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Featured

June 16, 2022
Our Investigations

Data-Driven SEO: A Keyword Optimization Guide using Web Scraping & Co-occurrence Analysis (Graphext + Deepnote + Adwords)

To improve our SEO, we built a data-driven method to analyze the content of top-ranking Google search results as part of a keyword optimization process. Starting with a single search term, our technique uses web scraping + NLP techniques to find specific keywords that are already proven to boost the rank of similar pages.

Andy Clarke
Andy Clarke
Our Investigations
Our InvestigationsShort StudiesCustomer StoriesData NewsEverything

Latest Posts

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
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 ...
July 5, 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
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
'España' and 'Españha' are just spelling variations. We built a way of grouping words spelt differently but referring to the same concept.
June 11, 2021

Graphext | Graphtex | Graphnext: Grouping Similar Spellings Using Chars2Vec and Agglomerative Clustering

'España' and 'Españha' are just spelling variations. We built a way of grouping words spelt differently but referring to the same concept.

Maria del Mar Ruiz
Data News
Taking on an investigation into the adverse reactions associated with the COVID-19 vaccination rollout in the USA, our team were aware of the increased need for transparency whilst conducting our analysis. This article documents the methodology behind our study of Vaccine Adverse Event Reporting System (VAERS) data.
June 8, 2021

The Method Behind Our Investigation of Reports of Adverse COVID-19 Vaccine Events

Taking on an investigation into the adverse reactions associated with the COVID-19 vaccination rollout in the USA, our team were aware of the increased need for transparency whilst conducting our analysis. This article documents the methodology behind our study of Vaccine Adverse Event Reporting System (VAERS) data.

Andy Clarke
Our Investigations
Modelling data from the Vaccine Adverse Event Reporting System (VAERS) - a US government-sponsored vaccine reaction monitoring service - our team set out to investigate reports of adverse health effects related to the seismic rollout of the COVID-19 vaccination programme in the USA.
June 8, 2021

Conspiracies, Complexity and Clustering: Investigating Reports of Adverse COVID-19 Vaccine Effects

Modelling data from the Vaccine Adverse Event Reporting System (VAERS) - a US government-sponsored vaccine reaction monitoring service - our team set out to investigate reports of adverse health effects related to the seismic rollout of the COVID-19 vaccination programme in the USA.

Paul Suddon
Our Investigations
Attempting to discover the most influential features of a loan application when considering risk, our team built a model using the features of a loan application to predict whether an applicant would have a good or bad risk rating.
May 6, 2021

Good Risk vs Bad Risk: Deconstructing the Features of 1000 German Loans

Attempting to discover the most influential features of a loan application when considering risk, our team built a model using the features of a loan application to predict whether an applicant would have a good or bad risk rating.

Paul Suddon
Our Investigations
Andy and María meet with Jake to talk about a dataset he's building about himself. From skating to people he sees to whether he flosses or not - Jake's data offers a unique and deeply personal insight into his life. But what makes the difference between good and bad days?
April 26, 2021

Jake's Project: Investigating the Data Behind a Good Day

Andy and María meet with Jake to talk about a dataset he's building about himself. From skating to people he sees to whether he flosses or not - Jake's data offers a unique and deeply personal insight into his life. But what makes the difference between good and bad days?

María López
Short Studies
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.
April 16, 2021

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.

Paul Suddon
Short Studies
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.
April 7, 2021

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.

Paul Suddon
Short Studies
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.
March 29, 2021

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.

Andy Clarke
Customer Stories