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Featured

Graphext raised $4.6M to create the best exploratory data analysis platform for building explainable AI models

Graphext has raised $4.6M in a seed round led by Hoxton, bringing their total seed funding to $7M, for their data exploration and predictive modeling platform that enables data scientists to explore data, create predictive models, and collaborate with the business in one place.

November 30, 2023
Data News
Victoriano Izquierdo
Victoriano Izquierdo
Our InvestigationsShort StudiesCustomer StoriesData NewsEverything

Latest Posts

We collected and analyzed answers to a Twitter meme, wherein people were asked to express their relationship with programming languages.
December 8, 2021

What People Really Feel About Programming Languages

We collected and analyzed answers to a Twitter meme, wherein people were asked to express their relationship with programming languages.

Victoriano Izquierdo
Our Investigations
Why are the social media strategies of Innocent Drinks considered as the gold standard for marketing teams the world over? We collected every tweet (10,521) posted by the communication department to deconstruct Innocent's content, style, reach and engagement with a simple topic analysis.
November 24, 2021

Reverse Engineering Infamous Marketing Strategies from Innocent Drinks

Why are the social media strategies of Innocent Drinks considered as the gold standard for marketing teams the world over? We collected every tweet (10,521) posted by the communication department to deconstruct Innocent's content, style, reach and engagement with a simple topic analysis.

Andy Clarke
Our Investigations
We spoke to the data science team at Aquaservice about how they used Graphext to build a clustering model to improve the way they forecast consumer demand. Their project grouped delivery routes using over 30 factors to calculate similarity and exposed patterns in the errors made by their prediction models. Models that are responsible for forecasting the number of water bottles that should be loaded into trucks for delivery across a huge number of routes across Spain.
November 15, 2021

How Aquaservice Use Graphext To Improve Their Prediction Models

We spoke to the data science team at Aquaservice about how they used Graphext to build a clustering model to improve the way they forecast consumer demand. Their project grouped delivery routes using over 30 factors to calculate similarity and exposed patterns in the errors made by their prediction models. Models that are responsible for forecasting the number of water bottles that should be loaded into trucks for delivery across a huge number of routes across Spain.

Victoriano Izquierdo
Customer Stories
Our team set out to build a type of analysis that could be used to measure the strength of association between variables in a dataset. Here's how we did it ...
November 11, 2021

Using Mutual Information to Cluster Variables and Discover the Associations Between Survey Questions

Our team set out to build a type of analysis that could be used to measure the strength of association between variables in a dataset. Here's how we did it ...

Alex Gonzalez
Data News
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. 
October 26, 2021

How to Perform Simple & Effective Customer Segmentation | A Walkthrough with Data from a Delicatessen (Dataset interactive)

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. 

Andy Clarke
Our Investigations
What's the most important milestone in a relationship? According to data from a Stanford study, it's a day like any other that occurs somewhere between the 4th and 5th anniversary of a relationship. 
October 19, 2021

Make or Break: After 5 Years ... Couples are Less Likely to Break Up

What's the most important milestone in a relationship? According to data from a Stanford study, it's a day like any other that occurs somewhere between the 4th and 5th anniversary of a relationship. 

Victoriano Izquierdo
Short Studies
We used sentiment analysis to model 5100 Billboard chart-toppers between 1964 and 2015. Our analysis predicted whether song lyrics were positive, negative or neutral as well as detecting the topic and intent behind the most popular tunes in music history.
September 10, 2021

Sentiment Analysis & Billboard Top 100: The Changing Mood of Popular Music .

We used sentiment analysis to model 5100 Billboard chart-toppers between 1964 and 2015. Our analysis predicted whether song lyrics were positive, negative or neutral as well as detecting the topic and intent behind the most popular tunes in music history.

Andy Clarke
Our Investigations
Testing out our brand spanking new integration with Hugging Face models for NLP, we analyzed speech from characters in all 9 series of the US Office. Added into our Graphext project, the language models focused on classifying the dialogue of Michael, Dwight, Pam, Jim, Daryll and all the other characters according to the detection of sentiment, emotion, offensive language, irony and hate speech.
August 23, 2021

The 5 Most Extreme US Office Characters

Testing out our brand spanking new integration with Hugging Face models for NLP, we analyzed speech from characters in all 9 series of the US Office. Added into our Graphext project, the language models focused on classifying the dialogue of Michael, Dwight, Pam, Jim, Daryll and all the other characters according to the detection of sentiment, emotion, offensive language, irony and hate speech.

Andy Clarke
Short Studies
How can we use text analysis of data from Twitter to improve our understanding of markets? This is the question prompting Paul, a strategist in our business team, to scrape tweets about Lloyds bank and conduct a Twitter topic analysis using advanced NLP and network creation. First, he collected tweets using Tractor, Graphext's scraping tool for social media analysis. Then, he analyzed the topics of tweets using network analysis. Here's how he did it ...
July 22, 2021

How to Study Brand Conversations with Advanced Text Analysis?

How can we use text analysis of data from Twitter to improve our understanding of markets? This is the question prompting Paul, a strategist in our business team, to scrape tweets about Lloyds bank and conduct a Twitter topic analysis using advanced NLP and network creation. First, he collected tweets using Tractor, Graphext's scraping tool for social media analysis. Then, he analyzed the topics of tweets using network analysis. Here's how he did it ...

Paul Suddon
Our Investigations
Market segmentation means splitting your customer base into distinct communities based on the similarity of their features. Depending on the data you use to segment customers, clustering a market dataset results in the grouping of customers based on geographic, demographic, behavioural and psychographic factors as well as their buying preferences.
July 20, 2021

A Beginners Guide to Market Segmentation: Types, Techniques & Examples to Better Understand Your Customer Base (with Data)

Market segmentation means splitting your customer base into distinct communities based on the similarity of their features. Depending on the data you use to segment customers, clustering a market dataset results in the grouping of customers based on geographic, demographic, behavioural and psychographic factors as well as their buying preferences.

Andy Clarke
Our Investigations
Drafting in a dataset containing information about purchases made over 4 years by 1590 customers on an online superstore, our team wanted to demonstrate the usefulness of RFM analysis.
July 5, 2021

Using Customer Data and RFM Analysis to Create Relevant Ad Campaigns

Drafting in a dataset containing information about purchases made over 4 years by 1590 customers on an online superstore, our team wanted to demonstrate the usefulness of RFM analysis.

Paul Suddon
Short Studies
Our team clustered 1000 supermarket sales in order to segment customers according to their buying habits. Our market segmentation analysis uses data on the demographics, income and geography of customers to identify key buyer personas and inform marketing strategies and campaigns.
June 29, 2021

A Market Segmentation of 1000 Supermarket Customers Using Data on Sales, Income and Demographics

Our team clustered 1000 supermarket sales in order to segment customers according to their buying habits. Our market segmentation analysis uses data on the demographics, income and geography of customers to identify key buyer personas and inform marketing strategies and campaigns.

Paul Suddon
Short Studies