Marketing science

Articles tagged marketing science

A quick guide to customer retention

There are two main ways you can grow the customer base of a business: you can either acquire more customers by increasing your customer acquisition rate, or you can improve...

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 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 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...

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 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 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 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 segment customers using RFM and ABC

While the Recency, Frequency, Monetary value or RFM model for 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 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 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 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 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 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 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 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 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...

A quick guide to catalogue marketing data science

Catalogue marketing is dying out. Over the past few years, virtually all the UK’s top catalogue retailers have stopped printing on paper and successfully transitioned their businesses online, either to...

How to visualise RFM data using treemaps

Recent papers on the Recency, Frequency, Monetary or RFM model, such as the one by Inanc Kabasakal in 2020, have started to adopt text-based labels to help people understand the...

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 a response model to improve outbound sales

The predictive response models used to help identify customers in marketing can also be used to help outbound sales teams improve their call conversion rate by targeting the best people...

How to analyse search traffic using the Google Trends API

The things we search for online can reveal a remarkable amount about us, even when viewed in aggregate on an anonymous level. For many years, Google has made some of...

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 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 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...

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 use GAPandas to view your Google Analytics data

Over the past decade I’ve written more Google Analytics API queries than I can remember. Initially, I favoured PHP for these (and still do for permanent web-based applications utilising GA...