Category Archives: Voice of the Customer (VoC)

Posts about Voice of the Customer (VoC)

Recorded webinar: Vertical Packs, VoC, VoE

Thank you all for your interest in our webinar “MeaningCloud Vertical Packs: the Fastest Way to Benefit from Text Analytics” that we delivered last December 20th, where we explained how to customize text analytics with only one click  and we presented our  Packs for the analysis of the Voice of the Customer and the Voice of the Employee.

During the session we covered these items:

  • Introduction to text analytics and MeaningCloud.
  • Why Vertical Packs? How they create value.
  • What are the components of Vertical Packs: models, APIs, integrations.
  • Available Packs: Voice of the Customer, Voice of the Employee.
  • Case study: analysis of the Voice of the Customer.
  • Coming developments: product roadmap

IMPORTANT: this article is a tutorial based on the demonstration that we delived and that includes the data to analyze and the results of the analysis.

Interested? Here you have the presentation and the recording of the webinar.

(También presentamos este webinar en español. Tenéis la grabación aquí.)
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Voice of the Customer in Excel: creating a dashboard

Excel spreadsheets are still one of the most extended ways of working with big collections of data, especially among non-technical users. Two of our Vertical Packs, Voice of the Customer and Voice of the Employee, are particularly useful for typically non-technical teams, which can now carry out their analyses easily with our last Excel integration.

In this tutorial, we are going to show you how to use the add-in provided in the Voice of the Customer Vertical Pack, how to carry out a VoC analysis, and how to work with its output by creating a dashboard like the one on the right. Working with the Voice of the Employee Pack would follow a similar pattern.

A practical case

Let us imagine we work for a market research department or agency interested in analyzing the Insurance industry. Customer comments in forums and social networks constitute an extremely valuable source of spontaneous information about their opinions about insurance providers.


We are going to focus specifically on auto insurance reviews extracted from ConsumerAffairs, a website that collects reviews from several domains.

The reviews we are going to use have been extracted from the top five companies in the Auto Insurance section: for each one of them we’ve picked ten items. You can download here the Excel spreadsheet we will be working on. It contains a single sheet where we have included two columns: one with the selected reviews, and another with the name of the company they refer to.

As we have mentioned, for this tutorial we are going to use our Vertical Pack for Voice of the Customer analysis. Vertical Packs are a combination of preconfigured models or dictionaries, powerful APIs and specific add-ins for Excel that enable you to adapt text analytics to your domain with only one click. Just by registering at MeaningCloud, you have a 30-day trial for all Vertical Packs available. The trial starts the moment you first analyze a text, so users that have been using MeaningCloud for a while will also be able to try it out.

To get started, you need to register at MeaningCloud (if you haven’t already) and download and install the VoC Excel add-in on your computer. Here you can read a detailed step by step guide to the process.

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Accelerate text analytics’ time-to-benefit with our Vertical Packs

At MeaningCloud we have published our first Vertical Packs.

Our goal for them is to provide you with the fastest and least costly and risky way to make your text analytics initiatives profitable.

Preconfigured models and dictionaries

Usually one of the main costs of text analytics projects lies in building the models and dictionaries needed to adapt the tools to each application scenario, and at MeaningCloud we have always made it very easy thanks to the customization tools that the product includes.

But for those who do not have the resources to carry out this adaptation, the Vertical Packs give it to you already prepared for a set of scenarios. The Packs consist of a series of pre-prepared resources (dictionaries, deep categorization models, and sentiment models) focused on a series of typical scenarios (analysis of the Voice of the Customer, the Voice of the Employee, etc.) ready for immediate use and that provide analyses with an increased precision, recall, and relevance in these applications.

Use them from our add-ins for Excel

To make it easier to leverage the Vertical Packs, we have made them accessible through new add-ins for Excel, with support for the most useful operations, models, and analysis in each vertical.

Add-in for Excel

If you work for Marketing, Customer Support, or Human Resources and have thousands of comments from your customers or employees to analyze, sign up to MeaningCloud, download the corresponding add-in for Excel, paste your verbatims in a spreadsheet, press the relevant MeaningCloud button, and you will see how your comments are automatically tagged with meaningful categories for the analysis of the Voice of the Customer or the Employee.

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MeaningCloud Release: new add-ins for Excel

In the last MeaningCloud release we presented our new Deep Categorization API, a new Premium API that gives us access to two of our new vertical packs: Voice of the Customer and Voice of the Employee.

We also know that many of the target users of these functionality may not be necessarily know how to code, so with that in mind, in this latest release we are publishing two new add-ins, one for each vertical pack:

Both add-ins provide an integration with the Deep Categorization API, but focus on giving a more user-friendly approach for the analysis each one of them provides.

MeaningCloud release

The add-ins are adapted so anyone can obtain the analysis they want with just a few clicks, without worrying about API parameters or leaving the environment where they have the data to analyze.

This release also contains minor security updates as well as bug fixes in our core engines.

If you have any questions or just want to talk to us, we are always available at!

Applications of Text Analytics in the Tourism Industry

Understand Your Visitors, Improve Your Offering

Tourism is one of the largest economic activities, with statistics indicating that people spend more discretionary income on travel than on home improvement, financial investment, or even health.

But how people travel is changing. For example, people are spending more and more time researching trip details on their mobile devices. In 2016, 40% of US travel site visits and 60% of searches for destination information came from mobile devices, and travelers are increasingly consuming and publishing information on tourism in online travel agencies, social networks, or review sites such as TripAdvisor,, etc.

A new generation of contextual semantic analysis applications allow us to leverage all that information and communicate more naturally with hyperconnected tourists. These applications range from analyzing comments on social media to understanding natural language which allows us to develop much more conversational assistants and bots.

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MeaningCloud Release: new Deep Categorization API

This is what we’ve included in MeaningCloud’s latest release:

  • New Deep Categorization API: we are happy to present the first of our Premium APIs, Deep Categorization 1.0, which lets you carry out an in-depth categorization of your data. In this initial release, we’ve included predefined models for analyzing the Voice of the Customer in several domains and the Voice of the Employee.
  • Language Identification 1.1: we say goodbye to Language Identification 1.0, so if you are still using it, you will need to migrate to the newest version. If you are using it through the Excel add-in, we’ve done it for you, so you just have to update your Excel add-in to the latest version.
  • New language for Text Clustering: we’ve added Catalan to the languages supported in the Text Clustering API.
  • General usability improvements: mainly in the developer area of the website.
New NeaningCloud release

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Surveys and HR: why do you need open-ended questions?

open and closed questions

Since everyone wants to understand employees better, text-based data sources are a key factor for any organization to understand the “whys” and act on them to make improvements. Open-ended questions are one of the most effective ways to gather employee opinions; they offer them an open forum to make suggestions and present innovative ideas.

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The amazing deeds of text analytics superheroes

In the last few years, the explosion of user-generated content in social media (networks, forums, communities, etc.) has significantly increased the need to extract information from unstructured content. Oddly enough, text analytics superheroes, wondrous as their achievements are, are just average guys who figured out what they could do with the right technology.

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Customer experience, a win-win in restaurants

 BLT sandwich, buttered or not?


Do you know if your customers prefer buttered toast in your BLT sandwich? Don’t worry; MeaningCloud is the kitchen helper you need to suit your dinner guest. Customer experience is the ingredient you need. Surfing the Internet you find hundreds of websites and apps to give feedback on restaurants. You could find by chance people talk about yours. Can you imagine people disparaging your BLT sandwich? For your information, I’d rather have it buttered.

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