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Artificial Intelligence  •  B2B  •  Data Science  •  Marketing Technology

Wärtsilä Achieving Measurability and Reaping the Rewards Beyond

“Getting to Denmark” for marketing – Wärtsilä achieving measurability and reaping the rewards beyond

Continuing on their path of data-driven marketing, Wärtsilä has now, together with Avaus, reached a significant milestone in the efforts to measure and validate the impact of marketing on revenue. By utilising machine learning and robust statistical analysis, a marketing attribution model has seen the light of day. We are now able to measure the impact and significance of marketing in monetary terms. We have gotten to Denmark, yet for us it is not the final destination. 


Building blocks

Getting to Denmark is a phrase used in political science and philosophy, coined by Francis Fukuyama. Essentially, it is a vision statement; a descriptor for the utopia which we endeavour to reach. Within political science Denmark is a symbol for a well-functioning and forward-looking society. 

For marketing, Denmark has been, since the invention of the billboard, measurability. 

As many people realise, the first step to solving a problem is acknowledging it. The marketing executives at Wärtsilä not only identified a lack of quantifiability in their operations, but, crucially, embraced a mindset of development toward data-driven thinking with a clear target state in mind: the measurability and impact of marketing on revenue. 

A multi-year scale-up of marketing capabilities and technology has laid the foundation for measurability. As marketing touchpoints with customers, in a highly complex B2B setting with long sales cycles, can be traced, collected and analysed we can see a two-fold phenomenon taking shape. Firstly, the now-available data can be rigorously analysed with new approaches, such as data science. And secondly, management is able to see what the monetary component of that investment over time looks like.


Attribution model for marketing’s impact on revenue

Customer, opportunity and marketing touchpoint data were matched from available platforms. By utilising variables defined for customer engagement in the marketing automation system and matching those with data on sales cases, the analysis produces a numerical value for the marketing impact. 

The machine learning model has established a significant, positive and verified causality between marketing and revenue with a confidence interval of 95%. 

The model is an operative and self-learning marketing attribution model. This means it resides in an operative environment, allowing us to create interactive dashboards displaying  real-time data. The fact that the model is based on machine learning means it is constantly calibrating itself, i.e. learning as it is fed more data, and thus becoming ever more accurate.

The ambition of Wärtsilä and the resourcefulness of Avaus’ data scientists fused to develop an operative decision making tool, able to answer the decades old question at the heart of marketing. 


We’re not done yet

When a marketing attribution model is put in place and its findings implemented, B2B companies can expect on average a 15-18% revenue increase*. This becomes all the more interesting, and noteworthy, if there is significant room to grow in the development of marketing within the company. When we know that a given level of investment gives us a certain degree of impact on revenue, we are able to channel resources more efficiently. Thus, the attribution model gives us the strategic insight to assess the standard of marketing excellence. 

Further, marketing impact can be interpreted in several ways. We could make the argument that with zero marketing revenue would be 10% smaller. This is not very likely to be consistently true. In some cases it might indeed be so, but as revenue in a business with huge deals involving long and complex sales processes, there are so many variables in play, that the impact may vary significantly across different timeframes. 

Off the back of this argumentation, it is likely more useful to see marketing as a strategic tool to minimise the volatility of revenue over time. In fact, if it can be consistently established that marketing reduces the volatility of revenue, it can be used by management to alleviate negative demand shocks in their operating environment in times of eg. recession. 

We have quite clearly reached the Denmark of marketing, so to speak. But as with societies, the work is never finished. We are now, as we are able to measure and quantify the impact that a multi-year development journey and scale-up of marketing can bring us, at a crossroads for marketing. The path forward is paved with data, advanced analytics and a fundamentally deeper understanding of how much and where value is created. 


Written by Jaime López, Erik Stenberg, Robert Halén, 




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