What is CRM analytics?
CRM (customer relationship management) analytics comprises all of the programming that analyzes data about customers and presents it to an organization to help facilitate and streamline better business decisions.
CRM analytics can be considered a form of online analytical processing (OLAP) and may employ data mining. As companies have added new and often faster ways to interact with customers, the opportunity and need to turn data collected about customers into useful information has become increasingly important. As a result, software companies have developed specific products that perform customer data analysis, and analytics is a component of many CRM systems.
Types of CRM analytics
There are three primary types of CRM analytics systems: collaborative, operational and analytical. Each comes with special features that provide functionality, such as storing customer data, identifying customer trends or automating certain tasks.
The following are the main characteristics of each CRM type.
A collaborative CRM system aims to streamline processes and workflows across various organizational structures. It facilitates cross-departmental collaboration by providing a centralized customer database that every department -- including marketing, sales and customer service -- can access. This centralized repository offers visibility into all customer communications, including purchase history, service requests and notes. This type of CRM equips customer service representatives with the tools they need to resolve customer issues and expedite services.
Collaborative CRM tools are based on the following two aspects:
- Interaction management. This entails keeping track of and aggregating all customer interactions in the customer account, from the communication medium -- email, social media and face-to-face -- to what was communicated.
- Channel management. Customers contact a business through many different channels, including chat, email, text and social media. To offer a great customer experience, businesses must keep track of their customers' preferred communication mediums. Channel management uses the information collected through interaction management and CRM records to determine how customers contacted the business, so the same preferred channels can be used to get back to them.
An operational CRM system automates numerous processes by providing a detailed picture of all customer interactions. Various company tasks, including sales forecasting, creating and evaluating marketing campaigns and identifying prospects are all automated through operational CRM.
An operational CRM system consists of the following three elements:
- Sales. The main purpose of an operational CRM system is to acquire new customers and maintain existing ones. It's used to set standards within a company, organize information efficiently to meet customer needs and increase sales. It employs various CRM sales modules, such as lead management, contact management, sales forecasting and quote-to-order management.
- Marketing. This involves finding the best way to offer products and target potential customers. For example, marketing professionals may reach out to specific audiences with personalized messaging. This may also include creating segmented marketing distribution lists for targeted sales, onboarding, repeat purchases and re-engagement campaigns.
- Customer service. This is the support staff who manages contact and customer communications. The customer support staff may present self-service options to customers, answer their queries, escalate issues, perform case routing and offer satisfaction surveys.
An analytical CRM system works behind the scenes to analyze the sales data gathered by operational CRM apps. It tracks various performance metrics to offer actionable insights into consumer behavior. This may benefit a company during its communications with the customer, as it can present the customer with relevant information to help satisfy their requests. Its primary components are a data mining module and OLAP tools.
An analytical CRM system provides reports on the following factors:
- Channel. It collects customer information from various channels and structures it for analysis.
- Customer. It helps examine customer characteristics and patterns of behavior.
- Customer service. Analytical CRM helps enhance customer relationships and loyalty by providing various business methodologies in sales, marketing and support.
- Marketing. It looks at past performances, customer demographics and buyer personas to optimize marketing campaigns.
- Sales. It determines the efficiency of the sales pipeline and sales prospects and also helps manage leads.
Applications of CRM analytics
Once CRM analytics are set up, they offer organizations insights into understanding and using the CRM data that's mined. Organizations can use CRM analytics in the following ways:
- Customer segmentation groups. Customers can be divided into one of two groups -- those most and least likely to repurchase a product.
- Profitability analysis and customer value. CRM analytics can provide insight into which customers contribute to the highest profits over time. This involves understanding not just how much a customer spends, but how many resources are dedicated to that customer.
- Personalization. Organizations can market to individual customers based on the data collected about them. This requires obtaining a 360-degree customer view.
- Measuring and tracking escalation. CRM analytics help measure how often problems arise with a product or service so an organization can rectify the issue and quickly achieve customer satisfaction.
- Predictive modeling. A mathematical process predicts future success by analyzing the customer knowledge base and the patterns and levels of customer engagement.
Data collection and analysis are viewed as a continuing and iterative process. Over time, business decisions are refined based on feedback from earlier analyses and consequent decisions.
Benefits and challenges of CRM analytics
CRM analytics improve business efficiency by storing and analyzing customer data. However, CRM platforms can sometimes present challenges.
Productivity. CRM platforms offer integrations with other tools, which increases the productivity of customer service agents, as they can perform tasks from a single user interface (UI). For example, if a sales team integrates its CRM platform with email and calendar apps, agents can view their sales pipelines, customer interactions and upcoming appointments all in one place. CRM analytics also increase productivity by automating mundane tasks such as completing approval processes, sending emails or updating user contacts. This frees up more time for agents to focus on building customer relationships. For instance, there's software that can automate the approval processes for business requests, such as expense reports.
Personalization. CRM platforms can efficiently record and track customer analytics, such as purchase history, demographics and customer service interactions. This enables organizations to offer tailored services to customers and customer segments through targeted advertising, which in turn leads to higher customer satisfaction rates.
Artificial intelligence (AI). Organizations can use AI-powered CRM platforms to acquire valuable business insights. For example, forecasting tools track trends in customer data and provide recommendations to sales reps on the next steps in the sales cycle. AI can also be used to speed up decision-making for the customer, as intelligent virtual assistants or online customer care can quickly answer a customer's questions. Certain AI-powered CRM platforms also use sentiment analysis tools to sense customer emotions, including anger or happiness during a given interaction.
Supply chain management. CRM analytics can provide insights that help organizations improve supply chain management by reducing inventory and speeding up delivery, thus providing partners and suppliers with lower costs and more competitive pricing.
Siloed data. While most CRM platforms are stored centrally, different departments may rely on separate CRM software, which can create data silos. For example, if a customer calls with a question, a sales agent may transfer them to the service department if they don't have access to the same information, which may cause the customer to repeat themselves. Most organizations can solve this problem by investing in a customer data platform. A CDP can centrally store information from CRM tools and other sources that all employees can access.
Software integration. A major challenge with CRM analytics is the integration of analytical software with existing and new systems. If the software doesn't integrate, the collected data can become difficult to use.
Data entry. CRM platforms without AI integrations require agents to manually enter data into the system to keep customer accounts up to date. Manual entry can be cumbersome for agents who travel frequently and may not have the time to constantly update the system. AI-integrated CRM can simplify data entry by automatically populating customer accounts with information from emails, texts and phone calls.
Adoption resistance. At times, organizations may face resistance to CRM adoption when convincing employees to get on board with the new CRM system and migrate away from the old processes. Leadership plays an important role in making this transition easier for employees who might be struggling to embrace the new CRM platform.
Common CRM metrics
To improve customer relations, an organization needs to quantify and analyze the data it receives from specific CRM metrics. These metrics offer better control over a company's sales and marketing efforts, as they can be used to measure success and the overall performance of an organization.
Here are some of the most common CRM metrics:
Customer turnover. This refers to the percentage of customers a business has lost over a certain period. This can be the most important and painful metric for a business to measure. While it isn't always easy to review the number of lost customers, it does enable a business to pivot its efforts toward containing the customer numbers. Customer turnover can be calculated on a monthly, quarterly or yearly basis.
Net promoter score (NPS). This metric measures a customer's satisfaction levels with a business and how likely they are to recommend it to someone else. To calculate the NPS, common customer research questions are presented to customers with a rating assessment.
Rate of renewal. This is an important metric to monitor for companies offering subscription-based services that require customers to renew their contracts periodically. The renewal rate assesses a company's growth in terms of retaining current and new customers, and it can be measured monthly or annually. For example, if a company has 100 contracts at the start of the year and renews 95 contracts, its renewal rate is 95%.
Cost of retention. This is the total cost a business incurs to retain a customer. It may include costs associated with customer service, customer success, customer engagement, marketing or any training provided by the business. To calculate how much a business spends on a certain customer, the following two factors should be considered:
- The calculation period, such as monthly, quarterly or yearly.
- Customer retention expenditures, which may include the cost of all services provided, personal time investment, fees for the premium tools needed, and the communication and marketing campaigns created by the business.
Once a company has gathered this information, it can divide its costs by the number of customers during the designated period, which provides the average cost of retaining a customer. The average retention cost should always be less than the average revenue generated from the same customer.
Length of the sales cycle. This measures how long it takes someone to buy from a business. The metric identifies the time between the moment when a prospective customer is identified to the time the sales process is closed. This helps accelerate the sales cycle of a business by identifying potential problems.
First call resolution (FCR). The success of a business depends on resolving customer issues as quickly as possible. The FCR measures the percentage of issues resolved on the first customer contact. A high FCR means the customer support team is diligent and responding to customer requests promptly.
Customer Lifetime Value (CLV). This metric measures the average value of a customer over time, based on the business and profits they've generated for the business. CLV is an estimate of the total revenue a business can expect to secure from a single customer throughout the length of its contract.
Analyzing CRM metrics
Once a business has gathered its CRM metrics, it's time to analyze them. This process is generally divided into a pre- and post-sales analysis.
Pre-sales CRM analytics
The pre-sales process includes all the activities that occur before a customer purchases a product or service. These activities often include product research, prospecting and identifying sales-qualified leads, as well as market research, customer analysis and selling propositions. It's crucial to track these metrics because they're the building blocks of a long-lasting customer relationship.
The most important pre-sales CRM metrics to analyze include:
- New leads. Most sales teams spend a big chunk of time trying to find new customers by sending proposals or making phone calls. Businesses should analyze these activities thoroughly to observe the effect they have on conversion rates.
- Prospects. Finding prospects is an integral part of a sales team's outreach efforts. Companies should analyze the conversion rates of prospects to weed out ineffective strategies.
- Personal interactions. Every customer interaction is a chance to create a potential customer and shouldn't be limited to cold calling or sending emails. It's important to analyze the number of calls that lead to conversations, along with their length and occurrences.
- Website and additional engagement. Many CRM programs can identify site visitors and potential customers who engage with a business through other channels, including social media or newsletter signups. By analyzing these metrics, businesses can come up with potential leads to target.
Post-sales CRM analytics
The goal of any business should be to turn a one-time customer into a returning customer. To achieve this goal, it's important to keep track of the sales funnel, including the customer experience. A business should also find ways to keep the momentum going after a sale is completed.
The following are the main post-sales CRM metrics to analyze:
- Problem tracking. Businesses must record any problems voiced by customers regarding products, services or other interactions. If a business is oblivious to the problems its customers face, it runs the risk of losing them.
- Additional purchases. It's important to track other products the customer has purchased from the business, as it presents the organization with an opportunity to determine which products to market to them in the future.
- Purchasing patterns. Businesses should investigate the purchasing patterns of those customers who have signed up for subscription-based services. The reasons behind their loyalty and motivation should also be assessed to discover whether the customer is likely to continue purchasing in the future. This can help a business avoid potential customer churn by offering discounts, coupons or other loyalty benefits to existing customers.
What to look for in CRM analytics products
A business should evaluate its needs and priorities before selecting a CRM tool. Since all CRMs aren't created equal, the following points should be considered before the selection process:
- Ease of integration. The main purpose of a CRM tool is to streamline business processes. Therefore, a business should ensure the prospective product is capable of easily integrating with existing applications and workflows.
- Features and functionality. Every business is unique in terms of its business model, customer growth and functionalities. Therefore, the best CRM isn't one that offers a one-size-fits-all approach, but rather a tool that provides scalability and adaptability to meet the changing needs of a business.
- Simplified usability. A CRM that offers an intuitive user experience is easier to adopt. It also helps if the potential product offers simplified usability, built-in tutorials and multichannel support, so employees across the organization can embrace it easily.
- Capacity for big data. A CRM analytics system should be able to store, capture and process massive amounts of real-time data regularly. Therefore, it's important to consider an option that supports big data
- Data visualization. The ideal CRM analytics tool should offer data visualizations, such as interactive graphs and charts, instead of reports that might be difficult for a business to interpret or follow. Interactive visualizations are an integral feature of a CRM, as they help turn data into actionable insights.
To pick the right CRM system, an organization must first understand the differences between the three main CRM types. Learn more about these CRM types and the pros and cons of each.