predictive analysis

Using predictive analysis in Marketing to drive success

The predictive analytics market is growing at a steady rate every year and it is common knowledge that predictive analytics can allow you to improve your marketing approaches (which means more sales), and you will have to act fast, if you have not already, before competitors catch on. Due to budgets, shrinking, marketing departments need more than ever to improve results and enhance every penny spent.

People have more choices than ever. They will quickly find useful solutions. They can expect companies to offer customized solutions to their needs and a tailored customer experience that is unrivaled by the competition.

If you make a mistake, your customers may shift to competitors.

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Predictive analytics is an effective way to create an exceptional experience for your customers. Using important consumer analytics can help you forecast future customer behavior. Integrating key insights that give you a profound understanding of customer trends into your marketing approaches can help you gain a competitive edge in your industry.

What is it?

Predictive analytics is a kind of advanced analytics that uses machine learning, statistical tools, and other instruments that can help companies get the most out of their marketing spending.

Here, companies use a variety of signals from the markets and customers they operate in to understand which offers, products, communications, and messages are appropriate.

In business, creditworthiness is one of the first and most intuitive uses of predictive analytics. Using historical data about a person’s loan applications, credit history, and past payments, financial institutions, such as banks use predictive analytics to calculate a score that echoes the likelihood that that person will make their future payments in time.

How to use predictive analytics in marketing

Predictive Marketing Analytics also makes use of past data to predict consumer behavior, future buying patterns or trends, and outcomes. To make forecasts about your marketing results and customers, Predictive Analytics uses data and a mixture of statistics, extrapolative modeling, machine learning, and artificial intelligence. You can make precise predictions or regulate the likelihood that something will happen in the future by looking at historic and existing data patterns. Here are a few steps to follow:

1.      Asking questions

This includes deciding what kind of questions you may want to ask, or the results you aim to achieve. The clarity in the questions you wish to ask will aid in projecting the right way to get the answers you want.

2.      Collect appropriate data

Develop a data collection and organization plan that will answer your questions. You may need to use past data, demographic information, and business characteristics to get the best results

3.      Data analysis

Examine your data for useful information that will help you draw deductions about your questions (for example, descriptive analysis). You can go further by asking more explicit questions and mining through the data to find answers. Once you have completed your list of questions and hypotheses, use the statistics to draw and assess your conclusions. Check every theory and trust the data you disclose.

4.      Establish a predictive model

After testing and analyzing each hypothesis against the statistical data and then testing or ruling it out, you can generate a predictive model. Again, use metrics to predict future happenings, results, or customer behavior. Use your data to get useful information and recommendations for future sales and marketing campaigns and strategies. Track and monitor newly developed tactics and campaigns you organize and record how they perform over time. Adjust models as needed and create new ones.

Examples of using predictive marketing analytics

·        Segmentation

This involves categorizing your customers into segments on the basis of demographics, behavior, buying patterns, etc. which can make it easier to conduct predictive analysis. Cluster models can be used to find any patterns that may be unprecedented. These are a type of algorithm used to segment audiences based on demographic data or purchasing patterns.

·        Identification modeling

If you take your segmentation a step further, you can use your customer statistics to create identity models. In practice, it is about intelligently identifying and targeting potential customers who are similar to your current customers.

·        Collaborative filtering

While virtually all successful e-commerce companies such as Amazon and streaming services like Spotify are professionals in using joint filtering to get appropriate recommendations, most marketers have yet to use similar approaches. In practice, collective filtering comes down to using past behavior, for example, content consumption patterns at an aggregate level in a given segment, to make endorsements for upselling, content consumption, or cross-selling.

·        Automated segmentation

Automatic segmentation allows you to explore the entire consumer base in real-time in a matter of seconds. Explore numerous dimensions, identify customers on different platforms, and reach specific audiences with SMS, in-app communication, push notification, email, or integration with marketing automation channels.

Final thoughts

Predictive analytics involves the incorporation of numerous measurement models and huge amounts of data. To take full advantage of predictive analytics, marketers need sophisticated marketing analytics software that can turn all this data into palatable information from which to extract actionable insights. Marketers should always be on the lookout for new ways to make their marketing campaigns more directed and effective. This improves marketing ROI, customer experience, and retention. To stay competitive, modern data marketers are leveraging innovations such as predictive analytics with integrated marketing metrics, analytics software, machine learning, and AI.

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