Data mining is a form of business intelligence and data analysis. It is the process of analysing data to draw useful conclusions or predictions from it. It’s a technique frequently adopted by large-scale ecommerce businesses to aid with marketing and product development.

Data mining in ecommerce to build user profiles

Because of the nature of the internet, ecommerce businesses will obtain a lot of data about their customers, or their prospective customers. Data is obtained whenever a purchase is made, an account is created, or a page view is made. This raw data (i.e., account and payment information) can come from the database associated with an ecommerce website, as well as from web analytics tools.

Today, Google Analytics 4 (GA4) collects first-party data through events and parameters. It does not expose personal Google account information but instead models users and sessions using consented signals. Ecommerce teams can enrich this with data from their CRM or customer data platform (CDP).

It’s also important to note the role of consent. In the UK and EU, businesses must implement Consent Mode v2 to send correct signals to analytics and ad platforms, ensuring compliance with GDPR.

What is data mining and market segmentation?

In practice, ecommerce data mining of a clothing website, for example, might show that:

  • 82 out of 100 visitors are female
  • 92 out of 100 live in the UK
  • 70 out of 100 are aged 18–34
  • 10% of them are shown as ‘interested in sport’ (based on consented behavioural data)

Using data mining techniques, it’s possible to start building profiles for the type of person that visits the site. This allows the business to market and create products based on those personas.

Using our example above, this combination of both acquiring data and analysing it through data mining techniques tells the business owners that, based on their audience, it would be beneficial for them to begin to market and sell female sports clothing.

Beyond simple demographics, modern segmentation uses behavioural data (purchase frequency, churn risk), RFM models (recency, frequency, monetary value), and predicted customer lifetime value (CLV) to guide marketing campaigns.

What is basket analysis?

Data mining software is how Amazon has perfected upselling by promoting other items with banners like ‘customers who bought these items also bought’, ‘recommended for you’ and ‘frequently bought together’ bundle packages.

Amazon acquires a huge amount of data through various methods that they then use to customise and personalise offers and promotions to the customer. This cross-selling has been proven to increase the average order size of a basket.

Basket analysis is a classic example of association rules in data mining. Retailers also use it to design product bundles and test complementary promotions, such as discounts when buying two related items together.

What happens if you don’t get as far as the basket?

Data is taken at every stage of the user’s journey through the site. In GA4 this is tracked as events like view_item, add_to_cart, begin_checkout and purchase. Consent settings now control whether remarketing audiences can be built.

This data can be processed to guide the company’s next actions – perhaps they should consider offering free delivery, or maybe they should send an email to that customer in a few days with a special offer to prompt them to return and finish their purchase.

From data to action

For ecommerce teams new to data mining, it helps to follow a structured workflow rather than diving straight into complex modelling.

  • Step 1: Set up data streams. Connect GA4, your ecommerce platform, and any CRM or email tool. This ensures you’re collecting behaviour, transaction, and engagement data in one place.
  • Step 2: Focus on three key views. Start by looking at pages (where users go), acquisition (how they arrive), and transactions (what they buy). These three perspectives give you a balanced picture of customer behaviour.
  • Step 3: Track the right KPIs. Many beginners get lost in dashboards full of numbers. At the start, focus on three essentials: sessions (traffic), conversion rate, and revenue. These give you a clear link between site activity and business outcomes.

A common mistake is treating every metric equally. Instead, use your KPIs as a compass. For example, if sessions are high but conversions are low, your problem lies in product pages or checkout design. If revenue is stagnant despite steady conversions, you may need to review pricing or product mix.

By building this foundation, you’ll avoid drowning in data and create a base for more advanced techniques like clustering and predictive modelling.

Sales forecasting

Sales forecasting with data mining software is where a company uses data to predict things that are useful for stock control, pricing and marketing.

An example of data mining and sales forecasting:

If you do your weekly shop online, the supermarket learns about your buying habits, and also your consumption habits. If you buy a bag of 100 teabags in the first week of January, and then again in the first week of February then the supermarket learns that it takes you about a month to use 100 teabags.

So, towards the end of February, the supermarket can target you with offers on teabags because they know that you’ve nearly run out. This makes you think ‘oh yeah, I do need teabags’, so you log in to your online shopping account to get some – and whilst you’re there, you might get some milk, bread, and notice there’s a sale on dishwasher tablets. That’s how they get you. Powered by data mining, this also lets them predict how much stock they need to have in store at various points of the year.

Modern forecasting also accounts for seasonality (holidays, paydays, sales events) and applies machine learning models to predict demand spikes. This helps retailers fine-tune pricing, optimise promotions, and avoid over- or under-stocking.

Optimising pricing and promotions

Data mining is increasingly used to understand how customers respond to different prices and offers. Instead of guessing, retailers can model demand sensitivity to fine-tune their promotions.

  • Test promotions before launch. Historical data shows how similar campaigns performed, helping teams forecast uplift and set realistic goals.
  • Time markdowns strategically. Instead of blanket discounts at month-end, data reveals when customers are most receptive – for example, paydays or holiday weekends.
  • Segment discounts. High-value or loyal customers may need only a small incentive, while price-sensitive customers might convert only with a bigger discount.

Consider an example: a fashion retailer planning Black Friday deals. Rather than discounting everything by 30%, they can use data to identify which products sell fast without discounts and which need a price cut to move. The result is higher overall margin and less wasted stock.

Dynamic pricing is also becoming more common. Airlines and ride-sharing apps have used it for years, but ecommerce brands are now adopting similar models. Data mining makes it possible to adjust prices in near real time based on demand, stock levels, and competitor behaviour.

When combined with forecasting, these pricing strategies ensure promotions lift revenue rather than erode profit margins.

Fraud prevention

Data mining techniques can also be used as a method of fraud prevention, by detecting abnormal patterns in a data sequence. For example, a bank might automatically temporarily suspend a credit card if their fraud prevention systems notice that it was used in McDonald’s in London and Burger King in New York within the space of an hour.

Alternatively, a card may be suspended if it’s only usually used to pay for a cup of coffee here and there, but suddenly it’s been used to buy a 54-inch television and a quad bike. That’s when data mining techniques notice an abnormality, and so the bank gives the account holder a ring to make sure everything’s alright.

In analytics, this falls under anomaly detection. Ecommerce merchants use similar techniques to identify suspicious account activity, fake reviews, or fraudulent orders before they impact revenue.

Emerging techniques and process

While the applications above cover the basics, many ecommerce businesses now use:

  • Clustering to group similar customers into segments
  • Classification to predict whether a visitor is likely to convert
  • Regression to model how price changes affect sales

A common framework for running these projects is CRISP-DM (Cross Industry Standard Process for Data Mining). It consists of six phases:

  1. Business understanding – Define what you want to achieve, such as reducing cart abandonment.
  2. Data understanding – Explore what data you have, like transaction logs, product views, or campaign responses.
  3. Data preparation – Clean, merge, and transform raw data so it’s analysis-ready.
  4. Modelling – Apply techniques such as clustering, regression, or classification to uncover patterns.
  5. Evaluation – Check whether the results meet the original business goals.
  6. Deployment – Put the insights into action, such as changing site design or launching targeted campaigns.

For ecommerce businesses, CRISP-DM offers a practical way to move from scattered data experiments to structured, repeatable insights.

Ethics and guardrails

With growing regulation and consumer awareness, ethical considerations are central to data mining. Customers increasingly expect transparency and fairness, and ignoring this can damage brand trust.

  • Consent and privacy. Compliance with GDPR and the UK Data Protection Act requires businesses to collect and process only the data users have agreed to share. Consent Mode v2 is one way of signalling those choices to analytics and ad platforms.
  • Transparency. Businesses should explain how they use customer data, especially when personalisation is involved. A simple note in a privacy policy isn’t enough – users respond better when they understand the value exchange.
  • Fairness. Algorithms can sometimes produce biased outcomes, such as offering better discounts to one demographic over another. Regular audits of data models help ensure decisions are equitable.

By embedding ethics into data mining projects, businesses reduce compliance risk and build longer-term loyalty. A customer who trusts a retailer with their data is more likely to keep coming back.

Tools and platforms

Ecommerce data mining can be incredibly beneficial to a company’s growth, with basic data mining techniques created with the use of Google Analytics and spreadsheets.

Today, the landscape is broader. Businesses often combine GA4, cloud data warehouses (BigQuery, Snowflake, Azure Synapse), and BI tools (Tableau, Power BI) to analyse their ecommerce data. For smaller teams, spreadsheets are still a good starting point.

Whether you’re running simple basket analysis or building predictive models, the right infrastructure matters. With Fasthosts Dedicated Servers, you’ll have the scalable compute and storage needed to support your ecommerce data mining projects – all backed by UK data centres and 24/7 support.