Two common problems CMOs face with data is having too much or too little of it.
For example, their inbound marketing teams struggle to find enough data to give insight on a target’s propensity to buy. Outbound marketing teams, on the other hand, often feel that they have too many leads, and need to find a better way to focus their efforts on those most likely to convert.
Having a strong data science team that can help wrangle raw data into actionable insights can alleviate both challenges. As such, it makes sense that the popularity of data scientists as a profession has grown in leaps and bounds. In fact, Harvard Business Review called data scientists the sexiest job of the 21st century.
But as the demand for data scientists continues to grow, the supply can’t keep up. A McKinsey study predicts that by 2018 the number of data science jobs in the United States alone will exceed 490,000, but there will be fewer than 200,000 available data scientists to fill these positions.
In searching for the right data scientists for their organizations, most hiring managers, including CMOs, understand they should consider candidates with the following attributes:
- A statistically driven mind that balances focus with analysis.
- A relentless determination that will answer the frequent need to continue digging through data and analysis for truly valuable insight.
- A strong sense of curious creativity, which would balance the analytics demands of a data scientist role with the more abstract thinking that brings innovative ideas to a wider marketing team.
However, while organizations may understand how to find a strong data scientist and navigate through filling a role with high demand across different industries, how many marketing executives truly understand how to best work with a data scientist either within their own team or as part of other departments within the broader organization?
The most effective CMO-data scientist teams speak at least weekly. Below are three topics they should be discussing regularly to ensure the success of the larger marketing team:
What new insight is available and applicable towards the team’s business objective?
Having a strong partnership between your marketing and data science teams is key. By making sure that both teams are aligned on business objectives from the outset, it ensures that you’re having productive checkpoints with your teams.
Additionally, understanding the insights and which metrics are being driven allows you make the best decisions and course correct where needed. A caveat to consider is the old adage, “statistics can be made to prove anything – even the truth.” In posing this question be conscious of any biases that may cause over-wrangling of the data. While using tools to analyze data is necessary, make sure the data can speak for itself and remains as objective as possible.
How do we best utilize the data available to our organization?
Data is the starting point of a holistic marketing insight-based strategy and leveraging internal first-party data is key to its success. CMOs should be asking their data science team about the data captured and if there is any additional data worth reviewing.
One popular but outdated approach to selecting data is built on the notion that quantity is king. A large amount of data is not nearly as beneficial as a much smaller amount of the right high quality data.
Before even deciding what tools to use, it’s also important to first ensure that the data be of the highest quality to avoid garbage-in, garbage-out problems.
Which solutions are available to us to attain additional insight to achieve our goals?
According to Salesforce’s 2016 State of Marketing report, on average, top marketing teams use more than twice the number of tools and marketing technologies than underperformers use. Also, these high performing teams are 10.7 times more likely than underperforming teams to extensively use predictive intelligence.
With marketing software innovation and predictive technologies providing tools that are more robust than ever before, CMOs and data scientists should consider which solution will derive the most insight and complements existing data and tools. Ensuring your team is using a combination of first-party data, the best third-party data and a robust layer of predictive insight is key.
As advancements in machine learning continue to revolutionize how to find insight from data, as a business leader it’s important to evaluate what capabilities you can handle in-house and where it makes sense to work with a company specialized in predictive analytics that provides the highest quality data.
Regardless on the approach, it’s important to align your strategy to your business goals.
Beyond speaking with their data scientists regularly, CMOs must recognize a key to success in this relationship is ensuring a strong partnership is forged. As CMOs become more data-driven, the data science partnership is poised to grow and become more established.
Pablo Stern is the CTO & SVP of engineering at Radius