Etuma is the leading multi-language customer and employee experience analysis company with over 200 million analyzed comments and customers in 9 countries.
Etuma is a customer and employee experience text analysis company. It's mission is to turn unstructured customer or employee feedback into statistical information fast and feasibly.
CX text analysis takes into account the peculiarities of analyzing customer's and employee's comments. It, for example, understands that a missing item in product selection is a negative comment.
Etuma's technology has 20 years of computational linguistics software development behind it. Etuma is a spin-off from a Natural Language Processing pioneer company.
We now live in experience economy. Good and consistent customer experience has become a competitive requirement. Loyalty is not just about delivering an exceptional product but making customers feel good about every aspect of your operations and brand. This is why many companies are putting in place systematic ways to monitor customer experience.
According to Harvard Business Review loyal customers are typically much more profitable than other customers.The reasons behind why they are so profitable aren't so well known or understood.
- Loyal customers stay longer
- Loyal customers buy more and more often
- Loyal customers cost less to serve
- Loyal customers insulate your company from price competition
- Loyal customers act as brand ambassadors
- Loyal customers provide honest, high-quality feedback
In their 2016 study Forrester found out that the customer experience leaders clearly outperform the customer experience laggards. "We found that the CX leaders in all five pairs of companies outperformed their relative CX laggard counterparts.”
With survey processes like NPS and CES an ever higher share of customer’s voice is open-ended comments. Social media discussions and incoming emails and web-forms further complicate the picture. Meanwhile, the feedback volume is increasing.
These methods are NOT effective for extracting insights from customer or employee comments.
- Extracting brands is useful information when it comes to social media and media discussions but fails to provide actionable information when it comes to your own feedback data analysis: it doesn’t tell WHAT people are talking about.
- Extracting whole comment sentiment based on the number of good and bad words in the whole comment and how far are they from the brand mention is quite easy but it fails to provide information about the topic of discussion: WHAT people are talking about. It also often fails to get the sentiment right because the sentiment analysis is based on a list of good and bad words and not the structure and rules of the language.
- Extracting keywords doesn’t work because keywords cannot be turned into statistical information: there is almost an infinite number of keywords in any language. The 'topic' of discussion is statistically distributed “too wide”.
CX text analysis is a an application, which connects customer and employee voice to company's business strategy, customer value proposition and corporate values.
A rule-based semantic analysis system understands language structures and the relationships between words. It has a massive set of pre-configured rules that enable automatic and accurate real-time analysis.
CX text analysis runs in the background of business processes like customer complaint handling, customer loyalty management and employee engagement.
CX text analysis adds value by making these processes more automated, more intelligent and faster. It also saves costs by reducing slow, expensive and mistake prone human work.
Developing a rule-based language analysis system takes a considerable amount of work and time, sometimes stretching even up to dozens of years. Very few companies have the skills or resources to develop such a system.
One of the problems in CX text analysis is that the analysis results are either too generic or too expensive. Teaching the system to analyze one company’s customer feedback is expensive to develop and maintain. Etuma has found the perfect balance between these two extremes. Etuma learns from all the incoming customer comments (not just teaching the system one customer at a time).
Above is the summary on how multi-language customer experience text analysis system works.
During the past five years Etuma Feedback Categorizer has analyzed over 200 million customer or employee comments. It is consistent, automatic and understands multiple languages.
- Intelligence is built into the analysis system
- Rule-based system detects sentiment accurately
- Rule-based system learns from all customer comments
- Rule-based system requires much less maintenance
Etuma has a highly productized API. Etuma has connectors to dozens of CEM, CRM or Surveytools.
You can visualize the CX or EX text analysis results using any dashboard or data analytics service or platform. This dashboard is created using Tableau but our customers also use Qlik Sense, Microsoft Power BI, Excel, IBM SPSS and SAS Analytics.
Etuma Feedback Categorizer is a pure cloud service hosted by Amazon Web Services in Ireland within EU. Etuma follows the stricktest privacy and secrecy principles and rules.
Etuma has customers in nine different countries and in many industries. Here are three examples from three different countries and three different industries.
Embedding Etuma feedback categorization service into your Customer Experience Management, Customer Relationship Management, Contact Center or Business Intelligence platform gives you access to high-quality text analysis results without the need to leave applications you are familiar with.
Is your current text-analysis solution solving the problem?
- Can you continuously monitor the customer journey?
- Do you know, for example, what drives your NPS score?
- Are your contact center tickets tagged automatically and consistently?
- Are you able to prioritize customer experience improvement actions?
Most CEM, CRM, BI and Contact Center platforms provide a text-analysis feature, which is typically based on keyword analysis. This type of feedback analysis doesn't provide statistically relevant information. There are just too many keywords, and the results aren't usable for decision making. You cannot use them to continuously monitor the customer journey, track hot topics, detect weak signals or benchmark your operational units, and compare multi-language feedback.
Etuma embedded analytics service is invisible to the end users. It is part of your platform.
At the end of the day nothing matters if you don't share the insights and close the loop. If you need help with this, read our white paper: "The CX Professional's Guide to Extracting Insights".
Want to dig deeper into CX text analysis business and Etuma? We've created an extensive Q&A from the dozens of RFQ's and RFI's that we have received.