If you work in Customer Service, by now you’ll know that customers have become more demanding than ever and that offering a good experience can be the difference between retaining a customer or giving your competitors an advantage.
Help desks today offer workflows and automation functionalities but there are still a lot of manual and repetitive processes being done in your team. As one of our customers said, “this repetitive work is just not good for your team’s morale”. This is where adding an AI layer to operations can help.
But what exactly should you be looking for in an AI platform? What common challenges in Customer Service can AI solve, and how can you be sure it won’t hurt your reputation in the market?
Biggest obstacles to improving efficiency and automation in Customer Service
When it comes to improving the efficiency of your operations, most companies face 3 challenges:
The first problem is that for most organizations the foundation for efficiency and automations —knowledge — is lacking. Knowledge, be it in the shape of macros, help center articles, or internal procedures and processes, tends to be scattered between various tools such as email, past tickets, and your CRM itself. Moreover, support teams are typically the last to be informed of any changes in product, promotions, or industry regulations. This makes it difficult for agents to respond quickly and effectively and it makes implementing any automations useless as you can’t automate if you don’t have the right up-to-date knowledge in the first place.
Secondly, customer service is well known for having high turnover rates. According to Contact Babel’s UK Contact Centre HR & Operational Benchmarking Report, in 2018 agent turnover rates were at an average of 21% across all industries, with that number being even higher in larger companies. This poses a need for constant hiring and training, as employees leave and need to be replaced.
Finally, there are AI/automation tools that can help support operations, but they typically have long implementation cycles that are time and resource-consuming for your team.
When not implemented properly, AI and automations end up hurting a brand by sending out replies that have nothing to do with what the customer asked.
That is not to say you shouldn’t be resorting to an AI platform to help your support team be more efficient. Bots, automated replies, and knowledge bases are valuable tools that can take over repetitive tasks, while also empowering your agents and increasing productivity.
But with all kinds of AI products available in the market, what factors should you consider for choosing the right AI platform for your business?
The Three Pillars of AI Model Efficacy
First, let’s start with the basics.
What exactly is an AI model?
There are many definitions of what an AI model is but fundamentally, an AI model is a program that is made to mimic human decision-making. The way AI models can do this is by looking at a lot of similar data (called training data) to recognize certain types of patterns. AI Models learn from this training data and over time become good at solving business problems they have “seen before”.
So as you can see just from the definition, the underlying training data that is fed to the AI model is the key to how good an AI model is. If you feed it bad data, the AI model will behave badly, and if you feed it good data, it will give great results. In addition to the training data, two other important considerations are to look at when evaluating an AI platform for customer service — cleansing and language architecture.
Let’s have a closer look at all three.
To build an efficient AI model, you need to look not only at your own data, but also at industry-specific data and best practices data, which improves the AI model’s pattern recognition process dramatically. Most AI platforms unfortunately use limited training data or take months to train the AI model on real operational data.
It’s not enough to feed the model the right type of training data. It is also important to ensure that what you are feeding is “cleansed” so the model learns based on clear examples. An e-mail, for example, has several parts such as the greeting, the body, and the signature. If the model is trained without cleansing this data first, it will end up reducing the efficacy of the AI model. A good AI model should extract only the relevant information from the data to train the model so that it’s more finely tuned.
Moreover, it is normal to train these models with data that has been wrongly labeled by agents and, unless these wrong labels are excluded when training a model, there’s a chance more is needed to get the best results.
With most solutions, you are responsible for choosing which examples to give to the model, so don’t neglect the work involved in picking the right examples.
The other advantage of doing this is that overall, you require fewer data to get better results.
And finally, having a multilingual approach. How do you ensure you have the same consistency and efficiency across different markets without having to invest in the same amount of time training the model for each market? An efficient AI model should be architected to train in one language but have the architecture to support and perform well in other languages too.
AI is not a silver bullet and not all AI are created equally. The key is to ensure there is a good match between your needs and resources and the AI platform you ultimately choose. If you have more questions about how AI can help your customer service team, schedule a call to chat with our expert or experience Cleverly for yourself, by downloading the Cleverly app from the marketplace integrations page.