Survival of the fittest is by no means a misnomer in the present situation. Every business has competitors, and each organization needs to stay in business despite stiff competition for its survival and growth. The uncertainty and changing needs of business metrics have to be studied, and suitable actions need to be taken to achieve the above objective.
Analysis of this scenario is a must and for which sufficient and relevant information is required. Fortunately, the availability of big data has come to the rescue. Analysis of past and current relevant data, can help to plan suitable measures at the right time for achieving the goals. Every business has its own infrastructure and an adequate workforce for all stages like planning, design, production, sales, and marketing, but these need to be adjusted and adequately managed.
Data analytics (a branch of Data Science) is a powerful and sure means to take needed measures from time to time. However, predictive analytics is an important type of data analytics as it depends on many challenging aspects and can help other stages of analytics. Knowledge gained from predictive analytics can guide other analytics sections to give proper inputs for the overall success of a business. Companies can use predictive analytics for businesses to answer questions like what the customer churn is, how to optimize marketing strategies, what do the customers expect and so on.
In this article, we will talk in-depth about predictive analytics, its objectives, techniques, and correct steps needed to achieve vital information the company or business seeks to stay proactive and progressive. However, it is essential to have a strong foundation in Data Science before handling Data Analytics. Try Data Science with Python tutorial to get started with your Data Science journey.
What is Predictive Analytics?
Predictive analytics is a planned and thoughtfully executed activity in any business. It starts with collecting past and current data, a proper study of this data, and then applying statistical or machine learning tools on this data to predict the future needs of any business.
It is essential to plan all business activities, such as management of inventory, workforce, infrastructure, sales, and marketing. This is further critical for the growth of any business, increasing profits, reducing losses, and optimum utilization of resources. It is now rare to find any organization in the world, small or big, that is not practicing predictive analytics at some level since it has become impossible to survive without it in this highly competitive world.
Predictive analytics for retailers can help them manage their inventories better by forecasting possible future demands using historical and real-time data. This way they can make provisions for adequate resources on a timely basis.
How Does Predictive Analytics Work?
The following steps or phases are required for predictive analytics to be successful:
- Deciding the Objective: A company has to first decide its aim for performing this analysis. Let us say whether to increase market share or increase profit, improve an existing product or launch a new one, and similar considerations corresponding to their business. Thus, it applies to all types of industries like manufacturing, trading, agriculture, healthcare, academics, etc. Once the objective is finalized, then further steps are taken.
- Data Collection: This is a crucial step involving collecting past and existing data from relevant sources. This information reveals shortcomings, hurdles, strengths, threats, and opportunities. Hence, this data must be appropriate, correct, and adequate. This is referred to as the descriptive analysis phase.
- Diagnosis Phase: The analysis of correctly accumulated and formatted data involves diagnosis, which aims at applying a cause-and-effect approach, getting all the facts about past records together with current ones so that patterns and trends can be identified later.
- Predictive Phase: Once these essential key factors are known, it is possible to predict what will happen in the future based on the identified issues. But this is tricky and requires applying statistical tools or machine learning models based on proper algorithms and some assumptions related to the marketing scenario. Many tools are developed by reputed data companies to make predictions suitable to the needs of a particular business and its objectives.
- Prescriptive Phase: After getting hints of possible happenings in the future, suitable measures are taken at appropriate locations to achieve the objective thought earlier.
Let us consider all this for an educational institute:
- Objective: A reputed educational institute wants to start a new technology course like ‘medical diagnostics.’
- Data Collection: This will be the descriptive phase in which:
- The institute will estimate the demand for such a course in the current scenario or in the recent past. To achieve this, they can discuss it with reputed hospitals, medical research institutes, perform surveys among public and industries, etc.
- Next, the institute needs to evaluate the existing infrastructure adequacy, like land, buildings, machinery, IT setup, etc.
- Additionally, it is also necessary to evaluate the existing expertise and staff for conducting the coursework and labs for the course of this medical diagnostic.
- It is also essential to estimate the needed finance to run this course in the institute efficiently.
- It is important to understand the government regulations and policies as this is a healthcare-related course.
- Diagnosis Phase: Based on the data collected, the institute can clearly understand what is available at the institute and what they need to procure or set up early if they decide to introduce the course at the institute.
- Predictive Analysis Phase: The institute can use a suitable prediction model like regression to predict the probability of success in setting up the course based on relevant features or factors. This prediction will assist in deciding about further measures to be taken for surety of success with good accuracy.
- Prescriptive Phase: In this phase, the institute can arrange for any needed adjustments, procurement of essentials, etc., like new mandatory equipment, expert staff, etc.
If the outcome of this exercise shows good potential, then the institute can proceed with its objective of setting up the course of the medical diagnostic. If reasonably good accuracy is not seen in this exercise, the institute can reconsider its model to improve accuracy.
Why is Predictive Analytics Important for Business?
Every business strives for sustainable existence and continuous growth. This is very important in the stiff competition and changing scenario in the marketing sector. If a particular business does not keep track of what is happening, what has happened, and what is likely to occur in the immediate future, it may face various problems like customer churning, loss of revenue, wrong investment, etc.
If a company takes the help of predictive analytics, it is constantly aware of what steps are to be taken. These steps will help to tackle any uncertainty or special requirements the business needs to maintain its status and growth. For example, if the predictive analytics hints at increasing demand in the next quarter for inverters due to likely load shedding in a particular state, the business can procure additional inventory.
They can also plan for more production hours in advance and try to push their product in that state suitably. Hence, there can be no denying that predictive analytics is necessary for any business. If you want to dive deeper into Data Science and want to know how much time it takes to get certified as a data scientist, please refer Data Science course duration.
Types of Predictive Analytical Models for Business
The six most commonly used predictive analytics models are as below.
1. Classification Model
The classification model is the relatively simplest of the various types of predictive analytics models in consideration. It allocates data into categories based on what it has learned from historical data.
Based on available historical data, classification models are best suited to answer yes-or-no questions. They provide comprehensive guidelines comparing various features in making a final decision. For example, it can be used to answer a few questions like:
- A perfume manufacturer wants to launch a new product: will it see a good demand?
- A bank sanctioning a housing loan wants to know whether this particular person will repay the loan promptly?
- A sports club wants to hire a reputed player from another club at a higher price. Will this deal prove successful?
The scope of possibilities with the classification model and the simplicity by which it can be trained and retrained with new data clearly indicates that it can be used for predictive analytics in many different industries.
2. Regression Model
In a regression model, based on available numerical data and the current scenario, factors can be identified that are likely to influence the outcome with a strong positive probability.
For example;
- Based on the number of bedrooms, floor level, square ft. area, nearby amenities, etc., a model can be built to predict the price of a house in a particular area with very good accuracy.
- A shopping mall wants to know how many products and quantities will likely be sold in the first week of every month? The main factors are salary week and other considerations related to the level of people, location of the mall, etc.
- Along similar lines, a nursing home may need to know; the number of patients likely to visit in typical months based on season severity, etc.
- The government may want to arrange for food supplies in public distribution systems, such as how many tons of rice are required.
- A general hospital wants to know how many beds may be required in the hard months of winter.
Many such examples can be cited.
3. Clustering Model
The clustering model segregates or divides data into separate, densely populated groups based on similar features or characteristics for making a particular decision.
For example;
- If a women's fashionwear brand wants to conduct focused marketing campaigns for its clients, they cannot consider checking purchase history data of each individual to determine a specific strategy. They can easily and quickly segment their customers into similar groups based on common but different characteristics evident from the data. Then they can come up with proper strategies for each group, favoring a particular brand by adopting the clustering model.
- Other use cases of this clustering model can be cited as grouping students based on their IQ and interest in a particular career, say science, research, engineering, medical, or marketing.
4. Forecast Model
This is one of the most used predictive analytics models. This model attempts to estimate the numerical value of new data based on prior data. This model may be used wherever there is historical numerical data.
For example;
- A travel agency can estimate the possible number of tourists likely to visit a specific famous hill station based on the records of a few previous years.
- A prominent temple administration wants to know the number of pilgrims likely to visit in the summer vacation months based on the interpretation of earlier year's records.
- Many such examples, like the sale of cars, two-wheelers, etc., are a good fit for this model.
5. Outliers Model
The outliers model focuses on typical values in a dataset that are anomalous in nature, meaning that they are absolutely mismatched or far away from most of the other values. For example, in a data set of fiction books, a comic book is an anomaly that can be easily identified. This model is very important in the finance sector to detect fraudulent transactions or fictitious cases for money transfers, loans, and insurance claims.
6. Time Series Model
The time series model requires an adequate sequence of the earlier recorded data points, using time as an input parameter for a specific period. It can use an entire last year's data to develop a numerical metrics model and, based on it, can predict the values for the next month or few weeks. Many cases where this model is applicable can be:
- If rainfall from July to August in a particular city like Mumbai for the last three years is studied, then can it be predicted what it will be this year?
- The amount of air traffic expected during the important festival season.
This model will now not only focus on average values but may also consider past and current data available on various issues. These can be seasonal fluctuations, newly built hotels or popular tourist spots in that city or location, bonuses or allowances, or increment declaration as a multi-variant analysis mode.
However, as any trend is not static or linear, the time series model can also consider exponential growth and adjust the model to suit the company's trend. It can forecast many projects or multiple locations in the country or world at the same time.
Best Practices for Better Predictive Modelling Results for Business
Here are some best practices companies tend to follow to extract more from data using predictive analytics:
- Identify and Collect from Best Data Sources: This indicates that companies need to identify high-quality data sources to feed into their predictive analytics interface while including any new data sources that become available from time to time.
- Building the Right Team: Even though a predictive analytics tool sounds like a solution, it is also critical to identify the available staff expertise and the coordination among them. Rather than focusing on the analytics provider, companies need to focus on building the right team involving BI and data analysts, data engineers, machine learning engineers, business analysts, etc.
- Making Predictions Visually Clear: Companies need to invest some time in carefully considering how they can integrate the forecasts visually so that they are effective for specific users and objectives.
- Selecting a Model for Prediction and Setting Acceptable Accuracy: It might be a good idea to set limits for the predictive analytics accuracy provided by the model. Companies can aim for higher accuracy. Sometimes, when the model has slightly lower accuracy, it can still be used as a general guideline for decision-making.
- Continuously Monitor and Evaluate Data: As data changes are dynamic in nature, suitable modifications or alterations must be made in the model selected for predictive analytics.
- Flexibility in the Approach: Apart from the above, running different AI/ML workloads on the cloud provides the flexibility required for a cost-effective, scalable, and secure AI solution.
Pros and Cons of Predictive Analytics for Business
Pros
- Makes an Organization Future-ready: A company can plan for different adjustments needed to cope with the likely dynamic changes in the market.
- Saves Money: Due to logical future information available beforehand, it is possible to avoid unnecessary investments and losses.
- Reliable Decision-making: Due to numerous ready-made accurate models with advanced technical algorithms suitable to company needs, there is little scope for faltering.
- Customer Retention and Increased Share of the Market: Due to proper and timely steps taken befitting to changing scenarios, chances of dissatisfaction amongst customers on any account like late deliveries or improper product or its packaging is prevented. This further increases the retention of existing customers, and word-of-mouth publicity may attract new customers.
Cons
- Failure of Correct Analytics: Improper and inadequate data might result in wrong decisions.
- Dependence on a Competent Workforce and Coordination: A lack of this issue might prove detrimental in achieving the desired objective.
- Tool Dependent: The success of predictive analytics depends on the selection of the correct and best-fitting model. An improper tool used may affect the outcome adversely.
- Expensive: Predictive analytics being complex is a costly affair.
Real-world Examples of Predictive Analytics in Business Intelligence
Initially, it might be difficult to believe in the power of predictive analytics for making critical business decisions related to real-world problems. Many organizations have now turned to data analytics as an integral part of their growth strategy. Let us look at some companies that are efficiently utilizing predictive analytics for:
- Amazon: The e-commerce giant Amazon employs predictive analytics to upsell products. Through predictive analytics, Amazon tends to know the purchasing behavior of each customer. This is also evident in the customized ads displayed based on the users' previous purchases and their provided product feedback and ratings. For example, Amazon knows that customers who buy a refrigerator are likely to purchase accessories like fridge mounts, ice trays, bottles, or containers to store food items. Hence, when users search for refrigerators on the Amazon website, they are simultaneously shown recommendations for products that are usually bought together.
- Coca-Cola: The company collects data on customers to increase the current consumption of their products and upsell new items. The collected data can assist in identifying purchasing habits, what appeals to individuals in a specific location, favored tastes, etc. For this, Coca-Cola listens to its customers' feedback on social media, such as their thoughts on the product. Using this data, the organization can modify and fine-tune its product marketing strategy to stay relevant to its customers' interests and desires. It then launches its products and provides users with a more tailored product experience. Coca-Cola also uses the gathered information to improve the brand experience and boost consumer loyalty.
- Netflix: According to Statista, Netflix has more than 220 million subscribers. The company uses AI-powered algorithms based on the user's watch history, search history, demographics, ratings, and preferences to predict what the users are likely to watch next. These predictions reveal what the viewer would be interested in seeing next, with an accuracy of 80%. Thus, Netflix predicts user behavior and recommends TV series and movies using predictive analytics algorithms in its recommendation engine.
Predictive Analytics Use Cases
In fact, predictive analytics is being used by a broad spectrum of businesses. Here, we list some domains and their respective uses of predictive analytics:
1. Manufacturing Industries
Regular log maintenance and sensor records can help identify possible breakdown locations in a manufacturing firm and take preventive steps to reduce these possibilities.
2. Financial Services
Banks, housing loan lending institutions, and insurance companies use machine learning and statistical tools to predict credit risk and fraud detection cases before finalizing loan approval.
3. Healthcare
Predictive analytics in healthcare is used to detect and treat patients with chronic diseases and predict the possibility of the spread of certain diseases in the future based on environmental and other causes.
4. Human Resources (HR)
HR teams use predictive analytics to select the most suitable workforce and predict the performance of all employees.
5. Marketing and Sales
Predictive analytics can be used for launching a new product into the market or determining the need for likely changes in an existing product based on information collected from different sources.
6. Retail
Retail businesses use predictive analytics to analyze customers behavior and product demand to ensure proper inventory management.
7. Logistics and Supply Chain
Businesses use predictive analytics to arrange the proper size, warehouse locations, and an adequate fleet of transport vehicles to ensure a timely and smooth supply of necessary goods to customers.
8. Educational Institutes
Looking at the past trends regarding choices of parents and students along with the upcoming demand of a particular workforce, educational institutions can plan for all the necessary resources to stay ahead in starting new courses. Predictive analytics can be an excellent tool for this purpose.
Predictive Analytics Resources
With the rapid growth in computational technology applications, it is now possible to use readily available software tools developed by top IT companies for predictive analytics. Each such tool has various capabilities essential to give correct predictions related to a particular type of business and the data provided by the business.
Let us list some widely available tools from top predictive analytics companies with their current uses. You can explore their features and offer conditions listed on their websites:
- SAP Analytics Cloud: This is the most popular overall predictive analytics solution offered by SAP.
- SAS Advanced Analytics: Another well-known analytics tool by SAS Institute Inc. is SAS predictive analytics which delivers accurate insights at the right time, leveraging the power of the available data.
- RapidMiner: This is an open-source end-to-end Data Science platform that provides predictive analytics models.
- Alteryx: Alteryx offers an analytical platform for team collaboration. This platform provides predictive analytics within the complete analytics workflow.
- IBM SPSS: This tool offered by IBM offers a good predictive analytics option for researchers.
- TIBCO Spotfire: Tibco offers a free predictive analytics software to predict the behavior of businesses and customers. Companies can then improve their analytics knowledge throughout the organization.
- H2O.ai: This is a good open-source tool that is in-memory, distributed, fast, and scalable machine learning and predictive analytics platform.
- Emcien: This is a leading predictive analytics tool for marketing.
- Sisense: This is a popular business intelligence software for data scientists that offers advanced machine learning algorithms for predictive analytics.
- Tableau: A very popular data visualization and dashboarding tool that also offers predictive modelling functions for building models and making predictions about data by regression.
Conclusion
Predictive analytics has become an indispensable activity for survival and growth in all types of organizations. Understanding its benefits and then accepting it for implementation is, hence, crucial. Fortunately, as it proved to be more beneficial when used along with business intelligence, many reputed data science companies have developed suitable models. To know more about these models, check out KnowledgeHut’s Data Science with Python tutorial.
These can be applied to different sectors' needs like healthcare, manufacturing, agriculture, travel and tourism, disaster management, weather forecast, etc. The vendor services required can be selected as per one's needs as many options are available, as mentioned earlier in this article. If possible, one can develop a system on their own. The need is only to be alert and proactive to the benefits of predictive analytics.
