Cracked Conversations: What to Do When Chatbots Aren’t Enough

Enterprise Search to Compliment Your Chatbot ExperienceBy: Robert Smith, Sales Engineer and John Finneran, Product Marketing

Conversational AI, or chatbot, vendors, are everywhere, deafening customers with the promise of AI-Powered solutions for their customer service needs.  According to Capterra, 158 companies currently offer chatbot software.  In Forrester’s evaluation of the emerging market for conversational AI for customer service for Q2 2019, the analyst firm identified the 14 most significant providers in the category – [24]7.ai, Avaamo, Cognigy, eGain, Indenta Technologies, Interactions, IPsoft, Kore.ai, LogMeIn, Nuance Communications, Omilia, Saleforce and Verint.

This makes understanding what works best to improve customer experience hard.

Chatbots work best guiding users along straightforward, well-defined conversational paths.  If a customer asks new, unpredicted questions the typical chatbot gets confused. More complex questions require complementary solutions.  

Sinequa offers one such complementary solution – Enterprise Search that can work with chatbots to help customers and employees find what they need.

We have spoken with a number of companies ranging from those considering the technology, to building prototypes, to deploying chatbots in customer-facing applications.

Several of the concerns about the value produced by chatbot deployments

  • Slow Conversation speeds
  • Conversation path-sets grow larger and longer
  • Low accuracy because the chatbot was unable to answer and was unable to maintain the chat
  • High development effort with too many expert hours spent conceiving, designing, deploying, and maintaining those conversational paths.

Some Reasons Why?

Chatbots work best when guiding a well-defined type of user through a set of preconceived conversational paths.

The typical chatbot’s tooling provides a graphical interface, and some testing capabilities; conceiving, designing, deploying, and maintaining those conversational paths will be up to you.

  • When you consider how many paths a user might take, multiplied by the number of user types, it can grow to an astonishing amount of work.
  • When chatbots have a lot of this work to do, they tend to slow down compromising, the chat experience
  • Most requests for information are ‘ad-hoc’ and therefore not well-suited for a pre-planned and pre-built conversation flow.

When Do Chatbots Make Sense?

An example is a chatbot at your local bank

  • They have a limited set of offerings for users to choose from
    • E.g. checking, savings, mortgages, lines of credit
  • Those offerings have a limited number of actions
    • Checking deposit, transfer, bill pay, balance inquiry
  • The site is often for reference, not as much for execution
    • To actually open up an account type, you typically have to apply in-person

If you can’t narrow the scope to specific user-types and paths like these, then the outcome of multi-step “chats” is by definition, less predictable, leading to a higher failure rate.

This also makes it difficult for some chatbots to get a PTO (Permit to Operate), because companies have not let applications go into production that couldn’t guarantee outcomes.  This is to avoid “Rogue AI” situations, among other things.

Addressing the Challenge

Enterprise Search, like Sinequa’s, leverages natural language processing (NLP) to get users the most relevant content, without the chatbot’s requirement that the conversational path be designed, built and maintained.

Where chatbot interactions are sometimes helpful, that chatbot can connect to enterprise search; when the chatbot gets a user’s request for information, the chatbot can refine and forward the request to the underlying Sinequa search, then channel the results back to the user’s conversation.

In Short

By using chatbots and a powerful enterprise search platform together for the jobs they were designed for, you can deliver profitable and productive solutions that enhance both customer and employee experiences.

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The Heat in The Trend Point: June 10 to June 14

Big data is usually mentioned at least a bit in The Trend Point, and last week was no exception. We noticed that many of the articles seemed to be pointing towards going beyond information retrieval.

Value must be added through the technologies of an information management, search and analytics system. An article quoted in “Big Data without Value is Just a Lot of Data” states the following:

Relying solely on the information gathered by Big Data is like watching a group of people from a relatively far distance. It’s possible to see what they’re doing while they interact with each other and engage in conversations, but it’s virtually impossible to understand why they’re holding those conversations, what are they feeling that drives their actions, what is the emotion underpinning those conversations, and most importantly, how they’ll determine the future behaviour of each individual and the group at large.

We heard a similar sentiment repeated in “Data Visualization Key to Data Understanding” with an emphasis on the end goal being easy access to actionable information. This post relayed the following:

It’s typical for an analyst who has been working on a project for more than two months to show all the frequency or statistical results with a presentation deck consisting of hundreds of slides. Stop! A few charts with great data visualization are worth 1,000 slides. Actionable visualizations such as Price or Attrition Alerts can help sales teams better engage with customers instead of analyzing a plethora of reports. The key: reports should be easy to understand as well as recommend the next actionable step for business leaders.

In another post, we saw another mumbling that big data is a misnomer better represented as big content. We noted some of the thoughts that followed — the necessity of extracting value from unstructured content — in the article “Big Data or Big Content“:

Unstructured content is often included almost as an afterthought, with extraction and enrichment applied on-the-fly, from scratch on a case-by-case basis. This undermines the potential of Big Data in several ways. It raises the cost of incorporating unstructured content while also increasing the opportunities for the introduction of inconsistencies and errors reducing the quality of the final product. Most importantly, the ad hoc approach also reduces the potential of Big Data by obscuring the extent of available raw materials.

It is refreshing to see that these several media sources are no longer discussing simply mashing up raw data from different sources. The important pieces are fusion of data (both structured and unstructured) and that comes through strong analytics that can detect what belongs to the same semantic category. Then a system like Unified Information Access from Sinequa can “fuse” results with other data, like geographic position or customer history, and others.

Jane Smith, June 19, 2013

Sponsored by ArnoldIT.com, developer of Beyond Search

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