What are some examples of BI tools that can improve your customer service?

Modern CRMs such as HubSpot, Salesforce (and in some cases CMS) are pivotal for data integration, enhancing organizational efficiency and customer interactions. It centralizes diverse datasets, creating a unified view of customer interactions. By automating data entry, CRM reduces errors and ensures consistent, up-to-date information across teams. Breaking down silos, it promotes collaboration among sales, marketing, and support teams. Integrated data enables personalized communication, allowing targeted campaigns and proactive customer support. That being said, there is hardly any single source of truth to rely on for decision making processes but there are different approaches only.

As businesses grow, they use more and more software tools to manage their business and keep customer data. Unfortunately, they can get to a point where they have so many data silos that it becomes very hard to fully understand their business and customers. They have no single source of "truth". Data integration software can help. Informatica PowerCenter, IBM InfoSphere, and SAS Viya Integration Platform are 3 of the top data integration platforms businesses can consider.

In addition, the Business Intelligence data integration facilitates strategic management, enabling the targeted delivery and promotion of products/services to discerning customers based on their preferences, interests, choices, and behaviors. This intelligent approach aims to secure a competitive advantage in the marketplace.

Data visualization tools can help businesses improve their customer satisfaction, reduce their costs, and increase their revenue. Businesses can use data visualization tools to provide better customer service by helping them understand their customers better, identify and resolve customer issues, and make better decisions about customer service. Popular BI tools include Tableau, Power BI, Qlik Sense, and Sisense.

The incorporation of BI in data visualization improves decision making as clear and visually appealing representations of complex data make it easier for decision-makers to grasp insights quickly, leading to more informed and timely decisions. BI visualization also help identify trends and patterns that may not be apparent in raw data and react promptly to changing conditions and make data-driven adjustments in real-time basis, which enabling businesses to capitalize on opportunities and address challenges proactively.

One thing at a time! Yes, that should be the only focus of visualizing data for interpretation. We can generate so many KPIs through different timeline but what matters most is to set what the ultimate goal is. With that, it is always recommended to seek for relationship between data visualization. A high revenue tax might mean high profitability as well or if you got unlucky, it may burn some cash as well! Only visualizing tax growth will not make sense (it might for revenue collectors though).

Data mining can provide a number of business benefits, including increased revenue, reduced costs, improved customer satisfaction, increased innovation, and improved decision-making. Businesses can try KNIME, SAP HANA Predictive Analytics, and RapidMiner for state of art data mining.

One thing I've found helpful is to keep in mind what audience is expecting from data story. Rather than focusing on information generation, it is better to set a target analytics endpoints or objectives. This approach offers clarity on the required data types and segmentation strategies. The tools employed will bridge the gap between raw data and meaningful information. Say, when aiming to understand reasons behind a high post-sales ticket SLA, the analysis should focus on customers who requested service and corresponding ticket data. Using a time-based moving average (month-on-month) compared to the average SLA unveils nuanced trend analytics, substantiating hypotheses about elevated or reduced SLA.