How B2B organizations can use their data

How B2B organizations can use their data

by Boxplot    Nov 25, 2023   

B2B organizations possess a unique advantage when it comes to leveraging data. Data on their institutional clients is often readily available. Some of the most valuable data-driven outcomes for B2B organizations include sector analytics, both retrospective and predictive, prospect analytics, and automated data cleaning. In this blog post, we’ll explore how B2B organizations can gain a competitive edge through superior data analytics.

Customer Stratification

Customer stratification is a pivotal strategy in business that involves segmenting a customer base into distinct categories based on various criteria, such as purchasing behavior, demographics, or customer value. This approach allows organizations to tailor their marketing and service efforts to different customer groups, ensuring more personalized and targeted interactions. By stratifying customers, businesses can identify high-value clients deserving of special attention and low-value customers who may benefit from re-engagement efforts. This segmentation not only enhances customer satisfaction but also maximizes the effectiveness of marketing and sales initiatives, ultimately driving business growth. Customer stratification serves as a valuable tool for optimizing resource allocation and fostering long-lasting customer relationships. Boxplot and Empirical Consulting Solutions have partnered to offer ar robust and proven customer stratification service. You can find more details here.

Product Data

B2B organizations, regardless of their industry, can harness the data they collect about their products or services to make more informed decisions and drive continuous improvement. Take, for example, a business offering phone services to other businesses; by analyzing usage patterns and call data, they can identify peak call hours, call drop rates, and customer preferences, allowing them to optimize service quality and pricing structures. Software companies can leverage customer feedback and usage metrics to refine their software’s features, ensuring it aligns with market demands. Similarly, firms supplying machines to manufacturers can monitor machine performance data to predict maintenance needs, reduce downtime, and enhance product reliability. In each case, data-driven insights empower B2B organizations to adapt and innovate, resulting in better service delivery, enhanced products, and ultimately, more satisfied clients and increased competitiveness in the market.

Sector Analytics

Sector analytics involves analyzing the composition of your client base using known data on these businesses. You’ll address questions like: How much of your revenue came from foreign clients last year? What was the revenue breakdown by industry? To what extent did you engage with large corporate clients versus smaller ones last quarter? These insights are invaluable and often lead to the creation of reports and dashboards that aggregate your organization’s data into concise, visual displays. Properly constructed, these reports can be configured for automated updating, creating a hands-free data ecosystem. Given the unique insights generated by sector analytics, it has become a standard practice among data-savvy B2B organizations.

Sector Predictive Analytics

Sector predictive analytics is an extension of sector analytics, where new or estimated data is generated. For instance, you can use known data on past sales to predict future sales to corporate clients. Data scientists often employ machine learning to build predictive analytics solutions, which optimize the process of extrapolation for accuracy and automation. Predictive analytics isn’t limited to forecasting; it can be used to estimate hypothetical scenarios, retrospectively evaluate decisions, and plan for the future. What sets predictive analytics apart is its ability to generate new, previously unknown information.

Prospect Analytics & Data-Oriented Lead Conversion

Data analytics is instrumental in identifying which of your sales leads are more likely to become clients. It also helps improve lead conversion rates, particularly for prospects who initially seem less likely to convert. B2B organizations often deal with a few large clients at a time, making the conversion of a higher number of clients crucial. Robust, data-driven lead conversion schemes are invaluable. While gathering data from various sources can be challenging, a skilled data analyst can help construct an industry-leading lead conversion strategy.

Automated Data Cleaning

Data quality significantly impacts the effectiveness of your data analytics. B2B organizations serving large corporate clients, with vast databases and numerous users, often struggle to maintain data quality. Modern data analytics tools, such as Excel and Python, allow for partial or full automation of data cleaning. Automated data cleaning not only saves time but also ensures the high quality of the resulting data, reinforcing the quality of the analytics they drive. Small to mid-sized B2B organizations often engage external data analytics experts to set up automated data cleaning processes due to their critical nature.

Superior Data Analytics for Your B2B Organization

Whether you seek to analyze your clients, enhance your lead conversion pipeline, or improve data cleanliness, superior data analytics is your path to a more advantageous future. If you’re unsure where to start, contact Boxplot. We’ve assisted numerous B2B organizations in realizing their data-oriented goals, regardless of their prior experience with data. Discover why leading B2B organizations have adopted a data-oriented strategy. The future is data-driven, and it’s time you experience its benefits. Contact Boxplot to embark on this transformative journey.

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