How To Improve Efficiency Of The Bots At The Early Stage Implementation?

Creating good support algorithms and content seems to be one of the most time-consuming and challenging aspects at the beginning of the journey with chatbots and phone bots. Hiring bot content engineers or customer service automation designers sounds like a rather natural step, to make sure that those tech solutions will work not only on the technological level but also fulfil their main role, which is supporting customers in an efficient way.

Bots content management starts to be even more challenging when more languages are to be handled. Whatever is the setup, instead of trying to create bot scenarios and algorithms from scratches that might be extremely beneficial to start with checking Contact Centre inventory of tools and resources for knowledge management. It can definitely speed up bots implementation and make them way more accurate from day one.

What may be a great help is to upcycle existing content being used by Customer Service.

If Contact Centre agents use call scripting, especially step-by-step scripting, this can be a very valuable input to be transferred into bots scenarios. Especially, if the company uses scripts already for a long time, they, most likely, have already transformed into codified, standardised, time-proven step-by-step solutions, which, if you look at them from a higher perspective, are the perfect customers support algorithms. In the complex, multi-lingual environments, it is very common, that those scripts already have proper administrators on contact centre, or quality management units, side. This might be a source of potential savings for the project, as long as those individuals might be involved in bots content management.

Unfortunately, in contrary to sales-oriented contact centres, for the after-sales customer support, call scripts are not always available. But what might be useful here, is to take a look at existing knowledge base, especially for the Customer Service procedures, which, if they were designed in a proper way, are just the operationalised depiction of the processes, hence, can be easily transformed into bots algorithms. One of the benefits of using already captured processes, as the input for the bots, and organising bots content management around the existing procedures management process, is that there are most likely already agreed with all relevant business units, including legal aspects like GDPR. That can be another significant time saver for the project set up.

Bot algorithms are just one aspect of customer service automation efficiency. The other one is to help them addressing Customers’ enquiries properly from day one, with a better understanding of Clients intensions. Therefore, a proper allocation of certain algorithms to the connected keywords and phrases is essential. Of course, the database of those keywords will grow very quickly after the platform rollout, but, as the starting point, there are at least three great sources of those collocations. Personally, I would suggest taking a closer look at live chat archives and analyse chat-starting sentences/phrases used by your Clients. It is not about statistics, but about how customers are talking about your company and products, how they are calling their issues with your services and how do they misspell the names. The other two sources of keywords data that are most likely available are the words and phrases, are the ones your customers are using in the search engine of the website and in the FAQ. The latter two should not be neglected, since that is how your customers may try to communicate with the chatbot when they realise that there is no human agent on the other side.

Bot Algorithms Are Just One Aspect Of Customer Service Automation Efficiency. The Other One Is To Help Them Addressing Customers’ Enquiries Properly From Day One

If the call centre agents in your organisation are still selecting contact reasons in a traditional, manual way, eliminating this 2-3 second activity can bring some hundred thousand Euros to the business case, for big-scale operations. That might be done by automated interpretation of contact reasons, by interactions with the phone bot, before the customer is getting connected to the agent.

Any bot platform can be an amazing early-warnings tool if some of the statistical process control mechanisms are created there during the implementation process. Assuming that existing scripts or process descriptions have been used to design certain bots’ algorithms, after some time of using the tool, it will be possible to define a standard absolute and relevant frequency of occurrence, for the certain steps of algorithms. That includes successful and unsuccessful steps and breaking the data by the language of the market of support. Hence, the Customer Service manager can get notifications about some unusual and potentially disturbing trends on the very early stage of its appearance, which could win some valuable time for the operational team to react. This concept will work extremely well, especially while bots are integrated with virtual agents and RPA platforms.

For the sake of the efficient bots implementations, it stays vital to take the most from the existing Customer Service experience and data, making sure that the quality management and knowledge management teams are the real part of the project team. As long as those teams can deliver up-to-date customers service processes description, SOPs, scripts and wordings (templates), it will make the moment, when the bots work in full, happening quicker. That will also save a lot of precious time of the bots product team that normally would be spent on capturing processes and agreeing with them with the stakeholders.