Unlocking the Power of Predictive Analytics in Business

A subset of advanced analytics called predictive analytics uses historical data, statistical modelling, data mining, artificial intelligence, and machine learning to predict future events. Although the technology has been around for a while, organisations have recently started using it. Companies discovered benefits in identifying patterns and relationships based on data with the rise of data mining, data analytics, and intelligent software. These patterns are then applied to develop tactical models that reduce risk, reorganise workflow, and increase output. 

Predictive analytics has become more potent since human and AI decision-making is growing more complicated and demanding. This forces businesses to rely more on data-driven, Deep-Learning techniques. By the end of 2032, the predictive analytics industry, estimated by Future Industry Insights Inc., is anticipated to be worth $55.5 billion.

What is Predictive Analytics?

Predictive analytics forecasts future trends using statistical algorithms combined with internal and external data, enabling organisations to optimise inventories, enhance delivery times, boost sales, and ultimately cut operational costs. Future forecasting will be more precise and timely because of the insights gained from these cutting-edge methods when combined with Artificial intelligence (AI). 

The development of Artificial Intelligence, Data Mining, and data analytics has led to the advent of predictive marketing. To determine the probable effects of their actions, businesses use predictive analytical models. AI assists in mapping layers of data patterns that companies may use to anticipate consumers' future behaviour and make tactical decisions in line with it by putting massive volumes of data via Machine Learning algorithms.

Why is Predictive Analytics Important?

Advertisers have had to reconsider predictive marketing as a result of recent shifts in the digital world. It's no longer a cool, futuristic tool but a tactical planning requirement. Discussed below are a number of the driving forces behind the current demand for predictive marketing below. 

• Competitive Advantage: Nearly 84% of people worldwide will own smartphones in 2022, up around 35% from 2016. Many companies are vying for customers' interest and support as smartphone adoption and demand for digital content rise. Marketers are using predictive analytics to study customer events, identify trends, and ultimately estimate future consumer behaviour in order to remain ahead of the competition.
• Changes in Privacy: With the introduction of iOS 14.5 and Apple's AppTrackingTransparency (ATT) framework, marketers are now required to obtain users' permission before delivering personalised adverts. Marketers have had to reconsider mobile attribution because they can no longer access many consumers' Identifiers for Advertisers (IDFA) and Google's Privacy Sandbox for Android is not far behind Apple.
• Advanced Predictive Models: Predictive data analytics have developed as artificial intelligence, machine learning, and data analytics continue to advance in sophistication and accuracy. Because of this, a lot of mobile measurement partners, including Adjust, now provide predictive analytics models to app marketers so they can improve their campaign and budget planning and optimisation.

Benefits of Predictive Analytics

The achievement of KPIs like retention rate, monthly/daily active users, stickiness, and average revenue per user, to name a few, is ultimately what determines a Business's success. Businesses can swiftly boost the number of users and revenue generated by their apps by utilising predictive analytics. 

Some of the key benefits of Predictive Analytics are listed below for your reference. 

• Driving Conversions: Predictive analytics can be used to identify variables that need to be changed in order to increase the conversion rates, such as creatives, channels, and user kinds. For instance, after identifying the channel that has the highest level of engagement, one can determine which creatives will probably result in the channel's highest conversion rate.
• Making Use of Predictive LTV: The lifetime value (LTV) of a user is an estimation of the whole money they will bring in while using the app. Businesses may now forecast a campaign's success days in advance using machine learning and compiled data from predictive marketing, and they can adjust their strategies accordingly.
• Increasing User Engagement: User engagement may be measured by a user's log-in, registration, in-app purchase, session length, or another in-app activity, depending on the application being evaluated. Utilising predictive analytics, one can examine user interaction throughout the user experience to identify chances for optimisation in areas such as onboarding, messaging style, scheduling of communication, predicting customer demands, and more.
• Increasing Upselling and Cross-Selling: Businesses can identify users who are most likely to upgrade their subscription or buy another product using predictive analysis. Then, in order to target these users for cross-selling or upselling, they develop dynamic audience segments.
• Reducing Churn: Predictive app marketing can assist to determine the likelihood that users will become inactive based on their in-app activity frequency, recent activity, and the price of their transactions. With the help of these insights, one can design a solid plan to lower churn and raise their app's retention rate.

Predictive Analytics Models

Discussed below are the five models for predictive analytics that are used in Businesses. Although each predictive analytics model has advantages and disadvantages, one may reuse and modify the algorithms for these models to suit their particular marketing requirements. So that one can improve and develop new models based on their unique app for more accurate forecasts. 

• Classification Model: This model, which makes use of the conventional approach of analysing previous data in order to generate predictions about the future, is one that all businesses are familiar with. The classification model can be used to respond to queries like the ones below. It is typically used to respond to yes-or-no questions.
  • Is this person relevant to your business?
  • Is the user on the verge of unsubscribing?

Now, rather than waiting days or weeks to generate predictions about users, companies could leverage Machine Learning and AI within a classification model.

• Time Series Model: To recognise and comprehend patterns across time, brands can utilise this predictive analytics model. The time series model offers businesses insights into seasonality or cyclical behaviour and can be used to anticipate prospective changes in data. It is frequently used to produce illuminating data visualisations. When it's possible that past trends won't have an impact on future results, marketers frequently utilise time series models. For instance, during the global pandemic's uncertainty, when patterns were wildly out of the ordinary, many companies followed this strategy.
• Cluster Model: A cluster model sorts through data to divide users into groups according to certain traits or properties. Businesses can, for example, set the cluster model algorithms' parameters to previously made purchases, brand interaction, or any other user data that has given consent. When organizations are unsure of how to categorise a large number of new incoming users, cluster models are very helpful because they employ predictive analytics to group data with comparable points.
• Forecast Model: A forecast model, which is an extension of the categorization model, is used to calculate the numerical value of fresh data based on old data. One of the most used predictive analytical models is the forecast model because, unlike the categorization model, it can manage numerous factors simultaneously. Even when there are no numerical values in the historical data, this model type can nonetheless produce them.

Jumping into the bandwagon of Predictive Analytics is essential now, as it is now used by over 80% of major firms. Factors like user base, user engagement, LTV, and eventually the total ROI of a business can all rise by making more accurate projections for the campaigns that an organization makes. Predictive analytics has received the support of a wide range of businesses, according to a 2017 report published by Zion Market Research. By 2022, the global market is anticipated to have grown at a compound annual growth rate (CAGR) of over 21%, reaching $10.95 billion [4]. Building a foundation in analytical abilities can benefit the organisation in the short and long term, whether it's to guide financial decisions, create marketing strategies, or change the course of action.