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The conversational revolution and the missing link: the understanding of the customer.
Data, data, data.

Conversational finally is becoming a full transactional digital channel (chatbots and voice assistants) able to gain marketshare from web and app business to consumer interactions. Once NLP is becoming better and better and messaging apps are becoming mainstream with business API the next step is make the interactions superb with hyperpersonalization and digital empathy. A focused CDP is the key.

Jan 27th, 2022

Finally it is happening, the conversational become true and from mere informative or FAQ chatbot/VUI approach companies in many verticals as banking, insurance, telcos, travel, etc. are deploying full transactional conversational experiences with different vendors. In the case of banking there are chatbot first banks as Buddy Bank, Bella, or including full transactionality in its conversational strategy as Itaí Unibanco. Many insurers are enabling conversational as the main way for claims.

This was a hard way that needed:

  • Improving NLP/NLU/NLG, the science of understanding and creating language.
  • Some saturation feeling around apps, the too frequent app updates needed and the rise of texting and voice notes using along messaging apps.
  • Progressive improvement of messaging apps: adding features (menus, carousels, buttons), some level of common set, offering B2C/A2P APIs to create the conversational experience even by long-tail of developers.
  • SaaS models reducing the entry barriers to test this new channel in all type of organizations.
  • Traditional A2P messaging players from SMS era creating connectivity with the new OTT messaging tools (Whatsapp, RCS, Viber, Telegram, etc.) offering business messaging models.
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.

What Whenwhyhow team (at Solaiemes, our previous venture, was pioneering the 1st RCS API Gateway then)  envisioned in 2010 (before Whatsapp) about texting becoming an B2C interaction model is here and is here to stay. Now the challenge is how to make this model the preferred one and the one helping more to improve the customer experience, loyalty and NPS in a myriad of verticals.

Once the AI has been successfully applied to language understanding with a myriad of very good vendors making easier for flow-designers to understand complex questions and create nice composed responses the challenge is time to extend the AI to “understanding customer mindset & behaviors”.

There are several types of software that can help to improve the conversational systems in terms of efficiency, transactionality, and some level of personalization:
  • Traditional approach of chatbot/vui analytics focused in improving the flows, detecting flow-stoppers and comparing chatbots but missing 
  • CRMs providing not only the data for transactional interactions but also the socio-demographic segmentation of the users to apply some personalization.
  • Traditional CDPs (customer data platforms) more oriented to web and app interactions but providing some useful info about journeys across other channels.
The challenge is to have a real 360º customer view (a long time martech industry promise) that could have into account the specifics of conversational: conversational enables empathy, make the customer feel “understood” and personally addressed. A fully capable 360º customer view should provide information beyond the omni-channel journey, basic segmentations, purchase history.

Whenwhyhow approach to the CDP for CPaaS/CaaS (and then for the conversational era) adds:

  • Chatbot/VUI analytics in both senses: in terms of profiling the chatbot performance and help to improve efficiency and detect bottlenecks, and providing user-individual skill profiling to adapt the conversation to their own style and capabilities (more or less text length, more or less menus, etc).
  • Ability to introduce in the journey not only digital interactions but also known customers (let’s say a payment with credit card in a shop) and able to get only the needed info (enterprise decided) from other type of channels interactions (not tagging all and web/app activity, from too little actionable data lake to actionable data-pool, less is more).
  • Behavioral experimentation framework and mindset profiling. It is needed to connect the customer actions and digital interactions, measuring the level of influence of external contexts (markets, weather, reputational news, general economy, etc) in the customer activity. Understanding the customer whys. AI may help from brute force prediction to causal understanding.
  • Profiles not only embedded in agent desktops for personal interactions with augmented customer information, API based enriched profiles to apply dynamically rules in automated conversational systems.
Whenwhyhow helps chatbot platforms, CPaaS, CCaaS or even messaging apps to extend their reach providing a customer-data actionable tool put CX to the next stage.

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