Predictive Analytics Tools

Predictive analytics is a set of methods for analysing current and historical data to make predictions on future behaviour. This means that you do not need to rely on your hunches or intuition to predict the future behaviour of individuals, companies public institutions and many other organizations.

Instead, by combining your current and historical data with predictive methodologies from a wide and diverse array of fields — including data mining, statistical modelling, machine learning, deep learning and many other artificial intelligence (AI) methods — you can create mathematical models to give you an edge over the competition.

Prior to hiring a machine learning consulting which offers predictive analytics, you should know what exactly they do. First of all, it’s important you understand that the predictive analytics usually involves at least three levels of your organization. The Predictive Analytics Company will involve in the process: developers (they build and maintain algorithms and other modeling tools), data scientists (they generate insights and results) and business intelligence professionals (they put insights into action).

Predictive analytics consultants help companies make better business decisions by improving their predictive models which can make them more efficient than competitors. Predictive analytics consultants are employed in various fields such as sales, marketing, healthcare and financial services industries.

The predictive analytics projects in organizations that are successful, often require strong sponsorship from top management. When the organization’s upper layers are involved in the process of setting the goals, planning, and evaluating results, major successes are much more likely to be achieved.

Data science projects are very expensive and complex in many organizations, which leads to skepticism from the top. And this is especially true when it comes to implementing products. Any new product can be risky, but products and services derived from data science are often more tricky because of the non-intuitive nature of such approach and the fact that predictive analytics projects might take longer than expected to mature.

Predictive analytics has changed the way companies do business. The technology and its methods have been integrated into many aspects of business:

• predictions of customer lifetime value (CLV)
• churn prevention
• lead scoring
• optimization of sales funnels (leads flow)
• customer retention
• finding product taxonomy examples
• identification of up-selling/cross-selling opportunities
• customer segmentation
• fraud transactions (anomaly detection)
• website categorizations
• sales and demand forecasting

Predictive scoring is a very popular term in business administration and modelling. With the speed and sophistication of available data, analysts have to be very careful with every aspect of model building, from the data cleaning, to the setting up of mathematical models and finally delivering the insights derived from these models. One of these aspects is computing the predictive value of a specific entity. Predictive analytics models goal can e.g. determine how likely it is that a person will engage in a specific behavior or how likely it is that an employee will quit his job.

As such it may be used to allocate budgets, improve marketing campaigns and help find investment opportunities. Initially such processes had to be performed manually by statisticians who were often intimately familiar with the structure of any given dataset.

Predictive scoring is a mechanism which enables organizations to make a wide array of different predictions:

• what is the credit risk of a client applying for a loan
• what is the probability that a given high value employee may leave the company
• what is the probability that a critical piece of component may break down
• what is the probability that a given flagged transaction is a potential fraud
• what is the probability that a customer, given the recent browsing and historical purchases will be interested in selected products
• what is the probability that a given sensor is malfunctioning

One of interesting recent applications of predictive analytics that we developed is in lead generation for technologies.

We have determined usage of 4000+ technologies over millions of domains. Based on this we built a collaborative filtering recommender which for given website will recommend technologies that are most likely interesting for this website, based on their technologies the website already uses.

Similar to what is happening on Amazon and their recommendations.

This also works off single technologies. On our website: https://www.alpha-quantum.com/technologies1 you can see the most popular technologies, categorized in 99 categorized.

Then, by choosing any technology, you can find which technologies would be most interesting for someone using that technology.

Here are recommendations for technology Wordpress:

Our website brings a lot of other interesting information about Wordpress, based on its usage on millions of domains.

It shows that Wordpress is above average used in Family & Relationships, Books, Religion & Spirituality verticals, but less than average in Style & Fashion:

Next, it is a bit less common among the top 100k most popular domains:

but its popularity increases for long-tail.

Another interesting analysis is with respect to domain age:

It seems that wordpress was a bit more popular in years 2010 to 2020 but slightly less so in recent years.

Predictive analytics can also be used in the finance field, e.g. in relation to prices of stocks, bonds and cryptocurrencies.

One way that helps is e.g. computing and using technical analysis, like support and resistance levels, here is an example of chart from BittsAnalytics dashboard:

In one of our next posts we will go into more detail on predictive analytics in relation to crypto market.