What is the Voice of the Employee (VoE)?

Voice of the Employee. Silhouettes with bubbles representing dialog

Finding committed employees is one of the top priorities for public and private organizations. Voice of the Employee is essential in that sense. They collect, manage and systematically act on the employees’ feedback on a variety of valuable topics for the company.

The relationship between Engagement and Voice of the Employee is so similar than the existing one between Voice of the Customer and Customer Experience. VoC provides information to improve customer experience. Voice of the Employee promotes employees’ engagement.

See: Voice of the Employee, Voice of Customer and NPS

The Voice of the Employee collects the needs, wishes, hopes, and preferences of the employees of a given company. The VoE takes into consideration specific needs, such as salaries, career, health, and retirement, as well as implied requirements to satisfy the employee and gain the respect of colleagues and managers.

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Activating social listening to protect online reputation (part 1)

EDITOR’S NOTE: this is a guest post by Leopoldo Martínez D., a researcher, consultant on social media corporate intelligence and lecturer at UCV and IESA (Venezuela), and it was originally published on his blog (in Spanish).

 

1. Introduction

In this previous post, I suggested that conversations taking place in virtual communities fostered by a digital marketing plan generated a feedback that was useful to assess and monitor the performance of a digital marketing strategy.

This feedback could generate a huge amount of valuable data (Big Data) that enable to create a knowledge base on what is being talked about, who is telling it, who is having a greater impact on brand image, products, people or organizations.

This knowledge base can also be fed by discussions arising from unexpected events, which are not part of the communication plan, but deal with the virtual community’s topics of interest.

To specifically assess the conversation’s impact, it is necessary to pay attention (more than just listening) to what is being said through metrics (qualitative and quantitative) that reflect the perception that the members of the virtual community have on brands, products, people or organizations, being this perception an online reputation measure.

In this sense, the purpose of this post is to show in a simple way how to carry out an active listening of conversations in social networks to evaluate the online reputation.
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Recorded webinar: When to use the different Text Analytics tools?

Last February 9th we presented our webinar “Classification, topic extraction, clustering… When to use the different Text Analytics tools?”. Thank you all for your interest.

During the session we covered the following agenda:

  • An introduction to Text Analytics.
  • Which application scenarios can benefit most from Text Analytics? Conversation analysis, 360° vision, intelligent content, knowledge management, e-discovery, regulatory compliance… Benefits and challenges.
  • What are the different Text Analytics functions useful for? Information extraction, categorization, clustering, sentiment analysis, morphosyntactic analysis… Description, demonstration and applications.
  • What features should a Text Analytics tool have? Is it all a question of precision? How to enhance quality?
  • A look at MeaningCloud’s roadmap.

IMPORTANT: The data analyzed during the webinar can be found in this tutorial.

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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Text Analytics & MeaningCloud 101

One of the questions we get more often at our helpdesk is how to apply the text analytics functionalities that MeaningCloud provides to specific scenarios.

Users find MeaningCloud knowing they want to incorporate text analytics into their process but not sure how to translate their business requirements into something they can integrate into their pipeline.

If you also add the fact that each provider has a different name for the products they offer to carry out specific text analytic tasks, it becomes difficult not just to get started, but even to know exactly what you need for your scenario.

homer-simpson-confused

In this post, we are going to explain what our different products are used for, the NLP tasks they trace to, the added value they provide, and which are the requirements they fulfill.

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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.
blt-sandwich

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Classification, topic extraction, clustering… When to use the different Text Analytics tools? (webinar)

How to leverage Text Analytics technology for your business

Text AnalyticsMost valuable information for organizations is hidden in unstructured texts (documents, contact center interactions, social conversations, etc.). Text Analytics helps us to structure such data and turn it into useful information. But which text analytical tools are the most appropriate for each case? When should I use information extraction, categorization, or clustering? Which applications can benefit most from Text Analytics? What are the challenges?

Register for this MeaningCloud webinar on Wednesday, February 8th at 9:00 PDT and discover answers to these and other questions through practical examples.

UPDATE: this webinar has already taken place. See the recording here.

(Este webinar también se realizó en español, ver la grabación aquí.)


Text Classification in Excel: build your own model

In the previous tutorial we published about Text Classification and MeaningCloud’s Excel add-in, we showed you step by step how to carry out an automatic text classification using an example spreadsheet.

In this tutorial, we are going a bit further: instead of just using one of the predefined classification models we provide, we are going to create our own model using the model customization console in order to classify according to whichever categories we want.

We are going to work with the same example as before: London restaurants reviews extracted from Yelp. We will use some data from the previous tutorial, but for this one we need more texts, so we’ve added some. You can download the spreadsheet here if you want to follow the tutorial along.

If you followed the previous tutorial, you might remember that we tried to use the IAB model (a predefined model for contextual advertisement) to classify the different restaurant reviews and find out what type of restaurants they were. We had limited success: we did obtain a restaurant type for some of them, but for the rest we just got a general category, “Food & Drink“, which didn’t tell us anything new.

This is where our customization tools come in. Our classification models customization console allows you to create a model with the categories you want and lets you define exactly the criteria to use in the classification.

So how do we create this user model?
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Learn to develop custom text classifiers (recorded webinar)

Last October 5th we presented our webinar “Learn to develop custom text classifiers with MeaningCloud”. Thank you all for your attendance.

We began by presenting how to do text classification with MeaningCloud and why it is necessary to develop models that are adapted to each specific application scenario. The bulk of the presentation consisted in using a practical case (analysis of restaurant reviews) to show how these models can be developed using our product.

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Text Classification in Excel: getting started

As you probably already know, Excel spreadsheets are one of the most extended ways of working with big collections of data. They are powerful and easy to combine and integrate with a myriad of other tools. Through our Excel Add-in, we enable you to add MeaningCloud’s analysis capabilities to your work pipeline. The process is very simple as you do not need to write any code.

In this tutorial, we are going to show you how to use our Excel Add-in to perform text classification. We are going to do so by analyzing restaurant reviews we’ve extracted from Yelp. If you have already read some of our previous tutorials, this first part may sound familiar.

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

Once you’ve installed it, a new tab called MeaningCloud will appear when you open Excel. If you click on it, you will see the following buttons:

excel add-in ribbon

To start using the add-in, you need to copy your license key and paste it into the corresponding field in the Settings menu. You are required to do this only the first time you use the add-in, so if you have already used it, you can skip this step.

Once the license key is saved, you are ready to start analyzing!
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Learn to develop custom text classifiers with MeaningCloud (webinar)

Learn in this webinar how to use MeaningCloud’s tools to create classification models completely adapted to your scenario

Users frequently ask us through our support line how to perform text classification according to application-specific taxonomies. For example, somebody needing to analyzing a bank’s contact center calls and open survey responses might be interested in classifying such messages according to the institution’s different types of products and services (deposits, loans, mortgages, etc.) or the type of interaction (request for information, contracting, complaint, etc.).

Custom classification

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