Scots are gradually starting to find a place on websites and Facebook pages…
According to the study conducted by Elu Customer Service of the year, chatbots are used by 9% of French people, with productivity gains for 59% of them:
But this is only the beginning, the Bots are now tackling new channels, especially the phone with Callbots.
Indeed, if there are already many tools like the Interactive Voice Server (the famous automatic switchboards with type 1 for, type 2 for…), callbots are becoming more and more important in order to bring more productivity gains but also a significant improvement in the Customer Experience.
This is confirmed by Gartner in his 2018 analysis of emerging technologies in CRM and Customer Service, with the arrival of “voice driven”, that is to say interactions by voice.
What is a Callbot?
The definition of a callbot is the following ” A callbot is software capable of understanding and interpreting the words of a person on the phone via automatic language processing (Semantic Analysis or Natural Language Processing). This program has the capacity to provide reliable and consistent answers to questions formulated by the person on the phone, if necessary by logging into the corporate information system to find or record information”
Here is an example of a Callbot in the insurance sector, the purpose of this callbot being to report water damage:
So he’s a cousin of the Chatbot who uses writing via the chat (messenger, web…) to interact with customers and prospects, and Voicebot which exchanges via connected speakers or virtual assistants in smartphones (SIRI, OK Google, etc.).
Google has also shown the interest of a Callbot via a rather impressive demonstration of the capacities for a Callbot to understand human language with Google Dupleix to make appointments:
Why should you care about Callbots?
There are three main reasons why Callbots will become a priority in businesses in the coming months, especially those with a lot of phone calls.
1 – The Callbot improves the productivity and responsiveness of the contact center
Despite everything we can hear, the phone remains the
N ° 1 channel to contact a company, with 57% of interactions (just
in front of email and web forms) according to the study conducted by “Elected
Customer Service of the Year 2018 ”
22% of consumers at the same time judge that the responsiveness of Customer Service is one of the major criteria of satisfaction (Source Elected Customer Service of the Year).
However on the phone, many requests are still very simple, with no real added value (order tracking, product returns, statements, stopping / subscribing options, making appointments, changing delivery times …), but still require waiting for an advisor on the phone, while they could be automated.
The use of a Callbot allows:
- Reduce request processing times. For example, a use case such as recording a water damage type incident, a glass breakage… takes 1 to 2 minutes less with a Callbot than with an adviser (the callbot goes straight to the point, and summarizes requests);
- Coping with peaks in demand, as during one-off events (technical problems), seasonal increases (eg sales, Christmas, etc.) or the influx of calls on Monday morning at 8 am to process weekend requests …;
- Open a 24-hour customer service (in French or in multilingual), whether to process requests, or to do a pretreatment (recording the request, transcribing it into text with creating a reminder ticket in the CRM + voice recording for later processing). Currently most contact centers are open during office hours, when consumers are at work…;
- No more “hiding” his phone number for fear of being overwhelmed with calls. This is the case in many companies (especially e-commerce) where the phone is placed at the bottom of the website, or even inaccessible without having passed a filter of multiple FAQs in order to “discourage” people from calling …
Chatbots have already captured 9% of interactions (source Elected Customer Service of the Year), but this indicates that‘There is still a huge potential for productivity to have simple and repetitive requests handled by a Callbot who are currently being treated by counselors over the phone.
2 – The Callbot to improve the Customer Experience
With increasing competition, it becomes crucial
not to lose any more customers because of a bad customer experience or a
chaotic customer journey.
It is in this context that the role of the contact center in companies is evolving: advisers are not asked to just process a maximum of requests in a minimum of time, but to offer a good customer experience. .
At the same time, and unlike this “customer experience” approach, operating budgets do not increase (or even are declining), while the communication channels are on the contrary in explosion (chat, video, click to call, WhatsApp, social networks…), with an explosion of the interactions and requirements of reactivity increasingly strong ( eg: less than 3 minutes to answer the phone, less than 2 hours on social media…).
The callbot intervenes at this level in order to respond immediately to simple requests.
The key is to carefully choose the right use case : it is essential to put the callbot to process a request to which
he will know how to respond perfectly.
We note that the opinions on chatbots and callbots are very polarized:
- Either they are very good (the bot has done its job well and it often happens that the client says “Thank you”);
- Either the opinions are very bad because the Bot was unable to respond to the request and an advisor had to take over to resolve the problem.
The other interest, it is also to avoid the “bad roads” in a customer journey, with for example the bad choices and errors returned in the SVI. In fact, thanks to the understanding of natural language, the Callbot has a much better ability to route to the right services (a traditional IVR having between 10 to 20% of misdirected calls).
Finally, thanks to the natural understanding of conversations, the Callbot can automatically transcribe conversations, to identify black spots in the customer experience, product irritants …
3 – The callbot meets the new expectations of customers and prospects
This is even more true among the younger generations, but it is also the case for most consumers. : 72% of customers prefer to solve their problem themselves rather than calling Customer Service (source Salesforce).
Having an advisor on the phone is usually only a “last resort” when Google & emails are no longer enough, or that the situation is delicate enough that a client must be reassured by an adviser.
The Callbot thus makes it possible to process “self-service” requests new generations who want to extend the self-service effect and immediacy they are on social networks.
What is important to note is that, on the other hand, the Callbot is also very popular with Seniors : these populations are generally less comfortable with new technologies (Web, Chat …) while the trend is to impose everything digital.
The callbot makes it possible to reconcile the two worlds, the telephone and the digital.
Duo Gagnant Advisors – Callbot
The establishment of a Callbot is relevant as soon as one is in B2C, that one has a volume of important calls or peaks of activity.
From then on, there is a winning duo that emerges:
- Advisors to process value-added requests or complex requests or low volume requests;
- Callbots to process simple, personalized and with a minimum of volume.
Conversely, a Callbot is not suitable if:
- When the number of calls to be treated is too low or too heterogeneous;
- The data used by the Callbot is incomplete or unreliable.
- The request to be treated is sensitive or with emotion (eg a serious accident …) …
How to succeed on a Callbot project?
If there is only one piece of advice to remember, it’s good
choosing your use case is the key to success.
You must select a request (= a use case), which has
both volume (several tens / day minimum), which can be automated
and if possible with a minimum of customization.
Avoid at all costs the callbot (or a chatbot) which tries to understand all the requests in FAQ mode (or pretending to be a human), and who will ultimately misunderstand and meet expectations for ambiguity problems.
To this are added other key tips to remember:
- Always confirm the customer’s request by SMS, so that he has a record of his conversation;
- Anticipate voice constraints (avoid surnames to favor client codes) and systematically rephrase the request (ex: OK, I understood that your request was for …) to reassure the client;
- Always suggest escalation to a human advisor (after X bot misunderstandings, if the client insults);
- Validate customer satisfaction at the end of the conversation, and in case of dissatisfaction return the hand to an advisor;
- Analyze Callbot conversations for continuous learning (eg new ways to request something, new requests…), and ensure a minimum understanding rate of 90% of requests for the use case of the Bot before launch (with very specific monitoring during the first hours of launch);
- Communicate internally that CallBot will not replace customer service advisers but help them deal with low added value tasks.
You want to know more ?
If you want to know more about setting up a callbot, project methodology, budget … you can leave a message in the comments or contact me directly on the site Eloquant.
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