Data Strategy and CRM for B2B Companies: Guide

Everyone agrees that data and CRM systems (customer relationship management systems) are important for corporate management. However, only a few B2B companies also use this insight in practice and pursue a targeted data strategy.

It's terrifying: A Harvard Business Review publication (2018) on the topic of "data growth" shows that around 60% of all decision-makers are heavily focused on investments in data analyses. What sounds good at first glance means, conversely, that as many as 40% pay little or no attention to the topic of big data and data analysis.

When it comes to the data stocks in B2B and their quality in the assessment of B2B entrepreneurs, it is appropriate to speak of a crisis. For example, a recent study by CapGemini shows that just 27% of managers are satisfied with the data quality and only 20% trust their own data (CapGemini Research Institute, 2021). This is dangerous in an environment where data is the basis for success. The quality of the data will have a decisive influence on the success of the company in the future.

Poor data quality hinders process optimisation, automation, and digital transformations, which often leads to unnecessary additional work. In the digital era, where data is generated in large quantities, it is crucial to use effective methods to manage data. Even the most advanced technologies, such as machine learning, are useless if they're based on faulty data. It is therefore essential to improve the quality of the database first.

In this article, we'll show you what information you should collect for your database to strengthen customer loyalty and encourage potential customers to buy. For this, you can use CRM systems, such as Hubspot.

Introduction to Data Strategy

The Fundamental Importance of High-Quality Data

High-quality data is the foundation on which the company stands. They shape the customer relationship, the number of customers, and the effectiveness of the marketing automation systems used (e.g. Hubspot). It may be tiring to deal with long tables and statistical data, but a few, regularly compared data is enough for a sophisticated business and marketing strategy.

Few data points doesn't mean little effect. It means: the same numbers, every week, in the same format. Which ones exactly, I'll show you further down in the weekly review.

Defining and Differentiating the Data Strategy

A data strategy is a formulated, targeted process plan that enables a company to gain knowledge from data. This plan serves as a roadmap for using data analytics to optimise existing business and potentially develop new business opportunities. It is important to differentiate from a digital strategy, which focuses on digitising and opening up new digital channels, whereas the data strategy focuses on the profit-oriented use of data.

Key Aspects of the Data Strategy

Objectives and Framework

The data strategy design sets clear goals, including timelines. It includes the expected use of resources and the necessary framework conditions such as technical and legal requirements (data protection, data security) to achieve the goals. It is also checked whether existing workers have the necessary capacities and qualifications or whether training measures are required.

Avoiding Irrelevant Data Collection

A well-thought-out data strategy helps to avoid focusing on irrelevant data collections and instead focus on projects that move the company forward. This prevents frustration and ensures that only relevant data supports business growth.

The Three Pillars of Data Strategy

Your strategy should be based on the following data:

1. Definition of responsibilities: If it is not clearly defined who is responsible for which data and to what extent, no one will care about it in the end. This not only includes collecting and analysing the right data but also identifying responsibility for data hygiene and data protection.
2. Quality over quantity: Mere amounts of data won't get you anywhere. For a sustainable data strategy, you need a clear division that makes your data usable. To do this, you must keep the customer journey in mind and define exactly which data needs to be collected per phase.
3. Timeliness: With every action, customer data also changes. You should keep an eye on this because only up-to-date data ensures a solid basis for decision-making. At this point, consistency is the most important characteristic for developing a long-term strategy with your data audit.

Of course, you can use numerous tools to collect and evaluate data. For customer relationship management in B2B, CRM systems such as Hubspot are useful.

The minimal data model: 3 metric families instead of endless mandatory fields

Most CRM projects don't fail because of the tool. They fail on the question of what the system is actually supposed to answer. The tightest answer I know comes from Jacco van der Kooij in the SaaS Sales Method: a CRM has to deliver exactly 3 families of metrics. Volume, conversion rates along the customer journey, and the time between steps.

Volume means: how many contacts, meetings and opportunities come in per week? Conversion means: how much of that makes it to the next step, from first reply to contract renewal? Time means: how long does a deal wait between 2 steps? Van der Kooij stresses that the waiting time between steps drives the sales cycle, not the activity itself.

Behind it sits his core line: revenue is not the sum of deals but the product of conversion rates. If your CRM can't answer these 3 families, you're collecting fields instead of knowledge. Every additional mandatory field is then exactly the irrelevant data pile warned about above.

He adds a warning: one methodology has to span marketing, sales and customer success. If marketing measures leads, sales measures closes and customer success measures tickets, each team in its own logic, the customer journey tears at the handoffs. That's exactly where data, context and eventually customers disappear.

Leading indicators: how Mark Roberge reads CRM data

Mark Roberge, Hubspot's first head of sales, takes it a step further in The Science of Scaling. His discipline: use your own CRM data to define one single measurable usage moment that signals customer success. Then validate that indicator against retention.

His worked example: customers who hit the defined moment retained at 93%. Customers who missed it, only at 39%. Only that gap proves the indicator is worth anything. For context: Roberge works with constructed example companies from the US SaaS world. The numbers are illustration, not a study. The method behind them is still something you can apply to your own data this week.

Roberge's second point is governance in practice. He demands a change log for the ICP, the ideal customer profile, or simply your target customer profile. Every change is dated, justified and visible to the board. It sounds bureaucratic. It's exactly the responsibility, quality and timeliness from the 3 pillars above, poured into a single document.

Which numbers belong in your weekly review?

5 to 7 numbers, every Monday, always the same ones. Measured on opportunities and revenue, not on clicks and open rates. If you need more numbers to explain the state of your pipeline, you're measuring the wrong ones.

A proven practice is a Monday scorecard with a fixed format: new qualified opportunities of the week, conversion per stage, waiting time per stage, pipeline coverage against the quarterly target, revenue won and lost. Plus 1 leading indicator for customer health, as Roberge describes above. That's the minimal data model, condensed to a weekly rhythm.

Two heuristics help with the selection. First: measure replies instead of open rates. Anything the recipient doesn't actively do is noise. Second: plan pipeline coverage well above target, because part of every pipeline always dies. Where that data comes from cleanly is covered in my article on customer data in B2B sales.

Get Help from an Expert

The more extensive and complex your data volumes are, the more recommended it is to bring an expert on board at this point. That doesn't mean that you have to hire a data analyst. It is enough to get someone for the initial setup to avoid marketing automation mistakes, especially if the company does not have the appropriate competence.

A MarTech stack (marketing technology stack) comprises all technologies such as tools, CRM, and IT systems that a company or its marketing department uses to manage, execute, measure, and optimise marketing measures and can be used as a great data basis.

What changes with AI agents?

Nothing about the logic, everything about the leverage. AI agents, meaning software that handles research, enrichment or first outreach on its own, can only work on data they can read. Your data strategy becomes the context layer for every automation.

An agent that researches accounts or drafts emails is exactly as good as the CRM underneath it. Garbage in, garbage out. If the field for the customer's current solution is missing, the agent invents a plausible assumption. If the ICP isn't documented, it writes to everyone. Bad data doesn't get better with AI, it just gets visible faster.

That's why the work on the 3 metric families and the change log pays off twice. Humans forgive data gaps because they fill them in their heads. Agents don't. More operator notes on this stack live in the marketing and sales automation hub.

Conclusion

An effective data strategy is the backbone of every future-oriented company. It makes it possible to extract valuable knowledge from data and to substantiate business decisions.

By setting clear goals, defining responsibilities, and ensuring data quality and timeliness, companies can make optimal use of their resources, avoid inefficient data collection, and utilise the full potential of their marketing.

Expert support can help to effectively design and optimise this process. However, there are also numerous tools, such as Hubspot, that take over automation processes and help you with tasks such as CRM.