Why use SAS Visual Statistics to maximise CLV

I have worked with a lot of businesses over the years, and I have seen an interesting shift in their marketing strategies. Most organisations are now looking to maximise customer lifetime value (CLV), rather than just focusing on acquiring new customers. There are several reasons behind this trend, but one cause is technology, like SAS visual statistics.

Businesses spend a lot of time and resources on customer lifetime value (CLV) and with good reason. Research shows that when customers are happy with their experience, they spend more money on subsequent purchases, raising revenue, while reducing customer acquisition expenses.

What is the best strategy for improving CLV?

Businesses have tried to improve customer retention using strategies like social media campaigns and niche buyer personas. However, one of the most effective strategies for improving customer lifetime value has been data analytics. According to a study, over 52% of companies and 50% of agencies stated that smarter use of data analytics is the most effective strategy for improving CLV followed closely by customer segmentation and more precise buyer personas.

One reason why analytics platforms have proven to be incredibly effective is because of the level of insight data analytics grants organisations. Platforms, like SAS Visual Analytics, can breakdown and present customer data in a way that is succinct and easy to understand, while also revealing trends that are hard to identify.

Let’s take a deeper look into how SAS visual statistics.

SAS visual statistics – The secret behind maximising CLV

SAS visual statistics is an effective tool for maximising CLV because of its ability to comb through data and use it to build predictive models like dynamic group-by processing, descriptive modelling, in-memory processing and flexible deployment options, all essential options for building predictive models. Predictive models that will provide businesses with insight into customer lifetime value.

The ability to dynamically explore datasets and predict future trends makes it easier to resolve problems. For example, if a business discovers that revenue fell compared to the previous year, they can breakdown data based on a specific variable to better understand the causes. Furthermore, it is possible to build multiple data analytics models, like linear regression data models and gradient boosting models, to better compare which output is more accurate.

One of the most useful benefits of SAS Visual Statistics is the incorporation of AI into the model for sharper analysis, making it much easier to breakdown a large volume of data relatively quickly.

The option to breakdown data based on different conditions eases the process behind maximising CLV because businesses have an easier time studying the data in a different context.

The new context can reveal problems not found before, making it easier to better understand the factors contributing to (or discouraging) CLV. The ability to dive into big data and identify problems quickly is one reason why organisations believe analytics platforms are an effective method to maximise CLV.

Thanks to SAS Visual Statistics, most businesses find it easier to identify the underlying causes that propel or hinder the lifetime value of a customer, causes that are not so readily found using other methods. Given the immense volume of data most organisations are dealing with, most businesses can save a lot of time and resources when optimising efforts to improve CLV.

One of the biggest benefits behind SAS visual analytics compared to other methods is that insight gained can improve operations in multiple areas. Whether it is smarter marketing campaigns, cost optimisation or improving retention strategies, the insights generated from SAS visual analytics can help businesses for years to come.

Incorporating data analytics into operations

SAS Visual Statistics is an excellent tool guaranteed to generate tremendous ROI in the long run. However, there is no denying that it is going to take a substantial investment to get the platform to work. This is because SAS Visual Analytics requires expertise to be properly installed and integrated into business operations.

While there is no denying that SAS Visual Statistics is easy to use (even people with a non-technical background can use the platform) you still need the support of SAS specialists to integrate the platform into operations and maintain it. Working with the right team can help businesses optimise their operations to cut costs while still looking for ways to improve CLV.

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