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Drivers Of Conversational AI

October 30, 2020  •  9 minute read

Fundamentals of a Growing Market

Conversational AI systems have become one of the most followed spaces across the spectrum of AI in marketing. With more companies now opting to deploy their chatbots, voice assistant, and NLP-powered bots, it seems as if there is a revolution in the market. 

The conversational AI market is forecasted to reach $22.6 billion by 2024, with North America, APAC Region, and Latin America leading the frontier. The actual insight here is that the market is expected to grow at a CAGR of approximately 30%. This means there will be several new technology platforms, use-cases, and growing data points that will make the decision-making around conversational AI a little more complex. 

Here are some basics you should have a grip on before you decide on the right route for your conversational marketing platform idea:

  1. Conversational AI vs. Scripted Responses: Scripted answering machines are static with their responses. Conversational AI systems are adaptive. Even if a query comes outside the paradigm of its training dataset, the conversational AI system is programmed to ‘learn’ from it. Hence, by design, conversational AI platforms are evolutionary. 
  2. Applicable for Internal and External Customers: You can use the same conversational capabilities for your internal customers to encourage self-service whenever they seek help. Use conversational AI systems for both D2C business cases and for enterprise knowledge services. 

Elements of an Effective Conversational AI System

When you start looking under the hood of bots or messaging apps with conversational capabilities, you will generally find the following coming together seamlessly. If you have plans to develop and deploy a conversational AI system, you should start with these foundational ideas:

  1. Performance Data & Analytics: You will need performance and data analytics capabilities on two fronts – the customer data and the customer-AI conversational analytics. It is better to use buyer personas as the building ground to help your AI system identify the right customer. The analytics on your AI system’s interactions will flow into improving its efficacy over time. 
  2. Cloud Storage and Processing: You can use Google Cloud for initial deployment. The cloud capabilities will help you store more historical, training, and analytics data. You will also get access to faster data processing with cloud RAM going as high as 12-13 GB. However, once the usage limit has been breached, you will have to start focusing on cost optimization. Microsoft Azure, AWS, Google Cloud, and Snowflake are great alternatives to fulfil your entire cloud requirement. 
  3. Fluid UI/UX: While you are busy deploying sophisticated technology systems, do not forget that eventually, you are developing a tool for conversational advertising. Hence, the user interface has to align with your brand identity while providing an optimal user experience. 
  4. NLP, NLG, and Machine Learning Capabilities: Natural language processing, natural language generation, and machine learning are the common forms of technological frameworks you will need. Hence, make sure your internal engineering team or external technology vendor has a clear roadmap for the cloud cost plans, access to talent, and a well-defined repository of tools and platforms to be used in the entire process. 

Now that you have a clear idea of what is under the hood of a conversational AI system, here are the common use-cases that are driving the market’s aggressive growth:

Read more about our NLP Engine

  1. Intelligent Virtual Assistants: Amazon’s Alexa, Microsoft’s Cortana, Apple’s Siri, and Google Assistant are the leaders in this category. Intelligent Virtual Assistant systems help your internal and external customers with efficient query resolution delivered over human-like interactions. Their scope of functionalities goes beyond just query resolution. IVAs are now being deployed for product recommendations, real-time information updates, and for providing an enhanced connected-device experience. 
  2. Chatbots: The key difference between intelligent virtual assistants and chatbots is that the latter is largely dependent on scripted responses and a smaller set of training data. Chatbots switch over to human agents when the query posted by a customer is outside their training dataset. Plus, chatbots are largely used for text-based interactions, whereas IVAs have a wider set of features.
  3. NLG Engines for Text-Based Interfaces: These natural language generation engines are specially used for email and text-based campaigns. Tools like MailChimp help you send programmed responses with templates. If you develop an NLG engine, you will make these templates more dynamic and provide each customer with more tailored response for her query. You can also use these engines for generating ad copies, website content, and other marketing collateral. 

The Drivers of Conversational AI

The market for artificial intelligence-driven conversational systems is bound to grow at an aggressive rate. Here are some primary driving forces behind the rise of conversational AI: 

Need for Personalized Customer Service: The biggest driver for messaging apps and AI-powered bots is the imperative urgency of providing personalized customer experiences. While stores had the luxury of having supporting sales staff, websites, and digital mediums cannot replicate the same experience. While manual chat boxes on websites are a possible alternative, they are often inefficient since it is expensive to scale people-driven customer service systems. 

Conversational AI, on the other hand, can provide a more personalized experience across the customer journey. Here are some common features a conversational AI system can provide across the sales process:

Pre-SalesDuring SalesPost-Sales
Frontend: Product Recommendations & ComparisonsBackend: Transaction ProcessingFrontend: Cross-Selling
Frontend: Feature-Specific QueriesBackend: Collecting, Aggregating, and Segmenting DataFrontend: Product Query Resolutions and Customer Complaints
Frontend: ‘How To’ QueriesBackend: Forecasting Consumer Behaviour (Future Purchases, Preferences, Trends, etc.) 

Optimization and Standardization Across the Conversion Cycle

One of the biggest benefits of using a conversational AI system is visible in the operations. Since the human agents are no longer responsible for working on each lead and customer query, it unlocks a bag of otherwise unrealized value:

Reduced Churn:

Personalized, timely, and effective communication for each customer generally contributes to high retention rates. A customer going through the query resolution process is already at risk of opting out of the brand. When implemented with precision and updated with time, conversational AI systems like video assistants, messaging apps, NLG engines can help in ensuring the customer’s query is resolved with a standardized but efficient process, to keep the customer satisfied and retained.

Controlled Customer Service Costs by Up to 70%:

Deloitte estimates that customer service costs can be reduced with conversational AI systems. This is a fair estimate as most customer queries are near the mean of the normal curve. Human agents can be utilized only when the query is too idiosyncratic. Since the incremental cost per query for using a conversational AI system is marginal, compared to the opportunity cost and expense per minute of having a human agent, the NLP-powered bots can produce tremendous value in the long run by saving costs.

Mapping and Executing Cross-Selling Opportunities:

Even the most effective salespersons might have a tough time in cross-selling since they use a very humanistic approach to selling. AI bots and assistants are engineered to develop contextual and sentimental awareness. As this awareness is developed, cross-selling shifts from a humanistic approach to a heuristics and signals-based approach. Systematic cross-selling helps in augmenting the topline, suggesting product bundles, and enhancing the user experience – all at once. 

Efficient Human Capital Allocation:

Most managers believe that conversational AI will replace all human agents. The reality is that conversational AI takes up most of the redundant, large-scale, and operational responsibilities. This leaves the human agents to explore more value-creating opportunities by focusing on high-value accounts, strategic inputs, and interfacing with enterprise/large-scale clients. 

Big Data-Driven Decision-Making and Predictive Analytics:

For most online businesses, a lot of data on consumer behaviour is available in the form of heat-maps, traffic graphs, clicks, CTRs, and a dozen other metrics. Segmenting all of this data and allocating it to each user profile is nearly impossible. Having a conversational AI system that interacts with users and visitors on the website creates a dedicated pipeline for accumulating and segregating data. Most such systems have good sentimental analysis and contextual awareness faculties. This helps it create effective segments of the audience with clear guidance of what can be done to convert all the traffic. 

Automated Content Production Capabilities:

Since a pipeline for information is already built on a digital platform’s homepage, a conversational AI system can also generate content that addresses the most frequent consumer queries. Based on trend analysis and past-query resolution, conversational AI can be used for everything from personalized and conversational advertising to producing textual content for helping users in the form of blogs and FAQs.

Business Continuity with Remote Assistance:

As the pandemic spread across the globe, more businesses saw a dire need to provide remote assistance. This created a unique opportunity for businesses that had actively invested in conversational capabilities. You can automate regular customer interactions with intelligent systems that provide a personalized experience without even being around the customer. 

How Can You Develop Your Conversational AI Program?

Identify the Use-Case and Define the Features:

Begin with defining a problem statement. You are engineering a solution to solve this problem. For example – your conversion rates have been going down. You would want an interactive conversational AI system that can help customers navigate easily on your website. Based on the problem statement and the possible solution, you will start seeing the scope of features necessary to make the solution work. 

Launch a Pilot with a beta Chatbot:

This is a counterintuitive step. Do not start with a full-fledged conversational AI system. Instead, launch a pilot program with a beta chatbot that can be a plug-in on your home page. Post this, monitor every interaction with your customers. Make sure you have enabled the feature of a human agent to take over the conversation. 

Collect, Aggregate, Process, and Analyse Data:

At the end of the aforementioned step, you will have enough data on what are the common questions posed by your customers when they interact with a bot. You will also have a clear understanding of where the conversational capability of your static bot fails; this will reflect the gap that your conversational AI system is meant to fill. And finally, you will have some benchmark data to see whether your conversational AI system is performing better than a well-engineered static chatbot. 

Hire Experts for Engineering Features:

In the beginning, you might be tempted to develop the project internally. Refrain from doing this. The entire journey of an AI project is critically dependent on the initial stages. It is difficult to fix an older AI algorithm with just new data. Instead, have a team of experts to help you with creating the exact conversational capabilities you will need. 

Deploy, Monitor, and Reiterate:

Conversational AI may seem like a great solution. If the implementation is done correctly, you will start seeing the impact of your quarterly results. Retention will improve, CPA will go down, and customer satisfaction scores will go up. However, your goal is to ensure this momentum is retained. Your audiences are dynamically changing. Hence, look at conversational AI systems as evolutionary projects. Put them through stress tests and see their performance. Your systems have to grow alongside the changing behavioural traits of your customers.

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