Predictive Analytics Consulting
With a Difference
Predictive analytics is essential to getting the most out of your data. It allows you to make more accurate predictions based on hidden patterns and trends uncovered in your data, and allows you to make better, more informed business decisions as a result.
At Cyber Group, our team of professional consultants will work closely with you to implement predictive analytics solutions that will benefit your business. Using predictive analytics, you will improve your ability to forecast demand, detect fraud, engage with customers, optimize product and service prices, improve risk management, improve quality assurance, and identify more up-selling and cross-selling opportunities, just to name a few possibilities.
We Help You Foresee
Developments And Respond To Challenges
Not only can we implement effective predictive analytics solutions that will allow you to foresee future developments (allowing you to make important decisions that aren’t solely based on guesswork and instinct), but you will also be able to determine the best way to respond to those developments. We can help provide you with insight into future events based on the data you have as well as help you leverage that insight in the right way.
Our team at Cyber Group will perform a thorough audit of your data strategy before determining what predictive analytical solutions will benefit your business goals. We are capable of implementing a variety of predictive analytics solutions and can offer the following services:
There are many tools that we can implement into your data strategy that can formulate predictions based on historical data. For example, lead scoring is a commonly used predictive analytical tool that predicts whether leads are ready to make a purchase. Lead scores are determined by specific actions and once their score reaches a certain number, they can be labeled as a SQL (sales qualified lead). These scores are determined based on past lead behaviors and conversions.
Not only will we help to make accurate predictions based on your existing data, our team will also recommend ways that you can leverage those predictions by taking action. We will advise our adjustments to your existing tactics and strategies to your decision makers based on the results of the predictive analytics solutions we’ve implemented.
Identifying patterns in customer behavior can be challenging, especially when you have large datasets from a variety of different data sources. Predictive analytics can allow you to detect patterns that can be incredibly useful. For example, you could identify a certain pattern in repeat customers’ behavior before they stop purchasing products from your company. This pattern could help you determine how to prevent customer churn.
There are many types of algorithm-based approaches to predictive analytics. Here are some of the different predictive analytics solutions that our team here at Cyber Group can implement into your data strategy:
Statistical Analysis and Visualization
Statistical analysis and visualization makes it easier to identify patterns and trends. An algorithm will structure statistical data and create visual representations of that data to make it easier to read; for example, by plotting the data in a chart or graph.
Data Mining and Modeling
Data mining is a process in which an algorithm is designed to search large sets of data for consistent patterns and/or relationships. The model built by these patterns and/or relationships is then applied to new data to generate predictions.
Linear regression is one of the most common forms of predictive analytics. It’s a statistical method that identifies the relationship between two or more variables within your dataset. The algorithm does this by examining whether the dependent variable is influenced by any independent variables. For example, comparing the relationship of your product’s price to the number of its sales.
Logistic regression is used to predict an outcome when a dependent variable has more than one discrete outcome. This outcome can be in the form of yes/no or true/false, to name a few examples. Instead of attempting to predict the value of a numeric variable given a set of inputs, the output is a probability that the input point belongs to a specific class.
Logistic regression can have many applications. For example, you can use logistic regression to predict whether your company will turn a profit, operate at a loss, or break even based on the characteristics of your operations. Your HR department can also use logistic regression to predict the pattern of absenteeism for your employees based on their characteristics.
Social computing refers to the social aspect of online behavior (as opposed to personal computing, which only involves the individual user). It includes interactions via blogs, social media, instant messaging, open source development, and multiplayer gaming. There’s a massive amount of data available as a result of these interactions and the use of social computing predictive analytics will help you identify patterns and trends among your audience’s social habits and behaviors.
Predictive analytics requires a lot of compute power due to the fact that the process involves matching current datasets against historical patterns in order to generate predictions about the future. Most companies don’t have the servers on site or the storage capacity to handle such an intense process, which is why cloud-based predictive analytics will function much more effectively. We can help you migrate to the cloud if needed, ensuring that your predictive analytics capabilities won’t be limited.
Machine learning is considered an extension of traditional predictive analytics. The main difference is that machine learning algorithms use cognitive learning techniques to make predictions without input from a user. There are three main types of machine learning algorithms that we can help implement:
Supervised learning – A type of machine learning in which the model is trained using a set of labeled data. Supervised learning algorithms can help determine patterns of behavior to predict customer churn.
Unsupervised learning – A type of machine learning in which a dataset is provided without instructions, requiring the algorithm to analyze the data and identify useful information on its own. Unsupervised learning algorithms are often used to identify fraudulent purchases.
Reinforcement learning – A type of machine learning in which the model is trained to make decisions and predictions based on trial and error. Reinforcement learning algorithms are often employed for predictive maintenance.
Deep learning is a type of machine learning algorithm used for much larger datasets. Unlike machine learning algorithms, deep learning algorithms are also capable of identifying whether the predictions they’ve made are accurate or not by using artificial neural networks, which allow deep learning algorithms to continue learning.
We Help Your Business Capitalize On Future Trends
Identifying patterns in your data can help you identify potential future trends. Your company will be able to position itself to take advantage of those future trends and give it a competitive edge.
Gain Insights For Operational Efficiency
While our predictive analytics solutions can greatly benefit your marketing and sales efforts, they aren’t limited to just these facets of your business. The use of predictive analytics can also improve your operational efficiency. For example, the data that you have on the purchase, maintenance, repair, and replacement costs of your equipment can help you predict how long your equipment will last and how often it will require maintenance or repairs. This information can help you predict equipment failure more effectively, and allow you to plan and schedule more cost-effective maintenance.
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