Questions? We got you.
Data is complex but Graphext is here to make it easy. Take a look at our frequently asked questions.
You don’t have to have a technical background to use Graphext. Our product is designed to be used by everyone. We provide step by step guidance to help users create and explore their data, but we also offer an advanced “editor” so more experienced users can get more out of the product.
We use dimension reduction techniques to map out multi-variable data sets in two dimensions so users can visually identify patterns and outliers in their data.
We are a lot more than that. We pride ourselves in having cool visualizations, but in reality, we offer an end-to-end data analytics solution that covers data preparation, exploration, analytics, and reporting so we could help you in each step.
When you work with a unfamiliar data set, you may need to establish and test many hypotheses in order to find insights from your data. Working under the wrong hypothesis could bias your analysis and produce inaccurate results. Our suggestion is to explore the data with Graphext in order to first confirm your hypothesis. That way you can develop more accurate analysis and save time.
Within Graphext, we connect with a few external APIs and we are developing an increasing amount of API connections to improve data enrichment, so when you choose to enrich your data, we can retrieve additional information such as location, demographic, contact that will complement your existing data.
We cluster data based on their similarities. We use un-supervised learning so the clustering result is unbiased.
The clustering result is subject to interpretation. We aim to help users to put their data in business context so they can easily explain their finding to their audience.
Of course! In fact, our platform is built using Docker, and it is ready to be installed in laptops, local or cloud servers
Although Social Listening is one of the most popular use cases of Graphext, we offer much more! Basically we can analyze any type of data. And many customers use us to do customer profiling, retention analysis, survey analysis, product recommendation, text analysis, etc.
Is there a size limit? - This question depends on the type of dataset we are working with. The upload limit to Graphext is 4GB. With complex datasets including texts, each project can process up to 300MB of data. If you are data set is larger than that, we may be able to analyse it on case by case basis. Send us an email and we will get back to you.
In our text analysis, we are able to process all major European languages including English, German, French, Spanish, Italian. For the Spanish market we also integrated Catalan in our language processing capability.
Our pricing is seat based. Request a demo and we will get back to you. However, you can give Graphext a try for free!
Yes. We offer a limited version of Graphext which you can use completely for free. Sign up here
Yes. We have a dedicated team of Data Scientists who provide advisory and consulting services to customers on project base.
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Simple Solutions to Prevent Customer Churn
Our team analyzed 7043 current and former customers of a telecoms provider in order to better understand what types of people are most likely to cancel their contracts.
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How Data Can Help You Keep Your Workers
To showcase how a company could reduce employee turnover, our team clustered a dataset containing information about IBM employees to discover the reasons why employees left their jobs.
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Menhir & Graphext: Analyzing the Intangible Value of Financial Assets
Working at the intersection of data science and finance, Menhir is using Graphext to understand the composition of financial portfolios, performing analysis that typically takes analysts between two and three weeks in just two days.
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The Moneyball Method: Using Data to Build a Football Dream Team (On a Budget)
Our team set out to build an exceptional football team for less than 100M Euros. Using data provided in the FIFA 2020/2021 dataset - the video game - we built a prediction model in order to find the key performance attributes for each position. Then, we used this to pick out a team of excellent but undervalued players.
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