Customer insights you never thought to look for in your data, and how to find them

Data-driven marketing is a type of marketing that uses data related to consumer demographics and behaviors — as well as market trends — to develop an optimized strategy. Data-driven marketing is valuable because it allows marketers to gain insights and develop strategies based on hard data rather than having to rely on guesses or vague correlations in the market. 

There’s research to support the value of data-driven business operations. For example, a 2018 report from Forrester suggests that data-driven organizations are growing at a rate of over 30% more than their competitors annually. Though data-driven marketing can prove invaluable for the success of a business, it’s vital that marketers first understand how to collect accurate, high-quality data and apply it to their efforts.  

The power of customer data

Consumer data can help marketers understand consumer behaviors and preferences in a variety of ways. It can identify things like who your target demographic is (or should be) by determining who is searching for or interacting with services and products similar to those offered by your business. 

Meanwhile, by tracking customer information — such as where your target audience spends time online and what current marketing efforts they are responding to — you can decide where you should focus your marketing efforts. These examples, along with other valuable customer data, fall under four categories: identity data, engagement data, behavioral data, and attitudinal data. However, for any of it to be useful, it’s vital to work beyond surface-level analysis and search for any subtle patterns of hidden insights. 

Uncovering hidden insights from your data

At first, you may not be aware of or even notice all of the potentially valuable insights you can glean from your data. This could happen for a variety of reasons, such as subtle trends, unfocused data gathering, or failure to recognize the importance of a trend. In fact, according to a report from Salesforce, up to 41% of business owners consider a lack of understanding to be a major barrier to deducing important information from data. 

Though learning to interpret data with a high level of efficiency is a challenge, you can make great strides in this arena by looking at the right metrics. 

Customer satisfaction insights

It’s important to track and analyze customer satisfaction and sentiment to determine what your business is doing right and what it’s doing wrong. In the long term, tracking customer satisfaction can help your business not only attract new customers but also maintain loyalty with existing customers. 

One of the most common metrics for tracking and measuring customer satisfaction is the customer satisfaction score (CSAT), which rates customer satisfaction on a five-point scale. However, there are several other useful metrics as well, such as:

• Net Promoter Score (NPS): An NPS asks survey respondents how likely they would be to recommend a product or service on a scale of -100 to 100. 
• Customer Effort Score (CES): A CES measures how much effort a customer has to put in to interact with your business or its products and services. This is determined by a single question to the customer regarding the ease of engagement, which they’re asked to rate on a scale between one and seven. 
• Social Sentiment Analysis: Social sentiment analysis is the process of determining the emotional underpinnings of text using AI technology. 

Once a business has gauged customer satisfaction, such as solicited customer feedback and analysis of consumer behavior over time, it’s important to further determine the specific reasons behind their level of satisfaction. These detailed insights can be more accurately measured and reviewed using tools such as customer relationship management (CRM) or Customer Data Platform (CDP) software. 

Customer value insights

Customer value refers to the perceived value that a customer attaches to your product or service. By determining customer value, you can assess what priority your products and services take in terms of typical consumer expenditures. This allows you to assess the likelihood of a sale, as well as how economic concerns such as inflation might affect your bottom line.

However, the perceived value of your product to the customer shouldn’t be your only concern. It’s also important to assess how valuable each customer is to your business.

Customer lifetime value (CLV) is a metric often used to determine the approximate profit that a company can expect from each customer throughout their relationship with the company. With this measurement, you can more efficiently make decisions about which customer relationships to prioritize. 

Engagement insights

Engagement insights reflect how, when, and where consumers engage with a company’s products and services, as well as their larger marketing efforts. For example, engagement insights can be gleaned from reviewing consumer responses to social media posts, determining what time of year engagement is the highest, and assessing which social media platforms garner the most engagement.

Common metrics for customer engagement include:

• Engagement score: An engagement score is the sum of values given to various engagement indicators, such as frequency of usage of your products and services and the number of specific actions taken on your website. This will result in a single number that reflects the amount and quality of engagement.
• Email open rates: Email open rates track how many subscribers open a particular email out of the total number of subscribers. 
• Click-through rates: Click-through rate is meant to measure how much traffic an ad is gaining. It’s calculated by dividing the number of clicks on an ad by the number of times your ad was shown. 
• Website interactions: This tracks traffic to your website, as well as different types of interactions with the website.
• Post interactions: This tracks the visibility of your social media posts, as well as how often people click on, comment on, or share a post. 

These assessments and metrics will be most effective if you track them over a long period. In doing so, you can optimize outreach efforts and increase customer engagement. 

Conversion rate optimization insights

Conversion rate refers to how often a user completes a desired action. In the context of marketing and business goals, this desired goal is usually something like clicking on a link, sharing a post, or buying a product. 

Meanwhile, customer rate optimization (CRO) is the process of increasing the number of users who complete the desired action. You can optimize conversion rates through several means, such as adding more effective elements to the marketing funnel and attracting more customers from a demographic with high conversion rates. 

Useful CRO metrics include:

• Cart abandonment analysis: Cart abandonment analysis is meant to determine how often a digital shopping cart is abandoned before making a purchase. This is calculated by dividing the total number of completed transactions by the number of times items were added to the basket. 
• Heatmaps: Heatmaps are visual data that reflect how often certain areas of a page are clicked on.
• A/B test scores: A/B testing is the process of testing two variations of something to determine which version performs better based on a predetermined goal.

Tools such as Google Analytics and various customer data platforms can help marketers gain more high-quality insights related to conversion rates and ultimately improve conversion rates.

Behavioral insights

Behavioral data refers to data that reflects how consumers interact with your company, your outreach efforts, and your products. Demographic data is a common source of customer insights, but behavioral data often offers more detailed, high-quality information. This is because behavioral data is multidimensional and can be used in conjunction with demographic data to gain more complex insights. 

It’s one thing to know who is interacting with your marketing efforts, but another to know how they’re interacting with your marketing efforts. As such, behavioral data can offer more accurate, predictive insights. Behavioral data metrics include:

• Purchase frequency: Purchase frequency refers to how often a customer has purchased your product within a specific period. 
• Average order value: Average order value (AOV) tracks the average amount spent each time a purchase is made. This is calculated by dividing total revenue by the total number of orders.
• Product affinity: Product affinity tracks what products are typically bought together. 
• User journey mapping: User journey mapping is a visualization of the steps a customer has taken to reach a certain goal (often, a purchase).

CDP and data management platforms (DMP) can help you gather, analyze, and refine data related to consumer behavior.

Putting it all together: Data integration and predictive analytics

It’s vital that you not only collect data but also effectively compile and compare it for compelling, predictive analysis. Various data platforms and analysis tools can further assist in developing highly predictive insights. By developing these high-quality insights, you can gain a competitive edge over your competitors and create resilient, long-standing relationships with customers.