Building a Data-Driven Foundation to Super Charge Your AI Journey

5/5 (1)

5/5 (1)

AI has become a business necessity today, catalysing innovation, efficiency, and growth by transforming extensive data into actionable insights, automating tasks, improving decision-making, boosting productivity, and enabling the creation of new products and services.

Generative AI stole the limelight in 2023 given its remarkable advancements and potential to automate various cognitive processes. However, now the real opportunity lies in leveraging this increased focus and attention to shine the AI lens on all business processes and capabilities. As organisations grasp the potential for productivity enhancements, accelerated operations, improved customer outcomes, and enhanced business performance, investment in AI capabilities is expected to surge.

In this eBook, Ecosystm VP Research Tim Sheedy and Vinod Bijlani and Aman Deep from HPE APAC share their insights on why it is crucial to establish tailored AI capabilities within the organisation.

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AI Research and Reports
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Understanding the Difference Between Predictive and Generative AI

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4.7/5 (3)

In my last Ecosystm Insights, I spoke about the implications of the shift from Predictive AI to Generative AI on ROI considerations of AI deployments. However, from my discussions with colleagues and technology leaders it became clear that there is a need to define and distinguish between Predictive AI and Generative AI better.

Predictive AI analyses historical data to predict future outcomes, crucial for informed decision-making and strategic planning. Generative AI unlocks new avenues for innovation by creating novel data and content. Organisations need both – Predictive AI for enhancing operational efficiencies and forecasting capabilities and Generative AI to drive innovation; create new products, services, and experiences; and solve complex problems in unprecedented ways. 

This guide aims to demystify these categories, providing clarity on their differences, applications, and examples of the algorithms they use. 

Predictive AI: Forecasting the Future

Predictive AI is extensively used in fields such as finance, marketing, healthcare and more. The core idea is to identify patterns or trends in data that can inform future decisions. Predictive AI relies on statistical, machine learning, and deep learning models to forecast outcomes. 

Key Algorithms in Predictive AI 

  • Regression Analysis. Linear and logistic regression are foundational tools for predicting a continuous or categorical outcome based on one or more predictor variables. 
  • Decision Trees. These models use a tree-like graph of decisions and their possible consequences, including chance event outcomes, resource costs and utility. 
  • Random Forest (RF). An ensemble learning method that operates by constructing a multitude of decision trees at training time to improve predictive accuracy and control over-fitting. 
  • Gradient Boosting Machines (GBM). Another ensemble technique that builds models sequentially, each new model correcting errors made by the previous ones, used for both regression and classification tasks. 
  • Support Vector Machines (SVM). A supervised machine learning model that uses classification algorithms for two-group classification problems. 

Generative AI: Creating New Data

Generative AI, on the other hand, focuses on generating new data that is similar but not identical to the data it has been trained on. This can include anything from images, text, and videos to synthetic data for training other AI models. GenAI is particularly known for its ability to innovate, create, and simulate in ways that predictive AI cannot. 

Key Algorithms in Generative AI 

  • Generative Adversarial Networks (GANs). Comprising two networks – a generator and a discriminator – GANs are trained to generate new data with the same statistics as the training set. 
  • Variational Autoencoders (VAEs). These are generative algorithms that use neural networks for encoding inputs into a latent space representation, then reconstructing the input data based on this representation. 
  • Transformer Models. Originally designed for natural language processing (NLP) tasks, transformers can be adapted for generative purposes, as seen in models like GPT (Generative Pre-trained Transformer), which can generate coherent and contextually relevant text based on a given prompt. 

Comparing Predictive and Generative AI

The fundamental difference between the two lies in their primary objectives: Predictive AI aims to forecast future outcomes based on past data, while Generative AI aims to create new, original data that mimics the input data in some form. 

The differences become clearer when we look at these examples.  

Predictive AI Examples  

  • Supply Chain Management. Analyses historical supply chain data to forecast demand, manage inventory levels, reduces costs and improve delivery times.  
  • Healthcare. Analysing patient records to predict disease outbreaks or the likelihood of a disease in individual patients. 
  • Predictive Maintenance. Analyse historical and real-time data and preemptively identifies system failures or network issues, enhancing infrastructure reliability and operational efficiency. 
  • Finance. Using historical stock prices and indicators to predict future market trends. 

Generative AI Examples  

  • Content Creation. Generating realistic images or art from textual descriptions using GANs. 
  • Text Generation. Creating coherent and contextually relevant articles, stories, or conversational responses using transformer models like GPT-3. 
  • Chatbots and Virtual Assistants. Advanced GenAI models are enhancing chatbots and virtual assistants, making them more realistic. 
  • Automated Code Generation. By the use of natural language descriptions to generate programming code and scripts, to significantly speed up software development processes. 

Conclusion 

Organisations that exclusively focus on Generative AI may find themselves at the forefront of innovation, by leveraging its ability to create new content, simulate scenarios, and generate original data. However, solely relying on Generative AI without integrating Predictive AI’s capabilities may limit an organisation’s ability to make data-driven decisions and forecasts based on historical data. This could lead to missed opportunities to optimise operations, mitigate risks, and accurately plan for future trends and demands. Predictive AI’s strength lies in analysing past and present data to inform strategic decision-making, crucial for long-term sustainability and operational efficiency. 

It is essential for companies to adopt a dual-strategy approach in their AI efforts. Together, these AI paradigms can significantly amplify an organisation’s ability to adapt, innovate, and compete in rapidly changing markets. 

AI Research and Reports
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Evolving Landscape: AI Startups Take Centre Stage in 2024

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The tech industry tends to move in waves, driven by the significant, disruptive changes in technology, such as cloud and smartphones. Sometimes, it is driven by external events that bring tech buyers into sync – such as Y2K and the more recent pandemic. Some tech providers, such as SAP and Microsoft, are big enough to create their own industry waves. The two primary factors shaping the current tech landscape are AI and the consequential layoffs triggered by AI advancements. 

While many of the AI startups have been around for over five years, this will be the year they emerge as legitimate solutions providers to organisations. Amidst the acceleration of AI-driven layoffs, individuals from these startups will go on to start new companies, creating the next round of startups that will add value to businesses in the future. 

Tech Sourcing Strategies Need to Change 

The increase in startups implies a change in the way businesses manage and source their tech solutions. Many organisations are trying to reduce tech debt, by typically consolidating the number of providers and tech platforms. However, leveraging the numerous AI capabilities may mean looking beyond current providers towards some of the many AI startups that are emerging in the region and globally. 

The ripple effect of these decisions is significant. If organisations opt to enhance the complexity of their technology architecture and increase the number of vendors under management, the business case must be watertight. There will be less of the trial-and-error approach towards AI from 2023, with a heightened emphasis on clear and measurable value. 

AI Startups Worth Monitoring 

Here is a selection of AI startups that are already starting to make waves across Asia Pacific and the globe. 

  • ADVANCE.AI provides digital transformation, fraud prevention, and process automation solutions for enterprise clients. The company offers services in security and compliance, digital identity verification, and biometric solutions. They partner with over 1,000 enterprise clients across Southeast Asia and India across sectors, such as Banking, Fintech, Retail, and eCommerce. 
  • Megvii is a technology company based in China that specialises in AI, particularly deep learning. The company offers full-stack solutions integrating algorithms, software, hardware, and AI-empowered IoT devices. Products include facial recognition software, image recognition, and deep learning technology for applications such as consumer IoT, city IoT, and supply chain IoT. 
  • I’mCloud is based in South Korea and specialises in AI, big data, and cloud storage solutions. The company has become a significant player in the AI and big data industry in South Korea. They offer high-quality AI-powered chatbots, including for call centres and interactive educational services. 
  • H2O.ai provides an AI platform, the H2O AI Cloud, to help businesses, government entities, non-profits, and academic institutions create, deploy, monitor, and share data models or AI applications for various use cases. The platform offers automated machine learning capabilities powered by H2O-3, H2O Hydrogen Torch, and Driverless AI, and is designed to help organisations work more efficiently on their AI projects. 
  • Frame AI provides an AI-powered customer intelligence platform. The software analyses human interactions and uses AI to understand the driving factors of business outcomes within customer service. It aims to assist executives in making real-time decisions about the customer experience by combining data about customer interactions across various platforms, such as helpdesks, contact centres, and CRM transcripts. 
  • Uizard offers a rapid, AI-powered UI design tool for designing wireframes, mockups, and prototypes in minutes. The company’s mission is to democratise design and empower non-designers to build digital, interactive products. Uizard’s AI features allow users to generate UI designs from text prompts, convert hand-drawn sketches into wireframes, and transform screenshots into editable designs. 
  • Moveworks provides an AI platform that is designed to automate employee support. The platform helps employees to automate tasks, find information, query data, receive notifications, and create content across multiple business applications. 
  • Tome develops a storytelling tool designed to reduce the time required for creating slides. The company’s online platform creates or emphasises points with narration or adds interactive embeds with live data or content from anywhere on the web, 3D renderings, and prototypes. 
  • Jasper is an AI writing tool designed to assist in generating marketing copy, such as blog posts, product descriptions, company bios, ad copy, and social media captions. It offers features such as text and image AI generation, integration with Grammarly and other Chrome extensions, revision history, auto-save, document sharing, multi-user login, and a plagiarism checker. 
  • Eightfold AI provides an AI-powered Talent Intelligence Platform to help organisations recruit, retain, and grow a diverse global workforce. The platform uses AI to match the right people to the right projects, based on their skills, potential, and learning ability, enabling organisations to make informed talent decisions. They also offer solutions for diversity, equity, and inclusion (DEI), skills intelligence, and governance, among others. 
  • Arthur provides a centralised platform for model monitoring. The company’s platform is model and platform agnostic, and monitors machine learning models to ensure they deliver accurate, transparent, and fair results. They also offer services for explainability and bias mitigation. 
  • DNSFilter is a cloud-based, AI-driven content filtering and threat protection service, that can be deployed and configured within minutes, requiring no software installation. 
  • Spot AI specialises in building a modern AI Camera System to create safer workplaces and smarter operations for every organisation. The company’s AI Camera System combines cloud and edge computing to make video footage actionable, allowing customers to instantly surface and resolve problems. They offer intelligent video recorders, IP cameras, cloud dashboards, and advanced AI alerts to proactively deliver insights without the need to manually review video footage. 
  • People.ai is an AI-powered revenue intelligence platform that helps customers win more revenue by providing sales, RevOps, marketing, enablement, and customer success teams with valuable insights. The company’s platform is designed to speed up complex enterprise sales cycles by engaging the right people in the right accounts, ultimately helping teams to sell more and faster with the same headcount.  

These examples highlight a few startups worth considering, but the landscape is rich with innovative options for organisations to explore. Similar to other emerging tech sectors, the AI startup market will undergo consolidation over time, and incumbent providers will continue to improve and innovate their own AI capabilities. Till then, these startups will continue to influence enterprise technology adoption and challenge established providers in the market.

AI Research and Reports
0
Verint Acquires Conversocial for Greater Digital Engagement

5/5 (1)

5/5 (1)

Verint has announced the intention to  acquire Conversocial, a US-based social media management system provider for USD 50 million to integrate social messaging capabilities across Verint’s cloud platform. The deal is expected to be closed in Verint’s third quarter subject to customary closing conditions and regulatory clearances.

Verint has been expanding their digital engagement capabilities through acquisitions. In June, Verint expanded their Workforce Management (WFM) offerings to include AI-driven insights for better hiring decisions through the acquisition of HireIQ. To extend Verint’s omnichannel cloud Voice of the Customer (VoC) portfolio, Verint acquired Foresee. Verint is also building IVA capabilities and recently launched a low code version of their IVA solution to make it easier for brands to build the solution without the need for technical knowledge.

The Need to Enhance Digital Engagement

Using self-service and messaging as the first point of connection to engage with a brand is growing rapidly. It accelerated during the pandemic, and it is common now for individuals to engage with their financial institution, airlines, retail and others through social media. Now that customers are demanding it, brands are lifting their game and engaging with customers on the platform of their choices. This acquisition will allow Verint to deepen their digital engagement with customers across Marketing, Contact Centres and digital functions – it follows the pulse of today’s customers.  

Digital discussions are accelerating and having a platform that can orchestrate as well as understand all the data from each digital and social messaging channel is important. Verint is taking the data discussion seriously and earlier this year they launched Engagement Data Management Solution (EDM). The ‘’data’’ piece is huge and cuts across functions – from back-office communications to gathering data across all channels and touchpoints. However, where this is going wrong for some enterprises is that all the data they collect sits in multiple repositories; in some instances the data has not been analysed for years! Managing the multiple social experiences, including data management and insights from these multiple sources, will be key to delivering proactive customer experience.

The data discussion is particularly significant for a vendor such as Verint – they are well known for their speech analytics and compliance management capabilities. These are all critical to managing multiple channels of conversation. They help agents to be accurate, efficient, and compliant; allow organisations to use asynchronous channels and social messaging and digital channels; immediately rectified errors through monitoring the data on the channels and so on. More importantly, they allow organisations to pick up points from conversations that can be passed on to Marketing to gauge the effectiveness of the messaging and campaigns. Organisations can ‘’identify and fix” problems by truly listening to customers.

Ecosystm Insights Tech Vendor Guidance

Why Conversocial

If we look at the Conversocial customer stories, we realise how relevant their offerings are to industry requirements. They offer brands the ability to engage through an automated channel and chatbots. Whirlpool appears to have benefited by integrating channels to deliver better customer care as well as communicating with field engineers through WhatsApp. Another customer, Freshly – a meals delivery company – saw a spike in incoming queries at the start of the pandemic. They were able to use automation to ease of the load and say that 50% of the conversations were handled in-channel through automation without the need for human agent intervention. They were also able to use Facebook Messenger as a preferred contact channel and decreased their cost-per-contact.

This acquisition demonstrates how Verint is taking the digital and the data discussion seriously. CX Vendors that do not move fast in building end-to-end digital capabilities will find it hard to compete in a highly competitive CX market.

Experience Economy
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The rise in Conversational Commerce – meeting customers on their terms

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Customer needs are changing. Quickly. In 2020 having a great digital strategy went from being a nice-to-have to an absolute necessity. And in 2021, businesses that have great omnichannel experiences will go from a small minority to a majority as customers demand that they are served on their terms in their chosen platform. Only 14% of businesses in Singapore offer a complete omnichannel experience today – serving customers on their terms regardless of the location or platform (Figure 1). These businesses are setting the benchmark that the rest of the market needs to meet soon.

Singapore Businesses Struggle with their Omnichannel Strategy

The Growing Importance of Social Media in Delivering Customer Experience

Chat and messaging are quickly becoming the normal way to interact with businesses – the view of a few years ago that “no one wants to chat with a bot” has quickly turned around. Now virtual assistants and chatbots are the second most important self-service channel for businesses in Singapore (Figure 2).

The growing relevance of Virtual Assistants and Chatbots

In fact, Zendesk’s global study shows that most customers (45%) use embedded messaging over social messaging apps (31%) and text/SMS (20%). That might be great for self-service, but for commerce, boundless opportunities exist to move to where the customer lives, communicates, and socialises today.

Smart businesses understand that customers spend their lives in other chat and social media platforms – such as Facebook Messenger, TikTok, Instagram, WeChat, Discord and WhatsApp. More customers expect to be served in these channels; they expect to be able to transact with their brands of choice. Why should they go to a mobile banking app to find their balance? Why can’t they get it in WhatsApp? They are often learning about the next Jordan or Yeezy shoe drop from their social network in Messenger – so why not transact with them there? Consider all your own personal WhatsApp, Messenger and other messaging platform groups discussing social activities, sporting teams, school activities or the latest fashion – these are ALL opportunities for commerce (Figure 3).

Number of Monthly Active Users on Social Media Platforms

And there are use cases now. Airlines – such as KLM and Etihad Airways – are engaging customers on WeChat, Kakao Talk, and WhatsApp, helping them reschedule flights and answering customer service queries.  Telecommunications providers are allowing customers to raise issues on messaging platforms – and are also using them to upsell and cross-sell new services. Transportation providers are making it easier to find a car or the the next scheduled bus right there in the messaging platforms. Retailers – such as 1-800 Flowers and Culture Kings – are not only serving customers but finding new customers on these messaging platforms.

Going beyond the messaging platforms, businesses are also looking to serve customers on their smart devices – such as Amazon Alexa/Echo and Google Nest/Home devices. Alerting customers to order updates, shipping details and product promotions is becoming standard practice for leading businesses. Digitally-savvy banks are allowing customers to not only track their balance but also make transfers and payments using these smart platforms.

Customers are more comfortable with these conversational commerce options – and they actually expect you to offer such services on your site, in your app, on their smart devices, and on their messaging platforms of choice. Your ability to provide outstanding customer experiences will not only be your ticket back to revenue growth but the recipe for long term business success. Meeting customer needs on their terms is a good place to start.

Delivering a Personalised Conversational Customer Experience

Customer experience (CX) decision-makers will have to rethink how they approach building richer CX capabilities to deliver personalised conversational interactions with customers.

Messaging should become part of a wider AI, Data, and Mobile strategy. Contact centre teams might feel that this is too ambitious a project and would prefer to continue to serve customers through the more traditional channels only. So, it is important to identify the key stakeholder/s who will drive the initiative. And the contact centre team should work with the Digital, Innovation and Marketing teams.

Designing the mobile experience and in app messaging for CX should have some of the following features:

  • Ability to click a button to request for a service or escalate an issue that will, in turn, result in the company contacting the customer either by messaging or calling.
  • Giving customers the option to contact through popular messaging platforms such as Facebook Messenger, WhatsApp, LINE, WeChat, and others. Unifying these systems in a single interface that integrates with your customer service application is best practice.
  • Having one single interface to manage and make payments – within the app itself or on the social messaging platform. Conversational commerce is about creating an ongoing relationship with customers throughout the entire customer journey. Don’t just focus on the sale or the post-sales experience – customers expect to be able to interact with your business from their platform of choice regardless of their need or stage in the customer journey.
  • Embed deep analytics into the communication services to help the organisation better deliver a personalised CX.
  • Ensure you have a solid, unified knowledge management interface at the backend so that all questions lead to the same answers regardless of channel, platform or touchpoint.

Your opportunity to drive greater business success lies in your ability to better win, serve and retain your customers. Refresh your customer strategy and capability today to make 2021 an exceptional year for your business.

Improve Customer Experience eBook
1
The Value of the Human Touch in 2021

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5/5 (2)

Authored by Alea Fairchild and Audrey William

There is a lot of hope on AI and automation to create intellectual wealth, efficiency, and support for some level of process stability. After all, can’t we just ask Siri or Alexa and get answers so we can make a decision and carry on?

Automation has been touted as the wonder formula for workplace process optimisation. In reality it’s not the quick fix that many business leaders desire.  But we keep raising the bar on expectations from automation. Investments in voice technologies, intelligent assistants, augmented reality and touchscreens are changing customer experience (Figure 1). Chatbots are ubiquitous, and everything has the potential to be personalised. But will they solve our problems?

Important customer touchpoints

100 percent automation is not effective

Let’s first consider using automation to replace face-to-face interactions. There was a time when people were raving about the check-in experience at some of the hotels in Japan where robots and automated systems would take care of the check-in, in-stay and check-out processes. Sounds simple and good? Till 2019, if you checked into the Henn-na Hotel in Japan, you would be served and taken care of by 243 robots. It was viewed by many as a template for what a fully automated hotel could look like in the future.

The hotel had an in-room voice assistant called Churi. It could cope with basic commands, such as turning the lights on and off, but it was found to be deficient when guests started asking questions about places to visit or other more sophisticated queries. It was not surprising that the hotel decided to retire their robots. In the end it created more work for the hotel staff on-site.

People love the personal touch when they are in a hotel; and talking to someone at the front desk, requesting assistance from hotel staff, or even just a short chat over breakfast are some of the small nuances of why the emotional connection matters. Many quarantine hotels today use robots for food delivery, but the hotel staff is still widely available for questions. That automation is good, but you need the human intervention. So, getting the balance right is key.

Empathy plays a big role in delivering great Customer Experience

Similarly, there was a time when many industry observers and technology providers said that a contact centre will be fully automated, reducing the number of agents. While technologies such as Conversational AI have come along where you can now automate common or repetitive questions and with higher accuracy levels, the human agent still plays a critical role in answering the more complex queries. When the customer has a complicated question or request, then they will WANT to speak to an agent.

When it reaches a point where the conversation with the chatbot starts getting complicated and the customers need more help there should be the option – within the app, website or any other channel – to escalate the call seamlessly to a human agent. Sometimes, a chat is where the good experience happens – the emotional side of the conversation, the laughter, the detailed explanation. This human touch cannot be replaced by machines. Disgruntled customers are happier when an agent shows empathy. Front line staff and human agents act as the face of a company’s brand. Complete automation will not allow the individual to understand the culture of the company. These can be attained through conversations.

Humans as supervisors for AI – The New Workplace

Empathy, intuitiveness, and creativity are all human elements in the intelligence equation. Workers in the future will need to make their niche in a fluid and unpredictable environment; and translating data into action in a non-replicable way is one of the values of human input. The essence of engineering is the capacity to design around human limitations. This requires an understanding of how humans behave and what they want. We call that empathy. It is the difference between the engineer who designs a product, and the engineer who delivers a solution. We don’t teach our computer scientists and engineering students a formula for empathy. But we do try to teach them respect for both the people and the process.

For efficiency, we turn to automation of processes, such as RPA. This is designed to try to eradicate human error and assist us in doing our job better, faster and at a lower cost by automating routine processes. If we design it right, humans take the role of monitoring or supervisory controlling, rather than active participation.

At present, AI is not seen as a replacement for our ingenuity and knowledge, but as a support tool. The value in AI is in understanding and translating human preferences. Humans-in-the-loop AI system building puts humans in the decision loop. They also shift pressure away from building “perfect” algorithms. Having humans involved in the ethical norms of the decision allows the backstop of overly orchestrated algorithms.

That being said, the astute use of AI can deepen insights into what truly makes us human and can humanise experiences by setting a better tone and a more trusted engagement. Using things like sentiment analysis can de-escalate customer service encounters to regain customer loyalty.

The next transformational activity for renovating work is to advance interactions with customers by interpreting what they are asking for and humanising the experience of acquiring it which may include actually dealing with a human contact centre agent – decisions that are supported at the edge by automation, but at the core by a human being.

Implications

Ecosystm research shows that process automation will be a key priority for technology investments in 2021 (Figure 2).

Digital Technology focus for 2021

With AI and automation, a priority in 2021, it will be important to keep these considerations in mind:

  1. Making empathy and the human connection the core of customer experiences will bring success.
  2. Rigorous, outcome-based testing will be required when process automation solutions are being evaluated. In areas where there are unsatisfactory results, human interactions cannot – and should not – be replaced.
  3. It may be easy to achieve 90% automation for dealing with common, repetitive questions and processes. But there should always be room for human intervention in the event of an issue – and it should be immediate and not 24 hours later!
  4. Employees can drive greater value by working alongside the chatbot, robot or machine.

Ecosystm Predicts: The Top 5 Customer Experience Trends for 2021

Download Ecosystm’s complimentary report detailing the top 5 customer experience trends for 2021 that your company should pay attention to along with tips on how to stay ahead of the curve.

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Ecosystm Predicts: The Top 5 Contact Centre Trends for 2021

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5/5 (1)

Running a contact centre has been extremely challenging in 2020. Contact centres have had to ensure business continuity, keep the focus on customer experience, and manage and motivate a largely remote workforce. Since the outbreak of COVID-19, not only have contact centres seen high inbound activity, but they have also had to manage agents who are dispersed and working remotely. 2020 has seen many contact centres starting, accelerating or re-focusing their digital transformation initiatives (Figure 1).

COVID Impact on Contact Centres

2021 will see contact centres focusing on transformation, not only to survive but also because their organisations and clients will expect more process efficiency and better customer experience. Ecosystm Advisors Audrey William and Ravi Bhogaraju present the top 5 Ecosystm predictions for Contact Centres Trends in 2021.

This is a summary of our predictions on the top 5 Contact Centre Trends for 2021 – the full report (including the implications) is available to download for free on the Ecosystm platform here.

The Top 5 Contact Centre Trends for 2021

  1. Remote Working Will Force Contact Centres to Re-evaluate Security Measures

Security has always been a concern for contact centre leaders. Improper data use by agents and agents breaching confidentiality are the biggest security challenges for contact centres. This has been further heightened, especially the fear of agents purposely breaching confidentiality while working from home.

Contact centres are still trying to figure out the best security measures when managing customer data, especially in the work-from-home environment. There is greater scrutiny over security and compliance measures – what agents view, how agents access the data, when agents log in and out of the system. Outsourcing providers will also have to guarantee high levels of security – a trusted relationship and defining the best practices on working from home will not be sufficient.

Many contact centres will trial different methods – from installing video surveillance cameras, desktop monitoring tools and access controls. Others will test technologies that can mask the information captured through mobile devices. This presents immense opportunities for vendors, as contact centres will rely heavily on technology to re-invent their security practices.

  1. Contact Centres will Invest in Conversational AI – Chatbots will No Longer be Enough

Many enterprises have rushed into deploying chatbots with expectations that these engines can solve the problem of high call volumes. The outcomes have often been poor, leaving customers frustrated and opting to interact with a live agent instead. Implementing a basic chatbot does not fully solve the problem and will force companies back to the drawing board.

Conversational AI offers a different experience by designing multiple forms of dialogues and conversations. It requires conversational design and the algorithms go through rigour from the start. The aim should be to make the channel irresistible – one that customers have confidence in, and that can reduce the need to email or call an agent. Successful uses cases have shown that conversational AI can reduce calls and repetitive queries by 70-90%.  Ecosystm research finds that contact centres are ramping up their self-service capabilities and their adoption of AI and machine learning.

  1. Offshore Centres will Re-invent Themselves and Make a Comeback

2020 has seen contact centres in offshore locations struggle to offer services to global clients. Many of these operators have been plagued by poor internet connectivity at agents’ homes, and unfavourable home working environments. These outsourcing locations remain vital however, for multiple reasons – for example the range of services offered, agent specialisation, costs or diversity in agent profile.

Contact centre outsourcing providers will make a comeback in 2021 and we can expect new models to appear. Many providers across the globe have been running successful work-from-home only operations for years – other outsourcing providers will learn from these best practices. Organisations will find that bringing jobs back to high-cost locations will incur more costs. A full onshore model may not be the right model for business continuity, and organisations will prefer to have back-up locations to ensure continuity of services if another pandemic or catastrophe happens. Organisations will want to see the outsourcing providers offer them a choice of location – they will prefer some services to be delivered from offshore locations and others to remain onshore.

  1. Digital and Mobile will be the Cornerstone of Deeper Customer Engagement

COVID-19 has changed how customers want to be served, and organisations have had to re-evaluate how they use their channels – e.g. email, web, chat and voice. Customer profiles and expectations have changed over the year and they are more digital savvy and are more likely to interact with brands through digital and mobile apps. They will expect a single point of interaction – for their enquiries and to complete their transactions. For instance, they will expect to chat while filling up shopping carts. Introducing chat capabilities within mobile apps is a good way to impress customers – this can be an effective way to push promotions and upsell. Capabilities such as the ability to directly place a call from a website will make the customer experience exceptional. Customers will expect to move between channels easily when interacting with a brand.

  1. Workplace Collaboration Will be Fully Integrated into Contact Centres

Contact centres will reassess their business and talent models. The focus on employees will be in two major areas:

  • Productivity. The contact centre floor dynamics have changed in how agents are spread out across outsourcing locations and in-house contact centres. Agents are no longer located in the same room or floor and do not have access to their usual way of work – continual training, digital signage that provides guidance and demonstrates KPIs, conversations with supervisors, managers, and team members for guidance or assistance, easy access to back-office functions and so on. This can impact their productivity.  
  • Engagement. Contact centre staff often work in high-stress environments -chasing sales targets and deadlines, handling complaints – and it is important for managers and supervisors to be able to engage and motivate them constantly. Remote working has further exacerbated the stress for those agents who do not have a conducive working environment at home.

Contact centres will increasingly look to workplace collaboration platforms and  tools to improve employee productivity and experience.


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