One solution to increase employee productivity and satisfaction


Contact centre agents are the shopfront of their company – they interact with customers daily, and their attitude is what forms the customer’s opinion of the company. The currency of such interactions is customer satisfaction – one poor experience can make the customer drop the brand and choose a competitor. Yet, a positive experience could increase the customer’s loyalty and bring revenue growth for the company.

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No spotlight for female artists

In the past years, many festival-lovers have expressed dissatisfaction with the unfair gender representation in music festivals’ lineups. And while festival organisers took in fans’ criticism and worked on creating more diverse and inclusive lineups, it seems that it will take more effort from organisers, sponsors and the fans until we see a fair gender representation on the stages of music festivals.

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The Game of Letters and Numbers

Wordle has taken the world by storm as it gained popularity among people from all walks of life. For those who haven’t yet had a chance to be introduced to this curious word-guessing game, there are just a few rules to know.

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Data strategy – The first step towards a successful business


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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Net Promoter Score – Can you predict it?

Net Promoter Scored

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.


  • 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:

Or join the dialogue as part of the Idiro Predicted NPS group here:

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.

Continue reading

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, 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.