Technical ecommerce

Articles tagged Technical ecommerce

How to create a Google rank checker tool using Python

Import the packages First, open a Jupyter notebook and install my EcommerceTools Python package. EcommerceTools includes a range of modules for performing a range of ecommerce and marketing tasks, including...

How to use the Feefo API for ecommerce competitor analysis

Most ecommerce websites use review platforms, such as Feefo, Trustpilot, and Google Reviews, to allow customers to give feedback on their service and the products they sell. The reviews help...

How to compare time periods using the Google Search Console API

One common task you’ll perform in Google Search Console is to compare the data from two different time periods to see how impressions, clicks, click-through rate (CTR), or average position...

A quick guide to the RFM model for data scientists

The RFM model is probably one of the best known and most widely used customer segmentation models by data driven marketers. It’s used for both measuring customer value and predicting...

How to run time-based SEO tests using Python

One of the problems with search engine optimisation or SEO is that search engine algorithms are essentially black boxes. They analyse so many on-page and off-page factors, and use multiple...

How to create content recommendations using TF IDF

After work, when I’m not learning about data science, practising data science, or writing about data science, I like to browse classic car auction sites looking for cars I can’t...

How to create a contractual churn model in scikit-learn

A growing proportion of what we buy regularly is purchased via a subscription, or some other kind of contract. Most people have contracts for their internet, mobile phone, car insurance,...

A quick guide to customer segmentation for B2B e-commerce

Customer segmentation, and the similar and related field of market segmentation, are particularly relevant to the field of business-to-business (B2B) e-commerce. B2B customers often have a higher Customer Lifetime Value...

How to detect Google Search Console anomalies

There are some great anomaly detection models available for Python, which let you examine complex data for a wide range of different anomaly types. In this project, I’ll show you...

How to classify customer support tickets using Naive Bayes

In ecommerce, customer service staff are often among the busiest people in the organisation, handling hundreds of tasks every day, often simultaneously. However, CS managers often get so bogged down...

How to infer the effects of marketing using the Causal Impact model

One common conundrum in e-commerce and marketing involves trying to ascertain whether a given change in marketing activity, product price, or site design or content, has had a statistically significant...

How to identify SEO keyword opportunities with Python

One of the most useful Python SEO projects you can undertake is to identify the top keywords for which each of your site’s pages are ranking for. Sometimes, these keywords...

How to analyse Google Analytics demographics and interests with GAPandas

The demographics and interests data provided in Google Analytics can be a useful way to understand who is visiting your site or purchasing your products, without the need to perform...

How to identify striking distance keywords with Python

Striking distance keywords are those which appear just off the bottom of the first page of search engine results. Keywords that appear on the first page have the greatest visibility...

A quick guide to lead scoring for B2B e-commerce sites

Lead scoring is a Customer Relationship Management (CRM) process that involves segmenting CRM contacts based on their likelihood to make a purchase. Lead scoring is applied to both existing customers...

How to trigger marketing automations using the Mailchimp API in Python

Mailchimp is one of the most widely used email service providers (or ESPs) in ecommerce and marketing. Since it is popular with those who only need its basic campaign features,...

How to create monthly Google Search Console API reports with EcommerceTools

Google Search Console is a really useful tool for search marketers since it shows what is happening data-wise before organic search visitors reach your website. Google Analytics only shows you...

How to use the Mailchimp Marketing Python API with Pandas

In ecommerce, email marketing remains one of the most effective (and cost-effective) digital marketing techniques, especially when combined with data science techniques. The vast amounts of customer data generated in...

How to use the eBay Finding API with Python

The eBay Finding API gives you direct access to eBay search listings using a simple SDK. This API lets you search or query eBay to fetch specific search listings for...

How to export Zendesk tickets into Pandas using Zenpy

The Zendesk customer service platform is widely used by ecommerce businesses, but its functionality for analysing ticket trends and automatically classifying them is somewhat limited. In many cases, you might...

How to query the Google Search Console API with EcommerceTools

The Google Search Console (GSC) API is a great source of information for those working in SEO, marketing, or ecommerce. It can tell you which of your pages are appearing...

How to read Google Sheets data in Pandas with GSpread

GSpread is a Python package that makes it quick and easy to read and write data from Google Sheets spreadsheets stored in your Google Drive into Python. With a tiny...

How to calculate the Lin Rodnitzky Ratio using GAPandas

The Lin Rodnitzky Ratio is a calculation designed to help search engine marketers assess the management of paid search campaigns and account structure. When managing paid search advertising accounts you...

How to analyse product replenishment

Subscription commerce was all the rage for a while, but it’s not really become as popular as many in ecommerce perhaps envisaged. While we may have subscriptions for certain things,...

A quick guide to customer segmentation for data scientists

Customer segmentation is the process of using data science techniques to create discrete groups of customers which share common characteristics or attributes. For example, a company might segment customers into...

How to create an ecommerce purchase intention model in Python

Ecommerce purchase intention models analyse click-stream consumer behaviour data from web analytics platforms to predict whether a customer will make a purchase during their visit. These online shopping models are...

How to predict employee churn using CatBoost

In the field of HR analytics, data scientists are now using employee data from their human resources department to predict employee churn. The techniques for predicting employee churn are fairly...

How to auto-generate meta descriptions with EcommerceTools

Meta descriptions are strings of text added to the head of an HTML document to describe its content to search engines and search engine users and are of critical importance...

How to create a basic Marketing Mix Model in scikit-learn

Marketing Mix Models (MMMs) utilise multivariate linear regression to predict sales from marketing costs, and various other parameters. A Marketing Mix Model (also called a Media Mix Model), even at...

How to create a simple product recommender system in Pandas

Product recommender systems, or recommendation systems, as they’re also known are ubiquitous on e-commerce websites these days. They’re relatively simple to create and even fairly basic ones can give striking...

15 ways you can use data science to boost ecommerce performance

Major internet retailers, like Walmart and Amazon, have been at the forefront of ecommerce data science and data analytics for many years, contributing lots of interesting papers to data science...

How to create PDF reports in Python using Pandas and Gilfoyle

While reporting is often quite a useful way to stay on top of your data, it’s also something you can automate to save time, even if your reports include custom...

How to create monthly Google Analytics reports in Pandas

Like most people who work in ecommerce and marketing, I spend a lot of time in Google Analytics. It’s a great tool, but when reporting on the numbers, it helps...

How to segment your customers using EcommerceTools

Customer segmentation can give you huge insights into your business and identify a whole range of different things about your customers, allowing you to change your marketing and improve results....

How to use EcommerceTools for technical SEO

There’s often a lot of faffing around required to get marketing and ecommerce data from various systems into Pandas so you can analyse it, or use it within more complex...

How to use k means clustering for customer segmentation

K means is one of the most widely used algorithms for clustering data and falls into the unsupervised learning group of machine learning models. It’s ideal for many forms of...

How to segment customers using RFM and ABC

While the Recency, Frequency, Monetary value or RFM approach to customer segmentation might be old, it’s based on sound science, so no matter what customer model you’re building, it’s generally...

How to perform a customer cohort analysis in Pandas

Cohort analysis is unlike most other customer segmentation techniques in that it typically uses a time-based element. It’s typically used to segment customers into groups, or cohorts, based on their...

How to machine translate product descriptions

Whether you’re analysing content written in other languages using Natural Language Processing, or you want to assist your content team by translating their writing into other languages, machine translating software...

How to identify the causes of customer churn

Understanding what drives customer churn is critical to business success. While there is always going to be natural churn that you can’t prevent, the most common reasons for churn are...

How to identify near duplicate content using LMS

In those ecommerce businesses where relatively few products are launched and products have a relatively long lifecycle, copywriters tend to be targeted on producing unique content that sells the benefits...

How to create ecommerce anomaly detection models

In the ecommerce sector, one of the most common tasks you’ll undertake after arriving at work each morning is to check over the recent analytics data for your site and...

How to create a product and price metadata scraper

In ecommerce, price monitoring is a really important consideration. If you offer your products at a price which is too high within the market, you may lose sales to rivals,...

How to create a non-contractual churn model for ecommerce

Knowing which of your customers are going to churn before it happens is a powerful tool in the battle against attrition, since you can take action and try to prevent...

How to classify customer service emails with Bart MNLI

Zero-shot learning, or ZSL, is a machine learning process commonly used for Natural Language Processing that allows you to generate predictions on unseen data without the need to train a...

How to calculate CLV using BG/NBD and Gamma-Gamma

Calculating Customer Lifetime Value or CLV is considered a really important thing in marketing and ecommerce, yet most companies can’t do it properly. This clever metric tells you the predicted...

How to auto-generate product summaries using deep learning

Several years ago, in one of my first Ecommerce Director roles, I worked with the ex-Myprotein founder to launch sports nutrition brand GoNutrition. As a “bootstrapped” startup, we were low...

How to assign RFM scores with quantile-based discretization

RFM segmentation is one of the oldest and most effective ways to segment customers. [RFM models] (/data-science/a-quick-guide-to-the-rfm-model) are based on three simple values - recency, frequency, and monetary value -...

How to assess product copy using EQA models

In ecommerce, writing good product copy is both an art and a science. Not only does product copy need to be written in the correct tone and style for your...

How to analyse product consumption and repurchase rates

You can learn many things about your products from the purchase behaviours and product consumption and product replenishment of your customers. Some items are purchased individually, some items are purchased...

How to scrape schema.org metadata using Python

As I’ve mentioned in previous posts on web scraping, the most efficient way to scrape data is to identify what Schema.org metadata is in use and then create a microdata...

How to join Google Analytics and Google Search Console data

Neither Google Search Console nor Google Analytics gives you access to the data found in both systems in one place. However, with a bit of ingenuity and some relatively simple...

How to find spelling and grammar issues on product pages

Ecommerce copywriters are busy people and don’t have the privilege of having eagle-eyed sub editors to sub-edit their copy and check it for spelling mistakes or grammatical issues, as magazine...

How to engineer customer purchase latency features

Purchase latency or customer latency is a measure of the number of days between a customer’s orders and is one of the most powerful features in many propensity and churn...

How to create targeted B2B company sector datasets

As I explained in my previous post, many B2B ecommerce businesses spend huge amounts on procuring third-party data for companies they wish to target. However, with some data science skills...

How to create a product matching model using XGBoost

Product matching or data matching is a computational technique employing Natural Language Processing and machine learning which aims to identify identical products being sold on different websites, where product names...

How to create a Naive Bayes product classification model

Assigning products to the right categories is crucial to allowing customers to find what they’re looking for, so product classification models are commonly used by online marketplaces to ensure that...

How to create a dataset containing all UK companies

In B2B ecommerce, there are two main approaches to new customer acquisition: you either rely on your website to acquire customers for you, or you target specific customers through sales...

How to calculate safety stock and reorder point

Although the techniques for reducing its impact have existed for decades, inventory management is still a huge issue in many businesses. Various things happen that can result in costly stock...

How to calculate operations management metrics in Python

Successful operations management is crucial to the overall growth of an ecommerce business. While those in ecommerce, marketing, or data science, can work together the sales coming in and encourage...

How to calculate marketing metrics in Python

Marketers can be just as obsessive about data as data scientists, so there are an abundance of well-researched marketing metrics available for analysing marketing performance. Most of the commonly used...

How to calculate customer experience metrics in Python

Customers are expensive to acquire but generate more and more profit as time goes on. Providing you nurture them, treat them kindly, and apologise and fix any mistakes that occur,...

How to calculate category management metrics in Python

Category management is a retail technique that breaks down a company’s product range into groups of related items, such as categories, or subcategories, or by their product type. By running...

How to use Extruct to identify Schema.org metadata usage

The downside to building datasets using web scraping is that every site has custom HTML. If you scrape sites in this way, you’ll forever be building bespoke scrapers, and they’ll...

How to send data to Google Analytics in Python with PyGAMP

The Google Analytics Measurement Protocol API lets you add data to your GA account that hasn’t been triggered by a user visiting a web page. Since it’s so flexible, you...

How to scrape Open Graph protocol data using Python

Many websites include Open Graph protocol data in their document head. This structured data allows social networks, such as Facebook and Twitter, to access specific elements of the page’s content...

How to scrape and parse a robots.txt file using Python

When scraping websites, and when checking how well a site is configured for crawling, it pays to carefully check and parse the site’s robots.txt file. This file, which should be...

How to scrape a site's page titles and meta descriptions

Scraping the titles and meta descriptions from every page on a site can tell you a great deal about its content, the underlying content strategy, or product ranges, and many...

How to scan a site for 404 errors and 301 redirect chains

Both 404 page not found errors and 301 redirect chains can be costly and damaging to the performance of a website. They’re both easy to introduce, especially on ecommerce sites...

How to resize and compress images with TinyPNG

Large, uncompressed images slow down your site, increase bandwidth costs, harm the user experience, and impact search engine rankings. In this project, I’ll show you how you can bulk resize...

How to parse XML sitemaps using Python

XML sitemaps are designed to make life easier for search engines by providing an index of a site’s URLs. However, they’re also a useful tool in competitor analysis and allow...

How to parse URL structures using Python

URLs often contain useful information that can be used to analyse a website, a user’s search, or the breakdown of content present in each section. While they often look pretty...

How to identify keyword cannibalisation using Python

Keyword cannibalisation occurs when you have several pages ranking for the same phrase, effectively putting them in competition with each other for search engine rankings. Since Google generally only shows...

How to calculate Economic Order Quantity in Python

The Economic Order Quantity or EOQ represents the optimum purchase quantity for a given product, while aiming to minimise holding costs, shortage costs, and order costs. It’s most commonly calculated...

How to build a web scraper using Requests-HTML

Unless you’re building a large and complex web scraper using Scrapy or Selenium, it’s probable that you’ll utilise Requests and Beautiful Soup. These two packages are brilliant for web scraping....

How to audit a site's Core Web Vitals using Python

Back in 2020, Google introduced Web Vitals, a set of metrics is designed to help site owners to optimise the user experience on their websites, so pages are quick to...

How to analyse non-ranking pages and search index bloat

If your site’s pages aren’t indexed by Google, you’re obviously not going to generate any traffic to them, so you’ll want to check that everything you expect to be present...

How to access the Google Search Console API using Python

Google Search Console contains loads of really useful information for technical SEO. However, there are limits to what you can do using the front-end interface, and it takes time to...

A quick guide to search intent classification for SEO

Search intent classification has been around for almost 20 years, but has only recently started to move into the mainstream in ecommerce and technical SEO. Here’s a quick guide to...

How to use Screaming Frog from the command line

The Screaming Frog SEO Spider Tool is widely used in digital marketing and ecommerce. It provides a user-friendly interface to a powerful site crawler and scraper that can be used...

How to create paid search keywords using Pandas

Setting up keywords for new paid search accounts can be a repetitive and time-consuming process. While it’s historically been done using Excel, many digital marketers are now taking advantage of...

How to visualise conversion funnels with Plotly

Funnels are arguably one of the most powerful data visualisations you can use within the ecommerce field. At a glance, they can show you the proportion of customers entering at...

How to create a dataset for product matching models

Product matching (or data matching) is a computational technique employing Natural Language Processing, machine learning, or deep learning, which aims to identify identical products being sold on different websites, where...

How to visualise RFM data using treemaps

Recent papers on the Recency, Frequency, Monetary (RFM) model, such as this one by Inanc Kabasakal which was published earlier this year, have started to adopt text-based labels to help...

How to group and aggregate transactional data using Pandas

Transactional item data can be used to create a number of other useful datasets to help you analyse ecommerce products and customers. From the core list of items purchased you...

How to create ecommerce sales forecasts using Prophet

Time series forecasting models are notoriously tricky to master, especially in ecommerce, where you have seasonality, the weather, marketing promotions, and holidays to consider. Not to mention pandemics.

How to create an ABC XYZ inventory classification model

As everyone who works in ecommerce will know, stock-outs on your key lines can have a massive negative impact on sales and your marketing costs. In many cases, you’ll be...

How to create an ABC inventory classification model

ABC inventory classification has been one of the most widely used methods of stock control in operations management for decades. It’s an intentionally simple system in which products are assigned...

How to bin or bucket customer data using Pandas

Data binning, bucketing, or discrete binning, is a very useful technique for both preprocessing and understanding or visualising complex data, especially during the customer segmentation process. It’s applied to continuous...

Ecommerce and marketing data sets for machine learning

If you read research papers on machine learning, you’ll notice that many researchers use the same standard datasets so other data scientists can reproduce their work or try and improve...

How to use the BG/NBD model to predict customer purchases

You might think human behaviour would be hard to predict but, in ecommerce data science, it’s not actually as difficult as you may think to predict whether a customer will...

How to use NLP to identify what drives customer satisfaction

While some people might naively interpret it as negativity, I think one of the best ways you can improve an ecommerce business is to focus on the stuff you’re not...

How to create a BI platform using Apache Superset

Apache Superset is a new “enterprise-ready” web application for building business intelligence (BI) applications and dashboards. Developed by the team that built Airbnb using the Flask Python framework, React JS,...

How to use Apache Druid for real-time analytics data storage

Apache Druid is described as a high performance real time analytics database and was developed at Metamarkets in 2011 for their internal analytics system. Unlike traditional relational databases, such as...

How to create ecommerce data pipelines in Apache Airflow

Like Apache Superset, Apache Airflow was developed by the engineering team at Airbnb and was open sourced in 2014. It’s a Python-based platform designed to make it easier to create,...

How to create an ecommerce trading calendar using Pandas

In both B2C and B2B ecommerce, special trading periods such as Christmas, Mothers’ Day, and Valentines’ Day can often greatly contribute to sales. Indeed, the introduction of Black Friday sales...

A quick guide to Product Attribute Extraction models

Product attributes, such as size, weight, wattage, or colour, are critical in ecommerce as they help customers find and select the right product for their needs. However, obtaining, adding, and...

A quick guide to Next-Product-To-Buy models

A Next-Product-To-Buy (or NPTB) model is designed to help retailers and marketers improve the effectiveness of cross-selling product recommendations by predicting the product each customer would be most likely to...

A quick guide to machine learning uplift models

Uplift modeling is a machine learning technique used in marketing and ecommerce to predict which customers are likely to respond to a particular marketing campaign. However, rather than simply predicting...

A quick guide to Learning to Rank models

On-site search in ecommerce has improved massively in recent years, thanks to search systems such as Lucene, Solr, Algolia, and Elastic. Despite on-site search generating massive amounts of revenue for...

How to use the Apriori algorithm for Market Basket Analysis

Market Basket Analysis, or MBA, is a subset of affinity analysis and has been used in the retail sector for many years. It provides a computational method for identifying common...

How to scrape JSON-LD competitor reviews using Extruct

In the ecommerce sector, you can learn a lot about your competitors and the expectations of your customers by analysing the reviews their customers leave for products and service on...

How to scrape competitor technology data in Python

In ecommerce, it pays to watch what your competitors are doing, so over the past decade or so in which I’ve managed ecommerce businesses, I’ve regularly undertaken competitor analyses. They’re...