The Importance of Data in B2B Marketing

Data is the key to success. Without data, there is no insight, no targeting and no optimization. This is true across both customer and business facing organizations but shows up in a nuanced way in B2B, as we need to understand and unify data across both individuals and accounts.

We all obsess over the plumbing, what the technology stack is made up of and what tools are available to your teams. Real success is driven by understanding the value that the stream of data unlocks through commercially driven insights that can help your organization drive both short- and long-term growth.

Marketing folk often share marketing metrics as success measures. I would urge you to think about how your metrics match up to the commercial objectives and translate from vanity and performance-based numbers to show how they align with business performance. Taking the conversation out of the functional focus will ensure that you are speaking the language of your business and taken seriously as a marketer.

Types of data

There are myriad types of data that need to be understood to grasp their importance in B2B.

• Market and audience data – showing trends, competitor insight and data enabling audience segmentation and targeting.
• Performance and analytics data - campaign metrics, performance and engagement data which can help you optimize your investments.
• Customer insights – audience survey data, client and customer buying signals, individual profile data and account metrics which all help a business deepen the relationship and drive greater upsell and cross-sell potential within current accounts.
• Propensity modelling – predictive analytics that enable a deeper understanding of what an individual and an account might buy next and where future opportunities might lie.
• Real-time optimization data – Data that can be used to optimize campaigns, channels and tactics to enable fast follow up by the aligned sales and account management teams.

Ensuring that your organization has an effective enterprise-level understanding of your data can significantly enhance the alignment and ultimately the success of your marketing plan. Building the data as a connected layer across your business will help you put the client at the centre of your business.

Structured vs unstructured data

There are a couple of fundamental categories of data that impact how easily your data can be used.

• Structured data is searchable and well organized – think of an excel sheet
• Unstructured data is free form – more like a word document.

The more structured, the more easily the data can be used and the old adage of ‘rubbish in, rubbish out’ applies here. The data needs to be cleaned up and unified so that it can provide you with actionable insight. If you have multiple data sources for a single individual it is ideal to unify the data profiles so that all data is connected to a ‘golden record’ with a unique identifier. This source of truth helps you understand any movement that the individual might make both through the buying journey and will also help you track any shifts as they move through any aligned segmentation.

In B2B there is the added complexity of selling to a business made up of many individuals in a buying unit. LinkedIn’s professional Services buyer research ishows that this now includes 7 individuals and that the process takes significantly longer than 12 months ago. Add to this the fact that we know only 5% of buyersii are in the market at any one point and we need to nurture many more prospective buyers towards a sale than ever before. Two key tech tools that you need to ensure that you are ready and able to target future buyers with the right message to sell them the right product at the right time are:

1. A unified customer data platform (CDP) that enables you to see all historic engagement both at the individual and the account level
2. An orchestration layer that enables you to be able to plug and play tools and technology seamlessly and compliantly into your stack. (Key word here is compliance!)

The combination of these two tools means that you can both understand the needs of the customer from their previous behaviours – that can aggregated and viewed as a buying group at the account level - and also bring in any additional intelligence to augment the data sources and provide greater understanding of the buyers.

Data driven decision making (DDDM)

This sounds much more complicated that it really is. Data driven decision making simply means making business decisions informed by the insights that are derived from data and analytics. It means that you rely on the facts rather than intuition.

To ensure that you maximize the potential of Data driven decision making you must establish as enterprise-wide an aligned approach as possible. If you can align the organization to a single taxonomy and data dictionary building a single customer view of your buyers at both an individual and account level, you are halfway there. Having clean, optimized data not only means that you can derive greater understanding from your data assets, it also means that you are well positioned to use AI across the enterprise and drive greater efficiency alongside opportunities.

Data driven operating models (DDOM)

Putting data at the heart of your operating model relies on enterprise level data driven decision making driving real-time optimization and insights that enable your business to thrive.

A switch to this approach means that your organization is built around your customer journey, with all functions and departments focused on driving the right outcome for your clients and customers. This results in an operating model that is finessed and optimized through the understanding of insights that drive a differentiated response enabling a focus on the next-best-action of an individual that looks to amplify the impact of every touchpoint to deepen relationships and ultimately optimize revenue.

A data driven future

For a business to make the leap and take advantage of the potential that AI and Agentic AI can bring to marketing from creative optimization to marketing operations, the data has to be AI-ready. It is only with these AI ready foundations – including both structured and unstructured data - that processes can be enhanced to drive efficiency, personalization at scale and real-time decision making to enhance business value. Data driven decision making using AI is about to blow open the potential of both real-time optimization alongside the much lauded betterment of creative testing and personalization. The optimization of data will unlock a customer-centric frictionless future at scale.

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