Predictive Analytics for Employee and Customer Success: A Data-Driven Approach

By Veljko Petrovic

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Since the AI industry has been blowing up lately, companies have found numerous ways to implement it in their processes. AI can indeed be considered overrated in some instances, but a combination of useful data and predictive analytics can be essential for your company’s success.

The importance of AI for marketing, development, writing and other industries can be seen in the fact that the market is predicted to grow in the following decade. While predictive analytics can help you make better decisions, it’s important to understand its complexities and regulations.

Companies that properly implement AI in their processes can significantly increase their effectiveness and competency. This article explores the idea of predictive analytics, aspects you should pay attention to and the process of implementing a data-driven approach. 

Predictive analytics explained

Predictive analytics is the process that leverages gathered data in order to make assumptions about how will certain metrics move. It leverages statistical techniques, machine learning algorithms, and data mining processes to understand historical data.

The whole process starts with defining a certain problem. For example, you can experience lowered productivity among your employees or an increased churn rate when it comes to customers. 

Once you recognize the problem that occurs over a longer period of time, you can use data that explains historical oscillations and where the problem lies. The data is analyzed by predictive analytics tools and predictive models.

Predictive analytics is considered as a form of AI since it leverages machine learning and algorithms. Of course, gathered data needs to be contextual and specific. You can download datasets from data depositories such as BigQuery.

However, if you’re looking to see actual benefits, you must ensure that the data and the metrics you want to measure and improve are relevant for your companies. There are three main methods of predictive analytics:

• Regression analysis,
• Decision trees,
• Neural networks. 

Regression analysis tries to understand relationships between variables. In other words, is used to give you an understanding of how changes in one variable impact another. Decision trees aim to provide the answer and different possibilities of an individual’s path.

Decision trees require a lot less data than the other two techniques. Neural networks is the most complex method of data analytics as it use mathematical formulas to determine non-linear correlations between variables. 

How to find and gather user data

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As mentioned earlier, you can find data that’s already published on the internet, but it’s unlikely that you’ll find it useful. Instead, you’ll have to rely on gathering information on your employees and customers directly.

Employee related metrics can be tracked by regular performance reviews and surveys. A marketing company and a software development company won’t have the same process for performance review. 

For example, a marketing company might want to understand how many words a writer wrote or how their copies impact sales. On the other hand, a software development company might analyze employee’s keystrokes and the number of bugs in their code. 

Customer data can be collected through cookies, interaction logs, demographic information, and feedback. 

Key considerations when gathering data

One of the most important parts of predictive analytics is to ensure that the data is collected in a compliant way. Your employees and customers need to give you explicit permission and consent for you to use their data.

Data collection can be a part of their initial contract, or you should provide them with an additional contract that provides them with insight into how is their data going to be:

• Gathered, 
• Processed,
• And for which purpose

Customers can give out their data based on online forms and cookies. However, you should ensure compliance by leveraging data consent management software. This way, you’ll be able to easily gather data and ensure a compliant process.

The reason why data gathering is problematic is that it should adhere to international regulations such as GDPR. Depending on the region where you operate and the location of your customers, you need to be quite considerate in the process of data collection.

While GDPR targets businesses that operate in the European Union, there are CCPA in California and PIPEDA in Canada. Regardless of your location, you should have an understanding of the regulations that apply to your business.

Failing to adhere to them can lead to costly fines and blows to your reputation. So if you aren’t ready to operate in a compliant way, you can end up with more costs and losses than you would without trying out prediction analytics. 

Steps to implementing a data-driven approach

Once you have a grasp of the ways in which predictive analytics can be used to understand your employees and customers, you need to devise an execution plan for the data-driven approach. 

The process starts with data gathering, which is a thoughtful process that must be done with consideration to your employees’ and customer’s privacy and rights.

The process ends after the data is properly processed and analyzed. However, once that’s finished, you’ll have to make proper decisions in order to make the whole process worthwhile and effective. 

1. Data collection

If you make certain mistakes in this step, then the whole process can turn out inaccurate. Data that you collect should be accurate, comprehensive and obtained in adherence with applicable regulations.

The quality of your data will directly impact the usefulness of the model you’ll later develop. By prioritizing data integrity, you’ll ensure that your results are helpful. Privacy concerns should be addressed depending on the regulations.

The way in which you collect data largely depends on your goals and the problems you want to solve. If you want to understand your employee’s productivity, then decide what data is relevant for each role and/or department. 

2. Choosing the right technology

To ensure that the data is properly analyzed, you need to choose great tools. They must align with your goals and the specifics of your industry. These tools must offer:

• Scalability: This should ensure that a growing demand for data analytics is satisfied. Furthermore, you should be able to analyze data in situations where your company suddenly grows. 
• Integration capabilities: Tools that you use need to have the possibility of integration with your current systems. For example. HR management systems, CRM, email marketing tools, and productivity trackers. 
• Easy to use: Although you’ll likely hire employees that are knowledgeable with a variety of data analytics technology, it’s essential that decision-makers will be able to understand the process as well. 

Failing to find a proper technology can result in subpar results. For example, if you’re developing a vehicle maintenance program, you’ll need to find tools that are able to adequately analyze and gather data such as fuel consumption, traffic, distance, etc. 

3. Building and testing models

Once you’ve gathered your tool, it’s time to build and test your predictive models. This process involves the use of ML algorithms to analyze the data you’ve gathered. Models should be trained based on historical data and taught how to make accurate predictions.

However, it’s important to understand that your models are going to take some time to be fully effective and developed. They need to be continuously trained and tested and it’s important to make changes and tweaks along the way.

4. Making decisions and understanding your results

If your models are operating well, your last step is to gather a team of decision-makers and understand what actions your company needs to take. 

The analyzed data is useless in it of itself, your job is to ensure that the proper actions are taken. For example, if a certain design change impacts a customer’s churn rate, you’ll understand that you need to alter it accordingly. 

Predictive analytics can give you a needed competitive edge

There isn’t a magical wand that will turn your failing company into a successful one. Furthermore, if your company offers poor products or services, it’s unlikely that an AI tool will make a complete turnaround.

However, companies that are already operating well and want to further improve their position in the market can significantly benefit from a data-driven approach. Based on predictive analytics, you can improve the productivity of your employees, as well as various customer metrics. 

Ensure that your company is ready for the implementation of predictive analytics, as it can require a certain amount of resources, experts, and data. Furthermore, the way in which the data is gathered and processed needs to be in accordance with relevant regulations and laws. 

 

Veljko Petrovic

Veljko is an IT student who has successfully combined his passion for technology with his exceptional writing skills. As an emerging specialist in cybersecurity, he has completed several courses and has been published in notable blogs in the industry. In his free time, Veljko enjoys weightlifting, reading, and programmi

Website: www.writerveljko.com

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