Organizations have access to a staggering amount of historical data, making it a powerful tool. Correctly harnessed, this data can enhance organizational performance, improve your decision-making process, and impact strategic planning.
The Global State of Enterprise Analytics report by business intelligence company MicroStrategy reveals that 56 percent of business owners, startup founders, and entrepreneurs say that an informed data-driven process is responsible for “faster, more effective decision-making” at their companies.
Asides from the significant benefit of a faster and more effective decision-making process, other passive benefits of working with data in business are:
Can your company experience these benefits by harnessing the power of data? You can drive impactful decision-making by concluding, predicting outcomes, and using actionable insights from data.
Four key types of data analytics could help scale your business’ thought and decision-making process. These include;
The most basic form of analytics, descriptive analytics, is the cornerstone for all other forms.
It enables you to extract trends from unprocessed data and quickly summarize what has occurred or is happening.
Descriptive analytics answers the question, “What happened?”
This type of analysis extends the data analysis process by comparing concurrent trends or movements, identifying correlations between multiple variables, and, when appropriate, identifying causal relationships.
Diagnostic analytics addresses the next logical question, “Why did this happen?”
You can accurately predict what the future may hold for your company by examining transactional databases along with current industry trends with the use of predictive analytics models.
Predictive analytics is used to predict future trends or events and answer the question, “What might happen in the future?”
Finally, prescriptive analytics answers the question, “What should we do next?”
Prescriptive analytics takes into account all possible factors in a scenario and suggests actionable takeaways. This type of analytics can be especially useful when making data-driven decisions.
All these types of data analytics are essential when trying to improve business processes and strategic planning. However, this article focuses on predictive analytics.
If a 5-year-old asked, “what is predictive analysis?” Here’s how I’d respond; a predictive analytics technique predicts future trends or events through and answers the question, “What might be coming, and when is it coming?”
Making informed predictions for your company requires analyzing historical data in conjunction with industry trends.
The fact that video game console sales have spiked every October, November, and early December for the past decade gives you comprehensive data to predict future events of the same trends.
This is a reasonable prediction based on upward trends in the video game industry, right?
You can formulate strategies for your organization based on future predictions and likely scenarios.
Prescriptive analytics is the next frontier for organizations that have successfully implemented predictive analytics. An analysis that predicts what will happen next is called predictive analytics; an analysis that prescribes how to respond to that prediction is called prescriptive analytics.
Predictive analytics aims to suggest actions based on the results of predictive models for optimal outcomes.
For decision-making, predictive analytics uses optimization and rules-based techniques.
For example, using predictive analytics to forecast the load on the electric grid over the next 24 hours is an example of predictive analytics, while using prescriptive analytics to determine how to operate power plants based on this forecast. check out what is sales analytics and how it’s working.
In predictive analytics, current data is analyzed using many methods, including data mining, statistics, modeling, machine learning, and artificial intelligence.
By combining management, information technology, and modeling business processes, the predictive analysis uses data mining, predictive modeling, and analytics to make predictions about future events. In order to identify future risks and opportunities, business people need to identify patterns in historical and transactional data points.
Scores and weights can be used by the predictive model, to assign to a set of conditions to determine the risk associated with those conditions. By successfully applying the predictive model, businesses can effectively interpret the big data collected r their benefit.
The data mining and text analytics, along with statistics, allows the business users to create predictive intelligence by uncovering patterns and relationships in both structured and unstructured data.
The data that can be used readily for analysis is structured data, such as age, gender, marital status, income, and sales.
Unstructured data are textual data in call center notes, social media content, or another type of open text which need to be extracted from the text, along with the sentiment, and then used in the model building process.
The predictive model allows organizations to become proactive, forward-looking, anticipating outcomes and behaviors based on the data and not on a hunch or assumptions.
Prescriptive analytics suggests actions to benefit from the prediction and provides decision options for the predictions and their implications.
Any business professional making decisions should be familiar with the fundamentals of predictive analytics.
Having access to data has become easier than ever. However, data reveals significant opportunities and red flags. If you formulate strategies and make decisions without considering them, you could miss a lot, and your business could lose precious benefits.
Professionals who can benefit from predictive analytics skills include:
Marketing professionals must use customer data, industry trends, and performance data from past campaigns to develop marketing strategies and decide on future risk behavior.
These professionals could leverage market, industry, and user data to improve a company’s products and predict future outcomes.
Financial forecasters use historical performance data and industry trends to make predictions about future events in the books and can even predict credit risk.
They combine predictive insights into employee opinions, motivations, and behaviors with industry trends and data to make meaningful changes within their organizations.
Lawton’s article, “5-Step Predictive Analytics Process Cycle,” offers a detailed description of the critical steps in deploying predictive analytics and the people skills required. Here is a brief summary of each step:
Analyze the problem you are trying to solve for your business. Are you managing inventory? Do you want to reduce fraud? Are you predicting sales? A wise place to start is to generate questions about the problem and rank them in order of importance. Measuring success can be easier with the help of a statistician at this stage. It is the responsibility of a business user or subject matter expert to initiate this process.
It’s a wise idea to involve a statistician, data analyst, or both in this process. To solve a problem and accomplish a goal, you need to identify data that supports your assumptions. Make sure the data is relevant, appropriate, clean, and of good quality.
It is critical to work with a data scientist to determine which predictive models are best suited to solving the problem. The key is to find a balance between performance, accuracy, and other requirements, such as explainability, by experimenting with different features, algorithms, and processes.
Data scientists determine how to retrieve, clean, and transform raw data required for the deployment of the model at scale and, above all, to make a meaningful difference — for example, by integrating a new scoring machine learning algorithms into sales teams’ workflow. Once the data scientist approves the model, a data engineer retrieves, cleans, and transforms the raw data.
Over time, the model’s performance can fluctuate because of shifting customer preferences, changes in the business climate, or unforeseeable events, such as pandemics. Business users and data scientists must work together in this step to determine thresholds for updating models. Check out some sales-related guides, B2B sales, tech sales, and SaaS sales.
Customer relationship management (CRM) objectives include marketing campaigns, sales, and customer service, which can be achieved using predictive analytics tools. Moreover, analytical customer relationship management can be used throughout the entire customer lifecycle, from acquisition to relationship growth, retention, and reconnection.
In health care, prediction analysis can help medical doctors and surgeons identify risk factors and patients who are at risk of developing cancer, the flu, and other infections. In this way, clinical decision support systems include predictive analytics to support medical decision-making.
Utilizing predictive analytics, you can identify the most effective collection agencies, contact strategies, and legal actions to increase recovery and reduce collection costs.
Organizations that offer multiple products can use predictive analytics to analyze customers’ spending, usage, and other behaviors, which can be used to cross-sell or sell additional products to current customers.
It is possible to detect inaccurate credit applications, fraudulent transactions made both offline and online, and false insurance claims with the help of predictive analytics applications.
To produce accurate forecasts, predictive analytics applications employ a capital asset pricing model and probabilistic risk assessment to determine the most profitable portfolio to maximize return.
Additionally, predictive analytics can assist in identifying the most appropriate combination of product versions, marketing materials, communication channels, and timing for a given target audience.
Underwriting quantities can be made easier with predictive analytics and predictive modeling techniques, which can predict values and the likelihood of illness, default, and bankruptcy. Additionally, predictive analytics can streamline the customer acquisition process by predicting future risk behavior based on application-level data. Check out the guide for what are the marketing metrics and how to calculate it.
The benefits of predictive technology are theoretically limitless because of its many potential applications. Using predictive analytics in commercial settings can be beneficial. Here’s how;
A predictive model can learn the behavioral patterns that precede customer churn and flag them as they occur based on historical and transactional data. As a result, a company can retain the customer by acting promptly. Find the guide about churn rate.
Businesses can provide personalized experiences to customers by using predictive technology to learn what they like and anticipate what they may desire next. A better understanding of typical consumer behavior and preferences can also help businesses better plan and design experiences for their customers.
In addition to this, predictive analysis can be used to improve customer service. For example, free text (e.g., responses to an open field on a survey or as part of a customer review) is information-rich but more challenging to analyze than numbers and rating scales because it differs in form and structure.
With predictive technology, text data can be processed at scale and grouped into sentiment or idea clusters. Businesses can use this to create a comprehensive analysis that anyone can understand at a glance by generalizing these.
The strength of predictive analytics is its ability to recognize patterns, which means it can also spot when something is out of place. Businesses can use predictive technology to find out-of-the-ordinary behavior patterns that might be signs of fraud. Predictive technology can help businesses detect unusual patterns of behavior that might indicate fraud.
Consider a scenario in which a US-based bank customer seems to make purchases quickly on several other continents. In that situation, the business can ensure the account’s security.
Organizations of all sizes can gain real advantages by integrating predictive data analytics into daily operations and using it to predict future events.
Planning ahead – The ability to use predictive analytics in business to help you see the future and plan accordingly across various domains like stock, staffing and customer behavior may be the most apparent reason to do so. This is perhaps the most obvious benefit of using predictive analytics in business.
Predictive technologies can tell you what is likely to happen in the future to plan ahead and change how you allocate your resources.
Let’s say you’re a fashion retailer, and an advanced analytics model tells you that natural materials are about to rise in popularity. So you can start collaborating with those who create these types of clothing and reduce the number of synthetic materials you use.
Time-saving and efficiency – Businesses can automate much of the routine, low-risk decision-making process using predictive technologies, freeing up humans for more strategic, high-risk tasks.
Predictive analytics can often determine whether a straightforward insurance claim can be paid out or generate a credit score. In addition, by using predictive analytics, healthcare providers can estimate success rates for new treatments, identify patients who would benefit, and predict the results of trials based on past outcomes.
Predicting and preventing risks – The predictive analytics model uses trends and patterns from your operational past to identify potential threats, what causes them, and how likely they are to occur. In advance, marketing professionals can use this information to develop risk or crisis management processes.
For example, as a food retailer, you depend on a steady supply of inventory to meet customer demands. Predictive info based on Big Data can help track weather and sea conditions affecting shipping and distribution. This can help you adjust your stock orders dynamically and prepare for a shortage.
We are in a sweet spot for predictive analytics at the moment. The technology is affordable, the know-how is available, and there are enough data available to make predictions that are valuable for the business world, government, and other sectors such as education and healthcare.
While predictive analytics capability is now more accessible than it used to be, companies can still gain a competitive advantage by mastering it.
Most businesses realize the value of applying predictive analytics for themselves and their customers, but not all are using it yet. So how can you start to use predictive analytics in your business?
You can solve this problem quickly: integrate predictive analytics applications into your technology stack. In most experience management software, predictive analytics capabilities are built-in – either as a user-directed tool or as an automatic one that does the heavy lifting automatically.
These software suites use machine learning to model future events and suggest actions based on the results. Thus, continuous data collection assists in self-improvement. As a bonus, you can use these action points to prevent problems from occurring in the first place.
Simply describe the concept as the prediction and analysis branch of advanced data analysis. It is an application combining data from historical and statistical models and machine learning methods. Several organizations employ predictive analytics tools to analyze data.
A predictive analytics model might identify the relationships in sensor data. The temperatures measured on a computer can correlate to how much time the computer runs at high power, resulting in a loss in power consumption for the machines. Data science and statistical algorithms can predict the future using measuring sensors and machine learning techniques.
All four categories complete the puzzle in data analytics: descriptive, diagnostic, predictive and prescriptive data analysis.
Predictive analytics can be applied in any industry or/and sector. A statistical technique like predictive analysis has applications in industries like insurance, banking, marketing, telecommunications, shopping, and lots more.
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