3 simple steps to Customer Service Automation with AI
When talking to individuals who want to understand how AI can be relevant for CS Automation, questions are often very similar.
“How do I get started?”
“Who needs to be involved?”
“Does it really work?”
“Where can I see AI?”
To answer this questions, here we are with an introduction of how AI can bring immense benefits from Day 1 to any customer service department of any organization.
I have highlighted 3 main steps to deploy AI in your Customer Service:
1. Problem definition
2. Dataset and model creation
3. Launch & iteration
The final goal is the creation of an hybrid Human + AI solution that understands human inputs and provides relevant answers, automating up to 70% of the first level (repetitive) customers enquiries.
This solution helps with providing better customer support to your consumers, while having your agents focus on higher value added tasks.
Let’s start! 👇
1. Problem definition
As always, a clear definition of your business pain is the starting point.
Once the problem to be solved is clear and well defined, we look into the right solution and approach to improve your KPIs.
Examples of solutions:
- AI powered chatbot on Facebook Messenger or on your website, for real time answers.
E.g. Your company is getting a high amount of consumer inquiries through the company’s Social Media pages- AI integration with your Zendesk/Salesforce/etc, to speed up the answer process with macro suggestion, auto tagging, smart routing.
E.g. Most of your customer service runs through e-mail.- In-app AI powered chatbot.
E.g. If you want to provide support inside your app.
There can be more applications that can fit your specific case, but we find these to be the most relevant in the market right now.
Once the problem and the solution are defined, we move on to the next phase.
2. Dataset & Machine Learning model creation
Here the magic happens!
But no worries, we take care of it behind the scenes 😎
For you, this step will still be very simple. A bit manual, but simple!
What we need to do at this point is to create a knowledge base, so that the engine can understand human inputs and come back with relevant answers.
In the majority of the cases, we will together create a dataset with 2 columns: one with a list of questions, and one with a list of answers.
The more questions we create, the better the engine will understand human inputs from the very first day.
It can be that simple! 😉
Once done, we will then work to create the best performing AI model for your specific case, before moving to the next phase.
3. Launch & iteration
After creating the model, it is time to design the solution (from a UX perspective, but I won’t be covering this now) and make sure we offer the greatest conversational experience to the final users — especially when we are talking about chatbots.
If we are designing a Zendesk/ Salesforce integration, no major design process is required.
Next step is….LAUNCH! 🥂
Especially when dealing with CS Automation solutions that allow people to enter free text, it is important to keep training the engine.
Indeed, users will most likely enter several variations of the same question so it is important to continuously update the engine, and make it smarter and smarter over time!
Our thoughts on AI & CS
At BotSupply, we see many applications of machine learning as an augmentation of the role of a support agent.
AI technologies should work side by side with support teams to help predict insights, provide recommendations, and automate simpler tasks — but step aside as soon as a human touch is needed.
Backed by machine learning, the support agent’s role is evolving to be more strategic and productive.
In our humble opinion, organizations will spend more time designing UX and training algorithms to automate specific tasks along that journey in the future.
And remember, AI can be very simple!
Next, we will be presenting some case studies with more in-depth analysis.
Stay tuned!
For any questions or doubts, please comment below or get in touch at francesco@botsupply.ai
