Predicting customer behaviour to improve customer experience – a case study

Predicting customer behaviour to improve customer experience – a case study

Predicting customer behaviour to improve customer experience - a case study

predictive-analytics-customer-experience-idiro-analytics-digicel

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.

The first step Idiro had to take was to combine data from multiple data sources then build a data pipeline to manage and put structure to the multiple disparate data sources. Idiro’s own proprietary software was installed as the platform to manage crunching the data.

Using our 5-step process to collect and analyse the data, Digicel experienced a +12% improvement in churn and a +53% increase in repeat purchases.

Learn more about how we helped Digicel predict customer behaviour and improve their customer experience!
“Idiro has helped us over the last 7 years to rapidly leverage a top-notch churn prediction model across a majority of our global operations, combined with some consultancy services to help us to gain insights and delight our subscribers with intelligently selected relevant and personalized offers. They always try to use best-in-class and recent machine learning algorithms to improve the accuracy of their models”.
Marc Buekenhout
Director of CVM/CRM and Innovation, Digicel Group
Evaluate Your Data Asset through Customer Journey Analytics

Evaluate Your Data Asset through Customer Journey Analytics

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.

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

In-house vs Outsourced – Building an analytics function that hits the ground running

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

Advanced Analytics, Customer Churn and the Appliance of Science

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.

Big data – will it solve your marketing problems?

Big data – will it solve your marketing problems?

As ever, Tom Fishburne has a point.  Increasingly, organisations are turning to their data to improve decision-making and improve commercial results – but buying big data infrastructure won’t solve your marketing problems.  In many ways, installing the big data infrastructure is the easy bit.  The real challenge, as Idiro has found time and time again, is turning all that data into money.  For this you need people with the BI and analytics skills to mine all that newly-available data for dashboards, insights and predictions.  And of course the organisation needs to be ready to change – to try new ways of using data to drive commercial activity – and it needs to be prepared to fail.  Samuel Beckett said:

‘Ever tried. Ever failed. No matter. Try Again. Fail again. Fail better.’

With the right analytics partner, the journey to excellence in data-driven marketing should be a lot easier than Beckett paints it – but nevertheless, it takes skill and a ruthless focus on the results.  However, the results from using your organisation’s data to drive its business are nearly always well worth the effort.

Major International Telco chooses Red Sqirl

Major International Telco chooses Red Sqirl

Major International Telco chooses Red Sqirl

 

One of the first commercial users of the Red Sqirl analytics platform for Big Data is a multinational telco, which has deployed Red Sqirl in two countries and is using it to deliver analytics on its Hadoop platform. This customer has asked to remain anonymous.  

Red Sqirl is a flexible drag-and-drop Big Data analytics platform with a unique open architecture. Red Sqirl makes it easy for analysts and data scientists to analyse the data on your Hadoop platform.  

The problem

 

This multinational telco operates on four continents worldwide. Unfortunately, for historical reasons these operating companies use a wide variety of different database systems and analytical platforms.  

As data becomes an increasingly important asset for organisations, many companies are taking steps to maximise the value of their data. In addition, most large companies now have access to many new types of data – for example, social media posts.

To exploit synergies across the worldwide organisation, this multinational telco decided to standardise database platforms across the group. In order to best meet the challenges of storing and using ‘big data’, the company chose Hadoop as its standard database platform.  Hadoop is currently being rolled out across the company’s operations.  

The company now faces the challenge of migrating existing code onto Hadoop, and allowing asset re-use and swapping across business units who have multiple historic platforms and assets.  Moreover, the Hadoop ecosystem does not contain a ready-made data analytics module.  The market leading traditional data analytics software platforms are not designed for the Hadoop ecosystem and tend to be inefficient when analysing Hadoop data. The telco searched for a native Hadoop analytics platform with an easy-to-use graphical interface.  In addition, because of the wide variety of requirements, the platform had to be highly flexible and cost-effective for a worldwide rollout.

Solution chosen: Red Sqirl

 

Following a thorough technical / usability evaluation of a number of analytics platforms, the company agreed a contract to trial Red Sqirl – initially in the company’s head office and in one operating company.  

Red Sqirl exceeds the telco’s requirements as set out above. Of particular interest to this company is Red Sqirl’s unique capability for sharing, via the Red Sqirl Analytics Store.  Moreover, the Red Sqirl development has deep experience of telco analytics.  Red Sqirl is already proven in predicting telco churn, as shown in the workflow below.

A Red Sqirl screenshot showing a telco churn modelling workflow
Telco churn modelling in Red Sqirl – a workflow

This telecoms operator now has the confidence that their analytics solutions are scalable, deployable and easy to use.

Implementation

 

Red Sqirl was installed in both the head office and the country telco, which took a matter of minutes in each case. Training workshops were held in both locations, to ensure that analysts could get the most out of the Red Sqirl platform. Red Sqirl’s drag-and-drop interface is similar to that of most GUI analytical tools, so the training was quickly completed.  Feedback from students was very positive – as the graph below shows, students scored the training very highly.

Student feedback on Red Sqirl training course
Student feedback on Red Sqirl training course

The company then needed to migrate analytical assets from legacy infrastructure to Red Sqirl.  The Red Sqirl development team showed the way by taking a particular analytical model that had been developed in another language and quickly porting it onto Red Sqirl.

The company is now using Red Sqirl as its primary analytics platform in the country in question – and early results are promising.  

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

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

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


 

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.

– Ends –
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

housing

Idiro & Alternatives.ie host a breakfast seminar on analytics for Irish executives

Idiro Analytics and Alternives.ie

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 info@idiro.com

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

Idiro Analytics Herald