Data-driven GTM strategy: Building your data model

Today's complex marketplace demands more than intuition-based decision-making. In this series, we'll guide you through transitioning from relying solely on past experiences to adopting a methodical, data-centric approach.

Before diving into building your market strategy, it's crucial to assess your organisation internally. You need to ask yourself:

• Do I collect all the necessary information?

• How efficiently and accurately do I gather it?

• Where, and how securely do I store it?

• How effectively can I utilise it?

Your responses to these questions will determine your readiness to embrace a data-driven GTM Strategy. It's important to note that the only correct GTM Strategy is the data-driven one. If you lack the necessary data, experimenting is acceptable, but it's essential to set yourself up for long-term success.

In the following weeks, we'll provide practical advice to tackle increasing competition and evolving customer expectations. Each segment will focus on key elements, guiding you through prioritizing segments, optimizing resources, and developing a customer-focused GTM strategy grounded in data.

Today, let's start by addressing the fundamental question: Do I collect all the necessary information?

Building your data model

“Do I collect all the information I need?”.  Answering and diving deeper into this question is your first step toward a data-driven Go-to-Market strategy. It involves identifying all the different types of data (objects) you need as well as the specific information about each data type (fields/properties) and works as the foundation of your strategy. 

Note that it is important to actively go through that exercise, and not just rely on the default data model of the CRM you are using. While most default CRM objects (data types) are good enough for most organisations, they often lack the specific fields and the necessary granularity to create a great customer experience. Customising your data model to fit your unique business needs and customer journey insights can significantly enhance your strategy's effectiveness, and secure your revenue organisation’s scalability and optimisation potential. 

If this is the first time you come across the Data Model term, stay tuned. We will soon publish an article to explain in detail what a data model is, what purpose it serves, why you should make sure to establish one, no matter the size of your business, and how to. 

In a nutshell, a data model is a blueprint that organises and structures your customer and business information. It provides a clear understanding of how different data points relate to each other and serves as a guide for organising, storing, and accessing data in a way that supports your strategic objectives.

Some of the benefits of designing your data model are:

1. Improved decision making

Enables revenue teams to access and interpret data efficiently, enabling better forecasting, pricing strategies, and identification of revenue-generating opportunities. 

Imagine a sales team targeting the wrong demographics for a new product. A data model that captures all the necessary information can enable you to combine customer data (age, location) with sales history to predict which audiences are most likely to buy, boosting revenue.

2. Enhanced data quality

Helps in maintaining consistency, accuracy, and reliability of data, which is critical for effective sales and marketing campaigns.

3. Increased efficiency 

Streamlines data management processes, reducing the time and effort required to gather and analyse data, thereby speeding up reporting and insights generation. 

For example, sales reps shouldn't waste time searching for customer information. A data model can streamline data management, allowing them to quickly access all relevant customer information to personalise pitches.

4. Better customer insights

Facilitates a deeper understanding of customer behaviour and preferences, allowing for more targeted and personalised marketing efforts. Actionable customer segmentation is only possible if you are capturing the necessary data about your customer. 

5. Optimized sales processes

Supports the identification and implementation of the most effective sales channels and tactics, based on data-driven insights.

Instead of relying on guesswork, a data model can analyse sales data to reveal which marketing channels (social media vs email) generate the most and best quality leads, allowing sales teams to focus their efforts.

6. Strategic alignment

Ensures that data management practices support the overall business strategy and objectives, fostering a data-driven culture.

Imagine prioritising social media marketing, while your data reveals most customers come from email campaigns. A data model can expose such misalignments and ensure marketing efforts support the overall business strategy.

But most important of all, a data model will allow you to optimise processes, and journeys, construct an efficient tech stack that enables your team to perform better and future-proof your Go-to-Market strategy by ensuring your data infrastructure can adapt to evolving customer needs and market trends.

Once you have established what are the different types of data you need to capture and how they connect with each other, it is time to put it into practice by defining your processes.

As we wrap up today's discussion on the importance of collecting necessary information, we invite you to join us next week as we dive into the next crucial question: “How efficiently and accurately do I collect it?” Stay tuned for practical insights and strategies to optimise your data collection processes and further enhance your data-driven GTM strategy. See you next week!