In the past, businesses focused on conveying messages to their customers through different channels to reach as many people as possible. However, things have changed – today, customers are being encouraged to communicate more during their buying journey.
As a result, we saw the birth of a science called customer engagement analytics, which is based on data that is analyzed to provide solutions to increase customer engagement during the customer buying journey.
By definition, customer engagement analytics is a process that focuses on using analytics to improve customer engagement. In other words, it’s an effort that helps businesses analyze structured and unstructured customer data obtained from different channels.
In today’s guide, we look at the key details around customer engagement analytics and explore the processes in detail.
Customer engagement analytics and their benefit to organizations
Data is a segment that can help organizations improve their interactions, identify gaps, and execute the best strategies for customer engagement. If your organization collects data, you can use it to make better decisions and gain insights into what your customers want.
By definition, customer engagement analytics encompasses processes that include:
- Collection of data: Capturing, aggregating, and integrating data
- Data analysis: Detailed analysis, which includes predictive analytics, descriptive analytics, and prescriptive analytics
- Application: Applying the analyzed information to increase customer engagement
Using various data sources is one of the best ways to analyze and diversify your results. Whether it’s customers, suppliers, or competitor companies, there are many ways to exploit data sources and make well-informed decisions. Below, we list some of the key benefits of customer engagement analytics for businesses.
A detailed perspective into the customer journey
Analyzing data from multiple sources can help you become more proficient with the customer journey. You’ll also be able to locate all the touchpoints with the customers and the appropriate channels for each customer segment.
While there is initially some effort into leveraging data from all different sources (transactions, interactions, customer feedback, third-party data, etc.), this can help you become more efficient at collecting, researching, and analyzing your customer journeys. Most importantly, you’ll have a better picture of the customer experience.
Pinpointing the exact causes behind your problems
Customer engagement is a process that involves a lot of steps of interaction between customers and your brand. Because of that, there is always room for potential mistakes during this process, and they are usually difficult to spot with the eye.
However, data can help you analyze consumer behavior and make the most of it. You’ll be able to pinpoint the interests and needs of your customers but also discover the reasons for their dissatisfaction, churn, or the cost of each interaction. As a result, you’ll be able to improve the customer experience significantly.
Promoting multi-channel engagement
Another key feature of customer engagement analytics is tracking and predicting customer behavior during their journey. You can get insights on this across all different channels – for instance, you can consider some of the best webinar platforms to host your next webinar, engage with customers directly through emails, or leverage other forms of data to increase customer engagement based on their preferences and habits.
Along the way, you’ll be able to pull more information from their side and drive customers to more of your locations, landing pages, or other touchpoints where they can engage with your brand. Ultimately, the point of the multi-channel approach is to create more touchpoints, pull more data, and grow your business.
What are the most critical customer engagement analytics metrics?
Analytics may seem like a lot of work for many people. However, research shows a direct connection between them and customer engagement. Researchers have found that the more sophisticated an organization’s analytics capabilities are, the higher the level of customer engagement.
Now that you know the ins and outs of customer engagement analytics, let’s look into some of the best metrics used by large businesses to collect data. These will help you segment your audience and make the most out of each group.
Purchase frequency (PF)
First on the list is the purchase frequency, which is a number that shows the average frequency a customer purchases from you throughout a year. The higher this metric is, the more it proves that your business interacts with your customers. The formula for it is as follows:
Purchase frequency = Number of orders over the last 365 days / Number of unique customers in the previous 365 days
Repeat purchase rate (RPR)
This metric applies to customers who buy more than one time from you. So, the Repeat Purchase Rate (RPR) is a metric that measures the additional interactions and engagement and is calculated.
Repeat purchases = Number of customers who bought more than once / Total number of customers
Guest checkout rate
Lastly, the guest checkout rate is a percentage of customers who buy goods online from you but won’t register for an account on the website. These people are labeled “guests” – to motivate them to register, you need to try different strategies. The formula is as follows.
Guest checkout rate = Number of orders from a specific guest / Total number of orders.
Without customer engagement analytics, it’s challenging to know what’s working and what’s not. However, it is also a way to understand what to replicate, fix, or change and deepen your customer engagement by having it precisely measured.
In the end, organizations that demonstrate higher analytical maturity levels will have access to more data and use it to their advantage. The most analytically mature organizations are twice as likely to report strong customer engagement and would therefore gain a competitive advantage.