Analytics is undoubtedly the most valuable tool for knowing about customer insights. Therefore, Big Data is going to reach $273 Billion by next year, and companies like Google, Amazon, and Microsoft are so heavily invested in not only collection of structured and unstructured data but enabling data for the enterprise.

As the fundamentals of Machine Learning and Artificial Intelligence continue to develop, so do the ways we use analytics as well. Previously, organizations focused on descriptive data about their customers and various products, but now they are increasingly focused on both prescriptive and predictive learning from the information they collect from that same dataset. So – what is the difference between prescriptive and predictive analytics within a company? And do you need the former more than ever?

For my readers who are new to the field of data analytics or have just started with my articles, let’s do a quick recap:

  • Predictive Analytics: This branch of analytics is all about supplying information about what will happen in your organization. With the application of complex Artificial Intelligence (AI) and Machine Learning (ML) algorithms, predictive analytics help you to determine the predictions related to every aspect of the operations, like how well a product will sell, who is more than probable to buy it, and which types of marketing strategies and tools will have the greatest effect on the success.

  • Descriptive Analytics: It revolves around using the data for generating insights related to whatever happened in the past for a company. There are diverse types of reports one can generate like monthly sales reports, web analytics, and others. It can also give you insights into how well a project performed. This is the most basic form of analytics.

  • Prescriptive Analytics: This is another interesting form of analytics which is about noy only to discover what will happen in your company, but how it could be better if you did x, y, or z. It means that predictive analytics is not only about prediction, but also giving you recommendations related to how can you make it even better to the highest degree.

Honestly, there is still a considerable amount of confusion related to what constitutes predictive and prescriptive analytics, and you may use it interchangeability as per your need. Regardless of that, these three analytical branches play a significant role in our organizations nowadays. Complex algorithms are not always needed running on our data. Sometimes we need to know the overall functioning of our organization. However, if we need to improve efficiencies, prescriptive analytics plays an increasingly key role.

Marketing is always Easier Through Prescriptive Analytics

In the past, teams in the department of marketing would draft campaigns and use descriptive analytics to target who they ‘felt’ will be most suitable one for them about a particular product or service they offered. Customers in the range of age in between 20-30 received a much ‘younger’ message than those who were in a much mature age, i.e., in between the range of 45-60 years. They even might be pitched a different product or service. This would lead to better overall performance of the campaign, and honestly; still, many companies’ markets through this strategy. However, this type of marketing still isn’t efficient and effective. There are still numerous assumptions related to the variables for the success of the campaign, and even the results won’t necessarily supply insights as on why the campaign did was a success or a failure.

When we analyze the data through predictive analytics, things get a bit cleaner. The algorithms related to AI and ML can pinpoint us more specifically which type of customers should be targeted, and which products or discounts to offer to maximize impact on the profits. But the findings will still be descriptive, and it won’t tell specifically what you should be doing to improve your solutions even further.

Here, prescriptive analytics enters, and it takes three main forms of analytics – guided selling, guided pricing, and guided marketing. It uses AI and Machine Learning to guide the organization in noy only selecting the right buyer, at the right time, with the right content, but also it tells salespeople which product to offer using which worlds – informing you what price at specific time and situation. This information extracted from historical data not only allows us to maximize just sales but also price and profit overall.

This example of increasing the effectiveness of marketing teams withing an organization is just the beginning. The benefits of predictive and prescriptive analytics go beyond that. It effectively saves time, increases efficiencies, human capital, and even transaction costs. Predictive analytic, when automated, can help make you fruitful real-life business decisions – something which petroleum companies do, for instance, changing prices of their products throughout the day to maximize their products. Achieving the advantages of data comes down to having the proper technology, impressive enough systems, and procedures to boost available data.

Finally, if a company needs to start investing in a type of analytics, then they need to start with the most important question: what do you want to conduct?

As I have mentioned earlier, prescriptive analytics are indeed powerful, but they won’t be necessary for every company. They also need to tweak its algorithm. No algorithm was developed perfectly to fit the needs of every organization of domain in the world. It takes a considerable amount of time, effort, and focus to make prescriptive analytics work effectively. However, if you are working in a highly competitive marketplace, being one step ahead of the competitors through prescriptive analytics could mean a huge boost to the productivity, profit, and overall operations of the company.

I’m guessing we’ve only seen the top of the iceberg in terms of what prescriptive analytics can accomplish. (Furthermore, for the small and medium-sized businesses out there, no worries: Prescriptive analytics as a service isn’t far behind in transforming your businesses to reach new heights in the near future).


Please enter your comment!
Please enter your name here