Guest Blog: Six Ways Predictive Analytics Enhances Customer Relations

This week we feature an article by Jarrin Howard who writes about how predictive analytics can enhance customer relations. – Shep Hyken

Have you ever gone down a rabbit hole of suggested videos on YouTube? Or maybe you’ve lost yourself in a thread of related products on Amazon (regretfully). These are common examples of sites using predictive analytics catering to individual customer preferences.

With the rise of machine learning, predictive analytics is becoming the status quo. It applies to various fields such as banking and travel, with customer relations at the forefront, putting Predictive Customer Analytics (PCA) in a field of its own. PCA utilizes historical data from a CRM to guess the likelihood of future events. These predictions aren’t without error, but they assist in making informed decisions concerning customers.

Here are 6 ways predictive analytics can enhance customer-related interactions:

1. Targeting Warm Leads

If you can determine which leads will turn out to be warmest, converting customers will be easier, and all marketing efforts will be worthwhile. Predictive analytics can assist in this process by comparing existing loyal customer data to the leads in question. This will provide a prediction on whether the leads will turn out to be warm and help decide where to direct the bulk of your marketing efforts for new business.

2. Segmenting Customers

Manual segmentation is extremely tedious work, especially if there is a bulk of customers with a myriad of interests. With machine learning, PCA can simply group customers into distinct segments based on diverse characteristics more efficiently than doing it manually. Segments that are more detailed and precise work to make your marketing efforts more efficient. This makes it easier to access and allows personalized content to a heightened degree.

3. Personalizing Content

Personalization is key in today’s sophisticated market; creating campaigns for customers based on their personal interests can spark new interests and provide memorable experiences. Again, utilizing historical information, PCA helps with this process by comparing the customers in question with past customers. From there, machine learning creates models to predict what customers are most likely interested in.

This matter of specialization doesn’t just relate to products but also to product discounts, promotions, upselling, and cross-selling. When marketing efforts are personalized, customers tend to respond more readily.

4. Choosing Channels

After producing content that will be of interest to customers, deciding which marketing channels to use is important. Just like there is a wealth of products and services on the market, there are countless channels available to reach customers such as email, social media, blogs, webinars and more. Machine learning can help with this by utilizing a regression model to determine which channels will customers prefer.

5. Predicting Satisfaction

When there isn’t enough feedback from customers, it may be difficult to determine how satisfied they are with your services or products. However, predictive analytics can aggregate various criteria to predict satisfaction.

For example, if satisfied customers typically click on ads at a high rate, it may be easy to assume that a customer who consistently has a low click-rate is not satisfied. However, there are multiple variables needed to measure and predict satisfaction. Luckily, machine learning can synthesize multiple variables in one model, such as neural network design.

6. Calculating Customer Churn

Predicting customer churn and satisfaction are two sides of the same coin. The two processes might even occur at the same time, and similar data can be used for both. Knowing which customers are likely to churn helps decide whether to direct special attention to them to change their minds, or focus on customers who are most likely to stay.

Final Thoughts

Predictive analytics helps in all aspects of the customer journey. From the time you are trying to identify your warmest leads, to the point where your most loyal customers want to promote your business, predictive analytics is a necessary aid. It is already commonplace in many industries. Don’t get left behind.