How To Measure Customer Engagement, and Put it to Use

How To Measure Customer Engagement, and Put it to Use

Customer engagement data can hold significant insights for business strategy, especially when shared beyond the marketing team and interpreted collaboratively.

Relationships with customers or clients are at the core of any business strategy. No matter the type of business, there is no revenue without reaching the right customers and satisfying their expectations. It’s hard to overstate the importance of customer relationships for a business, but it can be hard to measure the success of those relationships and find opportunities to improve. 

Marketers may tout metrics like impressions and click-through rates, but determining how these numbers translate to revenue often feels like a guessing game. For auditors especially, the ultimate influence on a company’s bottom line can be uncertain at best, and incalculable at worst.

This doesn’t have to be the case, however, when your team understands the power of customer engagement data. Modern analytics tools can collect and deliver data-driven insights that keep everyone from marketers to auditors well-informed and collaborating effectively.

What Is Customer Engagement Data?

In a marketing sense, engagement is any interaction that customers or clients have with a company’s products, services, or content. From an analytics standpoint, engagement data can be thought of as measurable elements of the overall customer or client experience. 

A recent survey reported that 28% of all business is today conducted online. That number trends upward every year and is of course already much higher for many industries. Thanks to this proliferation of digital interactions, much of the customer experience — from discovery to conversion and beyond — can now be captured by engagement data found online.

Types of Customer Engagement Data

With the right tools and expertise, companies can gain all kinds of insight from engagement data including how customers find the brand, what they buy, how they use products or services, and how they talk about the brand to others. 

This data can come from a wealth of touchpoints including a company’s websites, mobile apps, social media, email, and from third-party sources. Across all of these digital channels, customer data generally falls into four main categories:

  • Basic data forms the foundation of any customer database. It includes personal or anonymized identifiers along with demographic data. While they do not record interactions per se, basic customer data is fundamental for understanding a consumer audience.
  • Interaction data refers to most types of direct engagements, such as actions on a website, likes or shares on social media, and purchases or other forms of conversion. 
  • Behavioural data can be thought of as higher-level customer insights that encompass various types of interactions. Relevant data will vary by the type of business, but examples may be subscription details, content preferences, or a customer journey map from brand discovery to conversion.
  • Attitudinal data is often the most difficult type to collect, as it measures a customer’s perceptions of the brand and overall satisfaction with their experience. This information can be collected through direct means like customer feedback and reviews as well as indirect means like social media sentiment analysis.

When harnessed and analyzed effectively, these data types together can reveal critical insights for companies regarding their customer and client relationships.

The Importance of Customer Engagement Data for Business Intelligence

The significance of engagement data is never lost on marketing professionals, and certain forms of engagement are commonly tracked by sales, project management, and other client-facing teams within an organization. When it comes to business intelligence and other performance analytics, however, the relevance of a hyper-specific marketing metric like email open rate may seem to fade.

While it’s true that not every type of engagement metric belongs in a financial report, analysts who ignore marketing data might be missing out on key insights. This is because customer engagement, when evaluated effectively, can reveal far more than each customer’s buying habits. It can provide critical information for financial forecasting and strategic planning.

Early Indicators of Campaign Performance

Engagement data can reveal certain indicators of campaign performance even before sales numbers or other metrics become available. Here are some ways to use customer data for proactive analyses:

  • A/B testing of content or advertising.
  • Identifying and predicting patterns of customer behaviour.
  • Monitoring social media to detect popular sentiment and trends.
  • Identifying lapsed customers and reaching out to them to re-engage.

As a marketer or analyst, paying attention to all the available information will allow for more agile campaign testing and adjustment. In the same way, considering more diverse data points associated with a campaign can lead to better-informed decisions moving forward.

Optimizing Future Campaigns

Evaluating both the successes and failures of customer engagement should help shape future strategy and lead to improved performance. Here are some ways to use engagement insights in strategic planning:

  • Modify content, products, or services based on customer experience insights.
  • Identify opportunities to cut costs or improve efficiency based on past performance.
  • Personalise customer experiences based on the demonstrated preferences of individuals or groups.
  • Find ways to further segment customer profiles based on behavioural and attitudinal insights, and therefore deliver more personalized experiences.
  • Predict consumer trends and market changes ahead of the competition.

Collecting and analyzing customer engagement data should be an ongoing process that allows for cyclical evaluation and continuous improvement. Although this is no easy task, crafting the right strategy and using the best tools can go a long way toward streamlining the process and even automating much of the workflow.

How To Measure Customer Engagement

Although tech companies often preach the mantra that more data is always better, few businesses have the luxury of unlimited resources for big data management and analysis. Capable software and cloud solutions can lessen the workload immensely, but it always helps to know what data to prioritize and how best to interpret the results.

Determining the Most Relevant Metrics

The ideal analysis of customer engagement data will not look the same for every company or every campaign. Each business must begin by defining its conversion goals and deciding what key performance indicators to track. From there, teams can determine what types of data are most directly relevant. These are the metrics that should be actively monitored in real-time throughout a campaign.

That does not mean that less relevant data points should be ignored, of course, but they can be assessed at intervals as appropriate. Unexpected patterns or correlations can always occur and may be significant, so it’s important to keep an eye out for anomalies. This is one area where artificial intelligence can play a key role by automatically identifying trends in the data that warrant further attention.

Measuring Customer Engagement With Technology

With the expansion of online business has come an explosion of software solutions for businesses to use. When vetting the myriad options for software tools, it is important to keep sight of customer engagement data’s primary purpose — analyzing customer relationships to inform business strategy.
To that end, it’s critical to choose a cloud-based software that makes marketing data easy to interpret and communicate across an organization. For example, marketing teams should be able to share insights with accounting, and vice versa, using cloud solutions that facilitate collaboration. With capable systems in place, customer engagement data can seamlessly contribute to the 360-degree view that every business needs for making the best decisions.