Advanced Analytics Techniques


There are numerous advanced analytics techniques used in today’s business world. Some of the most popular techniques include data mining, machine learning, cluster analysis, retention analysis, predictive analysis, cohort analysis, and complex event analysis.

Businesses use these tools to gain many competitive advantages such as informed, timely decision-making, better preparedness for possible future events, quick response to changing conditions, more accurate prototype testing and improved customer satisfaction and retention.

Here, we take a more in-depth look at some of the most popular advanced analytic techniques used by businesses:

Data Mining

Data mining involves the effective collection of data, storage of data and the computer processing of the data. Using data mining to identify patterns and predict future outcomes has a long history in the technology field. At its foundation, data mining employs three interconnected scientific disciplines: machine learning, statistics and most recently, artificial intelligence.

Data mining has become a hotly trending phenomenon ever since the introduction of big data.  Big data made hoards of data available, which has always been key for data mining. The more data available, the more effective and accurate the technique can be.

Data mining is all about identifying patterns, relationships, or other anomalies in huge amounts of data. Connections can then be made between different elements in the data, which can help generate information about the possible opportunities and risks in the market.

The various data mining techniques can also be instrumental in helping a business cut down on costs, generate more revenue and improve customer relationships.

The advance in processing power has also meant that data mining can now unearth complex relationships. This means that as a technique it now has even more potential to offer hidden insight into the market environment. Data mining’s ability to sift through huge quantities of data quickly and give it meaning and structure allows a business to make timely, informed decisions. Additionally, its relatively reliable ability to predict likely outcomes helps a business be prepared for any upcoming changes in the market.

Data mining is a technique that is relevant across a number of industries including banking, retail, manufacturing, and in research projects such as those of genetics, mathematics, and cybernetics.

Data mining has three main types of models:

  • Descriptive Modeling: This approach involves unearthing similarities in data to identify reasons behind success or failure. Clustering and anomaly detection are two of the more sampling techniques in descriptive modeling.
  • Predictive Modeling: This modeling delves deeper into identifying possible future events and predicting the likelihood of an event happening. It can also offer precious insight into customer turnover and campaign success/failure. Regression and neural networks are two of the two most common sampling techniques in this type of modeling.
  • Prescriptive Modeling: This modeling looks at the relevant internal and external elements to recommend the best course of action in a given situation. Marketing optimization is a commonly used sampling technique in prescriptive modeling.

Data mining’s ability to quickly sift through huge quantities of data means that it can provide a company with quick responses about their market, giving them the time necessary to act on those findings before it becomes too difficult or expensive.

Data analytics and data mining have become the backbone of nearly every company that needs to stay competitive.  With the help of data mining, businesses can make quick and informed decisions to keep up with trends and changes in the market.

Machine Learning

This advanced analytic tool employs computational methods to identify patterns in data, which it then uses to create statistical models capable of producing reliable results without requiring much human intervention. Machine learning is seen to be a key component of the AI subset of advanced analytics. It can be divided into the following categories:

  • Supervised learning involves teaching machines by example and it usually needs huge amounts of labeled data for the process. As a result, datasets used for supervised learning are large in volume and continue to grow in size because the more data there is, the more accurate the results of the supervised learning process. Supervised learning is the more prevalent form of machine learning as its labeled data sets allow you to look for specific patterns in the data.
  • Unsupervised learning uses various algorithms to identify correlations and patterns in a subset of data. However, these algorithms can’t identify specific sets of data. They simply group the data based on similarities or anomalies. And it is because it tends to find random patterns in the data that it is less popular in the market since it has limited obvious applications. However, cybersecurity is one area that has found unsupervised learning a relevant technique.
  • Semi-supervised learning uses the techniques of both supervised learning and unsupervised learning. This technique uses both unlabeled and labeled data to better help the systems understand the problem.  The labeled data set is then used to help train the model, the outcomes of which are used to label the rest of the unlabeled data. When all the data is labeled, the model is trained using said data. This approach takes away the hassle of needing vast amounts of labeled data sets while at the same time allowing the machine to be partially trained to identify more specific sets of data.
  • Reinforced learning: This is among the newer developments in machine learning. Its reinforcement algorithm learns and evolves by a process of trial and error to achieve a clearly stated objective. It tries out various options then uses rewards or penalties to make the right choice to attain the objective.

While machine learning has been around for a while, the surge in popularity in recent times has happened for three main reasons: an unprecedented amount of data is available with very low cost of storage; powerful computers have allowed for the development of machine learning techniques that process huge amounts of data, and finally, new complex machine learning techniques have only motivated businesses to invest more in researching this field.

Related post: How to Inventory Your Analytic Ecosystem

Cohort Analysis

Cohort analysis is a technique in which users are grouped based on similarities and then it establishes broadly applicable insights by studying the behavioral pattern of each group of people.

Cohort analysis is particularly useful in helping improve customer satisfaction and retention. By studying behavioral patterns, it can offer precious insight into what kind of campaign is likely to be most effective, which is the consumer group most likely to buy your product and what particular requirements they might have of said product.

Additionally, cohort analysis is also instrumental in helping you time your promotional and advertising campaign just right. By identifying how long the consumer interest stays with your product, it can tell you when sending the reminder can ensure the best results. Cohort analysis can provide several positive benefits for the business:

  • Increased Customer Lifetime Value (CLV): The ability of cohort analysis to help a business improve its customer retention helps improve the business’s CLV, which is the total revenue a business earns from a customer during the lifetime of their association.
  • Stronger Relations with Loyal Customers: Cohort analysis helps identify your loyal customers, which allows you to take on a more customized approach to targeting and encouraging them to continue their association with you for as long as possible.
  • Better Testing of New Designs: Typically, tests cannot give much indication of how a new design of your product will fare in the market. Cohort analysis can make things easier. You can create a cohort based on interactions with the new design and then compare it with the conversion rate of those who have not interacted with the new design.

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Cluster Analysis

Cluster analysis involves recognizing the similarities and differences in sets of data and visually presenting it to help make comparisons easier. Box plot visualizations are often used in this method of presenting data in a cluster.

Cluster analysis’ ability to group similar elements and to present volumes of data in a simple and clear fashion can help businesses make informed, effective decisions in a timely fashion.

It is a powerful and popular data-mining tool and is particularly valuable to organizations that are looking to identify distinct consumer groups, sales transactions, or other behavioral patterns.

Cluster analysis is predominantly used for classification. It works to group elements in such a manner so that they are most similar to the members of their group and different to elements outside of it. Cluster analysis is particularly applicable in the fields of marketing, insurance, and geology. There are three primary cluster analysis types:

  • Hierarchical Cluster: This is the most commonly used method of clustering. It involves the creation of numerous models with the most basic cluster containing all the elements and the smallest cluster comprising the individual element. This type of analysis can incorporate the review of scale, nominal, ordinal data.
  • K-Means Cluster: This approach is best when you cluster large datasets. Researchers specify the number of clusters before even beginning the process. This type of cluster is ideally suited when testing different models with a different number of assumed clusters.
  • Two-Step Cluster: This technique uses a combination of K-means and hierarchical and cluster analysis. A cluster algorithm is first used to identify groupings by performing pre-clustering and then the hierarchical methods are applied. This method is best when hierarchical methods are needed but the datasets are too large to carry out a traditional hierarchical cluster analysis.

Retention Analysis

Retention analysis is an advanced analytics technique that studies the different cohorts of customers. These techniques are capable of offering deeper insight into consumer behavioral patterns and what factors influence consumer growth and consumer retention.

Retention analysis is an integral component of a business’s marketing strategy and its customer retention. It predominantly aims to answer the question of how many new customers turn into recurring, profitable clients of the business.

However, there are many other key questions it answers: How long does it take for a new customer to come back to the product again? What is the customer profile for the product you are selling? Have recent changes to the product led to improved customer retention? How has consumer retention been negatively impacted by new conditions? What changes can help improve the customer retention of the business?

To get the most value and accurate information from your retention analysis, it is essential to run it regularly because consumer behavior and your offers tend to change constantly. Only regularly performed retention analysis can provide you with a clear, comprehensive picture. Additionally, using the right KPIs, such as the customer churn rate, customer lifetime value, and customer engagement score to measure your retention rates will also help in getting the right picture.

In summary, if you track customer behavior, get feedback, and study the key metrics, then your retention analysis can help reduce your customer turnover, improve customer retention and grow revenue.

Complex Event Analysis

Also known as complex event processing (CEP), this advanced analytics technique gathers and analyzes data of an event from several different sources as it happens. It then works to identify cause-and-effect relationships and unearth complex patterns in the gathered data, providing real-time insight into factors and variables, which can facilitate more effective decision-making.

Complex event analysis equips systems to respond to events and patterns in data as they happen.

Complex event analysis is typically most relevant to cases where there is a large volume of events happening and there are very low latency requirements. This is why this technique is often employed in the following scenarios:

  • Real-time marketing: Complex event analysis allows marketers to identify patterns in consumer behavior helping them present customized offers to customers in real-time.
  • Stock market trading: Here, the complex event analysis can identify the stock price, match it against a pattern, and trigger the appropriate response of buying/selling.
  • Predictive maintenance: Relevant to manufacturing facilities mostly, this involves continuous gathering of data to identify whether there are patterns in the data that indicate the necessity of shutting down equipment for predictive maintenance.
  • Operation of autonomous cars: A very recent development, CEP is being used by manufacturers as a system that gathers data to determine when to take specific actions like identifying a stop sign in the distance, calculating the distance and choosing a deceleration rate to ensure complete stopping at the sign.

Predictive Analysis

Predictive analysis is a technique that is typically used in conjunction with data mining, machine learning and statistical models to study data and predict the likelihood of an event happening in the future and facilitate business forecasting to plan accordingly. The scientific approach of predictive analysis enables it to offer future insights to a great degree of accuracy. It has seen a great surge in popularity among a wide range of organizations in recent times.

Predictive analysis adds to business performance in several different ways:

  • Optimization of marketing campaigns: Predictive analysis can be instrumental in predicting consumer response to changes in product offering and can help a business identify the means through which it can attract the most customers and hold their attention.
  • Streamlined operations: Predictive analysis can ensure you manage your resources as needed such as maintaining inventory to keep your storage costs to a minimum at all times or hiring temporary additional staff only at peak times, cutting down on your HR costs. This helps in streamlining the operations of the business leading to improved efficiency and reduced costs.
  • Improved cybersecurity: Behavioral predictive analytics can help identify anomalies and patterns in real-time, which can identify fraud or potential persistent threats that need to be addressed.
  • Reduced risk: You can use predictive analysis to assess and predict whether your buyer is likely to pay his dues to you on time.  This can be accomplished by a predictive model generating a credit score of the buyer based on credit worthiness.

With valuable wide-ranging benefits that predictive analysis has to offer, it is not surprising to know that it applies to a wide range of industries including retail, manufacturing, banking, health insurance, the government sector, and oil and gas.