Telemarketing, telecommunications and service call centers traditionally relied on human service operators interacting with customers over the phone. This was for many years just done over the phone until the Web and things such as e-mail took off. Over time, more and more service centers started to outsource employees and customer service to third world countries where cheap labor could be found (there are also companies specialising in such services).
For many years customer service relied on customers calling up a service center and asking for help in fixing their system or an error with the software they are using. A call center representative would try to help the customer over the phone from a single location and if that did not work they would route them to a technician or specialist that could potentially access their system remotely. Customers would spend hours over the phone with the potential of a connectivity loss. Cell phone usage becoming widespread alleviated this issue somewhat.
However, synchronous chats and online communications really took customer service to a higher and more individualised level. Nowadays the practice is expanding into artificial intelligence or machine learning along with the cloud and other technologies replacing many employees or humans sitting at these call centers.
We have these programs being deployed across websites and messaging services called chatbots that even Facebook uses in its messenger to help customers instead of relying on human operators waiting to take a call at all hours of the day and night: often not a feasible task.
A chatbot is available on demand and can serve as the first line of help for any customer that may be accessing the system from a different geographical one where the company is based out of and in the middle of the night in the company’s location. It can also aid customers with both text and voice as well as through visual cues.
Chatbots are becoming more robust and effective due to them implementing AI or machine learning techniques in helping solve customers’ needs and understanding human language better or the questions people input on their keyboards. Some of you may recall a time when you entered a question or even a search query on Google and got a completely different answer than what you intended to ask about. Chatbots are increasing efficiency in the ability to weed through questions asked in a variety of ways by the end user.
Besides chatbots, there is also contact center software available that allows companies to manage all their communications from a single source (or cloud-based CRM) and respond effectively to customers on demand. An example of this is Vocalcom. Communication is becoming much more digital and Vocalcom also has a enterprise contact center service that is integrated with SalesForce.
Service center solutions such as these and CRM systems like SalesForce’s allow companies to stay in touch with customers through smart business intelligence and analytics to stay ahead of their customer’s needs. Another good example is that of Ujet. Both of these service center solutions rely on cloud integrations.
Machine learning and chatbots are really driving much of the global communications today amongst help desks or C2B assistance. It isn’t easy to sometimes hire interpreters or communication experts in a wide range of languages if the company is expanding to other geographical regions. A chatbot can do this on demand. It will also start to understand and pick up customer habits in terms of what they are asking for and how to help them effectively as the languages and audiences change.
A smart solution for business today is to have integrated systems in place that include both chatbots and cloud service centers on demand. Ujet has an interesting solution in place that allows for real-time monitoring of agent activity, intelligent call routine logic and the ability for supervisors to problem solve before issues arise, which is smart business intelligence. Personalization is also key as companies are becoming more tightly focused and specialized on particular market segments and customer needs.
“Customers can contact your call center, schedule an agent callback or launch a chat session directly from your website or mobile app,” according to Ujet’s website. “They can use their smartphones to share visuals that help agents better understand their issues. And service is speedier because agents already have key details about them, and their potential needs, right at the start of a call or chat.”
Ujet is referring to traditional chat communications between clients and companies, however a chatbot can also be integrated into such solution for times when company reps are not at work and as a first-step communication mechanism for companies experiencing heavy traffic load from customers.
Companies can implement multiple solutions today with the aid of SaaS and leverage various tools available from the cloud or CRM systems. They can then start integrating machine learning, such as chatbots, and have various ways to service clients through effective communication. Ujet has more information about the potential of such integrations in customer service on their company’s about us page. It summarizes this form of customer support well:
Customers can easily reach contact centers by phone, web and mobile app and use the power of their smartphones to help explain their problems. Agents have the contextual information they need to assist customers quickly, as well as more time to focus on resolving their issues. And supervisors have intuitive tools and real-time intelligence to get the best performance from their teams, increase customer satisfaction scores and help grow the businesses they serve.
In terms of the first layer of communication, I argue this is where the chatbot should come in —- at least for international companies with customers worldwide and those experiencing heavy traffic spikes (where call centers may be slow to respond to clients and even cloud contact centers can experience issues effectively addressing customer complaints on an individual level. Examples of this would be not enough employees for example and too many emails and chat clients.
SalesForce has an interesting video embedded on its site about chatbots and their use of machine learning advancements titled Chatbots: Using AI to Deliver the Future of Customer Service. You can watch it here and learn how chatbots are hot to implement right now and will only improve in the future as machine learning improves and the longer chatbots are active the more effective they become as tools for customer communications and services.
Companies can even utilize their own ideas for chatbots and deploy a chatbot they customized for their needs from scratch on botpress. This is an “on-prem, open-source boy building platform for businesses,” according to its website.
Some of its key highlights include NLU: natural language processing (NLP); analytics; flow editing: the ability to edit conversational flow; multi-channel across various messaging channels; authoring UI: ships with a GUI of its own for non-developers and SDK along with various APIs that are extensible and customizable.
I have experience creating chatbot variants from three different providers that I covered in depth for Pluralsight as technical guides. These three chatbot platforms I used to create them include Microsoft Azure, MotionAI (now Hubspot) and IBM Watson.
Natural language is not east to interpret or understand as a question can sometimes be asked a thousand times. It is in this way that machine learning is strongly being pushed. Google search, for instance, relies on machine learning to interpret search queries as do chatbots. Combined with effective CRM tools that leverage the cloud and cloud customer communication solutions is a great way for a company to service customers on a personalized level and identify problems before they arise on a massive scale. Customer service is not just becoming more personalized, but data driven and intelligent.