10 tips for implementing a data-driven b2b marketing strategy

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“The pandemic has really accelerated the digitalization of US B2B SMEs,” said Erica Chan, Head of Marketing for North America at Alibaba, in October 2020. After analyzing the results of a survey of 2,401 B2B SMEs, the company concluded that for these companies, being online is no longer an attractive option, but a necessity.

The growing digitalisation, the automation of routine activities in daily life and the growing importance of the Internet, where society spends most of its time and where 85% of consumers make their purchases, have led to an ever-increasing amount of information being generated about buyers and sellers.

Evolution of Marketing
What does this mean? Marketing professionals and sales representatives in B2B companies have had to change their way of doing things. Traditional marketing has also had to evolve as part of its adaptation to the social, economic and technological changes that mark the interests and habits of the new society.

If before the market trend was to create false consumer malaysia whatsapp lead needs or “spray and pray” (reaching an audience with a non-personalized treatment), now it is the data generated by the “digital footprint” of Internet users that dictates to companies which products and services to develop and even the best way to market them. This is due to the data-driven marketing strategy.

What marketers think about data-driven marketing
For B2B companies, which are characterized by their highly professional audiences and complex purchasing cycles, the time invested in attracting potential customers is key. This is why the implementation of marketing techniques based on data processing is so attractive, because they are highly effective and objective. Therefore, campaigns are not designed taking into account intuition or personal experiences.


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Promoting a data-driven culture in your company is ensuring its future. To achieve this, follow these 10 recommendations as a guide to its implementation:

Think of data-driven marketing as a business strategy or a new marketing approach. Data-driven marketing is not a project you should embark on with short-term thinking. You should see it as the path to success in automating your B2B business.
Define a data strategy appropriate to your business. It is essential that your company, based on the objectives that have been set, establishes a guide with all the procedures for handling data. To do this, it must be clear: how to standardize the data generated from multiple sources, what the business rules are, the data access and protection policies, and the tools that may be needed. In addition, this strategic plan must include the technical details of the environment where the data is located.
Don't skimp on employee training, hiring qualified personnel, or technology. Developing a new, more data-focused business culture requires a great deal of effort and the involvement of all departments in the company. Each one must understand the importance of data analysis to make more rational decisions.
Create your own data-savvy marketing team. When implementing a data-driven marketing strategy, you will have to deal with a large amount of information, in the most challenging of cases, in the form of “big data.” You will need to make this information actionable and useful for your objectives. To do this, you will need teams of specialists who know how all the company’s departments, databases, and especially marketing automation work.
Be clear about your goals before you worry about the source of data. You need to determine what you intend to achieve with the data and what you need it for. For example, to decide which new products to add to your production line, to identify how to increase efficiency, to decide where to open a new warehouse, or to generate more leads. This is the first step in developing a data-driven marketing strategy. Then you need to analyze where you can get that data from and define the tools and technologies you will need to achieve this.
Make a centralized database of customer data your primary source of information, but not your only one. That’s the role of CRM (Customer Relationship Management): helping you organize and manage customer relationships from a single place. Your CRM should be a primary source of competitive intelligence, but not your only one. B2B marketing and sales leaders are improving the quality of their CRM data and complementing it with data obtained directly from their buyers.
Convince your sales representatives about the benefits of collecting customer and sales data. If necessary, carry out internal marketing. Having well-stocked and error-free databases puts you in a better position compared to the competition. To obtain quality information, your salespeople have to be aware of the importance of correctly collecting and entering into the systems they use, as much data as possible resulting from each interaction with the customer.
Collect data only from trusted sources. Make sure that the information from your sources has been generated by certified and frequently audited software tools and that it has been handled by responsible and qualified personnel.
Collect data that allows you to target your audience with a highly personalized approach. Experience the benefits of customer segmentation. Look for patterns within firmographic data (key information that characterizes a company such as its size, credit history, growth rate, etc.) and the digital body language of your audience. Make the most of your data, reach your potential customers through the right channel and with greater chances of effectiveness, offering them only what interests them.
Avoid the formation of data silos. In companies eager to collect as much data as possible and as a result of process automation, it is very common for data to be trapped in multiple platforms, with different formats and for exclusive use by a certain work team or department. Also for security reasons, sometimes there is data that remains inaccessible, outside the network, on isolated storage devices. In this case, its usefulness should be seriously questioned. One way to avoid this is by defining a data strategy, as we mentioned above.