Predictive analytics has become the main focus for cloud computing due to the cloud’s increased computing power. Companies and organizations of all sizes and industries are looking to the data they are collecting to see if they can use it to make predictions that will help them be more efficient, effective, customer-focused, and ultimately more profitable. The rise of Big Data or data in different formats and in huge quantities has sharpened this focus on predictive analytics. New varieties of data create new analytic opportunities, while the increases in volume and velocity create new challenges.

Businesses and organizations need to know and understand how to use predictive analytics and the cloud in combination. Enterprises want to know what opportunities are available with predictive analytics in the cloud, what trends exist, and what impact Big Data has on the choices they make.

Predictive Analytics in the Cloud

Predictive Analytics is a form of shorthand to develop mathematical models and algorithms capable of making predictions by applying various mathematical techniques to historical data. The models created can demonstrate patterns of association, such as clustering in the data to assess the probability that something is true or statistically significant.

Predictive analytic models are developed and used to predict four basic elements: risk, fraud, opportunity, and demand:

• What is the risk level of this deal?
• What is the likelihood that this claim is fraudulent?
• How can we maximize customer profitability?
• What will the future demand look like for this product or service?

Many different techniques can be applied to a wide range of data. The data can belong to an organization or come from external sources. There are increasing structured and unstructured data that are currently available for analysis. The goal of data analysis is to gain actionable insights for a business or organization. The ability to use predictive analytics to generate new insights to improve decision-making quality creates great value for companies and organizations in every industry, regardless of their size.

Enterprises cite customer engagement as the most dominant use for predictive analytics. When asked which areas had been most positively impacted by predictive analytics, most areas related to customers. The most positive outcomes were identified as customer satisfaction, profitability, retention, and management. This focus on customers was also specifically around improved customer satisfaction rather than around marketing or selling to customers. While the use of predictive analytics in marketing and cross-sell/up-sell is very important, the clear message is that customer management and engagement can be improved using predictive analytics too.

There was a very wide range of specific areas cited for using predictive analytics to improve business results.

In digital marketing, use cases for predictive analytics include:

• Predicting which advertising will be most effective
• Predicting which marketing campaigns, channels, touches, behaviors, and demographics are delivering positive business outcomes
• Predicting how customers will respond to specific segments, tests, or personalization

Predicting the probability that a user will click on an ad, download a whitepaper, respond to an email, or respond to an offer

• Predict which leads are the most likely to convert
• Predict which customers are the most likely to buy one or more products for a cross-sell or upsell.
• Predict the number of purchases or revenue that will occur in the future from a specific customer or customer
• Identify and predict which customers will provide a high/medium/low lifetime value.
• Predict which customers are the most likely to stop purchasing products or services (attrition rates)

Other business activities that utilize predictive analytics include:

• Health plan resource utilization
• Fraud detection
• Customer buying patterns
• Collections strategies
• Planning and scheduling optimization
• Reducing Operational Risk
• Optimization of care
• Propensity to buy across product categories
• Predicting and understanding the customer journey
• Allocating budgets effectively

Experts in the predictive analytics arena remind us that algorithms search for patterns among values and not the values themselves. Furthermore, they do not believe insufficient data will hold back the expansion of predictive analytics.

More and more user-friendly SaaS platforms are emerging. For most businesses, the ability to create models and predictions from historical data still requires dedicated employees to navigate often complex software solutions or outsourcing that work to a third-party vendor. Even so, the benefits of predictive analytics make those investments in staff or a third-party vendor worthwhile.

For companies postponing predictive analytics projects, it is important to continue filling your data lake so that when you are ready to implement big data analytics, you have enough data to get started.