NEW YORK (PRWEB)
December 05, 2017
MeaningCloud, a heading association in calm analytics and low semantic analysis, currently announced a launch of a initial Vertical Packs, focused on a research of a Voice of a Customer and a Voice of a Employee. The Packs yield a chronicle of their methodical solutions blending to a needs of Marketing, Customer Experience, or Human Resources executives.
In sequence for calm analytics to yield suggestive and actionable insights, a research contingency be blending to a focus domain. To accelerate this routine to a maximum, a Vertical Packs mix a set of pre-prepared semantic resources (models and dictionaries) and worldly methodical APIs with add-ins for Excel specifically blending for any scenario.
In this way, barriers are lowered both in a pattern of a complement and in a era and expenditure of specific insights.
Initially, dual Vertical Packs have been published:
- Voice of a Customer: To investigate a feedback of business in surveys, a hit center, or amicable media and in industries such as banking, insurance, or sell and specify it according to a form of product, charge of quality, channel of interaction, or sentiment.
- Voice of a Employee: To learn a opinions, competences, and concerns that employees demonstrate in surveys, opening assessments, or exit interviews, and so improved conduct a talent in your organization.
Learn some-more during MeaningCloud’s blog.
Discover Vertical Packs in MeaningCloud’s entrance webinar
If we wish to know how to request a Vertical Packs to immediately get calm analytics blending to your application, register for this webinar that will take place on Wednesday, Dec 20 during 9:00 AM PDT. Register here.
MeaningCloud is a easiest, many powerful, and many affordable approach to remove a definition of any kind of unstructured content, from amicable conversations to inner files. Use a plug-ins to simply perform calm analytics in your spreadsheet, graphically customize a calm sequence and view research functions to your specific domain to obtain forlorn accuracy, and hide semantic research into your applications but risk by a pay-per-use web-based APIs.