Since launching our Decision Science practice, we have received a lot of interest regarding Decision Science, what it is and how it can be beneficial in B2B marketing. This will be the first instalment of a three-part blog series where we will discuss the distinguishing aspects of Data Science and Decision Science, its value in B2B marketing and how BNZSA’s Decision Science practice works.
What is the difference between Data Science and Decision Science?
It goes without saying that data is crucial for an effective B2B marketing campaign, and both Data Science and Decision Science are powerful tools needed to understand the needs of a B2B buying team and where in the purchasing cycle they fall.
However, Data Science and Decision Science have distinct strengths which can be utilised depending on your business objectives and requirements.
Data Science is primarily fact based, focusing on big picture analysis and large quantities of data in order to identify patterns. Whereas, Decision Science looks to trigger action. A decision scientist focuses on a question posed by the business and selects the data sources and methodology appropriately in order to answer it.
Cassie Kozyrkov, Chief Decision Scientist at Google describes the practice as “bringing together the best of applied data science, social science, and managerial science into a unified field that helps people use data to improve their lives, their businesses, and the world around them.”
Why implement a Decision Science approach in B2B marketing?
In a fast-paced marketing and sales environment, it’s critical to strategically direct your resources where they will have the most impact.
Companies often struggle with disconnected siloed data and inconsistent tracking. This results in an inability to derive the information required to find opportunities, nurture relationships, communicate effectively, learn, and improve go to market strategies.
However, a Decision Science approach can help companies answer specific questions such as:
- Which companies to target?
- What’s the best stage of the buying cycle to reach out?
- How to adapt your messaging to specific stakeholders?
- How to respond to individual contacts’ motivations and preferences?
Decision scientists will select the best data sources from what is available, clean it, restructure it, and enrich it using machine learning, foundation model and predictive tools. In doing this, companies can answer the above questions, identify clear actions to take and define their strategy. It can determine: which content to produce, which markets to invest in, where to focus BDR or SDR resources, what additional data might be required and so on.
Come back for Part 2 of our series where we will discuss which decisions can be influenced in a B2B marketing campaign and how they can ultimately facilitate more sales.
