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.
What technology do you use to visualize the Graph?
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.
Is Graphext a data visualization company?
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.
What is the advantage of data exploration before analysis?
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.
What is data enrichment?
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.
How does Graphext cluster data?
We cluster data based on their similarities. We use un-supervised learning so the clustering result is unbiased.
How accurate is your clustering?
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.
I have sensitive data. Can I install Graphext on premise?
Of course! In fact, our platform is built using Docker, and it is ready to be installed in laptops, local or cloud servers
Is Graphext only a social listening tool?
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.
How much data can you process?
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.
What languages can Graphext process?
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.
How much does Graphext cost?
Our pricing is seat based. Request a demo and we will get back to you. However, you can give Graphext a try for free!
Do you offer a free trial?
Yes. We offer a limited version of Graphext which you can use completely for free. Sign up here.
Do you offer professional services?
Yes. We have a dedicated team of Data Scientists who provide advisory and consulting services to customers on project base.
I just asked for a demo. When will you get back to us?
Within 24 hours
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We collected every tweet published about the 2021 UN Climate Change Conference (COP26) to study how people have engaged with events during the summit. Using topic analysis and emotion detection, our project dives into people's visceral reactions to agreements on deforestation, commitments between China and the USA and the appearance of Barack Obama.
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Customer segmentation involves splitting a customer base into distinct groups. These customer segments are defined by specific and shared characteristics, behaviours or preferences that help businesses to spot patterns and associate customers with one another. This article walks through the steps involved in a simple customer segmentation analysis. Using sales data from a delicatessen, we'll segment customers according to their buying preferences and behaviour. To achieve this, we'll use a powerful machine learning technique known as clustering.