Technology has transformed the landscape of business operations. In contemporary business practices, numerous avenues, such as social media, website interactions, and email communications, serve as channels for customer engagement. Within these engagements, a significant volume of data is generated. According to Statista, businesses collectively generated over 64.2 zettabytes of data in 2020, with projections estimating a rise to 180 zettabytes by 2025.
While interpretations of the value of this data may vary among businesses, many perceive it as a valuable resource for enhancing customer experiences. For instance, Amazon strategically utilizes insights from customer interactions to tailor promotions and offer personalized product recommendations to its customers. This process involves collecting, structuring, and analyzing customer data to learn customers’ behavioral patterns and preferences.
What is CRM Analytics
Customer relationship management (CRM) analytics is a process of gathering, structuring, and analyzing consumer data acquired across various touchpoints within a business. Its primary objective is to facilitate the comprehension of customer behavior through reporting tools or dashboards. This understanding then informs strategic business decisions tailored to customers’ evolving needs. CRM analytics encompasses three pivotal business domains: marketing, sales, and customer service. It draws data from diverse sources and consolidates it to be accessible to multiple organizational stakeholders.
Understanding Big Data in CRM
Big data refers to vast collections of data accumulated by a business, sourced from various channels like email lists, sales records, social media interactions, and even website traffic.
Big data is often facilitated by efficient data warehousing technologies for storage and management. When aggregated and analyzed, these large datasets serve as a valuable resource for understanding and predicting customer behaviors and preferences within CRM strategies. Here are some essential roles big data plays in CRM analytics:
Product Development
Big data is helpful in businesses when they want to develop products that will resonate with their target customers. With big data, companies can learn much about what their customers want. It lets them observe customers’ purchases to determine trends and popular preferences. Then, they can use the insight to decide what features to focus on when developing new products. Big data helps businesses make better choices about what to produce based on their customer’s preferences.
Predictive Maintenance
Through advanced CRM analytics and machine learning algorithms, big data can enable organizations to detect early signs of product failure or degradation by identifying patterns or anomalies in product performance data. These insights allow for early maintenance interventions and prevent potential product failures. IBM offers Watson IoT, a platform that combines IoT data with advanced analytics and AI to enable predictive maintenance on IoT devices. It monitors and analyzes product performance in real-time and predicts maintenance needs.
Customer Experience
Companies use the insights they gain from big data analytics to enhance their CRM strategies and deliver exceptional customer experiences. Companies can send targeted promotional emails or newsletters that resonate with individual customer preferences with proper customer data analytics.
They also use this data to recommend products their customers will be most interested in. Netflix, for example, analyzes their customers viewing history, ratings, and interactions with the platform to suggest content likely to be of interest to each user, thereby enhancing user experience and engagement.
Fraud Detection
Businesses integrate customers’ transaction data, profiles, and behavioral information to detect anomalies and deviations from a customer’s regular transaction pattern. This can flag fraudulent activities, especially in the financial sector. Banks, for example, leverage fraud detection AI models to analyze historical transaction data across all customers and identify common characteristics of fraudulent transactions, such as unusually high transaction amounts or frequent purchases from high-risk merchants.
Harnessing Big Data for Customer Insights
With approximately 328.77 million terabytes of data generated daily, the potential for companies to gain valuable customer insights is immense. However, despite this abundance, some businesses struggle to develop effective CRM analytics strategies to capitalize on this resource. Companies must adhere to specific steps to get the full potential of big data. The following steps will enable businesses to extract better insights from big data:
Define Your Business Goal
Before implementing big data initiatives in your company’s CRM, it is essential to establish clear goals for what you want to achieve. Consider specific objectives such as identifying customers who abandoned their online shopping carts, predicting which businesses are likely to grow, or recommending personalized products to particular customer groups. These objectives will guide you in making informed decisions and improving your business outcomes.
Start With The Data You Already Have
Your CRM system is already brimming with data waiting to be explored. Beginning with this existing dataset will provide a solid foundation you can build upon. As highlighted by the Harvard Business Review, numerous companies possess a wealth of data they’ve yet to leverage fully.
Utilizing CRM reports can be valuable for dissecting and aligning this data with your business objectives. For instance, delving into purchase patterns can uncover trends, revealing which products are popular and in which regions. With this information, you can strategically focus on areas with higher product demand and optimize your marketing strategy for maximum impact.
Maintain High-Quality and Accurate Data
Gartner revealed that poor-quality data can lead to significant financial losses and poor business decision-making. Companies should prioritize regular data cleansing and validation processes and invest in advanced data management systems to ensure data reliability and accuracy. Moreover, companies should adopt cloud-based CRM tools to provide uniform access to real-time data across all staff members.
Employ Skilled Professionals
Hire skilled professionals and use reliable CRM software for big data analysis. Experienced experts can handle complex analytic techniques like sentiment analysis and predictive modeling, providing accurate customer insights. Also, invest in a trustworthy CRM tool with advanced AI features to streamline data organization and gain valuable customer behavior insights swiftly.
CRM Analytics Techniques
CRM analytics leverages various advanced data analysis techniques to get insights from customer data. Here are some methods used:
Machine Learning (ML)
Supervised learning algorithms, like decision trees or random forests, can be employed to analyze vast customer datasets. These algorithms are trained on historical customer data labeled with desired outcomes, such as customer churn or product purchase. By identifying patterns in the data, the models can predict the likelihood of future customer behavior. This allows businesses to take measures, such as targeted marketing campaigns or loyalty programs, to prevent customer churn or recommend products that are likely to be of interest to the customer.
Sentiment Analysis
Sentiment analysis leverages Natural Language Processing (NLP) techniques to extract sentiments from textual data. NLP techniques like sentiment lexicons can categorize customer opinions as positive, negative, or neutral. This analysis helps businesses gauge customer satisfaction and identify areas for improvement. For instance, sentiment analysis of social media posts about a product launch can reveal whether customers are excited about the new features or if any common concerns need to be addressed.
Customer Segmentation
Customer segmentation utilizes clustering algorithms to group customers with similar characteristics into distinct segments. K-means clustering, or hierarchical clustering algorithms, can segment customers based on features like demographics or purchase history. This segmentation allows businesses to develop promotions that fit each customer segment’s needs and preferences.
Statistical Analysis
Statistical analysis techniques such as linear or logistic regression model the relationships between customer data points. These models can be used to identify which factors have a statistically significant impact on customer behavior. For instance, regression analysis can be used to understand how factors like price, product features, or marketing channels influence customer purchase decisions. This knowledge helps businesses optimize their offerings and marketing strategies to drive desired customer actions.
Conclusion
Building genuine and lasting customer relationships is critical to thriving in today’s competitive market. But to truly meet your customers’ needs, you must first understand their behaviors, preferences, and desires. With the wealth of customer data available today, businesses can seamlessly achieve this through effective CRM analytics. It’s like deciphering a code that reveals valuable strategies for keeping customers satisfied and loyal to the brand.
About Arun Gupta
Arun Gupta is a seasoned solutions architect with extensive experience in the IT industry, specializing in Microsoft Dynamics 365/CRM and Power Apps. Holding a Master of Science in Information Technology degree from the University of North Carolina at Charlotte, Arun has developed a robust portfolio in leading enterprise-level solutions and strategic technology implementations. His expertise encompasses a deep understanding of Dynamics CRM, customization, configuration, and the Power Platform, including Power Apps, Power BI, and Power Automate. Arun’s career is marked by successful tenures at prominent companies where he led teams and orchestrated the integration and upgrading of complex CRM systems. For more information on Arun Gupta, visit https://www.linkedin.com/in/arungupta86/
Disclaimer: The author is completely responsible for the content of this article. The opinions expressed are their own and do not represent IEEE’s position nor that of the Computer Society nor its Leadership.
