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If there is one industry that should be leveraging data in every way possible, it’s telecommunications. The telecommunications industry services billions of people each day, generating massive amounts of data. Though not many telecom companies are leveraging this data, the introduction of data science, machine learning, and artificial intelligence in this industry are inevitable.

A study by McKinsey, Telcos: The Untapped Promise of Big Data, based on a survey of leaders from 273 telecom organizations, found that most companies had not yet seriously leveraged the data at their disposal to increase profits. And only 30 per cent say they have already made investments in big data.

So while there is certainly debate within telecom companies about whether the return on investment is worthwhile, there is no doubt that data science, machine learning (ML), and artificial intelligence (AI) are inevitable when it comes to the industry’s future. Those that figure out how to leverage these techniques and technologies will thrive; those that don’t will be left behind.

By using data science, machine learning, and artificial intelligence strategies, telecommunication companies can improve four areas of their services.

The importance of data science, ML, and AI to the telecom industry will likely present itself in these four areas in particular, which this paper will take a look at individually:

 

1. Troubleshooting:

One of the major challenges for telecom providers is being able to guarantee quality service to subscribers. Analyzing call detail records (CDR) generated by subscribers at any given moment of the day is key to troubleshooting. However, CDRs are challenging to work with because the volume of data gets massive and unwieldy quickly. For example, the largest telecommunication companies can collect six billion CDRs per day.

With data science, machine learning (ML), and artificial intelligence (AI), companies can instantaneously parse through millions of CDRs in real-time, identify patterns, create scalable data visualizations, and predict future problems.

 

2. Fraud Detection: 

Verizon estimated in 2014 that fraud costs the telecom industry upwards of $4 billion a year. However, the faster that telecom companies analyze large amounts of data, the better off they are in identifying suspicious call patterns that correlate with fraudulent activity.

Cutting-edge ML and AI strategies like advanced anomaly detection make it much easier for telecommunication companies to identify “true party” fraud quickly.

 

3. Marketing:

The high churn rate in telecommunications, estimated at between 20-40% annually, is the greatest challenge for telecom companies. Telecommunication companies can use data to build better profiles of customers, figure out how to best win their loyalty (in the most scalable and automated way), and adequately allocate a marketing budget. With improved data architecture, they are able to harvest and store a greater diversity of data that provide insights into each customer such as demographics, location, devices used, the frequency of purchases, and usage patterns. By combining data from other sources like social media, they can have a stronger understanding of their customers.

Using machine learning gives a more accurate picture of which channels are most responsible for customer conversions for better ad buying as well.

 

4. Customer Experience: 

Telecommunication companies can enhance their services by analyzing the millions of customer complaints they get every year to figure out which types of improvements will have the greatest impact on customer satisfaction and thereby increase customer retention. They can also leverage data at a larger and more automated scale to gain insights into the performance of their technicians.

The more that telecommunication companies can analyze data on customer calls, the more they can begin to recognize which types of problems are most likely to lead to unwarranted “truck rolls” and put in place measures to prevent those calls. Given the number of calls and the depth of analysis required, this necessarily dictates a machine learning approach - more specifically, a deep learning approach. Because analyzing the calls themselves means dealing with lots of unstructured data, it’s the perfect place to expand into ML and deep learning for big gains.

 

The future of data in the telecom industry

Data science is already a big part of the telecommunications industry, and as big data tools become more available and sophisticated, data science, ML, and AI will all continue to grow in this space.

In the coming years, companies that succeed will be those that figure out how to best use the massive number of data points that are flowing both through their network and around it to reduce labor costs, develop better technology and, to better understand what the seven billion potential customers around the world want to do with their smartphones and computers.

To learn more, download the whitepaper White Paper: Top 4 Growth Areas of Machine Learning in Telecommunications.

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In this episode of The Field Service Podcast, Field Service News Editor-in-Chief talks to Kevin McNally, Sales Director for Asolvi about how technologies such as Cloud, Mobile, Artificial Intelligence and IoT have enabled smaller companies to not only meet the service standards of their larger peers but in some instances exceed those standards...

Did You Know? You can now subscribe to The Field Service Podcast on iTunes! Check it out here and subscribe to get the podcast straight to your phone, desktop or tablet as soon as they are released! 

Want to know more? Field Service News have published a white paper sponsored by Asolvi that explores this topic in further detail. This white paper is available exclusively for fieldservicenews.com subscribers.

If you are not yet a subscriber and are a field service professional you can apply for a complimentary subscription below (after reading our T&Cs here first) and we will send you a copy of the white paper as soon as we receive your application.

Click here to apply for your complimentary industry subscription to fieldservicenews.com and access the white paper now!

Note: Please do take the time to our T&Cs (available in plain English at fieldservicenews.com/subscribe) and note that this content is sponsored by Asolvi)
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Atos, a global leader in digital transformation today announces that it has been identified as a ‘Leader’ by global research and advisory firm NelsonHall in its latest Vendor Evaluation & Assessment Tool (NEAT) for Cognitive IT Infrastructure Management...

Atos supports businesses in their digital transformation by providing the tools, services and consulting to enable them to successfully implement next-generation IT infrastructure and workplace services, such as those which use Artificial Intelligence (AI), cognitive, machine learning, deep learning, virtual agents, advanced analytics and robotics.

Atos’ brand new Codex AI Suite, announced recently, supports businesses and research institutes in the development, deployment and management of AI applications. It offers an easy-to-use, efficient and cost-effective solution to rapidly build and deploy AI applications, better extract value from data and develop new business opportunities.

Atos’ end-to-end Digital Workplace offering includes a range of intelligent solutions to enhance the user experience.Atos’ end-to-end Digital Workplace offering includes a range of intelligent solutions to enhance the user experience. This includes the Atos Virtual Assistant (AVA), which leverages Cognicor’s next-generation AI engine, to offer help and support for users, resulting in reduced downtime, increased user productivity, and cost reduction.

Commenting on this ranking, John Laherty, Senior Research Analyst at NelsonHall, said: “Atos is driving digital transformation across both infrastructure and service desk to improve business outcomes and end-user experience; it is embedding automation into all its standard infrastructure managed services offering for clients.”

Elaborating on Atos’ role as a leader in Cognitive IT Infrastructure Management services, Peter Pluim, Head of Infrastructure & Data Management at Atos, said: “We are delighted to be recognized as a Leader in Cognitive IT Infrastructure Management by NelsonHall. We offer an end-to-end approach to automation and robotics, thereby reducing costs, increasing quality, and creating differentiation with real-time insight for our clients.”

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Paul Whitelam, VP Product Marketing, ClickSoftware, puts across the case that in the race towards AI adoption we shouldn’t forget to see the value and importance of human input in the service cycle...

Like many industries, field service has seen an increase in the adoption of artificial intelligence-driven automation.

The benefits are many: improved efficiency, schedule accuracy, workforce productivity, responsiveness, cost savings and higher profit margins, and, importantly, happier customers.

Naturally, the onset of automation causes some anxiety in workers whose tasks are being handed over to AI. As with previous industrial revolutions, we’re not likely to find ourselves in a low employment high-leisure utopia. While the nature of work might change, plenty will remain to be done. Getting the full benefits of AI and machine learning still requires some human participation and a good understanding of who (or what) is best for each job.

People provide context

Service management solutions powered by artificial intelligence and machine learning can rapidly process high volumes of data to use as a basis for automated decisions. But when it comes to learning, machines can be a lot like humans— garbage in, garbage out.

When Microsoft launched its Tay chatbot on Twitter in 2016, few would have guessed that in just a day it would become a bigoted bully. The problem, of course, was that Tay was learning to converse by interacting with Twitter users, some of whom seized the opportunity to educate it on humanity’s worst impulses. Even with less shocking or inflammatory outcomes, AI learns from what it is shown and told. It’s likely to replicate bad behaviour if that’s all it’s shown.

AI-based tools can also provide simulations and modelling for multiple scenarios and highlight the interaction of various policy and process changes. For example, if the objective is the fastest response time available for every job, more technicians might have to be available for dispatching, increasing labour costs and decreasing utilization.

People must still define the process and priorities for automation to ensure your system optimizes for the right business goals. While intelligent computing power can grease the wheels of daily service operations, the real value comes from informing businesses to foster improved decision making.

Managing the unique and unusual

While humans can grow bored with the rote and routine, machines have yet to complain. Tasks that are repetitive and predictable are best handled with automation.

AI can manage most routine and ordinary tasks – chatbots, scheduling, appointment confirmation, routing, showing a mobile worker’s location and travel path to a job, it can even reassign and redistribute jobs around disruptions, addressing unplanned work with urgency. One UK gas utility can dispatch engineers to address a leak emergency in 13 seconds from the initial customer call—without human intervention.

AI can use a variety of inputs to increase schedule and travel time accuracy and optimize in real time, but what happens when you just don’t have the data?

One of the challenges faced by self-driving car producers is how to navigate remote areas, especially with routes that lack landmarks or distinguishing features.

Too few inputs can stump the machine. There is also additional context in some situations that will not be gleaned from data analysis, and impact from factors that perhaps are not being measured.

Hands off, humans

Applying new technology to solving problems in old ways yields minimal benefits, if any. Field service organizations see the greatest benefits from automation after reviewing their processes, KPIs, and business goals to leverage exactly the kind of data processing and analysis they didn’t have before.

They guide machine learning by providing good and plentiful data, filtering out the unimportant, and prioritizing the right goals. Specificity is key.

AI will do exactly what you tell it to—including replicating inefficient processes or making dubious decisions to optimize for a single outcome.

For example, prioritizing the shortest possible wait times for a technician to arrive could result in overstaffing and idle time—costing a lot of money.

Use projections and simulations to see how various goals interact to find the optimal balance, and remember that instructing your system includes telling it what not to do.

The vision of zero-touch service scheduling and dispatching enabled by AI and the Internet of Things is increasingly becoming a reality for service providers. Resist the temptation to interfere when unnecessary so you can give the machine a chance to learn, and reap the full benefits of increased productivity and efficiency.

What can your employees do with the extra time in their day? Focus on the people stuff, of course: training and coaching, brand ambassadorship, cross- and upselling, remote support—you name it.

There is still plenty for humans to do in the increasingly automated field service world, and it’s the work that relies on person-to-person connections and trust. While your people are improving service quality and strengthening relationships with colleagues and customers, trust that automation can handle the rest.

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Manuel Grenacher, CEO, Coresystems explains that although Artificial Intelligence (AI) isn’t necessarily a new innovation with the global enterprise value derived from AI set to total $1.2 Trillion this year you need to make sure it’s working for you...

You know that voice that answers the phone and tells you to input your account number after the beep? That’s one of the numerous applications of artificial intelligence (AI) we encounter daily. If you’re reading this you probably know what AI is, but as a refresher, the term refers to a machine’s ability to imitate human cognitive abilities like problem-solving, language, strategic thinking, and learning. But its innovations and opportunities go far beyond asking Siri for driving directions to your next appointment – specifically for enterprise organizations. According to Gartner, the global enterprise value derived from AI will total $1.2 trillion this year – a 70 percent increase from 2017 – with significant expectations in areas like customer experience.

With an almost infinite number of data points and constant generation of new data, it is now impossible for the human mind to sift, sort, analyze, and draw insights from that torrent of information – yet AI uses these data sets to empower companies with strategic, informed decisions. Considering this explosive growth of AI, the following are some examples of how the field service industry can implement the technology to innovate and improve the customer experience.

Increase Productivity

Artificial intelligence optimizes the scheduling, planning, and dispatching of service and maintenance calls in real-time. Whereas traditional dispatchers must fall back on manual searches, an AI-based system values data points to generate efficient results, leading to smoother operations and the best possible utilization of your resources - which also translates into a sizable return on your investments.

An AI-supported system can factor in a variety of data with an increased level of speed and accuracy – for example, technician availability or skill level. An AI-supported system can factor in a variety of data with an increased level of speed and accuracy – for example, technician availability or skill level. Other restrictions, such as work time hours, legally mandatory lunch breaks, and travel time and distance – among others – are also be taken into consideration. Moreover, such systems can automatically notify technicians (via text, email, or other channels) about the necessary tools and parts needed for the job, not only guaranteeing higher first-time-fix rates, but also reducing wait times for available technicians.

Elevate the Customer Experience

Artificial intelligence technology also streamlines and optimizes the customer experience. With an AI-enabled platform, customers can expect real-time solutions, competent technicians equipped with the right tools and parts, and early detection of potential breakdowns. By connecting via mobile, they can track arrival times and progress, easily schedule and change appointments, and count on a quick and reliable billing system.

Support Predictive Maintenance

Lastly, with predictive maintenance, artificial intelligence is solving problems before they arise. This not only eliminates unnecessary machine condition checks but also addresses the growing skill gap between service technicians, as AI solutions can identify and address trivial tasks, which then allows the technicians to focus on customers and solutions instead.

The strides made in AI are in the direction of chatbots, language processing, image recognition, and machine learning. Even though these are considerable cost-saving and productivity benefits, many workers are afraid of AI replacing their jobs, and the entertainment industry has capitalized on an image of AI (like Star Wars, Iron Man or Black Mirror) that is more in line with types that are still being developed like Artificial General intelligence, or types that are fiction like Artificial Superintelligence.

In reality, the strides made in AI are in the direction of chatbots, language processing, image recognition, and machine learning. Rather than new technologies replacing jobs, artificial intelligence supports employees by helping them become more efficient, in areas such as predictive maintenance and customer experience. The forecasts for this inventive and advanced technology are promising, and we look forward to working with our customers to make the implementations successful.

What are some other uses that you see through your work in the field service industry? We invite you to share your thoughts in the comments section.

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Mashed up by machine learning? Dumbfounded by data science? Agnostic about AI? Nick Frank, Managing Consultant, Si2 Partners doesn’t promise to the provide all the answers, but he can offer some crucial insight into the management process on turning your field service data into profits...

Recently I have been working with Data Scientist Eric Topham co-founder of The Data Analysis Bureau, to understand why many company leaders are struggling to turn data into profits. Eric solves data problems. He is the professional who will understand if it is a Data Science or a Data Analytics challenge and then deliver the appropriate math-based algorithms.

Data Science is about discovering new patterns in data in order to make predictions and take real-time action. The mathematical technologies used in this process are dynamic and self-learning, sometimes being grouped under the ‘Artificial Intelligence’ label. In Field Service, the types of data problems addressed by these technologies might include scheduling or predictive maintenance.

Data Analytics deals with historical and more ‘static’ data, where the desire is to test ideas or hypothesis, understand relationships and develop insights into historical patterns.Data Analytics deals with historical and more ‘static’ data, where the desire is to test ideas or hypothesis, understand relationships and develop insights into historical patterns. Here techniques such as statistical modelling, data mining and visualization are used to gain results. Common examples you might recognize are knowledge management or performance reporting.

Data problem solvers such as Eric will tell you that the hardest part of his job is not developing the data solution, it is defining the problem to be solved in terms of reducing costs or increasing revenues or hopefully both.

The companies who can to articulate their business problem in terms of money and performance, make it much easier for his team to create the mathematical models to answer the questions posed.

One of the ways of defining the business problem is to use value mapping tools, such as the Value Iceberg described in February’s issue of Field Service news “Don’t be caught in the Emperor’s new clothes. First focus on the customer”.

These help companies articulate not only the direct benefits to the customer, but more importantly the hidden value of their product or service, such as improved material through-put, lower energy costs or reduced risk.

A good example would be a manufacturer of air conditioning systems who targets facility managers for whom 30% of the building’s running costs is energy. This company targets their products and services to reduce their energy by 10%, enabling a very compelling sales argument.

However, the vast majority are far blander and generally fall into three broad categories:

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• Bland USPers: Ask people about their value and they will trot out a predictable unique selling point(USP) such as 24/7 spare parts delivery. The question is do they know what this means to the customer and price accordingly.
• The Easy and Obvious: Many can tell you what their customers tell them, but not much more! Do you hear phrases such as. ‘My customer needs fast and right-first-time resolution!’. What does this really mean to the customer in terms of money and performance?
• Know, but cannot say: Then there is also a significant proportion who intuitively know their customers, but struggle to move themselves beyond the immediate need. They need help to articulate how they make their customers more profitable.

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If the key to monetizing the data is to never separate the business problem from the data problem, how should companies approach this challenge. Many lack the confidence to take the journey due to the intimidating jargon and fast pace of change.

This high-level roadmap is our attempt to demystify the process by breaking it down into 5 key common-sense steps:

[ordered_list style="decimal"]

1. Define the business problem: Whether it’s internal service operations or new services, a value mapping exercise such as the Value Iceberg is the essential start point. But do not just look at the customer. Look at the end to end industry supply chain and in particular the data hand-offs between the different actors in the supply chain. We discussed this more in our 2016 Field Service news article ‘ 5 patterns to discovering new data-driven service revenues’.
2. Solution and data needs: Identify the solutions you might offer, the critical data you need and how you will collect it. In their rush to create data services solutions, many companies jump to this step first without a clear view of the business problem. The result can be developing IoT platforms with no revenue stream or data they cannot analyse.
3. Define data problem: Formulate and scope the problem. Then scope and design the solution. Here matching internal capabilities matched with external expert partners is often the key to success.
4. Implement & evaluate: Start with a manageable pilot, revisit the business problem and ensure the solution is able to add the value you desire.
5. Scale Up: When successful, you are ready to scale up across your organization

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If data is particularly relevant to growing your field service business, then you can reach me @ nick.frank@si2partners.com

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Barrett Coakley, Product Marketing Manager, ClickSoftware offers some crucial advice in the complex and crucial area of change management...

Organisational change is hard but, given constantly shifting market conditions and the rate new technologies are released, dealing with transformation is now a requirement at most firms.

However, McKinsey reports that 70% of change programs fail to achieve their goals, largely due to employee resistance and lack of management support. With that type of failure rate, you might be wondering why even bother. Nonetheless, when done correctly, change management can have an enormous impact on employee engagement, operational efficiency and financial success.

There are three areas that are causing change within field service teams that leaders must address Field service organizations are being asked to address multiple reforms but there are three areas that should be high on your change management list; talent management issues, technology advances and new customer attitudes.

Here are some recommendations to help your field service group succeed on this change management journey.

Talent Management

According to The Service Council, 70% of service organizations report they’ll be facing a pinch as they lose workers to retirement in the coming years. The retirement of baby boomers has the potential to leave a vast knowledge and experience gap on many field service teams.

There is hope, however, as the 75 million large millennial generation has entered the workforce and they have the skills to fill these open positions.

However, field service managers must understand the drivers that motivate millennials and how they differ from the retiring baby boomers, including:

• Tech savvy: The millennial generation grew up with all things digital. They embrace technology and expect the organizations that they work for to provide the most current technology for them to perform their job.
• Mission: Millennials are looking from a deeper meaning from work. They want to feel that they are having an impact both on the company as well as greater society.
• Retention: You might have some members on your field service team that have worked in the group for 10-20+ years. Millennials, however, tend to change jobs frequently. In fact, Gallup revealed that 21% of millennials report changing jobs within the last year, which is more than three times the number of non-millennials.

Here are some areas your field service team should focus on to facilitate the changes this generation will bring to your team.

Offer Incentives:

While you might think a raise would be sufficient for millennial retention, you should instead focus on benefits you could offer.

According to Gallup, millennials are more likely than any other generation to say they would change jobs for a particular benefit or perk. They especially appreciate perks that directly impact their lives and the lives of their family. It makes sense considering many millennials are starting families, have large student loans, and desire a work-life balance.

Popular benefits for Millennials include:

• Paid paternal and maternity leave
• Student loan reimbursement
• Childcare reimbursement
• Tuition reimbursement

So instead of just offering a pay raise next year, poll your workforce to determine what they truly value.

The responses might surprise you.

Development opportunities: The best way to attract millennials is by leveraging two of their biggest desires—development and purpose.

67% of millennials are engaged at work when they strongly agree that the mission or purpose of their company makes them feel their job is importantFor instance, Gallup reports that “rallying millennials around a mission and purpose dramatically increases their employee engagement: 67% of millennials are engaged at work when they strongly agree that the mission or purpose of their company makes them feel their job is important.”

Focus your attraction and retention strategies on delivering learning opportunities and career development. This way millennials are assured that their jobs provide plenty of opportunities for skill development and career advancement.

Keep in mind millennials may want to pursue independent project work, attend conferences, take classes, and join professional organizations.

Give them the flexibility and resources to do so, whether this means tuition reimbursement, or time off work to ensure they are fulfilled.

The Impact of New Technology

New technologies like the Internet of Things (IoT), artificial intelligence (AI), machine learning, and data analytics are having a huge impact on field service operations.

These new technologies are providing real-time insights into field assets that can be used to predict when a piece of equipment might fail, allowing for proactive maintenance. However, with all of this technology, there comes the need for change across your field team in order make sense of all this new information. Here are a few steps you can take to make sure your team is prepared for the impact of technology on your field service group.

Make a Plan:

First off, you will need a plan to prepare for the impact these technologies will have across your field service organization. For example, you will need to train field engineers on how to potentially service IoT-based equipment, build a roadmap for incorporating new devices, and identify which technician or dispatch behaviours will change based on this new technology.

Will customer issues be identified at a server level when equipment fails? What does this do to the dispatch workflow? Are you incorporating wearables at an employee level to improve communication or field-based efficiency? What software will you need to ensure these devices operate smoothly within your current frameworks and infrastructure?

Create a roadmap that accounts for the short, and long-term implications of devices, services, and technician needs.

KISS Principal:

Albert Einstein once stated, “Everything should be made as simple as possible, but not simpler.” This is where the KISS principle comes into play during change management exercises. Stepping up to the challenges associated with all of these different technologies is difficult and complicated.

Everything should be made as simple as possible, but not simplerWith any digital transformation, the best possible course of action is to simplify by starting with small, simple changes. Select a small behaviour, or wearable device that your customers are using, and optimize around that. Then, scale what you have learned across more devices, customer behaviours, and internal processes. With a change this impactful it is best to keep it simple, sir.

Uberization of Service

As Amazon, Uber, Airbnb and other upstart organizations continue to heighten customer expectations, field service organizations have struggled to keep pace with these new demands.

Customers now expect transparency around service delivery such as the real-time location of the field technician responsible for the appointment as well as personalized communication preferences like text or email.

However, the delivery of exceptional service requires changes to the technician’s traditional role and skill set.

Here are few areas that should be looked at to change.

Product and Service Training:

Field service professionals understand the inner workings of the products they maintain but they might not be knowledgeable enough to upsell a new product or service to a customer.

To enable this ability, sales and marketing training should be provided to field service professionals so they understand the features and benefits of different services. Sales and marketing is a new type of training and skillset for most field service professionals but one that can really benefit the top line.

Increasing revenue is an important focus for many organizations but it is proving to be a difficult one as 76% of field service providers report they are struggling to achieve revenue growth, according to the TSIA. Sales and marketing training could be the support ticket that helps change this trend.

Soft Skill Training:

Field service professionals are now required to interact with clients in a way that elevates the customer experience, resulting in upsell opportunities and less customer churn.

64% of consumers have switched providers in at least one industry due to poor customer service.Preventing customer churn is especially important as Accenture reported that 64% of consumers have switched providers in at least one industry due to poor customer service.

To provide a higher level of personalized service requires better soft skills, something not every person has, but this ability is a key to this new service delivery model. In fact, study conducted by Development Dimensions International found that for every $1,100 invested in soft skills training, employers earned an average return of $4,000.

Training soft skills can help a technician provide more empathy towards the customer, improve communication and the ability to provide a more personalized experience.

Soft skill training is especially important for millennials as they often lack these abilities. An investment in soft skills training is worthwhile for any organization but can be particularly important in delivering a great customer experience.

Conclusion

The key to handling all of these changes is a commitment from all involved. In fact, McKinsey found that when people are truly invested in change it is 30 percent more likely to stick.

However, making the challenge even more daunting is that organizations no longer have the luxury of implementing changes over a 3-5 year period of time as in the past.

Change is no longer a periodic event, but one that is constant as the market and technology continue to evolve at faster and faster rates. Field service teams need to prepare now.

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Wiraya, a Marketing Technology firm based in Sweden which develops a Managed Mobile Customer Activation software, has been awarded €2 million in innovation grants by the European Commission, for the development of Wiraya Activation Intelligence (“Wiraya AI”).

The contribution is intended to enable further development, validation and optimisation of Wiraya’s artificial intelligence software to help Europe’s mobile operators improve customer value for their subscribers, and thereby customer loyalty.

The new functionality, Wiraya AI, automatically creates interactive voice and text communications, which allows individual dialogue with large customer groups. With such deployments of AI being predicted to potentially revolutionise the customer experience across a range of use cases including support issues, this development could be an interesting development for field service organisations.

We are really proud to be one of the few companies selected by the European Commission. The grant gives us a great opportunity to be able to drive the development of artificial intelligence within customer communication in Europe - Oskar Klingberg, CEO, Wiraya SolutionsCurrently, European mobile telecom operators are facing continuing rising industry challenges to tackle low customer satisfaction and loyalty and despite advanced churn prediction models, operators still often communicate with their customers as if they were still prospects, using generic communication that erodes trust and commitment. This is a challenge that Wiraya are aiming to tackle.

Using machine learning, the software predicts and customises what, when and how to communicate with each individual, by matching the individual’s profile with specific communication journeys. With the implementation of Wiraya AI, 5% of the annual churning customers can be saved each year, corresponding to substantial savings for the operator, and increased customer satisfaction.

“We are really proud to be one of the few companies selected by the European Commission. The grant gives us a great opportunity to be able to drive the development of artificial intelligence within customer communication in Europe. We have always tried to challenge ourselves by identifying and solving important industry-specific business problems. For the telecom sector, AI functionality will solve resource-intensive and complex customer communication flows with highly effective, automated personal dialogues.” says v, CEO of Wiraya Solutions.

The development of AI functionality begins with pilot projects in 2018 and then full commercial launch in 2019. Initial tests suggest up to 5 times higher conversion rates compared with today’s way of communicating, while delivering substantial yearly savings.

Klingberg adds: “We are now developing the functionality specifically for mobile operators, but our plan is to implement the functionality across other industries, proving the same opportunity for a whole range of businesses.”

Should the pilots be successful it will be interesting to see if this could as act as a proof of concept to roll out across over service-centric sectors.

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Manuel Grenacher, CEO Coresystems, explores the major trends that he expects to impact field service companies across 2018...

While 2017 introduced innovative new technology-based trends with the likes of artificial intelligence (AI) and augmented reality (AR), 2018 will bring real-world applications that put those buzzwords into practice. Here are the top trends we see this coming year.

The IoT will drive more proactive device maintenance, service and repair

In 2016, Gartner, Inc. forecasted that 8.4 billion connected things will be in use worldwide in 2017, with 5.5 million new devices being connected every day. The vast majority of today’s devices and machines come equipped with sensors, which transmit signals, status updates and warnings.

Field service innovators are finding ways to use the IoT and increased connectivity to their advantageThese alerts not only allow people to address problems proactively but also accelerate expectations around service time. The alerts notify users the moment their device needs maintenance or repair, which immediately puts the technician on the clock to resolve the issue. Challenges such as long response wait time and excessive appointment windows already plague the field service industry, so the IoT threatens to exacerbate these existing issues.

However, field service innovators are finding ways to use the IoT and increased connectivity to their advantage. In 2018, further innovation in the field technology space will enable technicians to take advantage of the IoT’s increased connectivity and automation in today’s devices, enabling them to provide service in real time to meet and exceed customer expectations.

Artificial intelligence will simplify and automate service appointments

2018 will focus on not the adoption of AI, but the implementation of it in real use cases. One industry that stands to greatly benefit from AI is customer service, particularly field service. Gartner forecasts that 85 percent of customer relationships will be through AI-powered services by the year 2020.

AI technology will make strides in streamlining the customer experienceIn 2018, AI technology will make strides in streamlining the customer experience. Chatbots will troubleshoot issues with customers, determining all necessary information before dispatching a technician. Powered by machine learning, chatbots will understand if a customer needs assistance in resolving an issue or wants more information about a certain piece of equipment. Logistic regression capabilities will enable chatbots to walk customers through equipment problems step by step.

AI will also automate the technician’s workflow. One of the biggest pain points in customer service – particularly field service – is dispatch time. To combat this, heuristic search functions in AI technology will determine which technicians are not only available but also knowledgeable enough to properly service the request. AI will also consolidate relevant customer details – from device history and prior appointments to technicians who are qualified and available to resolve an issue – to ensure customers receive the most efficient and painless experience possible.

Augmented reality will provide unprecedented visibility into worksites

Augmented reality enhances the way we see, hear and feel by bringing elements of the virtual world into the real world. Many people associate augmented and virtual reality with the gaming industry, but the technology offers far more than entertainment for a niche group. Industry forecasters predict that by 2020 the market for AR will reach $100 billion in value.

The increased connectivity that the IoT brings will continue to propel the application of AR in the field service sector. The increased connectivity that the IoT brings will continue to propel the application of AR in the field service sector. Using standard mobile devices and AR glasses, service technicians are finding unique ways to approach service. AR applications that allow technicians to look into machines without disassembling them have proven enormously helpful for pinpointing malfunctioning parts. Also, the ability to share data from onsite with offsite experts allows for a more collaborative approach to finding solutions. And this capacity to share knowledge and access an endless stream of information is increasing the first-time-fix rate and thereby improving the customer experience.

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