Business Intelligence is often associated with dashboards, reports and KPIs. Those are essential parts of the job, but they are only one part of what you can do with data.

A traditional BI dashboard is very good at answering questions such as: What happened? How are we performing? Which products, regions or teams are above or below target?

Advanced analytics goes a step further. It can help you explore why something happened, identify patterns that are not immediately obvious and, in some cases, estimate what is likely to happen next.

For a BI analyst, that does not mean becoming a data scientist overnight. Techniques such as customer segmentation, churn modelling and regression often sit closer to the world of data science than traditional Business Intelligence. But understanding them can significantly expand the kinds of business problems you are able to tackle.

What is advanced analytics?

Traditional BI tends to focus heavily on understanding historical and current performance. You might use a dashboard to analyse revenue by month, compare conversion rates between channels or monitor customer-service KPIs.

Advanced analytics can help answer deeper questions. Why are customers leaving? Which products tend to be purchased together? Which factors have the strongest relationship with sales? Which customers are most valuable? What are people saying about a product, and is sentiment improving or deteriorating?

The boundary between Business Intelligence and advanced analytics is not always clear-cut. Some techniques can be performed entirely in Excel or a BI platform, while others may require statistical software, Python, machine-learning tools or specialist applications.

For a BI analyst, the important thing is not necessarily to master every technique. It is to understand what is possible and recognise when a business question could benefit from something beyond a standard dashboard.

1. RFM analysis

RFM stands for Recency, Frequency and Monetary value. It is a customer-segmentation technique designed to help businesses understand the value and behaviour of their customers based on three simple questions.

Customers are scored according to those three dimensions and can then be grouped into segments such as high-value customers, loyal customers, new customers or customers at risk of becoming inactive.

The exact names and scoring system can vary, but the underlying idea is straightforward: recent, frequent and high-spending customers are generally more valuable than customers who purchased once a long time ago.

What business question does RFM analysis answer?

RFM is particularly useful when a business wants to understand which customers deserve different types of attention. A company might want to identify its most valuable customers for a loyalty programme, find previously strong customers who have stopped purchasing or create different marketing campaigns for different customer groups.

Rather than treating every customer identically, RFM provides a practical way to prioritise them according to observed behaviour.

How does RFM analysis work?

You typically need transactional data containing a customer identifier, order identifier, purchase date and transaction value. For every customer, you calculate how recently they purchased, how many purchases they made during the period being analysed and how much they spent.

Each measure is then converted into a score. A simple approach might score customers from one to five on each dimension, with five representing the strongest performance. Those scores can then be combined or used individually to create customer segments.

When would a BI analyst use RFM analysis?

RFM fits naturally into BI because it can be performed with tools many analysts already use, including SQL, Excel and BI platforms.

You might use it to enhance a customer dashboard with meaningful behavioural segments rather than simply reporting total customers and revenue. It can also feed directly into marketing activity. For example, a BI analyst might identify a group of formerly valuable customers whose recency score has fallen and pass that segment to the marketing team for a re-engagement campaign.

This is a good example of moving from reporting what happened to helping the business decide what to do next.

2. Market basket analysis

Market basket analysis identifies relationships between products that are frequently purchased together.

The classic example is an online retailer recommending another product because customers who bought one item often purchased the other as well. But the technique can be useful in physical retail, subscriptions and many other environments where customers purchase combinations of products or services.

The objective is to uncover associations that can support cross-selling, upselling, bundling, merchandising and recommendations.

How does market basket analysis work?

The analysis is often based on three measures: support, confidence and lift.

Support tells you how frequently a particular item or combination of items appears in all transactions. If you have 1,000 transactions and 100 contain both bread and butter, the support for that combination is 10%.

Confidence measures how often one item is purchased when another item has already been purchased. If 200 transactions contain bread and 100 of those also contain butter, the confidence of the rule “bread → butter” is 50%.

Lift helps determine whether the relationship is actually meaningful by comparing the observed combination with what you would expect if the products were purchased independently. A lift above one suggests that the two products are bought together more often than would be expected by chance.

What business question does market basket analysis answer?

At its simplest, market basket analysis answers: Which products or services tend to go together?

That can lead to several practical actions. A retailer might create bundles, place complementary products closer together, recommend an additional product during checkout or design promotions around combinations that customers already tend to purchase.

When would a BI analyst use market basket analysis?

A BI analyst working with transaction-level data may already have everything required to explore product associations. You may not be responsible for building a full recommendation system, but you could identify interesting product relationships and surface them through analysis.

Instead of merely showing that Product A and Product B both sell well, you might discover that customers who purchase A are disproportionately likely to purchase B in the same order. That is a much more actionable insight.

3. Churn analysis

Churn occurs when a customer stops doing business with a company. In a subscription business, churn may be very clearly defined: a customer cancels their subscription.

In other businesses it can be more complicated. A retailer, for example, might classify a customer as churned after they have gone a certain number of months without purchasing.

Churn analysis attempts to understand the characteristics and behaviours associated with customers who leave and, in more advanced cases, predict which existing customers are most likely to churn next.

What business question does churn analysis answer?

There are really two related questions: Why are customers leaving? and Which customers are most likely to leave?

The first can often be approached with exploratory BI. You might compare churn rates by acquisition channel, product type, customer tenure, region, support interactions or purchase frequency. The second moves more clearly into predictive analytics.

How does churn prediction work?

The first step is defining churn correctly for the business. You then examine historical customers and look for characteristics associated with churn.

These could include time since last purchase, declining purchase frequency, product usage, support contacts, contract type, customer demographics or almost any other relevant variable.

A predictive model can then be trained using those historical examples. Common techniques include logistic regression, decision trees and other machine-learning algorithms. The resulting model might assign every customer a probability of churn, allowing the organisation to prioritise those at greatest risk.

When would a BI analyst use churn analysis?

Even if you are not building machine-learning models yourself, churn is an excellent area for BI analysis. You can monitor churn rates, identify segments with unusually high churn and analyse how customer behaviour changes before departure.

If you do have access to a churn score produced by a model, you can also incorporate that into BI reporting. A dashboard could show how much revenue is associated with high-risk customers, where those customers are concentrated and whether retention interventions are working.

4. Sentiment analysis

Not all useful business data arrives in neat rows and columns. Customer reviews, survey responses, emails, support tickets and social-media posts can contain valuable information about how people feel about a product, service or brand.

Sentiment analysis uses natural language processing to classify that text according to the sentiment it expresses, commonly as positive, negative or neutral.

More sophisticated approaches can also produce sentiment scores or identify particular emotions and topics.

What business question does sentiment analysis answer?

Sentiment analysis helps answer questions such as:

• How do customers feel about our product?
• Is sentiment improving or deteriorating?
• What issues are causing negative feedback?
• Which parts of the customer experience receive the most praise?
• Did sentiment change after a product launch or service problem?

Imagine a sudden increase in negative customer reviews. A sentiment analysis might reveal that many of them contain references to delayed delivery. That gives the business a much more specific problem to investigate than simply knowing the average review score has fallen.

When would a BI analyst use sentiment analysis?

The most obvious opportunity for BI analysts is to bring qualitative data into reporting environments that would otherwise rely entirely on structured metrics.

A customer-experience dashboard might already contain NPS, complaint volumes and response times. Adding sentiment from customer comments could provide another useful dimension.

The BI analyst may not necessarily build the language model. The role may instead be to integrate sentiment outputs with other business data and help the organisation interpret what they mean.

5. Regression analysis

Regression analysis is one of the oldest and most widely used statistical techniques, but it remains extremely useful in business. It is used to examine the relationship between an outcome and one or more factors that might influence it.

For example, a business might want to understand the relationship between advertising spend and sales, price and demand, customer tenure and lifetime value, delivery times and satisfaction, or staffing levels and productivity.

The outcome you are trying to explain is called the dependent variable. The factors that may influence it are the independent variables.

How does regression work?

A simple linear regression looks at the relationship between one independent variable and one dependent variable. You might examine whether increasing advertising spend is associated with increased sales.

The model estimates a line describing that relationship and produces a coefficient representing its direction and strength. A positive coefficient indicates that the variables tend to move in the same direction, while a negative coefficient suggests an inverse relationship.

Multiple regression expands the same principle to several independent variables at once. This is often much more useful in a real business setting because outcomes rarely depend on a single factor.

What business question does regression answer?

Regression is particularly valuable when you want to understand what is associated with an outcome and by how much.

A company may know that sales increased last quarter, but a regression analysis could help investigate whether that increase was associated with advertising spend, price changes, seasonality or some combination of factors.

The output can also be used for scenario analysis. That does not mean the relationship is automatically causal, and regression results need to be interpreted carefully, but the technique can provide much more insight than simply comparing two lines on a chart.

When would a BI analyst use regression analysis?

Regression is one of the most accessible advanced techniques for a BI analyst because it bridges the gap between descriptive analysis and statistics.

Excel can perform regression analysis, as can Python, R and many statistical applications. Even if you do not regularly build regression models, understanding how they work can improve the way you think about relationships in business data.

Advanced analytics vs traditional Business Intelligence

It can be tempting to think of advanced analytics as the next level after Business Intelligence, but the relationship is not quite that simple.

Traditional BI remains essential. Businesses still need trusted metrics, reliable dashboards and reporting that explains what is happening across the organisation. Advanced analytics addresses different questions.

A strong BI environment can support all four. A churn model may predict which customers are most likely to leave, but the organisation still needs reporting to monitor those customers, understand the financial exposure and measure whether retention strategies actually work.

Rather than competing with each other, traditional BI and advanced analytics are often most useful when combined.

Do BI analysts need to learn advanced analytics?

Not necessarily.

If you are just getting started in Business Intelligence, there are more important things to learn first. SQL, data modelling, BI platforms, visualisation, business understanding and the ability to define trustworthy metrics are still foundational skills.

You do not need machine learning to build an effective sales dashboard. But once those foundations are in place, advanced analytics can expand your capabilities considerably.

It allows you to approach questions that might otherwise be passed straight to a data scientist and gives you a broader understanding of what organisations can do with their data.

You also do not need to become an expert in every technique. Knowing that market basket analysis exists and understanding the kind of problem it solves may be enough for you to recognise an opportunity and collaborate with somebody who has deeper technical expertise.

Which advanced analytics technique should a BI analyst learn first?

If I had to choose one starting point, it would depend on the kind of data you work with.

For someone working with customer transaction data, RFM analysis is probably the easiest place to start. It is intuitive, practical and can be implemented using familiar BI tools.

If your organisation struggles with customer retention, churn analysis is an obvious next step. You can begin descriptively before moving into predictive modelling.

For retail and e-commerce, market basket analysis can produce highly actionable insights from transaction data. If you work with surveys, reviews or customer-support information, sentiment analysis gives you a way to bring text into your analytical environment.

And if you want to strengthen your statistical thinking more generally, regression analysis is arguably the most broadly useful technique of the five.

The important thing is to start with a genuine business question rather than choosing a technique because it sounds advanced.

Why advanced analytics can make you a more valuable BI professional

The value of a BI analyst is not measured by how many tools or statistical techniques they know. It comes from their ability to help an organisation make better decisions.

Sometimes a dashboard is exactly what is needed. Sometimes it isn't. If a business wants to understand which customers are drifting away, which products should be promoted together or which factors are associated with declining performance, a traditional KPI dashboard may only take you so far.

Understanding more advanced analytical techniques gives you additional ways to investigate those problems.

The objective is not to turn every BI analyst into a data scientist. It is to move beyond seeing BI as the process of building dashboards and start seeing it for what it really is: using data to help businesses understand problems, identify opportunities and make better decisions.

Frequently Asked Questions

What is advanced analytics in Business Intelligence?

Advanced analytics refers to methods that go beyond basic reporting and descriptive dashboards to identify patterns, investigate relationships, segment customers and predict possible future outcomes.

Is advanced analytics the same as data science?

There is considerable overlap, but they are not exactly the same thing. Many advanced analytics techniques are commonly used in data science, while Business Intelligence traditionally focuses more heavily on reporting, metrics and decision support.

Does a BI analyst need machine-learning skills?

Not necessarily. Machine learning can be valuable for predictive problems such as churn, but SQL, data modelling, visualisation and business understanding are usually more important foundational skills for BI analysts.

Can advanced analytics be done in Excel?

Some techniques can. RFM analysis and regression, for example, can both be carried out in Excel. Other methods may be easier or more scalable using SQL, Python, specialist applications or cloud-based analytics services.

What is an example of advanced analytics?

Predicting which customers are likely to cancel a subscription is one example. Historical customer data can be analysed to identify patterns associated with churn and used to estimate which current customers are at greatest risk of leaving.

Where to Go Next

Continue with these related Learn BI Academy guides.

 What Is Business Intelligence?  Understand the wider BI process and how reporting, analysis and decision support fit together.    What Skills Are Needed for Business Intelligence?  Explore the technical, analytical, business and professional skills that modern BI analysts need.    How to Learn Business Intelligence  Follow a practical route through the core concepts, tools and projects that make up modern BI work.    What Is Data Governance?  Learn how organisations create trustworthy, consistent and well-managed data for reporting and analysis.    Business Intelligence Tools: How to Choose the Right BI Platform  Learn what to compare when choosing a BI platform for analysis, modelling, visualisation and sharing.  

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