Predictive Modeling 101: A Beginner’s Guide to Building Accurate Models
The Essentials
Businesses can benefit from using predictive modeling to discover trends and improve their decision-making. However, one of the biggest challenges with predictive analytics is learning how to develop accurate models.
This article provides a step-by-step guideline for developing predictive analytics models and explains the benefits of implementing predictive modeling in your company.
Definition
Predictive analytics is a process that relies on past and present data to forecast future outcomes. Usually, this will require incorporating mathematical and statistical models and automation via machine learning and artificial intelligence.
Types of Data Analytics
Many people find it hard to differentiate between predictive and other types of data analytics. Data analytics is an umbrella term that has several applications depending on a company’s specific needs. There are four widely known types of data analytics, each with unique benefits.
Data Analytics Types
Descriptive Analytics
The most common form of data analytics practiced by companies. Aims to explore a data set and understand what it means without drawing further conclusions, answering the question “What happened?” Examples include a retail company summarizing revenue per quarter or the percentage of sales from different item categories. Results are usually presented in reports, easy-to-read charts, and dashboards.
Diagnostic Analytics
Diagnostic analytics goes a step further than explaining an event. It seeks to use data to discover the why behind an outcome. Although this process may begin with descriptive analytics, the goal is to identify the driving factors and possible correlations in datasets. For example, a bicycle manufacturer may perform diagnostic analytics to determine if there is a relationship between seasonality and bicycle sales. Diagnostic analytics heavily relies on the data professional’s ability to accurately interpret the results of their analytics and the reliability of the information used. This type of analytics often requires data collection from numerous third-party sources to increase the sample size and ensure adequate population representation.
Prescriptive Analytics
Prescriptive analytics is of utmost importance to business stakeholders because it sits at the core of the decision-making process. In prescriptive analytics, the focus is on what to do next. Companies that engage in this analytics seek to identify the best intervention measure. For example, a marketing company can compare responses to different ad durations and recommend an ideal duration for optimal user conversion. Proposing suggestions via data analytics can be done manually or using machine learning models. Furthermore, with the advent of advanced technologies for sorting big data, organizations can quickly process large volumes of information and make well-thought-out decisions.
Predictive Analytics
As explained earlier, predictive analytics employs advanced mathematical and statistical concepts to estimate the probability of an outcome. Like prescriptive analytics, prediction often requires creating or working with existing machine learning models. Companies that practice this type of analytics can stay ahead of the curve by anticipating customer needs and identifying new products for different markets. Many industries use predictive analytics, including healthcare, financial services, retail, and manufacturing. Finance companies, for instance, can use predictive models to evaluate customer’s credit risk and identify which clients are likely to default on their loans.
Steps to Build a Predictive Analytics Framework
Building a predictive analytics framework from scratch is a meticulous process. Failure to define and follow adequate steps at any stage of your model’s development can affect the accuracy of the results obtained from the data analytics exercise and lead to poor decision-making.
Here are five essential steps to follow when developing your predictive analytics framework or model.
Define the Problem
Building a predictive analytics model begins with defining the business problem. Defining the business problem involves deciding what you want to predict and how the insights from your analysis will be used. A clearly outlined problem statement can provide direction on the nature of the data to collect, the parameters to analyze, and the type of output to expect.
Gather and Organize the Data
After the business problem has been outlined, the next step is to collect relevant data for analysis. Usually, predictive models require lots of data, and larger datasets often translate into more accurate models. Therefore, you must identify potential sources of reliable information where you’ll extract data.
Due to the advancement of computational technology, data can be obtained from multiple sources, including machine sensors, weblogs, or call center notes. Third-party data can also be obtained from websites via web scraping.
Extracted data must be organized and stored in a database using JSON, CSV, AVRO, or any other desired format. This phase typically requires a professional with database management proficiency who also possesses some domain expertise and understands the business problem.
Clean and Prepare Data
Data cleaning and preparation is the most time-consuming step in designing predictive analytics frameworks. However, it plays a significant role in the subsequent performance and accuracy of your model.
Data retrieved from multiple sources often has many discrepancies, such as outliers, missing values, and wrongly formatted data points. After extracting relevant data and storing it in a database, a data professional must carefully examine the entire dataset to identify data gaps, correct any errors, and manage entries that can negatively affect the reliability of the generated insights.
Develop Predictive Models
Once data is cleaned and ready for analysis, data experts start designing their predictive algorithms. Many techniques, including decision trees, neural networks, classification models, and regression models, can be applied to a predictive model. The specific method used will depend on the business problem and the nature of the collected data.
Test and Implement the Results
The final step in building a predictive analytics framework involves fine-tuning the predictive model to improve accuracy. Refining a framework’s performance requires constant testing with already available data until it reaches a satisfactory level of reliability. When this is achieved, the data expert can deploy the model for the current business problem.
Benefits of Predictive Analytics
Businesses have much to gain by hiring a predictive analytics provider or incorporating this technology into their data analytics process. Customer interests and behavioral patterns constantly change over a period. Fortunately, organizations can employ mathematical techniques to anticipate these changes before they occur and pivot where necessary.
In addition, predictive models can help companies discover loopholes in their operations and plug these gaps to improve their services. Here are some benefits of engaging a predictive analytics company to aid your decision-making process.
Security Enhancement
Regardless of your industry and the nature of your product or service, preserving company and user data is paramount in building customer trust. Predictive analytics can benefit businesses extensively by beefing up their security based on customer behavior analytics.
By studying previous data from security breaches, organizations can identify behavior patterns that precede security risks and devise strategies to mitigate these challenges before they occur.
Risk Reduction
Organizations can use predictive analytics to pinpoint potential risk factors. For example, spot users who are likely to switch to another product or service or clients who will likely default on their loan payments.
Armed with information from predictive algorithms, business owners and institutions can take necessary steps to manage risks before they escalate. Many businesses have reported reduced churn rates and higher turnover by incorporating data-driven risk assessment and management procedures.
Operational Efficiency
Predictive analytics simplifies previously complex operational tasks such as inventory management. Rather than rely on guesswork, companies can apply predictive models to anticipate accurately when their stocks are expected to reduce and place orders early. This reduces instances of over or under-stocking goods and reduces logistics challenges.
Similarly, predictive analytics plays a significant role in improving operational workflows by reducing machine breakdowns. Organizations can hire data science experts to build models to identify the best times for maintenance activities. Consequently, this will help to save costs and reduce supply chain disruptions due to equipment failure.
Improved Decision-Making
Strategic decision-making requires consideration of short and long-term outcomes. Predictive analytics provides stakeholders with a window into future market trends. These insights can shape their decisions and ensure well-thought-out products or services.
Because most companies have limited financial and human resources, they must depend on data-driven insights to identify the most effective use of these assets. Predictive analytics companies allow executives to have broader perspectives on business problems and devise interventions and policies based on their industry’s current and future trajectory.
Fraud Detection
Although there are other manual means of fraud detection, most of these methods are cumbersome and may easily miss some cases or address cases after the fact. Predictive analytics, however, provides a more effective alternative.
Companies can build predictive models to identify potential fraud cases. Predictive analysis can use current and previous user behavioral pattern data to correlate specific actions and fraudulent activity. This insight can help businesses flag and keep out swindlers in real-time.
Optimized Sales and Marketing Strategies
Applying predictive analytics is pivotal to maximizing the outcome of marketing campaigns. With predictive modeling, companies can track customer data and evaluate the impact of previous campaigns to determine which approach is more effective. By identifying the most effective strategy, these businesses can allocate resources effectively and reap high conversion rates and increased revenues.
Furthermore, predictive models can help define personalized marketing strategies and identify unique sales opportunities for different age groups, locations, and income levels. This insight can help to ensure that each customer segment’s needs and preferences are met.
Predictive analytics can also optimize sales and marketing strategies by helping businesses categorize customers based on their average order value, purchase frequency, behavior, and other parameters. The outcome of this analysis will enable businesses to build lasting client relationships by targeting individuals with a higher customer lifetime value.
Satisfied Customer Needs and Preferences
Businesses that remain relevant regardless of the competition and other factors must be able to anticipate and fulfill customer expectations. Unfortunately, customer interests, habits, and income are never static. Companies must continue to study their clients and modify their offerings based on these changing factors.
Organizations are aware of the need for flexibility. However, this isn’t easy to achieve without relying on predictive models. Changes to a product or service can be risky and lead to revenue loss if not executed properly. Predictive analytics allows companies to forecast client needs relatively accurately based on past actions. Armed with this information, they can continue to take the initiative to keep users satisfied and generate organic growth.
Infomineo – Optimizing Business Processes through Predictive Modeling
At Infomineo, our approach revolves around harnessing advanced machine learning and algorithmic techniques to analyze extensive datasets and accurately predict market trends and customer behavior.
Our tailored predictive analytics solutions cater to a diverse array of industries such as financial services, retail, hospitality, healthcare, and more. By merging predictive insights with industry expertise and business acumen, we empower our clients to optimize operations, enhance efficiency, and proactively mitigate potential challenges, thereby safeguarding against losses.
Acting as a collaborative partner, we seamlessly integrate with our clients’ teams using an agile approach and ensuring a customized and smooth incorporation of new data analytics solutions into existing systems. Curious about our customized predictive analytics solutions?
FAQs (Frequently Asked Questions)
How do I conduct predictive modeling?
Conducting an efficient predictive analytics project requires four steps – defining the business problem, collecting reliable data via web scraping, developing and training predictive models, and deploying models to generate results.
What are the challenges to building accurate predictive models?
Some of the challenges to building accurate predictive models include data availability, reliability, and bias. In other words, there may not be sufficient data from reliable sources to develop a precise model, or the output could be skewed by the model developer’s bias.
Are predictive models always right?
Although a well-designed predictive model is often reliable, it might not be able to guarantee 100% accuracy. This is because real-life outcomes are sometimes influenced by factors that may not be accounted for during the development of the model.
What type of database can I use to store my data when building a predictive model?
Data for predictive modeling can be stored in a data warehouse, JSON database, relational database, or NoSQL database.
How can I extract data to build my model?
There are many methods for data extraction when building a predictive model, including database querying, web scraping, document parsing, and email extraction.
To Wrap Up
Although there are various types of analytics, predictive analytics remains the bedrock of long-term planning across all industries. Building a reliable, high-performance predictive framework requires careful consideration of the business problem, data collection, organization, and preparation. It also demands repeated model testing to generate accurate outputs.
While this process may appear demanding, it has several benefits for companies, including improved security, risk reduction, better operational efficiency and decision-making, and optimized sales and marketing strategies.
Twenty-first-century businesses cannot avoid implementing preventive analytics if they wish to stay ahead of trends, build brand loyalty, and grow their reputation. However, this involves following multiple steps to develop models that produce trustworthy insights for decision-making.