Category: Conversational AI

With no available standard Conversational AI agent testing method, businesses face the dilemma of how can they ensure the chatbot is error-free and user-engaging? How should performance testing tools be used? What are the most effective mechanisms for testing its functionality?

Therefore, a chatbot testing checklist below contains tools, ground rules, best practices, techniques, and critical considerations to help businesses set a standardized testing plan.

1. Test Your Bot’s Conversational Flow:

Engage the chatbot in a conversation. Start with the broad, user-greeting questions and critical use cases or chatbot testing scenarios. The list of questions, at this stage of the chatbot testing process, should include:

• Does the chatbot understand user questions?
• Does it respond promptly to them?
• Are its responses accurate and relevant?
• Are there sufficient conversation steps
• Does it keep the user engaged?

2. Include Developer Testing:

The developers working on the chatbot should test it at each development phase.

The purpose of developer testing is to verify and validate the chatbot development and confirm whether the chatbot provides accurate and relevant answers to user queries.

3. Run a Chatbot-Error Handling Test:

While building a chatbot testing strategy, businesses should program the chatbot to reply coherently if a user enters a meaningless sentence or a not so commonly used expression. Businesses cannot anticipate the irrelevant information that users might enter. However, developers should program the chatbot with emergency replies for the exceptions expected by the company.

Chatbot Testing Tools to Consider

A shortlist of 3 tools for streamlining the testing efforts is mentioned below:

1. Chatbot Test:

An open-source guide with 120 questions for assessing the user experience delivered by the chatbot. It operates at three levels:

• possible chatbot testing scenarios
• expected scenarios
• almost impossible scenarios

It provides 7 different metrics for evaluating the bot performance:

• Understanding: does the chatbot understand different kinds of user input such as curse words, small talk, idioms, and emojis?
• Answering: are the answers context-relevant and accurate
• Navigation: is it intuitive enough for the company to go through the conversation users have with the bot?
• Personality: Does the chatbot’s tone suit the audience and the nature of the ongoing conversation?
• Onboarding: is the chatbot functionality apparent to the user
• Intelligence: does the chatbot remember the user’s details and key information throughout the conversation?
• Error management: how does the chatbot handle errors and exceptions?

2. Bot Analytics:

From usability to conversational flow to the delivered user experience, this custom service enables businesses to test the critical aspects of the chatbot.

3. Dimon:

This chatbot testing tool integrates seamlessly with major platforms like Telegram, Slack, WeChat, and Facebook Messenger. Businesses can use it to detect any issues in the conversational flow and the user experience that the bot provides.

The Way Forward: Selecting the Right & Best Conversational AI

While it’s true that adopting conversational chatbots has many advantages, including a positive impact on business revenues, it does require a one-time investment. However, in the long run, it offers a high ROI.

Thus businesses need to evaluate their requirements before selecting a conversational AI provider as it is a long-term commitment. Deploying the right chatbot leads to increased customer satisfaction, and improvement in agent productivity, scale, and efficiency in handling customer queries, after implementing our chatbot for customer support.

Wrapping Up:

We hope this article will help you select the right chatbot provider among the myriad of chatbot companies currently operating in the industry.

But if in case you need help regarding the same, schedule a demo with our experts right away.

With the growing popularity of automation and AI chatbots, more and more businesses are interested in developing a chatbot of their own.

Searching for a reliable software vendor can be challenging. Thus, we have listed key aspects that you should consider when choosing a chatbot provider.

Implementing a conversational assistance solution is as much a business decision as a tech decision. This decision impacts key business metrics such as conversions, sales, customer experience, productivity and revenue through AI and smart automations.

The main objective of automating simple and repetitive requests is to scale the business without adding costs, freeing up agents’ time to respond to more complex tasks. This allows 24/7 availability allowing for extended support outside of call center hours, an almost instantaneous response without the need to wait for the availability of an agent, and create a delightful experience for existing and prospective customers.

Constructing a decision matrix is the best method to help select a software program. It consists of criteria weighted by relevancy and a list of solutions to be studied. Each criterion is scored for each solution. The sum of all scores of the weighted criteria provides an evaluation of the solution compared to the others.

What are the Different Types of Chatbot Companies?

Businesses need to understand the different types of chatbot softwares and providers and assess their requirements to select the correct partner.

A chatbot development platform is a platform that allows businesses to build a chatbot without the need for developers. When companies subscribe to a chatbot development platform, they can access a DIY platform that enables them to create the user interface, a knowledge base, and dialogues and allow them to manage integrations with their third-party tech stack.

At the same time, Natural Language Understanding platforms are linguistic technology platforms that power a chatbot and enable it to answer user queries. They are stand-alone platforms and must be combined with the framework to get a viable product. 

Criteria to Select a Chatbot

Once businesses identify the different options available, they should consider the following criteria when selecting chatbot providers:

1. Ease of Use of the Developing tool:

Ease of use is essential when building the chatbot and when managing it or developing new capabilities. A tool that is not user-friendly and difficult to use will lead to difficulties in maintaining and improving the chatbot. The platform’s ease of use is an essential factor to consider while selecting a conversational AI chatbot provider. Businesses prefer a UI that is easy enough for anyone in the company to use.

2. Artificial Intelligence:

Chatbots are of two types: rule-based and AI-powered. Even though both conversational AI and regular chatbots aim to engage customers and respond to their queries, the manner of engagement differs significantly.

Rule-based chatbots work on a bound system and are defined by commands and keywords to interact with customers. If the user doesn’t use one of the specified keywords, the chatbot will not understand the question and therefore not respond accurately. Additionally, they are not self-learning, and hence businesses will need to add new keywords manually. However, they are a good option if the user experience is not the top priority and the chatbots are required to handle only a limited number of questions and connect the user to a live agent.

On the other hand, the AI-powered chatbot uses AI, ML, Predictive Analysis, and Neural Networks to understand users and respond in a natural language giving the conversation a human feel. Conversational chatbots are powered by AI that understands customer intent, uses past data to understand the context and provides a personalized answer.

The conversational chatbot can continue the conversation by interpreting the context and shifting from one channel to another, regardless of where the user starts a conversation. Selecting a conversational AI over rule-based chatbots has another advantage.

The AI chatbot can hand over the chat to an agent to answer the question based on the context. It ensures that the customer does not repeat their question and the agents get all the information they need. 

3. Security, Speed, & Robustness:

Businesses cannot compromise on security as the customers share a lot of information with the company while conversing on the chatbot. Security is vital for businesses across industries. Banking, e-commerce, real estate, ed-tech, travel, and insurance. Therefore, it is paramount for companies to use a platform that offers the best security to ensure the data collected is safe. It is necessary to ensure that information is encrypted and GDPR and other guidelines are maintained.

Speed is another crucial criterion as businesses require a platform that provides users get their answers instantaneously regardless of traffic.

4. Dashboards and Actionable Statistics:

A platform that offers dashboards and statistics will enable businesses to monitor the chatbot KPIs and improve efficiency. Conversational AI is among the most potent behavioral analysis tools when extrapolated correctly. Simple decision-tree heatmaps tied to complex keyword mapping allow brands to have detailed insights into consumer behavior. This helps businesses identify general trends or dig deep to observe the finer details. When selecting a conversational AI chatbot provider, companies must ask prospective providers to send dashboards templates that can be used to monitor the KPIs.

5. Natural Language Efficiency:

Natural language efficiency refers to how the system matches answers with questions. Sentiment analysis is also another factor that affects the ability of the conversational AI system to understand conversations, dialects, slang and context.

For instance:

A user says “My experience was not bad”. This is usually a positive or neutral sentiment. A smart NLP engine will understand the context and sentiment around this sentence and classify it as a neutral or positive experience. However, a subpar NLP engine or a decision tree bot would pick up the keyword “bad” and classify this as a negative experience and take the conversation in a different direction.

6. Number of Languages Supported:

This ensures that the chatbot can answer queries in a range of languages and can be implemented on multilingual websites. Maintaining a language is relatively expensive since a complete set of data has to be optimized for each language, such as the different questions in the conversation tree and the answers’ translation.

Therefore, businesses should identify the languages used by their clientele to calculate the percentage of use of each of these languages. In case of low volume, your brand can calculate the viability of the associated cost and whether to replace it with an alternative language or maintain the current support for the language.

7. Response Time, Scalability & Personalization:

Chatbot response time is paramount. It may be possible that initially, the chatbot may have to manage only a couple of hundreds or thousands of user requests per month. Still, businesses would want to be sure that the technology that powers the chatbot can handle a higher number of user queries if needed. The two types of volumetry to consider are:

• The volume of conversations, i.e., the number of potential users and the number of requests made per month
• The volume of managed intentions, i.e., the number of questions the chatbot will have to manage.

Many chatbots are easy to use for a few scenarios but cannot scale up. Scalability is another important criterion for companies. While most chatbots can handle multiple queries simultaneously, only a few can handle them without crashing the server or showing a lag in response. While scalability is vital to ensure the customer experience is not affected, it’s also crucial to add personalization to the customer interaction, such as having previous chat history, speaking in the customer’s preferred language, or sharing recommendations based on their past behavior.

There’s no easy way to determine whether a chatbot provider can handle the scale. Thus, while selecting a conversational AI chatbot provider, businesses should inform the provider of the message load per hour and how many conversations the chatbot can handle in a specific time frame. Finally, companies should ask about scale-up plans to understand if the vendor can keep up with business growth.

8. Understanding Business Specificities & Requirements:

Businesses would want their potential chatbot developer to have a clear understanding of the specific use of the chatbot. This will ensure that the developer knows the utility of the chatbot concerning user expectations and best practices in the industry.

9. Reliability and Uptime:

Businesses prefer a reliable provider who can deliver what has been promised on schedule.

10. Documentation & Maintenance:

Businesses should ensure that the chatbot developer has good documentation practices and a low response time for maintenance requests. Clear documentation will help businesses understand the different functionalities of the chatbot and how they work. Enterprises want the provider to be available to fix a bug without waiting for long periods after raising maintenance requests.

11. Agent Assist:

A rule-based chatbot only answers customer queries, but a conversational AI chatbot also assists the agents in answering questions improving their productivity. Agents are crucial for customer support. While a conversational chatbot can resolve frequently asked questions, agents resolve complex queries that require special attention. When a chatbot escalates a query to the agents, they need the context and relevant information to answer the customer’s queries.

Conversational chatbots may help agents in a variety of ways:

• Previous chat history records
• Quick answers to frequently asked questions
• Ability to take notes, add tags, and more before closing the conversation
• Smart-plugins to show customer profiles with location, language preferences, and purchase history
• Context of the current chat to continue the conversation

With an agent assist feature, the customer experience is multiplied. It allows agents to resolve queries accurately and reduces the average ticket handling time. It saves their time and enables them to take up more tasks that increase company revenues. 

12. Available Integrations:

Businesses want a chatbot that can seamlessly integrate with existing tools and software ecosystems rather than having to adapt existing tools to the new chatbot. Therefore it is vital to check that the chatbot has existing integrations with the current apps being used by the business and can connect with third-party applications for:

• Identification and authentication systems
• Support related systems such as Livechat, messaging system, ITSM (IT Service Management)
• Systems linked to the business contexts covered like ERP (Enterprise Resources Planning), DMS (Document Management System), CRM (Customer Relationship Management), CMS (Content Management System), HR (Human Resources) software, banking software, etc
• RPA (Robotic Process Automation) systems execute manual actions on applications.

Integrations and delightful consumer experiences go hand in hand. A brand has multiple customer touchpoints and uses many software providers to track, engage and service its customers. If a conversational AI platform doesn’t operate in sync with these services, it will cost the brand time, money, and resources. Therefore brands must ensure that the chatbot integrates with an expansive suite of tools. Additionally, the brand must enquire about future integrations, timelines, and how receptive the provider is to custom requests.

13. Pricing:

Pricing is among the most critical aspects of the decision-making process. Different vendors use different pricing strategies based on a variety of factors. Businesses must remember that chatbot pricing has two elements – a flat fare + surcharges for add-ons. There are three primary methodologies to calculate pricing for a conversational AI chatbot.

Cost per message – ideal for easy-to-close interactions.

• The cost per message is the easy one here. A message is a string of alphanumeric variables exchanged between the brand and the end-user. Brands are charged a certain amount for each message, and that figure is usually in cents or a similar nominal denomination.
  
  Cost per chat/conversation – ideal for interactions with lots of back-and-forths.
• A conversation is a collection of messages. It doesn’t matter how many messages are in a conversation. The cost per conversation charges the business for the number of conversations they have in a specific timeframe. This type of pricing is usually tiered.
• (a) Cost per customer/contact/user – ideal for a small, dedicated user base.

• (b) Cost per customer is a bit tricky. Often used by enterprise-style chatbot providers, cost per customer means that the business pays for each unique customer.
• Although vendors may use other synonyms, they’re probably using one of these umbrella terms. In addition to this, the service provider may charge the business on the number of agents using the Chabot.

14. Testimonials & Reviews:

Finally, it is advised that businesses check the reputation of the chatbot provider by researching their clientele, company reviews, product reviews, and whether the provider has a stable working relationship with its clients, and develops new products for the same client again. These elements will help businesses make informed decisions and avoid surprises after the deal is closed.

15. Area of Expertise:

This criterion is among the most critical and challenging to value. A change in the conversational assistance solutions market has been taking place over the last few years with the appearance of solutions dedicated to a specific business domain such as banking, insurance, human resources, and e-commerce.

These solutions come pre-configured with a corpus of intentions, entities, dialogues, and responses, enabling a reduction in the creative effort, thus reducing costs and accelerating the deployment of the chatbot. In highly regulated and legislated areas, the advantage brought by these solutions is undeniable and will save time. This advantage is much weaker in more open and varying domains and should not be decisive for the final choice.

Therefore, a generalist solution should be adapted to all configurations but will require additional configuration work.

16. Complexity of Dialogues:

Defining how the chatbot will respond when designing response scenarios is essential. The chatbot can provide a simple answer or a complex dialogue to ask sequences of questions and search for information in third-party applications. A static or dynamic FAQ solution would be sufficient for the first case. In the second case, the solution to be selected should allow for complex dialogues and integration with third-party applications.

17. User Interface:

The chatbot’s user interface is the channel through which the requester can interact with it. This interface can be :

• An avatar that simulates an agent’s face can express emotions related to the conversation. Communication is done through speech and visuals.
• A telephone line (call bot or phone bot). The communication is carried out through the voice channel and can be completed by sending elements through email or SMS.
• A social network. The user uses the capabilities of the social network to communicate with the chatbot.
• A dialogue window (or pop-up also called webchat). The exchanges take place via a small window included on the company’s website.

18. Omni-channel Experience:

The customer experience is different between omnichannel and multi-channel. While considering a conversational AI chatbot provider, it is essential to determine the customer experience provided by the chatbot.

In a multichannel experience, the chatbot is present on multiple channels to engage with the customers, such as the website, mobile app, social media pages, or instant messaging apps. However, these numerous touchpoints do not fetch the context from other channels.

In an omnichannel experience, the chatbot platform syncs data with other channels and understands the context from a previous interaction on a different medium. Thus, leading to the continuity of the conversation.

Both systems work differently and serve different purposes. Hence, businesses need to define the chatbot’s purpose before selecting a conversational AI chatbot provider.

Now that we have understood the various elements to evaluate when considering a chatbot provider, let’s look at the various tools, techniques and metrics to consider when you are testing your Conversational AI solution or Chatbot platform.

The recently announced Convert by ORI and MoEngage partnership brings the power of 2-way automated sales conversations to Whatsapp business channels, helping brands lift conversions and improve customer value management through Whatsapp.

Through this partnership, brands can drive results across Customer Value Management journeys – including Lead Generation, Sales Conversion and Customer Win Backs.

Convert by ORI, was recently voted by Google as the #1 B2C conversational revenue acceleration platform for its ability to bring human-like, automated 2 way conversations to Whatsapp and other online communication channels.

What Does This Partnership Unlock for Brands?

This partnership brings some of the most powerful, high impact use cases to WhatsApp.

1. Product Recommendations & Selection:

Trigger human-like two-way conversations to recommend the right product at the right time to users.

Impact: A global lifestyle brand saw a 9% increase in total cart value through recommendations made during purchase.

2. Abandoned Cart Win-back & Re-engagement:

Trigger context-relevant and personalized re-engagement messages on WhatsApp to win-back users. Engage them with deeper conversations using conversational insights.

Impact: 165% improvement in recovery of abandoned carts for a major global D2C player.

3. Interactive Offers:

Engage users conversationally by offering relevant discounts via Whatsapp. Trigger them from MoEngage and build follow-up automation based on offer redemption status, response feedback (eg this offer is not appropriate, I want an offer on shoes not on bags etc)

Impact: 5X better conversions during the White Friday campaign for a high street Middle East retailer.

4. Post-Purchase Customer Value Management:

Personalize product replenishment notifications and relevant up-sell and cross-sell suggestions via 2-way conversations on Whatsapp. Enable deeper ongoing communication by using deep conversational insights.

Impact: A D2C fashion brand saw a 27% increase in sales through relevant up-sell and cross sell recommendations.

This partnership further strengthens the conversion and customer communications capability of the MoEngage platform and adds an additional layer of intelligent conversational insights  – that brands can use to understand their customers better.

With an enriched customer profile – brands can now enable context rich 2-way interactive campaigns from MoEngage on Whatsapp and other communication channels such as (In-App, Messenger, WeChat etc).

How Does the Integration Work?

The two solutions are now integrated at the platform level. This means if you are a MoEngage customer you can set up and use Convert by Ori to supercharge your customer communications on Whatsapp.

It’s a simple 2 step DIY set-up that takes less than 24 hours to make live.You can talk to your MoEngage customer success representative or write to contactus@oriserve.com to initiate this.

Wondering where to find the best open source chatbot platforms?

No worries we’ve listed the best among those for you here! Let’s dive right in.

Let’s first have a look at what open source software is, it’s basically a software code that can be seen, modified, and distributed by anyone. In simple terms, it is something that is publicly accessible.

Open-source software has given us some real jewels over the years. Mozilla Firefox, Linux, WordPress, VLC, Apache, LaTex, and Ubuntu are just a few standouts.

Chatbots have been a revolutionary invention, one of a kind that has changed how we view communication. Before diving deep into the best open-source chatbot platforms let’s quickly go through what open-source chatbots exactly are?

What are Open-Source Chatbots?

This is how you define a chatbot: ‘a computer program designed to simulate conversation with human users, especially over the internet.’

We have knowingly or unknowingly interacted with a chatbot at least once, maybe while ordering food, shopping online, booking a train ticket, and many more. These are the chatbots deployed by the companies to assist their customers Ex. Amazon has a chatbot on their website that assists customers throughout the process of shopping which we popularly call Alexa.

There is however another way lying underneath which we call open-source chatbots. They are just like modern web applications. They live on the interweb, use databases and APIs to send and receive messages, read and write files, and perform regular tasks.

Here are some of the best open-source chatbot platforms:

1. RASA:

RASA is a set of open-source machine learning tools. These tools can be used by developers to create chatbots and assistants. The two major components of Rasa Stack are ‘core’ and NLU. It’s quite simple.

The NLU understands the user message and Core decides the next move. Rasa is an Independent service which means that it doesn’t have to go through API (Application programming interface). It can be deployed on a cloud even if it’s private. 

2. Bot Press:

It’s a self-proclaimed WordPress of chatbots (Open source bot-building platform) It’s built using a modular blueprint. You can snap pieces off and add new bits to an existing code frame. It is based on a 3 step installation process.

After the developers build the bot they deploy it on the preferred platform and give them access to manage the respected person. The best thing about botpress is that it is developer friendly, comes with an intuitive dashboard, and is powered by flexible technology. It gives you full control over what comes in and out.

Ana’s SDKs ensure that you can integrate Ana into your app in a matter of minutes.  

3. Open Dialog:

It’s undoubtedly the most popular among the open-source chatbot platforms. It helps to design. Integrate and deploy chatbot effortlessly.

OpenDialog flaunts the ability to perform real-time STT processes while still using relatively low memory. It can work as a server unit as well as deliver the N-best/word graph output.

The USP of this product is that it allows you to build full-fledged conversational agents without having any coding experience.

4. Tock:

Tock is another brilliant platform for chatbot development and deployment. The best thing is that it requires no third-party API and can work independently. Integration becomes easier when you are using a platform like Tock. 

5. Deep Pavlov:

It’s a popular chatbot development platform based on TensorFlow, Keras, and PyTorch that gives developers flexible tools to build powerful conversational agents that are multi-skilled assistants.

The best thing about this one is that it’s compatible with NLP (Natural Language processing). The fact that makes it stand out is that it is easily deployable.

6. Botfront:

It is an enterprise open-source platform for Rasa teams. It helps you design and implement your conversations at once. It is a collaborative platform built on Rasa. The best thing is you can create complex conversational forms using this open-source chatbot with ease.

One more benefit is that it helps you create multilingual assistants hence the language barrier is removed. It is a good tool to analyze your conversations at a scale. 

7. Pandorabots:

What makes it unique is that it is built for developers as well as CX designers. This multilingual bot offers a wide range of solutions. It would be wise to call it a smart bot as it is always context-aware. It is compatible with speech to text and text to speech. It is flexible and extensible as it contains RESTful APIs to integrate with apps and systems. 

8. ClaudiaJs:

Claudia makes it easy to deploy Node.js projects to AWS Lambda and API Gateway. AWS Lambda and API Gateway are incredibly flexible, but they can be tedious to set up, especially for simple scenarios. Running Node.js functions requires you to iron out quite a few quirks that aren’t exactly well documented. Claudia automates all those steps for you.

It can be deployed and updated using a single command. It comes with multiple versions which are easy to manage.

The best thing about Claudia is that it’s easy to use and compatible with different versions. You should definitely try this unique platform.

To Conclude:

So here we end our list of the best open source chatbot platforms in terms of specific measures like customizability, 24/7 availability, NLP engine, and most importantly data privacy. However this is not the end here we selected the best ones among many available on the web, you can go and check them yourself.

Also, schedule a demo with our experts if you need help in selecting a Gen-AI solution that suits best for your operational and customer needs.

Companies have varied sales cycles depending upon their target market and audience base. Every business team out there has one common intention of closing deals faster and one of the best ways to fast-track this is by implementing Conversational AI for your sales cycle.

CX automation entails employing tools that replicate human cognition capability such as problem-solving and logical reasoning, to reduce manual labor in support processes. It involves identifying repetitive tasks across customer engagement cycles and automating them with the help of tools such as AI-powered chatbots, voice bots, and live chat.

There’s a difference between conversational AI and mere chatbots. Conversational AI is designed in such a way that it can actually recognize the conversation flow as well as human cognition. It optimizes the entire process of lead generation and helps you better understand your existing and potential customers.

What is the Relationship Between the Sales Cycle & Conversational AI?

The sales cycle refers to a repeatable and tactical process followed by the sales teams to convert leads into customers and minimizing time-consuming repetitions falls right in the domain of automation. Conversational AI helps you acquire more customers in a shorter period of time. It substantially helps convert the leads into actual sales. 

Here are the top five ways to fast-track the sales cycle using conversation:

We’ve listed how conversational AI boosts the sales cycle and facilitates B2B and B2C communication.

1. Lead Generation:

Lead generation is a key task every business has to perform. That is the reason lead generation is highly significant when it comes to businesses. Lead generation can be done in a better way using conversational AI. Manually generating the leads involves a lengthy process ranging from cold phone calls to online forms and despite all the efforts, it doesn’t guarantee that substantial leads will be generated.

Whereas, using conversational AI lead generation is quicker, easier, and precise. A Conversational AI system communicates with customers via human-like conversations, smoothly asks them about their preferences, and allows users to self-serve contact information along with these preferences. The data is recorded in real-time and is sent to the CRM segregated.

2. Automatic Lead Scoring & Qualification:

Spending time on leads that are not interested or relevant leads is a sure shot way of slow sales cycles.

It becomes crucial that leads are scored on the basis of relevance, interest levels, product fit, demographics, budget, and other product-specific factors.

Conversational AI facilitates the automated scoring of leads as per relevance and multiple conversational triggers such as depth of conversation, sentiment, and intent.

Once the leads are qualified, conversational AI can also move them further down the sales funnel by answering any questions they may have about the product and sending them hyper-personalized offers.

In the case of high-value sales, qualified leads are handed over to sales reps with complete context – helping them close the sale.

3. Behavioural & Preference-based Retargeting:

Conversational AI uses NLP, machine learning, and AI-trained models to interact with customers and potential clients. This includes observing and learning from the behavioral and conversational patterns of customers.

This enables chatbots to retarget and nudge prospective customers who are stuck in the middle of the purchasing funnel, resulting in the creation of new opportunities without extra human effort.

Conversational AI re-engages with customers, provides context-relevant information, and guides them to closure in the most optimal manner – automated.

This saves your sales time and effort – helping them focus on the most warm leads by jumping into the conversation at the perfect time.

4. Response Time Optimization:

No customer or prospect likes to wait. As per the latest research trends – 35-50% of sales go to brands that respond first. Web leads are 9 times more likely to convert if you follow up within 5 minutes.

Conversational AI allows your brand to create instant and immediate experiences for your prospects. You can stay in touch with the audience 24/7.

Conversational AI also has the ability to handle multiple customers simultaneously through even some of the most complex use cases. This helps you be there for the audience when they need you most and when the purchase intent is at its highest.

5. Conversational Commerce:

The popularity of social media and messaging channels is increasing each passing day. This highlights the need for businesses to be readily available on their customers’ most preferred platforms. That’s where conversational AI comes into the picture.

Conversational commerce is the process of conducting business over messaging apps like WhatsApp, Facebook Messenger, Instagram, and more. It enables companies to meet customers where they are and drive up to four times more conversions by creating relevant conversational experiences.

To Conclude:

The world of customer communications is evolving at a faster pace than ever before. Your business needs to be accessible to your customers at the most relevant points in their purchase journey.

A well-integrated conversational sales augmentation system like Convert AI can fast-track your sales cycle with intuitive human-like conversations and empathy. Schedule a free demo with our experts to experience it yourself.

The arrival of conversational AI to the consumer products industry opens new possibilities for the final consumers. Today, the consumer durables industry thrives because of three major innovations: advanced analytics with insights to support the decision-making process; autonomous business processes, eliminating manual processes to achieve faster time to market; and AI-powered immersive, conversational, and continuous interfaces that help deliver a ‘wow’ experience and gain more involvement from the user. 

Conversational AI focuses on three fundamental pillars to help the consumer durables industry: employees, companies, and final customers. Conversational AI solutions create direct dialogs through convenient channels without friction, at an affordable cost, with a 24×7 service.

Consumers’ constantly demand for better, personalized experiences has led to innovation across various products. Apart from being affordable and solving day-to-day problems, Conversational AI can capture the users’ attention and provide a delightful experience.

Consumer Durables Industry in Revolutionizing

The modern-day buyers’ unique decision journeys and preferences sort into distinct consumer segments, with different characteristics of each segment. Brands must use Conversational AI as they plan marketing spending, messaging, and positioning. Conversational AI helps brands grasp online behavioral nuances of each segment’s buying patterns and the information and influencers it relies on to target segments through relevant channels and messages.

Brands need to grasp what motivates consumers to consider a purchase since the trigger point determines whether a brand makes it to the initial consideration stage. Using Conversational AI and conversational analytics to understand the preferences and sentiments of multiple demographics helps a brand position itself appropriately in the consumer’s mind.

With loyalty not guaranteed in today’s market. Consumers tended to shop around rather than automatically choosing the same brand from one purchase to another. This suggests that marketers need to fine-tune their understanding of consumers in each segment, and what triggers them to shop by employing innovative tools like Conversational AI.

It is also, during the post-purchase experience, that marketers must continue to highlight quality and service or offer rewards to the most loyal customers. It is easier to lose customers than to add new ones in today’s environment. Hence, spending disproportionate marketing money on loyalty can prove risky. Usually, the persuasive information in the durable consumer business includes brands, price, warranty, product features, after-sales service, offers or special promotions, availability, and user reviews.

Marketing in the Shopping-driven World

Before the digital era, every communication with customers included a variable media cost that usually surpassed the fixed expenses of creatives. Thus, management began focussing on ‘working media spend’, which is part of the marketing budget to what today is known as paid media. Today, marketers must also consider “owned media”-the channels they control, such as websites and “earned media,” created by customers such as communities of brand enthusiasts.

During the functional evaluation, consumer behavior is greatly influenced by past experience with the product or service. Conversational AI can boost consumer awareness of their brand’s products and services through significant media or social channels, motivating consumers to learn more about them. Conversational AI can provide creative interactions that convey a fuller picture of the brand’s value and is an excellent approach to moving beyond simple awareness to real brand consideration.

In the consumer-durables sector, buyers tend to purchase a brand in their initial consideration set. But marketers who understand customer motivation can use Conversational AI along the entire consumer decision journey to influence final purchase decisions.

6 Ways Conversational AI Can Influence the Buyer Journey

Brands increasingly focus on becoming more customer-centric and elevating customer experience to improve customer satisfaction, loyalty, and lifetime value. Companies that supply high-value and complex consumer durables face unique challenges.

Ease of access to relevant knowledge:

Conversational AI offers easy access to an integrated knowledge base with support that will help reduce the cost of information delivery and help customers find faster, and more relevant products and answers round the clock.

Modern-day customers want seamless and unified integration, rather than being passed off to different departments. Centralized access to all knowledge sources, including registrations, product configuration, service plan entitlements, support queries, inspections, warranty & warranty claims, service orders, and service campaigns, will help customers solve their issues in no time. It will help reduce the cost of support systems and provide a complete picture of all customer support needs. It will also leverage enterprise-wide resources to provide a consistent and seamless customer service experience.

In instances where the customer demand is too complex for the Conversational AI to solve. In that case, it can escalate the issue to human agents with complete context of previous conversations – who can resolve the issue and add a new technical solution to the knowledge library, making it an invaluable asset over time.

Go Omnichannel & Digital:

Calling to wait in line and navigate the maze of multiple IVR options. In today’s world, brands must provide consistent support and service across multiple channels, including the web, mobile, messaging channels, and email.

Conversational AI lowers the overall costs for companies to create an omnichannel digital experience and is preferred by most customers because of ease of access, time savings, and convenience.

Use of Conversational AI to Improve Online Sales Performance

Conversational AI can help by analyzing customer support data to improve product quality and operational performance. 

Through customer conversations, brands can gain a deep insight into softer customer aspects such as color preferences, evaluation criteria, other brands etc and reposition and realign their product offerings in real time. These insights are called conversational analytics – and you can view more about these here.  

Further – brands can use insights gained from support data to improve their products and services by helping to fix the problems at the source. Repeat issues can be prevented by using support data to identify emerging quality concerns early and implement necessary actions. 

The study of customer complaints and their feedback also helps identify and fix operational performance issues within the company or channel partners.

Boost Customer Engagement:

Brands can leverage Conversational AI for push notifications across multiple popular consumer-facing channels to provide proactive alerts, announcements, and timely information.

Conversational AI can be made available on multiple channels, such as mobile phones, web, IVR, smart speakers, or even smartwatches, making it device/technology-agnostic. This will keep your customers engaged and improve their perceptions of responsiveness.

Additional revenue generation options can be generated by maintaining customer contact information accurately, registering their products, and offering related services, accessories, and other complementary products.

Customer loyalty, referrals, and advocacy arise from engaged customers.

Enable Self-Service for Customers:

Conversational AI enables customers to find answers to support inquiries fast and anytime by providing accessible and intuitive self-service options on various channels. As most customers prefer self-service, companies have the opportunity to minimize their call volume.

As mobile devices have become the primary communication channel, having a Conversational AI-powered mobile application or a mobile-friendly responsive website allows your customers to access the information they require on any device quickly, with the help of an intuitive conversational experience.

Convert Conversations into Revenue:

The Conversational AI interface uses a machine-learning algorithm and Natural language processing to make sense of buyer queries.

It then uses metas and API calls to pull up the right product that suits the customer, helping turn automated chats into new revenue. A revenue acceleration sales agent like Convert AI, constantly nudges the customer towards purchase by proactively prompting and guiding the user on the most optimal path along the buyer journey.

The Bottom Line:

Incorporating Conversational AI with consumer goods enables simpler and more direct shopping for producers and consumers. This allows consumers to spend more time doing (and finding) what they want, as they want.

Companies can leverage these innovations to contend with increasingly value-conscious and tech-savvy consumers. As increasing numbers of consumers depend on using digital and mobile devices to shop, many consumer goods companies invest in existing and emerging technologies to better understand, connect, and engage with consumers.

Schedule a demo with our experts to know how we can help you leverage Gen-AI, offering you a competitive advantage in this crowded market.