Predict future outcomes with extremely high precision
Take the human variability and guesswork out of decisions
Know your client's next action and when it will happen
Introduction
There are many different types of analytics today, each offering a different perspective to drive change and solve everyday problems.
Some analytics are retrospective, such as descriptive and diagnostic analytics. These analytics show “what” happened and “why” it may have happened, and are well suited for general reporting and traditional business intelligence.
Then there’s advanced analytics. It is good to know that these advanced methods are now accessible to businesses of all sizes.
What is advanced analytics?
Is advanced analytics important?
Advanced analytics provide deeper, more advanced insight into patterns, trends and themes that are hidden in your data. This enables your business to understand customers on a deeper level, predict future outcomes, reduce cost, increase revenues and much more.
In this guide, we briefly discuss the methods that make up advanced analysis, and why each method is important to consider for improving your business.
Type 1 : Data mining
Data mining is defined as “knowledge discovery in databases” and can be considered the foundation of advanced analytics.
The objective of data mining is to search larger data sets to uncover patterns, trends and other hidden insights that may not be clearly visible. Think of data mining as a deep dive into data analysis.
- Clustering analysis – Clustering methods can help your business to identify distinct groups within the client base. It can cluster different client types into one group based on different factors, such as purchasing or social interest patterns. The results can be used to optimise the services, products or information offered to these new clusters, and lead to higher conversion rates, whatever that might mean in your particular field.
- Anomaly detection – This method, also known as outlier detection, looks for data points that are rare and fall outside a defined group or average. Anomaly detection is often used to detect fraudulent behaviour in the financial sector and glitches in e-commerce transactions. But it can also be used, for example, to identify individual training needs or success indicators within your staff.
- Association rule mining – This analytical method is looking at how one variable relates to another. For example, if event A occurs, then event B is likely to follow. Commercial sites may apply this to predict the next purchase to be made, and other businesses may use these learnings to optimize their internal processes.
- Regression analysis – This data mining method looks at the effect of one variable on many others. It can reveal market patterns, such as increased demand on certain days of the week, or in response to weather forecasts. You can, for example, optimize stock and staffing before a peak in demand occurs.
Text mining
Another form of data mining, called text mining, is an advanced method for extracting text from documents and databases, or the web. Once extracted, natural language processing (NLP) is applied to transform the free (unstructured) text into normalized, structured data that can be used for further analysis and machine learning (ML) algorithms.
Type 2 : Predictive analytics
One of the most prominent forms of advanced analytics today is predictive analytics.
This type of analysis goes a step beyond “what happened” and “why it happened” by analysing historical data to predict future outcomes. This is done through a variety of statistical techniques such as data mining, machine learning and predictive modelling.
Predictive analytics extracts data from systems such as CRM, ERP, marketing automation stacks and other internal or external databases. The predicted results are then visualised in such a way that key business users can easily interpret them.
Once the predictions have reached a satisfactory level of accuracy and reliability, we can automate tasks and process decisions without human intervention.
Predictive analytics industry examples
There are many industry examples of predictive analytics today.
The Education Industry, for example, can use predictive analytics to more accurately predict where students are getting stuck and what is needed for them to succeed. Human Resources (HR) uses analytics to identify pain points and productivity spikes to predict future employee performance.
Here are some more examples how predictive analytics may apply to your industry.
- Retail & E-Commerce – Predictive analytics is not reserved for the big players. You don’t have to be Amazon to use predictive analytics to make personalised product recommendations based on buying behaviour. By analysing data, you can understand your customers on a deeper level and predict their behaviour in a more personalised way. Predictive analytics can tell you what customers will buy next and, more importantly, when they will buy it.
- Travel & Hospitality – In order to run a successful travel or hospitality business, it is very important to predict demand. Predictive analytics in the hospitality industry allows for improved inventory management, optimised staff management, improved guest experience, increased marketing efficiency, real-time pricing and streamlined processes. Advanced analytics can even predict the best restaurant menu based on the guest profiles that will be soon staying at your hotel.
- Real Estate Agents – A good example of predictive analytics in real estate is to take the guesswork out of lead qualification, in addition to making the process much faster and more efficient. Predictive analytics for real estate agents allows them to stop wasting time on leads that won’t buy, and instead focus on those who are (almost) guaranteed to go through with buying or renting a property in the next few weeks.
- Manufacturing & Logistics – A prominent example of predictive analytics used in manufacturing is with predictive maintenance. The purpose of predictive maintenance is to notify manufacturers of cautious activity regarding industrial equipment. By taking it large amounts of data, typically through the use of IoT-embedded sensors on the equipment, manufacturers are able to intervene before a break down occurs.
Why predictive analytics is important?
Predictive analytics is important for gaining a competitive advantage, understanding a variety of customer interests, finding new business opportunities, reducing costs and risks, and most importantly, spotting problems before they occur.
Type 3 : Prescriptive analytics
Predictive analytics allows us to understand what is likely to happen next, while prescriptive analytics provides calculated next steps to take. Out of all the types of advanced data analytics, it is perhaps the most actionable.
Prescriptive analyses are complex in the world of data science. Applied statistics, deep learning, computer vision, and other advanced methods are used in prescriptive analytics.
Because of the high barrier to entry, prescriptive analytics is not commonly used by many small and medium businesses today. Unless there is a strong use case to apply descriptive analytics, predictive analytics will be the more common method to solve real world problems.
- Descriptive analytics answers “what happened”
- Diagnostic analytics seeks “why it happened”
- Predictive analytics tells “what is likely to happen next“
- Prescriptive analytics provides “best actions to take next“
Why prescriptive analytics is important?
Prescriptive analytics help businesses identify the best course of action, so they achieve organizational goals like cost reduction, customer satisfaction, profitability etc. While figuring out what you should do is a crucial aspect of any business, the value of prescriptive analytics is often missed.
What's next?
As there are many applications for many sectors, we recommend you talk to one of our data professionals to help you better understand your specific opportunities.
They will go out of there way to introduce you to simple but effective solutions or go with you all the way to cutting-edge technologies to improve your business.
Data mining finds valuable patterns, trends and other hidden deep insights in your data that are not visible yet.
Predictive analytics uncovers customer insights and business opportunities, to predict what most likely will happen next.
Prescriptive analytics help your business identify the best course of action, so you achieve organizational goals with less error.
Get in touch and we’ll get back to you in a tick.
