B2B enterprises invest in various marketing strategies to expand their revenue pipeline. Database marketing is one such tactic deployed to bolster sales growth and build brand value. Most enterprises maintain a combination of in-house databases obtained through their inbound and outbound campaigns and external database obtained from B2B database providers.
However, it is not as simple as it sounds. In their bid to source and maintain accurate databases, companies encounter a range of challenges as they make sense of the huge amounts of data they are gathering. Various data architectures, data management concerns, new BI analytics capabilities, and varying levels of data maintenance literacy in the workplace are all adding to the challenges. Companies are also restricted by insufficient data governance and technology integrations while storing and using their database. Let’s take a look at some of the pressing database challenges hindering the demand generation and revenue generation exercises of B2B companies:
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1
Integrating data from varied sources
With an increase in the number of customer touchpoints, there has been a significant rise in data sources. Businesses are now required to collect data from a range of databases, big data platforms, a B2B database provider, and business applications- both on-premises and on the web. They also have to handle unstructured data that cannot be easily integrated and standardized in a regular CRM.
Unstructured sales intelligence requires AI/ML and NLP-powered database management and BI analytics tools to make them easily accessible and usable by sales enablement teams. The lack of data integration and data orchestration systems limits the scalability and increases the time it takes to examine the data. This further causes data stagnation and decay over time, reducing the productivity and ROI of the marketing campaigns.
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2
Use of conventional subscription-based database models
Enterprises that are still using the traditional subscription-based will more likely fall behind in the race. These models don’t provide a wide range of options to strengthen the AI-powered customer engagement strategies. Moreover, these models don’t support AI/ML or NLP technologies that restrict a company’s ability to process large-scale B2B databases. It is one of the primary barriers to creating an impactful result with their demand generation campaigns.
Most subscription-based database models are not built for targeted demand generation or ABM strategies. These models don’t provide critical information like buying intent or firmographic and technographic information. Furthermore, storing and processing huge volumes of data while maintaining data accuracy, cleanliness, and contact-ability turns cumbersome and time taking through these models. This creates gaps in the existing sales and marketing initiatives and undercuts the revenue goals of the concerned companies.
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3
Data silos with inconsistent information
Another prevalent issue that troubles modern-day enterprises is siloed systems. It is challenging for BI analytics tools to analyze siloed data with variable permission levels and security settings since database completeness is necessary for actionable business intelligence. To get the desired marketing and sales output, enterprises must disintegrate silos and unify the data inside them.
However, many companies struggle with data unification and standardization due to a lack of internal data standards across departments and business divisions. Inconsistent data in silos can lead to different versions of the truth, which can hamper coordination between different revenue generation teams.
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4
Lack of options moderate data consumption & standard attribute tags
Many database models don’t allow users to moderate their data consumption volumes and costs. Companies that need a smaller dataset end up paying for a standard, much bigger database that not only shoots up their overheads but also brings down the per-contact ROI. Apart from this, the lack of standard attribute tags limits a user’s access to meaningful and transparent storage. Fewer enterprises have the expertise to create algorithms, protocols, and languages to orchestrate meaningful and relevant data. This limits their ability to get the right data at the right time.
Advanced data attributes enable companies to process and store contextual information from unstructured data. On the contrary, standard attribute tags create the opposite effect inhibiting sales intelligence. Furthermore, with a lack of correct attribute tags, data errors become rampant within the CRM. This is one of the primary reasons why sales representatives fail to get a detailed picture of their target audience. It is essential for modern enterprises to build a structured database with the right attribute tags to support personalized sales and marketing campaigns.
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5
Absence of tech-powered processes to overcome database management challenges
As companies grow the amount of raw data they generate and store, they require tools and processes that can continuously cleanse, update, and enrich those data. Many enterprises still have low technology integration and obsolete database practices that cause their customer data to go stagnant and/or decay over time. A poorly designed data management system also creates compliance issues with local and national data privacy laws.
The lack of AI-powered standardization and deduplication engine fills a company’s CRM with duplicate, old, and redundant data. Even with traditional database service providers, companies don’t get the ability to validate and refresh databases using AI/ML tools, which reduces productivity and increases marketing costs for low output. Continuous data refresh is a modern-day mandate to keep track of key decision makers and influencers within existing marketing lists.
Conclusion
Data is the growth shot that B2B businesses worldwide need to kickstart their sales momentum and improve ROI. However, building and maintaining an accurate B2B database is riddled with multiple challenges that can tank the productivity and ROI of B2B sales and marketing strategies. Companies need to refine and clean their data for improved accuracy. They can also source company data from a reliable B2B data vendor that can offer standardized, human-verified, and contactable data to elevate sales prospecting.
Contact us to know more about our B2B database services and buy B2B data that is over 85% accurate and sourced from over 10Mn companies.
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