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Our Customer Excellence team led two round tables hosted by the Executives Global Network (EGN). Together with a group of more than thirty Chief Marketing Officers (CMOs) and Marketing Directors in The Netherlands, they discussed how both Business to Business (B2B) and Business to Consumer (B2C) organisations can create value with data. Read on to discover the insights from the B2B discussion.


Why is data-driven marketing in B2B important?

Data can help drive sales and marketing efforts through actionable insights. Organisations generally understand the importance of collecting data; however, there is some uncertainty around what an organisation should do with this data to ensure added value.

Clearly, there is no simple answer to this question. Every organisation will have its unique data science use cases. However, some use cases show up more than others. One of those is the use of Machine Learning (ML) techniques and other technologies to develop a robust forward-looking analysis of the market. This helps an organisation to identify market opportunities and generate leads more efficiently by making decisions based on data-driven insights.

Another application that was mentioned by multiple participants during the discussion, is the use of data to measure the effectiveness of a given strategy. Collecting relevant data while putting a strategy into place will determine how well the strategy is performing. This allows for insights on whether the strategic approach needs to be adjusted or changed over time.


How can you address the organisational challenges of becoming data-driven?

Organisational change is an essential part of becoming a data-driven organisation. This rarely goes without challenges. Oftentimes organisations face departmental resistance to change.

For example, a common data use case is developing a better understanding of an organisation’s client base. This is done through processes such as lead scoring, a methodology that uses a point system to rank sales leads based on a set of criteria or data points. However, this can alter the current way of working within, for example, the sales department. As a result, sales managers might resist the adoption of the lead scoring process, which results in potential value not being realised.

As such, it is essential to work with each department to effectively implement organisational change. By first demonstrating how data can improve the department’s current way of working and that it will support them in creating better results, you will face less resistance and be able to reach your organisation’s data-driven goals more easily.


Data architecture or strategy: where to start?

Organisations often face uncertainty of how to start their data-driven transformation to achieve maximum added value. Should the first step be fixing the data fundamentals and the data architecture? Or instead defining the strategy through use cases and begin prototyping as soon as possible?

There is no universal answer to this question, as both routes consist of benefits and drawbacks. If an organisation starts by fixing the data fundamentals and architecture before determining how they will create value, it can easily become a very lengthy and expensive project without clear revenues. This is because they involve collecting, transporting, storing, and accessing data. Consequently, before committing to a project of this magnitude, an organisation should be certain that it will deliver value. This can be determined by defining clear strategic use cases.

In contrast, if an organisation starts by developing a strategy and use case prototyping without first considering how to take these into production, it will struggle to create real impact. Without the data architecture in place, there is a chance that the value of the use cases will not be realised.


Do you have to choose between starting with either the data architecture or strategy?

At ADC, we typically advise to start with the strategy and use cases to see results more quickly and keep the focus on creating value for the organisation. However, an organisation does not always need to choose between starting with data architecture or strategy. Ideally, these steps should be done simultaneously. We recently took this approach in our engagement with the data-driven transformation of NIBC, a Dutch retail bank.

In this engagement, we started with a short strategy session consisting of a two-day design sprint. After this, we began prototyping to determine if there was value in the data, while also considering what architecture would best suit the organisation, its people, and its data. In just three months, we produced deliverables on both workstreams. By working on the data fundamentals and strategy in parallel, we were able to quickly deliver three internally validated prototypes and design a future-proof data architecture for the next phase: productionising the models that proved their value in the prototype phase.


Read more about our project with NIBC here:




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