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HOW VOICE ANALYTICS CAN IMPROVE YOUR CUSTOMER SERVICE

by Chris Robinson, Director and Executive Chairman, Awaken Intelligence

 

How to turn negative emotions into a positive conversation with CX. Why VA will help you to intelligently transform your customer engagement

According to the research firm, Technavio, the global voice and speech analytics market is poised to grow by USD 2.17 billion during 2019-2023, progressing at a CAGR of almost 18% during the forecast period. What’s exciting about this news is that more businesses are recognising that this technology can transform their customer experience (CX) while helping to drive greater efficiencies across their call centres. So, if you haven’t considered implementing voice analytics (VA) and speech analytics (SA) for your contact centre here’s a few reasons to get onboard with this astounding technology.

One of the key highlights from the Technavio research is the ability VA and SA will give organisations adapting to rising trends and changes in consumer behaviour. Consumers have had to rapidly modify their own behaviours this year and many will be more au fait with technology as a result. That familiarisation will result in a greater level of expectations so how can voice analytics help you to stay across your customers’ demands?

The first advantage of VA is the ability to better understand a customer’s perspective through their voice and tone. The pitch, loudness, timbre, speech rate and pauses in a customer’s voice will help you to analyse and assess whether a conversation is proving to be a positive, or negative, experience for them. What’s more this will be a two-way conversation so you can also measure your agent’s tone of voice too. This insight will help you to understand how they are feeling during a conversation, while at the same time measuring sentiment based on the words used.

Businesses are starting to realise how important their customer experience (CX) is therefore embracing the technology to help them stay ahead of customer expectations is a no brainer. Apparently 96% of American businesses close their doors within 10 years (of being established) and a contributing factor to that is poor customer service. Making sure your customers don’t have a negative experience or equipping your agents to transform a customer problem into a positive conversation is business critical.

VA not only helps to take the heat out of a tricky situation but because it provides data you and your team can use these insights to reduce call times and repeat calls helping to reduce customer churn and improve the quality of their experience. As Gartner recently highlighted 94% of customers with low-effort interactions intend to repurchase compared with 4% of those experiencing high effort.

So, if you’re new to this technology you may want to find out more about how you can utilise a combination of language analytics and behavioural analytics such as topic modelling, natural language processing (NLP) through to acoustics and vocal emotion detection to create greater insight into your agent and customer conversations.

From this crucial date you can extract actionable insights and introduce quick changes to improve agents’ performance while analysing calls to help them to identify complains. Ensuring that you’re both compliant and able to react to vulnerable customers means that you can rapidly transform your CX. And what’s really important is that with embedded machine learning you can continue to learn and therefore adapt to your customers’ ever-changing needs. Consumers don’t stand still and in the land of call centre technology nor should you.

 

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Technology

HOW TO ACHIEVE THE BEST POSSIBLE CUSTOMER EXPERIENCE THROUGH ARTIFICIAL INTELLIGENCE

By Craig Charlton, CEO of SugarCRM

 

Before high definition televisions were introduced, home entertainment was limited to a grainy picture on a small CRT box in the corner of your living room. It could in no way compete with the picture quality seen in cinemas, for instance, with details hard to pick out. The difference was like chalk and cheese. In the early two thousands this all changed with the introduction of high definition LCD tv’s, which took home entertainment to a new level and made it feel like a truly immersive experience. More recently we’ve seen the introduction of 4K and even 8K devices, which have taken the experience even further.

In the world of CRM and CX, we’re now at a similar watershed moment. Currently, most businesses have a fragmented, dated and distorted picture of their customers, which is affecting the level of service that they can achieve and their ability to grow. Poor data quality is hitting organizations where it hurts, costing them time and money. It’s important that organisations act now to replace their current hazy view of their customer, with a sharply focused picture that’s rich in breadth and depth.

We call this a high definition customer experience, or ‘HD-CX’, and by delivering on it, businesses can reach new levels of performance and predictability, and increase customer lifetime value. There’s a long way to go however, as research indicates that 91% of data in CRM systems is incomplete, and 70% of CRM system data goes bad each year[2].

 

Craig Charlton

 Where can CX take us?

Forrester states that over the next 5-10 years CX will become “crucial for brands to survive, for them to avoid disintermediation, irrelevancy, blandness, and/or cluelessness about customer sentiment.”[3] Those brands that choose to wait 5-10 years before delivering a HD-CX experience however, will have found themselves disrupted and behind the curve.

To leapfrog competitors and fuel growth, companies need to  obtain a high definition view of their market, business and customers as soon as possible. Right the way from formulating ideal customer profiles (ICP) and identifying sectors with a propensity or intent to purchase, through to customer lifetime loyalty. HD-CX is all about drawing on accurate, up-to-date information from multiple sources and from across the organisation to reach new levels of business performance and predictability. The value of performance and predictability applies to businesses of all sizes and in all industries.

 

Why is being ‘time aware’ so crucial?

It’s about time we redefined the 360-degree customer concept and added the key missing component: Time. It was Benjamin Franklin that said, “Lost time is never found again.” Although nobody can stop the flow of time, what we can do is ensure a complete historical record of every change event in the customer journey, and augment this information via a rich repository of relevant information to ensure full situational and directional awareness of a customer.

It’s no good just having a 360-degree view of one moment in time—right now. Recording every change event in the customer journey is essential for predicting future outcomes. Accurate predictions enable companies to make better business decisions, manage risk, respond to problems and take advantage of opportunities. Organisations need to gain insight into the past, present, and future of their customer-facing business processes.

 

Stay ahead of your competitors with artificial intelligence

Making sense of all this data can be a perennial issue for companies, with the average company holding on average 162.9TB of data[4]. To make sense of this data, create a competitive advantage and deliver an unparalleled level of predictability across a whole array of different business use cases, Artificial Intelligence is the key.

Understanding your current state and how you got there is essential, but what if you had the ability to look into the future toward what your business could be? I’ve already stressed the importance of having a complete historical record of every change event in the customer journey to ensure full situational and directional awareness of your customers and your business, but AI considers the other direction of time: the future.

AI has the ability to deliver exceptional predictions, even with limited or incomplete CRM data by leveraging vast external data to consider factors your data doesn’t cover and surface insights that you may not have known existed. These unparalleled predictions allow businesses to make confident decisions and focus on the highest priority activities across marketing, sales, customer service, and more. However, not all AI-powered prediction is created equal and predication accuracy is essential for success. A proven platform with deep learning models combined with both the best quality external data and CRM data, is a combination that most companies can’t provide their customers.

If companies successfully create a time-aware and high-definition picture of their customers, they will benefit from greater customer relationships and unparalleled oversight of their businesses. However, they must utilise AI to provide exceptional customer experiences and business predictions. If businesses don’t adapt to change and instead continue to operate with an old, outdated, standard-definition view of their customers, they will lose out to competitors as they won’t be able to deliver an experience that their customers expect.

 

[2] https://www.dnb.co.uk/content/dam/english/business-trends/b2bm-db-improve-the-quality-of-your-marketing-now-1-0.pdf

[3] https://go.forrester.com/future-of-cx/#:~:text=The%20wheel%20of%20change%20is,relationships%20stable%20amid%20unprecedented%20upheaval.

[4] https://cdn2.hubspot.net/hubfs/1624046/IDGE_Data_Analysis_2016_final.pdf?t=1496694598964

 

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Technology

USING ARTIFICIAL INTELLIGENCE TO ACHIEVE CIRCULAR ECONOMY

By Professor Terence Tse, ESCP Business School

 

It is really only a matter of time before the two main trends, artificial intelligence (AI) and circular economy, would come together. A milestone of this convergence was the white paper “Artificial intelligence and the circular economy”: AI as a tool to accelerate the transition, jointly published by The Ellen MacArthur Foundation and Google earlier this year. It has kick-started the discussion on how AI can be used as a tool to help accelerate and scale our transition to a circular economy. This can be achieved by unlocking new opportunities through improving product and material design, enhancing circularity-based business models, and optimising circular infrastructure. The paper draws on the food and consumer electronics industries to illustrate the circular benefits driven by AI. The forecasted value that can emerge from these is encouraging: up to $127 billion and $90 billion a year in 2030, respectively.

 

The pace will be slow

No doubt these are very good news. It also shows how innovative technologies can take circular economy to the next level. Yet, I believe the path leading there will be full of challenges, not least because, contrary to what general media would like to get us to believe, the development of AI is, in reality, really slow.

 

There are several reasons attributable to this sluggish pace

First, there is a general shortage of AI-proficient graduates. Training up AI researchers takes time. Universities are not churning out data scientists fast enough to meet the job market demand. For those who are graduating, they will most likely be snapped up by the technology giants. Indeed, it has been estimated that some 60% of AI talent are in the employment of technology and financial services companies, leading to a ‘brain drain’ in academia, which in turn, slows down the production of qualified graduates. Small circular economy-based companies (as well as AI start-ups) will struggle to have the same hiring power, as they often lack the ability to match the levels of salaries and prestige offered by large organisations.

Another reason why circular economy-aimed companies, large or small, will struggle to deploy AI is that the technology remains a very expensive investment. AI is, at the moment, far from a plug-and-play technology. Arguably, there are off-the-shelf AI applications available in the market. But what this one size fits-all technology solutions can really do is often very limited and their effectiveness low. Inevitably, for AI to work at an acceptable, value-creating level, it is necessary to integrate it into the existing wider IT system. Customising AI applications to be embedded in the system architecture is very complex and hence very costly.

To make matters worse, the market is seemingly inundated with self-proclaimed AI companies. A recent report has suggested that 40 percent of start-ups in Europe that are classified as AI companies do not actually use artificial intelligence technologies in a way that is “material” to their businesses. As someone who researches and works in the business of AI, I can readily observe this phenomenon has already eroded the trust of many companies, making them increasingly cautious when proceeding with investment and deployment of AI.

 

Gradual developments, not quantum jump

For these reasons above, the adoption of AI, and by extension, in the area of circular economy, will be slow. This, however, does not mean there will be no advancement. Instead of “big bang” new business model creations, AI will most likely produce circular advantages through baby steps in operational enhancement gradually. For instance, one of the important elements in achieving circular economy is better asset management. In a recent research project for the European Defence Agency, my colleagues and I have discovered that there is a wide spectrum of operations for ministries of defence to save money and practise circular economy, from refurbishing and repurposing small military equipment items to reduce waste and minimise the use of virgin materials to extending the service years of capital assets. Unquestionably, the same may be applied to civilian activities. For example, combining the power of AI and drones can extend the longevity of major infrastructure such as reactors and bridges.

Advancements in drone technologies have allowed them to be deployed to take pictures at heights that are dangerous for inspectors to reach. The contributions of AI come from its ability to analyse and identify cracks as well as defects on assets that are not always visible to human eyes from captured images. Consequently, problems are detected before the assets become irreparable, thereby lengthening their lifetime.

A seemingly insignificant but potentially huge possibility of waste reduction would be saving on paper use. In the insurance industry, for instance, there is still a huge reliance on actual paper, with the communications between various stakeholders, including the underwriters, brokers and insured, passing on a large number of physical documents. AI techniques, in particular natural language processing, can help speed up the digitalisation of documents as they can go beyond the point of just reading and processing text to recognising and recording signatures and rubber stamp marks. Little by little, it will be possible to lower paper consumption.

 

The future is now

Both AI and circular economy are by themselves breakthrough ideas that are set to change the world dramatically. Combined, it can be a very powerful force of good. But this can only be achieved if we can synthesise them. For AI and circular economy to work together, it is necessary to educate AI developers to be more familiar with the idea of circular economy as well as making circularity practitioners and researchers more AI-savvy. Holding just half of the equation, we risk missing out on most of the intelligence. After all, no matter how smart machines can be, ultimately, it is the human intelligence – or stupidity – that determines the kind of future that we will be having.

 

Extract of “The AI Republic: Building the Nexus Between Humans and Intelligent Automation”

 

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