Predictive analysis in marketing is like soothsaying or forecasting what will happen in the future. But, it’s not predicting an event exactly, but listing out the possibilities of an event to occur or not occur based on the past data.
An example of that would be how Netflix uses predictive analytics that uses AI-powered algorithms to make predictions based on the user’s watch history, search history, demographics, ratings, and preferences.
These predictions show with 80% accuracy what the user might be interested in seeing next and recommend it to the users. All their recommendations have proved to be true and have people subscribing to other shows.
What Is Predictive Analytics?
Predictive analytics uses data, statistics, and machine learning algorithms to identify the likelihood of future events and outcomes. The goal of predictive analytics is to go beyond knowing what has happened to predict what will happen in the future.
It allows businesses to take advantage of future events. It includes techniques such as artificial intelligence, data mining, modelling, and statistics to make predictions about the future.
Predictive analytics are used in weather forecasts, business forecasting, creating video games, translating voice to text, customer service, and in finance. All of these applications use existing data to make predictions about future data.
It is useful in businesses to help them to
- Manage inventory
- Develop marketing strategies
- Forecast sales
- Predict outcomes to have a competitive edge over others
- Set strategies that reduce the potential for risk
What Is an Example of Predictive Analytics?
To reiterate, predictive analytics determines what would happen next based on past data. The most common examples of predictive analytics could be seen in weather forecasting, where the next two days or the following week’s weather is forecasted based on the previous week’s weather.
One of the most intuitive predictive analytics applications is a credit score in the banking and finance sectors. It takes into account a person’s loan applications, past payments, and credit history to calculate a score that reflects the likelihood of that person repaying the loan in the future if offered.
Again, predictive analytics in marketing is used in fraud detection in banking transactions that examines trends and patterns. If there is any irregularity in the activity, it can be probed to verify any anomalies in the transaction.
Why Are Predictive Analytics in Marketing Important?
In what ways can predictive analytics in marketing be helpful? To answer this question, let’s first analyze what help does marketing need.
The key challenges in marketing are: identifying your target audience, understanding their needs, providing solutions that solve their problems, and taking your products to their doorstep through marketing and the volatility of the audience behavior.
Each marketing campaign differs based on the behavioral pattern, expected ROI, and demand for the product. Predictive targeting allows marketers to optimize their marketing campaigns based on these factors.
This type of targeting utilizes advanced analytics, artificial intelligence, machine learning, and historical data to make predictions about future outcomes.
Marketers can use predictive analytics to determine the right message for each campaign’s audience.
For instance, an apparel business can gather the transactional details of a customer when they buy a product. Using a predictive analytics system, thorough research of what type of apparel is sold and when it was sold can be analyzed, and patterns can be detected to be used in the next marketing campaign.
Forecast Seasonal Customer Behavior
Weather impacts the customer’s buying patterns and preferences. By integrating a predictive analytics system alongside weather data, marketers can design a perfect campaign for product sales.
For instance, sales of air conditioners, cool drinks, sun protection cosmetics, and cotton fabrics would be on the rise in the summer season. Therefore, seasonal businesses can leverage this factor and target their customers with the right message.
Similarly, year-end sales, and year-beginning activities, such as gym memberships, buying insurance, real estate investment, and starting a business, are other possibilities where marketers can make accurate predictions on the impact of seasonality or local forecast.
Develop the Right Content
With predictive analytics, you can understand their behavioral patterns using the wealth of historical data and give the right information based on the demographics.
Address your customer’s pain points, speak in a voice they can relate to, and appear on their preferred platform – social media – when they are likely to be online.
When your customers are at TOFU, give them more information about your products and services through engaging posts, such as blogs, webinars, and videos.
Construct content along with the sales team that would persuade your customers to move towards MOFU and BOFU.
Develop More Effective Marketing Strategies
For your target audience, design a buyer persona to know their needs, interests, and the solutions they are looking for. This will enable you to have a marketing strategy squared down to that specific group.
Using predictive analytics tools, develop a lead scoring system to score how many prospects will convert to leads. The historical data about prospects converting to leads and customers will serve as criteria in the lead scoring system.
Also, make use of the feedback from the customers to develop post-sales content like how-to guides, videos, and FAQs. This facilitates a good customer retention rate, as you care about them even after the sales are made.
Businesses must identify anomalies and outliers in the lead’s journey to become customers. By identifying patterns, personalized marketing strategies can be used to retain them to reduce the attrition rate of the customers.
How is predictive analytics used in marketing?
Predictive analytics is used in marketing to predict outcomes using data collection tools, analyzing patterns, segregating them based on a common term, and listing out the possibilities of an event.
What’s the difference between predictive analytics and data analytics?
Data analytics gathers and analyses data to make decisions about what has happened. On the other hand, predictive analytics gathers past data and processes it to make decisions.
Predictive analytics is used in healthcare, retail businesses, supply chains, and sports to predict outcomes and in human resource segments to make calculated marketing moves. It makes workflow easier, saves marketing costs, and improves sales by pitching to the right segment of people using the right strategy.
Use predictive analytic tools to create content that takes your business to your target audience. Write sales-oriented ad copies, well-informed landing pages, and relevant blogs based on what users want by knowing their mindset.