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Call Center, Omnichannel
[Ultimate Guide] How to Set Up a Call Center in 2022?
As we enter into a brand new year, we wonder what this new year is going to look like, what will be the new opportunities and challenges. The rapid transformation in technology has kept all of us on tenterhooks. It is filled with excitement (as it means more business opportunities) and nervousness because in business we hate disruption. We will focus on Customer Experience, Customer Engagement and the transformation that we can expect in 2025.
What have we witnessed so far?
Here's a comparison between traditional and modern customer engagement
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While the competition is intensifying, the modern customer is expecting the brand to provide a more personalised experience. Now with the advent of AI and Machine Learning models, predictive analytics can do a lot. From product recommendation to proactive support, hyper-personalisation is scaling newer heights. Faster internet with 5G is paving the way for video based communication and making it more pervasive. IOT is transforming the device much more intelligent and connected. Brands can monitor the products in real-time and intimate customers of potential need of maintenance, avoiding any downtime.
However, to provide hyper personalisation brands have to be careful as collection of personal data should not compromise on privacy and data security. Personal data protection regulation is getting strict across the world and its interpretation is different in different countries.
Brands need to be a lot more cautious in the pursuit of providing hyper-personalization.
Let us analyze the various trends that will get into the mainstream in this year. Some of them have already become more entrenched while business will be evaluating the others.
Hyper-Personalization
This is covered in the previous section. Brands are using various approaches to provide this experience. With customer data and user behaviour, predictive models using AI are able to tailor the customer experiences. Earlier e-commerce platforms like Amazon or Media platforms like Netflix were using recommendation engines to drive personalisation. Now, with the advent of better and pervasive ML models, it will be easier for brands to offer such hyper-personalization. Hyper personalisation can also help customers getting better offers or timely updates based on their activities.
Example: Customer is alerted when AI detects unusual transaction from customer spending pattern.
However dependency on clean data and concerns related to data privacy need to be appropriately considered.
This will result in enhanced customer loyalty and better business outcome.
Agentic AI
Agentic AI is a type of artificial intelligence (AI) that can make decisions, take actions, and work autonomously without human oversight.
This will be one of the most roaring buzzwords for 2025. Yes, we have already seen companies like Microsoft (copilot), Google (Agentspace) and Salesforce (Agentforce) showcasing the power of agentic AI. This marks the third wave of AI. It adds the ability to take action by integrating Generative AI with tools.
While voicebots and chatbots can provide responses and hold conversation, Agentic AI can think and perform action. Example - A customer is requesting for refund on his last purchase. The agentic AI can evaluate the authentication of customer request, validate his purchase history, can check the refund policy and eligibility and inform the refund agent. The refund agent can process the request, seek approval (as per business workflow) and intimate the same back to the customer as soon as the refund is processed.
There will be myriads of AI agents that will be providing the next level of automation and transforming the world of customer engagement.
Agentic AI will give rise to multi agent systems which can work in coordination to resolve customer requirements.
It will bring the following benefits:
Omnichannel 2.0
While Omnichannel is already becoming a table stake, brands will look for true channel-switching experiences without losing context. So far Omnichannel has enabled brands to communicate with customers using different channels - voice, messaging, email, SMS, social etc. Deeper integration with customer data, CRMs will assist agents to provide proactive response and improve the customer experience.
Brands would like to explore interconnection between the offline and online world to make customer engagement fully Omnichannel.
Example: The customer has purchased from the physical store and later checking the merchandise online should be recognised by the brand and offered seamless customer experience.
Proactive Customer Engagement
Anticipating the customer needs or issues and addressing them before the customer reaches out or creates a ticket will drive better customer engagement. This shift from reactive to proactive can be achieved by using predictive analytics.
Lets say the data shows that a particular customer usually reaches out to the business just before his plan is getting over. He wants to explore if there is any deal that can be availed. With predictive analytics, this customer can be reached out before he is reaching the business. This will ensure better customer retention.
Various brands offer renewal notification before plan expiry or loyalty notification for new offers based on the customer preference.
However, with predictive analytics and machine learning, this can go up by multiple notches. It can understand the user behaviour and the potential of conversion and send target messages or call them if possible.
Real Time Assistants
The support team - agents and supervisors can benefit with real time assistants. AI powered Agent assist can monitor real time conversations and assist the agent with the next best response. It can be a rebuttal engine for the sales agent so that they can quickly respond to any objections.
The supervisor can be alerted about the conversations which need their attention. AI powered Supervisor Assist can monitor real time calls and chats. The admin can configure the intent or nature of calls that should be flagged to him. Supervisor assist monitors the conversation in real time and flags the call which has some sort of anomaly. The supervisor can then review the call from live transcripts or barge into the call (whisper/conference mode).
Real Time Assistants provide numerous benefits:
While all the past calls and conversations are recorded and stored, it is not convenient to review it while on call with a customer. Conversation summary enables the agent or supervisor to quickly review the past conversation while assisting the customer. This is also useful to create the call notes and reduce the after call works.
Sentiment analysis is a good indicator of customer satisfaction. Since all customers do not provide feedback or CSAT score, it is even more important to know the customer sentiment.
Supervisors can filter the conversations based on the sentiment level and analyse the specific conversations.
Call transcripts are a rich source of voice of customer and can be used to analyse what has transpired in the call. This data can be further distilled and analysed by the product team and other relevant teams.
Interaction Analytics and Quality Assurance
Manual QA has limited ability to score the conversations and identify the training requirements. With AI powered Interaction Analytics, 100% of the conversations can be analysed instantly. This can help the business in scoring the agents, identifying the areas of improvement and providing real time insights.
A lot of escalations are hidden within a call which may not surface unless the agent highlights it to the supervisor or the customer actually escalates to the authority. With interaction analytics such escalations can be discovered immediately and corrective action can be taken before it blows out of proportions.
Top benefits with Interaction Analytics:
The Role of AI and Automation in Shaping Engagement
Key trends shaping the future of customer engagement in 2025 is heavily pinned on the power of AI. This is an indisputable fact that brands will leverage the capability that will be delivered by AI. However with AI, there are certain concerns which need to be addressed as well.
Data Privacy: With rapid use of LLM, brands need to be cautious how their data are being used and which data should be contained within the organization. This is also applicable to customer data, PII data masking and data purging.
Return on Investment (ROI): Usually, ROI throws the spanner in the works because tangible ROI may or may not be visible from the day one. Decision makers should be watchful of this and understand that AI and automation is planned for a long term goal. Needless to say, some automation discussed above will have a clearer ROI than others. But it is essential to budget it so that as AI takes over, your organisation is not left out.
Preparing for 2025: Actionable Insights for Businesses
To adapt to these rising trends there should be a preparation plan for the organisation. Few things that are essential for business leaders to consider are:
This year will be very transforming as the third wave of AI has just set in. This will provide numerous opportunities for businesses to scale and enrich the customer engagement. It will be necessary to align your business with these trends and benefit from the innovation that AI is offering.
