The rise of technology paved the way for Artificial Intelligence (AI) and chatbots. They brought new ways for businesses to communicate with their customers. The popularity of messaging apps has gone to stratospheric levels in the last 5-6 years. 2.3-billion people around the globe use some kind of messaging apps. The same report suggests that 66% of consumers trust a company when they are active on messaging platforms.
According to a report by Forbes, 37% of customers prefer chatbots for faster response. It is big news for businesses as they can leverage chatbots for increasing revenues. However, each business is unique with different requirements, so each will have a different strategy for using it. Here, we are presenting to you strategies that work despite the business type.
1. Understand your customers
2. Create an AI culture
3. Choose a very specific purpose
4. Use existing channels for knowledge capturing
5. Use journey maps for understanding customer intents
6. Identify technology suitable for your business
7. Define a framework for measuring success
8. Build the bot with intents/ responses
9. Leverage crowd-sourced bot learning
10. Test before rolling out
11. Launch the bot
12. Use different channels for continuous improvement
Let’s dive deep into the strategies:
Every successful business strategy begins by understanding your customers. Develop a deep understanding of customers for
It is not only important to collect data, but it is also important to make sense of the data. Nowadays it is easy to collect data from multiple sources/ channels. However, cohesively structuring the data to provide greater insights into customer interaction is complicated.
A successful bot strategy often requires introducing a bot into the overall channel. You shouldn’t think of it as replacing existing channels, but as an intuitive method for customers to interact with your businesses. The goal is to provide accurate and quick results.
Don’t guess or assume anything about customers. Prepare your strategy on solid data understanding.
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You should create an AI culture by keeping customers the focal point. Humans are better at doing creative work while bots are good at repetitive work. Your task should be to understand and allocate different tasks accordingly.
Some companies fall for thinking they can replace human interactions with bots. This can lead to their demise because at this point the bots are not developed to that extent. Humans can certainly understand and solve complex problems better.
Your support team should understand that bots are for complementing their work. They can concentrate their energy on tasks that are enjoyable and less repetitive. Staff can identify new intents and the responses that will create a better customer experience. Chat staff needs to understand the AI systems and dedicated to improving them. It often means training and developing a learning attitude.
Developing a bot is easier compared to narrowing its purpose. A bot for answering technical questions is harder to create than a bot that answers simple transactions. The narrower the scope, the more likely it is going to answer successfully. It is true, especially at the beginning.
With time you can train it to answer complex queries and provide additional support as you get a better understanding of customers behaviours. At the initial stages, aim to provide consistency in responses and 24*7 availability.
A bot is much more inviting to customers than using the website only. On a website, customers have to search and determine the beneficial tools. A bot can provide a specific response based on the customer intent.
Your goal should be to provide customers with a precise answer as soon as possible. Creating a narrow scope at the beginning ensures early success for both customers and the organization.
You don’t need to start from scratch for gathering data. In most cases, you already have the data/ knowledge for building quality bot conversations with customers. Start gathering data across multiple channels, like recorded phone calls, chat transcripts from customer support, knowledge articles written for direct use by customers. Live chat support software like Duvim captures data automatically as transcripts. You can leverage the data for bot training.
One of the best places to start is with structured data capture in the request fulfilment, incident management and general Q&As. Focus on capturing customer’s context using knowledge-centred service. Your organization’s context helps to improve the bot’s intelligence with the metadata to map the intents with accurate responses.
If you’re providing precise responses for customers in self-service, that knowledge can be used for training the bot. Using both structured and unstructured data, you can understand customers’ intent better and faster.
Mapping customer journeys helps you better understand their intent and engineer a superior overall experience. For a successful bot strategy, provide a successful customer experience within the defined scope. Customer experience is directly proportional to their satisfaction and loyalty. Create an experience that delights your customers and not only aim for using the bot as an alternative channel.
Identifying what a customer wants, e.g., questions, incidents, requests from the conversation, is important for developing successful conversations. It helps to define an accurate scope for the bot. Develop a subset of the most relevant intents that make sense to deliver from a bot. Use a subset of intents that are well understood and documented instead of trying to use all intents at once.
Few well-defined use cases are much easier to design and deliver than trying to map out every scenario. Start by tackling the most common use cases first and then expand the scope as you learn from the experience.
Don’t focus too much on the technology at the beginning. For a successful chatbot adoption, you must understand your customers. You are aware of the type of knowledge you require to serve them. Address all the issues which are not working today, like your self-service site. You don’t want to add a piece of technology on top of another without rectifying old technologies.
Choose a platform that makes sense for your business requirements. Use an interface that your customers are most likely to use. Nowadays most bot platforms integrate easily with Facebook, Slack, CRM & your website. You should lookout for a platform that provides natural language understanding and not only dialogue or interaction. It is important for developing bot learning.
While it is important to develop a bot, your organization needs to develop frameworks for measuring success. You should have different measures for bot success, customer journey success and much more. Never forget that a bot is only another support channel in the list of support channels. Your ultimate aim is to improve customer satisfaction and revenue.
Your framework must have customer satisfaction as one of the KPIs for measuring success. For the bot, you can have completion rate as an important KPIs. Start by tracking successful outcomes for comprehensively understanding customer success.
One of the important metrics you can consider is the bounce rate. It will show you how often your customers are leaving the chat with a bot. Did they require some kind of human interventions before bailing out? One important metrics to consider is the reuse rate. Check out how often your customers are returning to using the bot after a successful result.
It is human nature to choose the path of least resistance, so a customer can start in one channel but move to various channels before the successful completion. For example, they can start from the self-service portal, use the bot and need live agent help. You need to understand the reason for a customer moving from one support channel to another.
Now, the next step is to create the bot with customer intents, contexts and responses upon pre-defined use cases. Every use case should have a unique intent. For example, you sell computers online then build the bot using conversations that help to accomplish the sale efficiently.
It is important to have an in-depth knowledge of how the conversation engine works on your platform. Every customer outcome has questions, facts, responses to queries that the bot needs to understand for a successful result.
Does the customer is looking for a computer, laptop or tablet? What system configuration do they need? These entities are important for understanding follow-up intents. The input of one intent builds up the next intent like natural conversations between people. Looking at the data from various existing channels will help you in understanding intents.
To stop customers from falling out in their journey, you need to identify various fallout points. Work closely with staff for understanding exceptions, use cases, journey maps and data analysis of intents and responses. Test the intents for ensuring all paths work exactly as expected for the bot.
At the start of the adoption, the bot will have lower accuracy and a low containment rate. Before launching to the customers, ensure additional testing by staff and select customers for improving it. Additional paths/ intents might be identified in the testing phase. With natural language understanding the bot will learn new phrases. It can then map it to the existing intents.
You need to understand that it is not only that the bot is learning, your staff is also learning additional responsibilities of monitoring the bot’s progress, identifying exceptions, & measuring its success.
It may seem obvious but you need to do extensive testing before rolling out the bot to customers. You can first use it with assisted support. Check the containment rate when it is used by your staff for helping customers with different support channels.
Your goal should be to look out for ways to improve the user experience. Give final tweaks to the bot development while practising the skills required to improve the bot.
After you have done the above processes, it is time for the launch of the bot to the world. Market the new service full-fledged and don’t forget to celebrate the success on the way.
Measure the metrics for understanding customer adoption, its impact on other support channels, completion rates and fallout rates. Make long-term and short-term plans so you don’t panic if some things don’t go your way.
It is tempting to think that your work is done after launching the bot. But, it is only the beginning of a long journey. Your aim should be a continuous improvement by identifying additional intents, increase the bot scope, gather substantial data. Repeat the process after improving.
Don’t go into tunnel-vision by thinking only about making improvements to the bot. Try to make improvements to the overall business by providing better support, services and customer experience. Remember, a bot is only a channel of support in your many support channels. A customer journey pan across many channels.
Your organization should focus on ensuring that the experience is consistent across every channel. The responses need to be the same despite the channel.
As we conclude this blog, remember that artificial intelligence is the future. It is an exciting field that can serve customers in newer and better ways if implemented correctly.
With tools and technologies around us, it is now relatively easy to develop and implement a bot. Establish a strategy that allows you to experiment, learn on the go and making it better.
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