How To Better Align B2B Lead Generation And Prospect Development
By Daniel Hussem - Director of Marketing for Troparé Inc.
Click here to read the original article on Forbes.com
Business-to-business (B2B) lead generation and prospect development are key skills that can drive superior sales growth. They are closely interconnected, as prospecting is an essential part of lead generation. But this link isn't always obvious to sales and marketing professionals.
The lack of understanding of their interdependence tends to complicate the relationships between sales and marketing team members. Forrester Research states that “if marketing and sales are not aligned and they do not collaborate, they will be disintermediated,” resulting in lost sales or costly delays.
Aligning B2B lead generation and prospect development requires strong cooperation, reliability and trust. Both sales and marketing teams need to work together to develop a set of specific abilities, customer insight, knowledge and practices that help launch campaigns, nurture leads, collect and analyze data, and convert prospects to new customers.
Building An Analytical Foundation
Big data has become a game-changing factor for sales and marketing in companies across the globe. Data is penetrating all spheres of business, and the success of the entire lead generation process now depends on the competence of the team.
Competence in big data analysis is a prerequisite to work with various data types and disparate data sources to establish an efficient lead generation funnel. A successful lead generation strategy requires a high level of personalization, which in turn involves the processing of massive amounts of data (often from diverse sources).
Legacy/Disparate Data Sources
According to research by Econsultancy and IBM, most companies use only a small portion of data collected for personalization, yet still struggle to unify this data.
Disparate legacy systems and data sources require the accurate analysis, cleaning, comparing, blending and filtering of data before it is ready for migration. Legacy data sources are usually related to data quality issues, and the overlapping of datasets, duplication of data and missing data are frequent occurrences. Therefore, the integration of such data requires well-established, precise steps and complex programming. Patience and scrupulousness are major keys to success when dealing with disparate data.
A key reason legacy systems tend to be a burden for both sales and marketing teams is that they appear to be outdated. Due to the exponential development of digital tools and techniques, a gap often arises between the system adopted earlier and the data gained now. Owning an outdated legacy system can result not only in sales and marketing complications, but in errors, bugs and even unprofitable decisions. Moreover, many challenges can be avoided by modernizing legacy systems, including:
- Complications with system integration and scalability.
- Security threats.
- Slow or poor performance.
- Dependence on disconnected and siloed data and tools.
- Hardware and device dependency.
- Additional costs.
Storing data within in-house data centers seems to be an outdated solution and cumbersome to manage. The present challenge of data management has shifted to the integration of accumulated in-house data with data gained and stored in external sources.
Combining these two categories can prove to be a huge success. Industries face the need to answer complex questions; thus, data integration is a must.