Most companies are looking to see where chatbots can be plugged in to products for speeding up response times and offering helpful FAQs. Their ultimate goal? Reduce resources.
Sure there were more than 300,000 chatbots on Facebook in 2018 (Venture Beat), and more recent studies suggest the chatbot market value is expected to grow from $17.17 billion in 2020 to $102.29 billion by 2026. (Research and Markets). But what if chatbots have an even more valuable long-term benefit for SaaS companies in particular?
Learning from customers can both enhance the customer experience, AND hone machine learning. Developing a cutom chatbot within a product experience, is a great way to succeed at both goals.
Build a custom chatbot to feed product AI/ML
Yes, we’re saying build a custom chatbot. To capture missing customer data, or to go even further — you can build a custom AI chatbot to make your product better. And while you’re making your product better by adding your own custom machine learning, your chatbot is morphing into a conversational AI tool.
A lot of chatbots today use rule-based logic and only handle pre-defined queries, especially those you see for customer service. Throw them a multi-dimensional question, and these FAQ experts get stumped. However, there’s still plenty of opportunity for deeper integration into the customer experience. As these bots evolve towards a conversational AI tool, they will be able to manage more complex tasks and understand human language.
While we’ve focused on chatbots here, we should pause to highlight this is really just one part of a much larger data collection strategy that our teams map out for clients based on the product goals and startup growth plans.
If you’re already analyzing your options for data collection, you’re in luck. We’ve rounded up a few of the starting questions from the Gravitate network of data scientists and development professionals to guide you in deciding if a data-capturing chatbot belongs on your startup’s product roadmap.
First, what data can chatbots gather?
When we break it down into the most basic terms, a machine learning model uses any combination of text, image, and time series data.
Natural language terms get you the “text” you need for increasing the vocabulary and recognition within the algorithm. Creating prompts and opportunities for product feedback can be a way to timestamp information, and ask profile questions to enrich context. And for those looking to hone a predictive feature, quick pop-ups designed with conversation starters and smart tagging could lead to rich, dynamic data for testing the accuracy of your AI model. SAS.com has a great summary for those looking to better understand NLP.
Working to better define customer use cases? Just ask. Behavioral data is a key factor in segmenting and testing customer needs. Customers are likely to respond to specific chatbot conversations when they are well placed within a product experience. Whether the user is stuck or has just completed a task, a start-up's algorithm can learn from user behaviors to get better at anticipating customer needs.
Custom chatbots can be as unique as the customer, product, and brand using them. Teams combining product developers and data scientists can identify valuable points in the product strategy where the customer and the AI product will both benefit from a custom chatbot interaction.
Who can chatbots reach?
When building a custom chatbot, it helps to consider the type of person you need to reach and your key markets. Acquire reports that 1.4 billion people are willing to talk to chatbots, with the majority of all chatbot usage coming from the US, India, German, the UK, and Brazil. (Chatbots Life)
Depending on your customer base, your specific user type, and your activity, you may be able to gather more data from your target audience through short interactions with a custom chatbot than through surveys for other expensive product research efforts.
We have recently seen certain industries accelerate the adoption of chatbots in real estate, travel, and now fintech and healthcare. (Chatbots Life)
Not sure your users will like it? Your audience may very well be ready to interact with an AI messaging interface, especially if your users understand it can make the overall product experience better.
Lufthansa Group shares a great summary of their journey in developing a custom chatbot with elements of conversational AI. In this case-study walk-through, Product Manager of Digital Assistants Nick Allgaier shares three tips for success based on the team's experience: build a passionate team, choose a WOW use case and select a scalable, easy-to-use tech stack.
We couldn't agree more. In fact, we insist on these principles when we work with SaaS startups looking to build any type of AI product solution.
Then, after fully understanding the target audience, the data needed, and how to capture it, your teams can move on to determining what types of questions, and what points in the customer journey are best for incorporating a custom chatbot experience.
What questions can a conversational AI answer?
AI-driven bots are learning to pick up on the emotional and social cues within human word choice. There are also efforts underway for processing multi-topic conversations. As development in these areas advances, data scientists will need even more kinds of data to train the models.
To make the most of every customer interaction, SaaS startups can be capturing examples of the user's potential behaviors now to inform an algorithm planned for the near future. Defining these potential scenarios could be as simple as asking for feelings-based scales. An example question could be “how were you feeling before completing this task” (emoji scale) and now how are you feeling after completing it with our new feature? With the right controls in place, a chatbot powered with advanced AI can recommend on the fly the best answer to a custom question.
Crafting an authentic user experience works best when there are recorded examples to work with. A cleverly executed example of this is eddy, a bot that plugs into Slack to keep track of a new engineer’s progress but also to provide team tasks without the hassle of planning ahead. It answers a new developer's questions, like “where is the code repo for xyz?” And “which training modules are mandatory this month for security compliance?”. It anticipates the next step in the onboarding process in an organic, more human-centric way. Then, based on the company's processes and training, it finds the right information for that moment.
Adding Product Value with a Custom Chatbot
Proving AI solution value is a key element of success for a SaaS startup business strategy. The bottom line is chatbots can be good for the bottom line.
Embedding a chatbot with advanced AI into a startup’s product experience can offer greater returns beyond reducing customer call times. After identifying product and business goals, founders and data scientists and tech teams can lay out the roadmap for what data to collect, from which audiences, and for what types of use cases. The result will be rich data ready for data science models for enriching the customer experience for greater product adoption.
Find out what Gravitate thinks of your specific product roadmap plans for AI. Set up a consultation by giving us some times you are available to meet in the coming weeks. Our team of developers and data scientists are ready to partner with you.