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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Inspiration and Innovation: Learning From Artists

This article appeared in Wired Innovation Insights.

The crowd at an IT event in Paris was intrigued to see an art historian as the keynote speaker opening the conference. They had come to hear their peers talk about trendy topics in Big Data, Search, Content Analytics, Natural Language processing, etc. But the first thing they were asked to do was contemplate a painting by Veronese . Then, art historian Stéphane Coviaux showed them a drawing that looked like a sketch the artist made before embarking on the big painting. Yet the “sketch” had been created by a different artist some 50 years earlier! Veronese a plagiary?!

When asked to compare the two works, the IT audience was not shy and came up with many major and minor differences. It became clear to everyone that Veronese had been inspired by the work of his predecessor. Through his changes in the conception of the image, in his use of space and color and through his own symbolic, Veronese had produced a major work of art from a comparatively minor source of inspiration.

The message to the audience: do not expect cooking recipes or “best practices”! Transpose what you see from others – the innovations they have implemented – into your own environment. And, don’t be overawed by impressive projects that you may see, view them as sketches for your own projects and “go create!”

In an emerging market or one that is radically changing, there simply are no “best practices” and no “recipes.” Take inspiration from others but use your imagination to create innovations that advance your business. A specific message for the modern times IT-audience was added: Aim at “co-creation”; find partners you trust to help you along in the creation process and accompany you in uncharted (or not completely charted) territory. In uncharted territory, your procurement services cannot take a standard contract out of a drawer and hope it fits.

The “distance” between the artistic and the business environments worked well to get the message across. The presentation of even an exemplary business project coupled with the injunction “be inspired! Do not copy, but transpose,” would certainly have provoked reactions like “fine but not applicable in my environment.” In the distant world of art, everyone could easily agree on the necessity of inspiration to create innovation, took this lesson home to apply to their enterprises.

Xavier Pornain – VP of Sales & Alliances for Sinequa.

 

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Inclusion of Unstructured Data into Business Intelligence Analytics

data5Today’s large enterprises and administrations accumulate ever bigger volumes of ever more heterogeneous and volatile data. More than 90 percent of this data is unstructured, consisting of texts in many different languages. Information workers spend too much time searching for the information they need for their work and companies lose business opportunities when employees are drowning in these mounds of data.

Enter Sinequa.

Sinequa’s real-time Big Data Search & Analytics platform helps its customers meet this challenge. But what makes Sinequa different than other big data analytic platforms? See what Sinequa’s Vice President of Marketing Hans-Josef Jeanrond says about the value of Sinequa’s platform and how companies can best leverage the software.

Q: What differentiates Sinequa from other big data analysis platforms?

A: There are two basic reasons why Sinequa is different from other platforms. One being the data sources we are dealing with: We are not tackling the World Wide Web nor the complete internet of things. We focus on the enterprise world with different analytical tools.

The second reason that we stand apart from other big data platforms is that we do not choose between structured and unstructured data. We offer combined statistical and semantic analysis of big data and use the structured information to refine linguistic analysis. With Sinequa the semantic analysis of unstructured data is used to create structured data; one analytic method is used to refine the other. What sets Sinequa apart here is our capability to query an index of up-to 200 million documents in real-time with sub-second answers and our ability to deal with semantically related subjects in 19 different languages.

Q: Can you please further explain the importance of semantic analysis?

A: If you limit yourself to statistical data analysis, enterprise data often poses problems in sample size, sample error and sample bias. This is why tools from the Web don’t work well with enterprise data.

Keyword search is not a sufficient option either: The keywords you use in an information request may not occur in many of the documents that are actually relevant for your work. These keywords may not have been added as “meta data” by the people who classified and stored the documents in your document management system – they may have been looked at from a different perspective.  You need to find documents dealing with concepts that are semantically related to your request, thus needing semantic analysis to find them.

 

As you can see, the Sinequa platform offers functionalities that truly differentiates it from competitors. Sinequa combines deep content analytics, including Natural Language Processing with an extremely scalable IT architecture, offering users simple and secure access to the most relevant information. Stay tuned for our next post that will explain how a leading bio/pharma company implemented Sinequa to index millions of R&D documents and break down barriers between information silos, worldwide.

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Congrès Big Data Paris 2013 – Interview d’Hans Josef Jeanrond

Lors du congrès Big Data 2013 qui s’est tenu les 3 et 4 avril, Hans Josef Jeanrond, Directeur Marketing de Sinequa, a présenté l’intérêt des solutions de search et  des plateformes d’Accès Unifié à l’Information pour traiter et valoriser les données non structurées contenues dans le Big Data.

Retrouvez l’interview :
Big Data 2013

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On the road to Big Data

The Documation trade fair and conference was a resounding success for Sinequa: Visitors came with more concrete projects than in previous years. Conferences were very well attended, and the roundtable discussion on Big Data with Sinequa and partners was crowded, even standing room overflowing into the aisles.

Three major players EMC²; CGI Business Consulting and Sinequa focused on ROI of Big Data projects. They form an alliance to address the challenge of value creation in the various areas and phases of real life Big Data projects.

Philippe Nieuwbourg, specialized journalist, lecturer and book author on Big Data moderated the discussion and contributed his own experience. He provoked people to breathe life into “data cemeteries” that have accumulated in many companies, and extract value from it ion realistic projects.

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