the roads before the draw falls steadily as you get closer to Saturday – but your odds of winning Saturday’s lottery stay the same, at eleven million to one.

So keep your Saturday lotto draw money in your pocket, at least till the weekend!

p.s. your chance of winning the big Euromillions prize is one in 116,531,800 – so buy that ticket as close to the draw as you can. Good luck!

Some research tidbits for Christmas

Over the last couple of weeks a few interesting research items on social psychology and social network analysis have crossed our desks – so we have compiled them into this collection of research tidbits for Christmas.  Enjoy!

In-flight influence

First up, a study that shows how the decisions of people around us influence our decisions, even if we don’t know the people.  This elegant piece of analysis, written up in this working paper and covered by the Washington Post (albeit with a misleading headline) shows how our decisions about whether to purchase in-flight food and drink are influenced by those around us.   Because the study had access to reservations data, it was able to exclude groups travelling together, and control for parameters such as seat choice.

The research found that people sitting near other purchasers were 30% more likely to make in-flight purchases.  If this is the the level of influence that strangers hold over us, how much more is our behaviour influenced by those who we care about?  Answer: in Idiro’s experience, lots.

The same Washington Post article referred to an interesting piece of research demonstrating the power of peer pressure in schools.  Message to all parents: make sure your kids are in classes with people cleverer and more diligent than them.

A link analysis of languages

Multi-lingual Wikipedia editors: which languages?
Multi-lingual Wikipedia editors: which languages?

There are plenty of studies showing how which languages are spoken by the greatest number of people, which languages are economically the most powerful – but which languages serve as the pivots between other, less popular languages?  To put it another way, if you speak a minority language (like Welsh) and want to understand it written in another (e.g. Kikuyu), which other languages are necessary to make the link?  In this case, most Welsh speakers know English, as do many Kikuyu speakers – so the answer is simple: just English.

Quartz published details of an interesting MIT study looking at this in depth, using three data sources: multi-lingual Wikipedia editors, multi-lingual Twitter accounts, and book translations.  The data is displayed in an interactive website but it’s worth watching this video, as it’s a complex enough study.

One can criticise the data sources, of course (for example, the great firewall of China restricts Chinese Twitter usage) but nevertheless it’s a fascinating topic.  Here in  multi-cultural Idiro, the most common hub language is English (of course), followed, we observe, by Russian.

How many friends?

How many people do we have contact with through our mobile phones?  Idiro’s researchers took a week’s worth of connection data from a European mobile phone network, and counted the number of different phones that each person had contact with over a week.  We then plotted the distribution of the number of contacts each phone had – in other words, the total number of links per person.  As the graph shows, a number of phones were (as one would expect) used rarely or not at all that week.  A few users made over sixty unique connections in a week, and a large number of people made between 5 and 15 connections.  We compared Christmas with an average summer week. and found – no surprise – that people make more connections over Christmas week, as we renew old friendships.

Distribution of the number of mobile contacts per person
Distribution of the number of mobile contacts per person

Finally, here is a study by Hill and Dunbar demonstrating that Christmas card networks are (or were, when we used to send Christmas card to all our friends) a reasonable approximation of Dunbar’s number – 150.

Merry Christmas to all, from the Idiro team

Sex, teenagers and Big Data

A good friend said to me recently that Big Data and analytics is a bit like teenagers and sex; everybody is talking about it but very few are actually doing it. I think he may need to update his knowledge of teenage behaviour but I got his point nonetheless.

 

The rush to Big Data has the usual hallmarks of other past industry hot trends i.e. lots of hot air and hype. Additionally, there are a lot of definitions of what Big Data actually is and what differentiates it from, say, your bog standard Oracle BI/data warehouse.

So what’s my definition of Big Data? If pushed I’d say something similar to the following: “Big Data is the discipline of analysing vast volumes of structured and/or unstructured data with a view to generating insights and predictions that improve business performance”  (OK, I know that’s not very inclusive of non-business activity but you get the general idea).

My gripe about some soi-disant Big Data companies is that all they have done is moved their dashboard reporting tool to Hadoop (if even that). I can understand the temptation to rebrand an existing BI tool as a Big Data platform but it would be unfortunate if anyone actually fell for that.

Here in Idiro we like to differentiate between BI and predictive analytics – there are many companies offering BI tools of varying levels of sophistication. However, there are far fewer suppliers of predictive analytics platforms  (and even fewer still who provide predictive social network analysis like ourselves). In essence, BI tells you what did happen (i.e. after the horse has bolted) and predictive analytics tell you what will happen (while the horse is still happy in the barn). A smart company will use both.

But back to the definition of Big Data…some would argue that Big Data is all about analysing unstructured data such as blog postings, tweets and other such rubbish. Sorry, yes, I know there is useful information in there but there’s a lot of junk too. We prefer not to discriminate against data and believe that any data can form the input for a Big Data platform.

A word of caution lest anyone think that by installing some Big Data platform that all their problems will be solved. The analytics generated by any such platform need to be used to change business behaviour – this is probably the biggest challenge to the successful deployment of analytics within a company. Often there is political resistance within a company to the use of analytics that makes the Israeli-Palestinian problem seem like a walk in the park. Simply put, people and processes need to change if a company is going to capitalise on its investment in analytics.

As for the aforementioned teenagers, I think that when it comes to the adoption of behaviours that they find “beneficial” they exhibit a lot more openness to change than some large companies who desperately need to reinvent themselves. Big Data may or may not be a panacea for all a company’s problems but, once we step away from the buzz and the hype, what we see is that companies small and large, who intelligently leverage analytics for business really do get the edge over their competitors. Call it Big Data, call it analytics, the important thing is to call it right.

Aidan Connolly
Email: a.m.connolly@idiro.com