As business interactions around the world become increasingly digitized, massive amounts of data are created and can be evaluated through predictive analytics tools to give users a better understanding of market dynamics and underlying trends. 
 
Predictive analytics is already one of the most widely adopted intelligent automation technologies in the world, with more than 80% of major enterprises deploying smart analytics, according to Statista. 
 

What Is Predictive Analytics?

 
Predictive analytics encompasses a variety of statistical techniques from data mining, predictive modeling, and machine learning that analyze current and historical facts to make predictions about future events.
 
In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities.  solutions became increasingly important as companies realized the importance of using the data they already collected to generate insights, but also to detect patterns that can lead to determining the best course of action to reach their business goals. 
 
The technology is applicable in a wide range of industries, from fraud detection to medical diagnosis. Therefore, it is no surprise that predictive models rank as one of the top big data technology trends around the world.
 
The sudden increase in data traffic and data dependence across all industries, along with the adoption of AI, machine learning, and big data is expected to further drive the demand for predictive analytics solutions. 
 
 

Predictive Analytics Market Drivers

 
The global predictive analytics market size is expected to be valued at $23.9 billion by 2025, registering a CAGR of 23.2%, according to a study conducted by Grand View Research. 
 
According to market research, the customer analytics market is expected to gain the most traction over the next few years, especially in the context of an accelerated digital transformation. 
 
Demand for training and consulting services is also anticipated to increase, as well as demand for cloud deployment as a solution to reduce cost and offer scalability for businesses. 
 
The SMEs (small and medium enterprises) are expected to register a high growth rate over the next 5 years. These companies are fast to adopt new technologies, since their size offers them the possibility to quickly change solutions, train employees, and invest in infrastructure. 
 
Historically, data shows these companies are also more susceptible to competition and market shifts, so reducing operational costs and enhancing operational performance is always on the top of the manager’s agenda. 
 
Retail and e-commerce are anticipated to register a remarkable growth due to increased internet usage digitalization, which enables online shopping. 
 
Predictive analytics solutions could help businesses map the various stages of the buyer journey, which would enable the adoption of suitable campaigns and lead to higher sales and customer retention. This is especially important during this COVID-19 period. 
 
Other industries where predictive analytics is experiencing rapid growth are banking, financial services, and insurance, healthcare, government, sports, transportation and travel, IT, energy, utilities, and entertainment. 
 

Source: Predictive analytics revenues/market size worldwide, from 2016 to 2022. Data from Statista.
 
 

Why Companies Invest in Predictive Analytics Solutions

 
Predictive analytics is not a novelty. The technology first emerged years ago, but the widespread adoption was lacking proper market conditions. 
 
Now, with software and technology becoming more affordable and accessible, this is a viable resource that can be utilized across all economic sectors.
 
For example, in the manufacturing industry, these solutions can be used for equipment maintenance management, workforce management, and cross-selling and up-selling. The IT and telecom industry can adopt these solutions in sales, marketing, and CRM by implementing churn and pricing optimization, according to a market study
 
 

Predictive Analytics Use Cases During COVID-19

Predictive analytics and forecasting, along with machine learning, are skyrocketing during the COVID-19 pandemic. 
 

Healthcare

  • Solving problems with medical images and their use for biomedical research and clinical care through computational and mathematical solutions. to discover optimal parameters for tasks, such as lung texture classification. With this technology, doctors and analysts create reliable patterns of classification, which can help diagnose patients before the first symptoms.  

 

Finance & Banking

  • Leveraging predictive analytics and machine learning to investigate, assess, and then address financial risks. 

 

Transportation

  • Transitioning from a reactive business model to a proactive one, anticipating trends, optimizing delivery routes, and resource allocations.
  • Anticipating the dynamics of goods shipped and the demand for specific goods based on historical seasonal patterns. 

 

Retail

  • Leveraging data science and predictive analytics solutions to uncover hidden revenue opportunities through factors such as location, competitive prices, income availability, consumer purchase patterns, etc.

 
 

Value of the Predictive Analytics Industry

 
Since the COVID-19 outbreak, consumer behavior started shifting rapidly and significantly altered normal patterns. This mostly impacts business forecasts, such as trends or KPIs forecasts that depend on volatile market conditions. 
 
Predictive analytics and these algorithms at work are not only enhancing the cost-effectiveness of business decisions but also increasing model sustainability by analyzing massive amounts of data. Everything from stock moves, customer behavior, and algorithmic trading are being handled by data scientists
 
Even though several factors and government regulations (that change on a weekly, if not daily cadence) are contributing to economic shifts, data scientists have adapted to this new reality by creating multiple scenarios they run with at the same time. If we look at what government institutions did, they have forecasted three different economic scenarios (mild, moderate, and severe), and designed specific action plans for each. 
 
These models might be changing at a fast rate, with the datasets constantly being updated, but the statistical models offer businesses the chance to adapt to different scenarios and different outcomes. 
 
If you need help with implementing data science solutions for your business, contact us today. Having strong analytics implementations and data science technologies working for your business is essential, and we must adapt in order to survive and thrive.
 

About the author

Sebastian Stan

Sebastian is a journalist and digital strategist with years of experience in the news industry, social media, content creation and management, and digital analytics.



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