Post_20220324. The on-boarding and adapting the chat to each conversational customer.

The "on-boarding" and "chat restyling" for each conversational customer.

How a CDP, DXP, Messaging app or CPaaS can help conversational channels go beyond language recognition. Conversational is not only about "what to say" but also about "how to dialogue". Personalization matters!

March 24th, 2022

In the rising conversational era ever more individuals, mainly mobile-first customers, are exploring the business messaging (Whatsapp, Viber, RCS, etc) as the main point for customer-service and for conversational commerce. Today it is possible to look for a desired product, buy it and even pay for it inside a chat app.

The past years conversational chatbots grew slower than expected and the transition from mere informative or lead generation chatbots to real transactional chatbots took long. Initially poorly performing chatbots were improved through language AI (NLP,NLG,NLU) evolution. Today we are in a new scenario where chatbots and conversational could become the preferred business to consumer digital channel.

After this huge milestone achieved, it is needed to rethink about some key questions to walk the next step.

Is natural language understanding sufficient for an AI to run successful conversations with customers? In human interactions, considering a perfect knowledge of the language, there are no two equal interactions with two different clients (even when the conversation has the same purpose). The reason is the empathy component impacting “how” the conversation is created and followed, that adapts the conversation to the customer mindset and the customer communication skills.
Can the current gap between those that try to chat with services and succeed and those who try and fail (and thus return to the web or the app) be shortened or even removed?
How long does it take for customers to see/capture the info of a graphic element (picture/chart) before they initiate the next interaction or can answer a chatbot question?
Could chatbots be designed to be more supportive for those with less initial chat skills without penalizing the more efficient interactions of those who chat fast and get the desired results quickly?
How to provide end-user conversational-skills profiling to help the chatbot make decisions about “how” to interact with each given individual user?

Now we have a third type of digital interaction. The messaging fragmentation is not an issue, the same web can be used with several browsers and the same app experience is offered for Android and iOS mobile operative systems.

Trending interactive channels as chatbots o voice assistants should not only improve CX but also help know more about the customer. Chatbot analytics, however, are still underused compared to website or app analytics. Most chatbots (when using messaging apps) are not “anonymously navigated”, so they could collect valuable customer information that could help brands communicate in a more efficient way. If this information were smartly used, customers could be individually profiled and treated differently according to their skills and recorded data. The "on-boarding" of a conversational customer would mean to measure his skills and style and the particular chat restyling would mean adapt the flows and elements used in the dialogue to match better with each given user/end-customer skills/style.

To do that, conversational channels must be complemented by tools that provide user-skills profiling, like Whenwhyhow.

I am a CPaaS company, how can I add more value to my product suite and enhance the conversational CX?

For CPaaS without advanced analytical tools, the next step is to add Customer Data Platform features to their offering, that could complement the solution and make it more competitive. With an embedded CDP CPaaS may not only offer communications, but also collect first party data, create customer profiles and personalize every engagement with customers.

I am a CDP or DXP but I do not perform conversational analytics, how could I benefit from Whenwhyhow capabilities?

Whenwhyhow analytics/profiling features may also be offered as pluggable separated pieces for any data architecture, either embedded as a microservice or as an external data tool accessible via APIs.

I am a messaging app, how Whenwhyhow can help my business clients to deliver better B2C interactions?

Whenwhyhow can provide the app with end-user profiles with chat skills to help the enterprise chatbot adapt to their communication styles and make the communication smoother.

If you are a CPaaS, CDP or messaging app that wants to bring conversational channels to the next level, Whenwhyhow can help you. Let’s cooperate!

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The "on-boarding" and "chat restyling" for each conversational customer.

How a CDP, DXP, Messaging app or CPaaS can help conversational channels go beyond language recognition. Conversational is not only about "what to say" but also about "how to dialogue". Personalization matters!

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