CRM analytics: Unlocking growth through customer data analysis

What if it were possible to understand how your customers are feeling, thinking, and how they perceive your business, all without any crystal ball or mind-reading tricks? While it may sound like wishful thinking, customer data analysis give you a window into your customers’ minds and emotions, if you know how to use them.

Without customer data analytics, your business is in the dark when it comes to understanding how to improve customer relationships. With CRM analytics, organizations can house all their most critical customer data in one place so that it’s straightforward to work with it to implement meaningful change.

In this article, we’ll look at how CRM analytics can make a deep impact on your business’s success, its benefits, and how customer data analytics are used by different teams. We’ll also let you know how to choose the best analytics platform and have a look at how AI-powered systems like monday CRM pair analytics with automation and personalization for a well-rounded solution.

What are CRM analytics?

CRM analytics refers to the process of collecting, organizing, and analyzing customer data in a customer relationship management (CRM) platform to unlock insights into customer behavior. Using a variety of tools like reports, dashboards, and predictive analytics tools, businesses can get a deeper view into their processes and areas for improvement.

Customer data analysis in a CRM helps teams optimize various processes that affect different aspects of a business, such as sales, marketing, and customer service.

With a platform like a CRM to continuously gather and examine data, organizations have a regular, fresh stream of relevant data to pull from to make stronger decisions.

Why do companies need customer data analysis and why is it important?

Customer analytics are just as important as financial reports when gauging your business’s health. Analyzing your customer data helps you understand who your customers are, what they want, how they behave, and how satisfied they are with your company or a specific product.

Companies need CRM analytics to gain insights into what’s happening behind the scenes of their sales. Thanks to customer analytics, companies can:

• Understand customer behavior, including how they interact with products and services, what factors influence purchasing behavior, and what drives decisions
• Improve the customer experience by identifying what needs improvement, such as product features, customer support, or marketing messages
• Increase customer loyalty through identifying the most valuable customers and creating targeted retention strategies to keep them engaged

Look at UK-based grocery chain, Tesco, as an example. The grocer launched Tesco Clubcard, a reward scheme that offered incentives and was also used to track customer purchase history. After analyzing the data, Tesco discovered that a few loyal customers accounted for most of its sales, so the grocery giant created tailored coupons and offers to entice casual shoppers to the stores and encourage returning customers to spend more. Within months of launching, Clubcard customers were spending 4% more than non-Clubcard customers.

The benefits of CRM analytics for business growth

CRM analytics can be advantageous to every area of your organization. Regardless of whether your goal is boosting sales, improving customer service, or creating winning marketing campaigns, having access to a steady flow of customer journey data analytics can improve efficiency in more ways than one. Here’s a look at some of the core ways CRM analytics can benefit business growth.

• Make data-driven decisions: The data stemming from your CRM can be used to inform strategic decision-making that’s based on historical performance.
• Improve pipeline management: Learning more about what moves prospects through a pipeline more efficiently can make it easier to optimize pipeline management across the board.
• Increase conversions: When pipelines are optimized, it’s quicker and simpler to convert leads to clients and boost revenue opportunities.
• Enhance resource management: By using data to track workforce performance and resource usage, teams can forecast resource needs to stay more organized in the future.
• Optimize marketing campaigns: Getting information on marketing metrics helps improve campaigns by helping teams target the right audiences with the right message.
• Personalize customer messaging: Data reveals customer preferences and behaviors, which can be used to personalize messaging for each customer or segment.
• Establish proactive support: When customer service teams track pain points and see which solutions work best through data, they can improve common practices to be more proactive.
• Boost customer satisfaction: Isolating practices that improve satisfaction by analyzing data can produce happier and more loyal customers over time.

Which teams use full-funnel customer analytics?

It may seem like CRMs and analytics are tools just for sales teams, but the truth is that all revenue teams can benefit from integrating customer analytics. From initial awareness and acquisition to long-term retention and growth, sales, marketing, and service teams can use customer data analytics to improve internal processes and build a better customer experience, while other teams, like product development, can also benefit from client data. Let’s break it down a bit further by looking at how customer data analytics are used in different departments.

Customer analytics for sales

• Understand the source of new leads and track customer acquisition data and costs
• Improve sales rep performance across the entire sales cycle
• Learn where and why customers churn to improve retention
• Identify cross-selling and up-selling opportunities
• Build strong sales strategies through customer segmentation

Customer analytics for marketing

• Create stronger customer messaging for each funnel stage
• Build customer profiles to better target campaigns
• Measure campaign ROI and attribution across the full customer journey
• Personalize content based on customer journey stage
• Track brand awareness and sentiment over time

Customer analytics for service

• Identify common customer issues that impact retention
• Improve response times to enhance customer satisfaction and loyalty
• Predict which customers might need proactive support
• Track first-call resolution rates
• Identify training opportunities for support staff

Customer analytics for product development

• Understand customer preferences across different lifecycle stages
• Prioritize the development roadmap based on customer needs
• Track feature adoption rates and usage patterns
• Identify gaps in the current product offering
• Monitor product performance metrics post-launch

Common customer data analysis metrics

There are a lot of different metrics businesses can track to gauge progress and performance. To focus on improving customer relationships and enhancing the customer experience, here are some key customer data analysis metrics you should consider tracking through your CRM.

Customer lifetime value (CLV)

Customer lifetime value (CLV) measures the total revenue a company can expect to earn over the lifetime of a given customer relationship. It’s one of the best ways to link customer success efforts and revenue. You can calculate this by multiplying what a customer typically spends each month by how long they stay with you. So, for example, if someone spends $100 monthly and sticks around for 2 years, their CLV is $2,400.

Customer retention rate (CRR)

Customer retention rate (CRR) measures the percentage of existing customers your business retained over a given period, such as from one month to the next, between quarters, or year over year. If you started January with 1,000 customers, gained 150 new ones, and ended with 950 total customers, your retention rate would be 80%.

Customer retention cost (CRC)

Customer retention cost (CRC) measures the amount of money your business spends on retaining customers. It tells you whether your customer success efforts are effective. To calculate this, divide your total retention spending by the number of customers you kept. If you spent $50,000 on retention programs and kept 800 customers, you’re paying about $62.50 per retained customer.

Customer churn rate (CCR)

Customer churn, also known as customer attrition, defection, or turnover, is the opposite of customer retention and measures how many customers you lose over a given period. If you lose 50 customers out of your starting 1,000 in a month, that’s a 5% monthly churn rate, which translates to losing 1 in every 20 customers each month.

Customer satisfaction score (CSAT)

Customer satisfaction score (CSAT) measures how customers rate their experience with your company based on your choice of questions and rating scale. For example, if 425 out of 500 survey respondents say they’re satisfied with your service, you’d have an 85% CSAT score, which is a pretty solid indicator that most customers are happy with what you’re delivering.

Net promoter score (NPS)

Net promoter score (NPS) is a type of customer feedback analysis that measures customer responses on a sliding scale to a one-question survey, such as: “On a scale of 1-10, how likely are you to recommend [business/product/service] to a friend?” Responses are classed as follows:

• Detractors = 0 to 6
• Passives = 7 or 8
• Promoters = 9 or 10

Rate of renewal

For businesses that offer subscription-based services, such as software, the rate of renewal shows how many customers are staying after contracts expire. This helps assess a company’s growth and ability to retain existing customers. If 340 out of 400 customers whose contracts are up choose to renew, you’re looking at an 85% renewal rate, suggesting most customers see enough value to stick around.

Best practices for an effective customer data analysis strategy

While tracking the metrics we measured above is a great start, data without a strategy won’t give you the results you’re looking for. By implementing a few key strategies, you’ll be able to not only improve the data you collect but also make full use of it to grow your business. Here’s a look at some best practices to implement for stronger customer data analysis.

• Focus on a few core metrics: There are dozens of metrics you can track, but taking your short and long-term company goals into account, focus on a select few to track over a predetermined period before expanding to add new ones.
• Segment customers for targeted insights: Generic analysis of all your customers often misses nuances. Break down your customer base by demographics, behavior, lifecycle stage, value, or other relevant criteria to uncover patterns and opportunities relevant to specific groups.
• Clean and consolidate data: Unifying data is essential; otherwise, your source data may be filled with inaccuracies, irrelevant information, and duplicate data. A platform like monday CRM can help consolidate and clean up data automatically so that you can rest assured that the outcomes are reliable and accurate.
• Set a CRM reporting and analytics cadence: Set up automated dashboards and establish consistent review schedules so insights become part of your team’s regular decision-making process. This prevents important trends from being missed and ensures data insights influence business strategies.
• Use AI to optimize data: AI is your friend when it comes to customer data. There are so many ways to incorporate AI to optimize sales and customer relationships, including data-based personalization, sales forecasting, and more.
• Automate data collection: Using tools such as data analytics systems or CRM software helps streamline customer analytics efforts and save time for more strategic tasks. With the right CRM automation platforms, you can set it and forget it, making it easier to track key metrics and progress over time.
• Combine quantitative data with qualitative feedback: Numbers tell you what’s happening, but customer feedback tells you why. Don’t forget about surveys, support tickets, and direct customer conversations that allow you to add context to your analytics and validate what data shows.

How CRM analytics powers hyper-personalization

One of the best side effects of CRM analytics is the power it gives teams to personalize experiences across the sales funnel. According to research by Statista, over three-quarters of business leaders stated that personalization was instrumental in their business’s success. Personalized customer messaging, sales tactics, and service approaches can instill a sense of trust and loyalty in customers that keeps them coming back. When you use CRM analytics strategically to demonstrate a deep understanding of customers, both their pain points and relevant solutions, you create experiences that feel tailored specifically for them rather than generic one-size-fits-all interactions that risk falling flat.

Choosing the right CRM analytics software for your business

CRMs differ in their strengths and abilities, so it’s important to know what you’re looking for before picking one. If you’re in the market for a CRM analytics platform, there are a few deciding factors you should take into account before settling on a solution. Here are some things to consider that will make choosing the right platform a smooth affair.

Goals and strategic priorities

First things first, you need a solid grasp of what your goals are in general and for using a CRM. Once you understand where you want to go, it’s easier to find the right platform to map out your journey. For example, if your goal is to use analytics to increase sales, you’ll want to look for a platform that offers robust lead scoring, pipeline forecasting, and conversion tracking capabilities.

Growth and scalability needs

Do your future plans for your company include launching new products, expanding into new locations, or doubling in size by next year? If so, these are all things to take into account when selecting a CRM. You want a platform that can scale as your company expands so that you don’t find yourself outgrowing a limited system. Look for a CRM that’s easy to use so that onboarding is quick and easy, and that offers plenty of integrations so that you can easily connect other work tools you may need.

Deployment and mobility requirements

Do your team members work remotely, or does everyone work in the same office? Some CRMs offer cloud platforms, while others are on-premise. If your team members work remotely, or even if you want to give sales reps the ability to update their CRM on the go, then a cloud CRM might be your best bet.

Budget and pricing structure

Don’t overlook upfront costs in the total cost of ownership. Budget for things like implementation, training, and ongoing maintenance so that there are no surprises. Some CRMs charge per user, while others have tiered pricing based on features. Additionally, don’t forget to factor in potential costs for data migration, customizations, and integrations that might be essential for your business.

Harnessing the power of monday CRM analytics

With monday CRM, you can get closer to your customers, automate repetitive work, and organize your data in one place. As an AI-powered CRM, the platform offers users plenty of opportunities to optimize data collection and analysis to make the most of the information you already have. From predictive analytics to deep personalization, monday CRM puts your data front and center and uses it to improve customer relationships at every touchpoint.

To better illustrate the impact a data analytics-centered CRM can have on your business, let’s take a look at how Cenversa, an Australian pet care group, boosted sales and performance with monday CRM. The company was dealing with inaccurate data and an outdated CRM system. After implementing monday CRM, Cenversa was able to centralize its customer data and make more strategic business decisions based on real-time sales and territory data. With reps spending less time chasing down accurate data, the company saved an annual 3,500 hours of manual work by automating tasks like data entry, reporting, and customer follow-ups.

Now, here’s a closer look at some of the features in monday CRM that help teams put data first to work smarter.

Plan ahead with predictive analytics and sales forecasting

With monday CRM, teams get access to predictive analytics and forecasting features that help them turn data into a plan of action. By analyzing historical trends and patterns in user behavior, monday CRM can predict which deals in a pipeline are likely to close or fall through, allowing sales teams to focus their time and energy in the right place.

Optimize marketing campaigns with personalized insights

Marketing teams can make the most of the data in monday CRM to use real-time insights to improve campaigns. By looking at behavioral patterns, preferences based on segments, and engagement history, teams can personalize email and marketing messaging to fit what clients want to see, delivering timely campaigns that resonate.

Automate manual tasks for quicker pipeline management

The platform makes it simple to create AI-powered custom automations to put your pipeline on autopilot so that everything flows smoothly. From automating follow-ups, status updates, and even data extraction from web forms and uploaded documents, sales agents don’t have to spend a lot of time managing their pipeline and can instead focus more on nurturing relationships with high-priority accounts.

Analyze customer sentiment to stay one step ahead

Teams can use monday CRM’s sentiment analysis feature to get insights into how a customer is feeling. Through using AI to analyze chat histories, emails, meeting summaries, and call logs, monday CRM can determine if a customer or prospect is feeling positive or negative so that sales and support teams can proactively get involved with immediate solutions.

Master customer data analysis with the right CRM

Streamlining customer data analysis will help you understand who your customers are, what they want, how they behave, and how satisfied they are with your products or services. If you want to stay ahead of the competition by creating a memorable end-to-end customer experience, it’s important to listen to the data and use it to inform strategies in your company. Using a solution like monday CRM helps you make the most of the data you have while digging deeper to give you insights into customer behavior, sentiment, and preferences so that teams can forge deeper client relationships that form the foundation of your business.