Customer Analytics

Precisely understanding the behaviour of your customers is a realm where details count.

Customers are all unique but patterns in their behaviour allow you to recognise key characteristics. Market segmentation in Graphext allows you to quickly cluster and define customer bases using structured and unstructured data about their behaviour, income, demographics and actions. Our models deploy unsupervised learning techniques to identify target audiences and communities, enabling you to take specific and informed action to direct marketing efforts, prevent churn, acquire leads or deepen your understanding.

‍

Our Investigations

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

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

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

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

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

The Lipstick Effect: Did the 2008 Financial Crisis Drive an Increase in Positive Airline Reviews?

We analyzed 30K airlines services reviews and saw that there are clear jumps in ratings marked by the 2008 financial crisis and subsequent economic recovery. Could these factors have impacted consumers, or have airlines improved their services?

Brais Ramos

Zara vs. Gap: An Instagram Analysis

What are unexpected insights and surprising patterns of the social media strategies of Inditex’s flagship Zara and Gap? In this article, we analyzed their Instagram campaigns since Feb 2017.

Maximilian Zachow

Project Gallery

Segmenting Coffee Machine Customers

This project segments 30,000 customers of a coffee company based on their buying behaviour.

Models | Cluster

E-Commerce & RFM Analysis

This project segments customers of an e-commerce site using recency, frequency and money spent. The clustering model groups customers according to their interactions with the site allowing for identification of specific customer sub-communities.

Commerce | RFM Analysis

Customer Churn in a Telco Company

This project clusters 7000 customers of a Telco company exposing the reasons why sub-communities of customers churned. The project uses customer features like tenure, monthly charges and type of deal to create links and cluster similar customers.

Models | Cluster

German Loans & Credit Rating

This project reverse-engineers the features of 1000 German loans in order to understand the features most influential for lenders and banks when considering an application's risk profile.

Models | Train & Predict

What People Think About Fizzy Drinks

A topic analysis of tweets written about soft drink companies in 2019. This project looks at extracting language features in order to understand the brand perceptions of businesses like Coca-Cola, Pepsi and Gatorade.

Social Media | Topics

Analyzing Disneyland Customer Reviews

Extracting the language features of 30000 Disneyland customer reviews.

Text | Topics

Docs

Clustering Supermarket Transactions

Clustering is a machine learning technique used to group data points based on similarity. This guide is intended to walk you through the process of creating a clustering model to group your data. We'll be using a dataset of 1000 supermarket transactions from stores in Myanmar. The aim of our project is to group these transactions in order to find patterns in the buying habits of the supermarket's customers.

Use Case Guides

Group Similar Spellings

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

Technical Docs

Analyzing Reviews

This guide is intended to walk you through the process of analyzing customer reviews with Graphext. We will analyze a dataset of 42,656 reviews about 3 Disneyland branches.

Use Case Guides

Market Basket Analysis

This guide is intended to walk through the building of a market basket analysis project using Graphext. We will be working with a dataset of purchases made at a bakery in Edinburgh. The aim of the project will be to identify communities of products that are related to one another and to transform this information into useful business insights.

Use Case Guides

Collecting Twitter Data

A guide on collecting Twitter data through the API. Here we walk through the process of setting up a Twitter developer account through to collecting tweets using our open-source notebook.

Use Case Guides

Scraping with Tractor

Tractor is a tool to scrape data from popular platforms. Use it to build datasets from information on Google, Twitter and Facebook Ads.

How To

Building Models

You can train and deploy prediction models in Graphext. Models use existing features of your data to predict the value of a target variable.

Technical Docs