When Data Science and Marketing Automation fuse to create something amazing

Big Data opened up the spectrum of information that was once only accessible to research institutions, intelligence agencies, and your local Walmart. But now it is at the finger tips of every marketer who is lucky enough to work at a company that invests heavily in collecting and transforming large amounts of product, user, web, mobile data into useful data models that can be easily accessible to all. These datasets are essentially the raw materials needed for both understanding your customers and their usage behaviors (the data science part) as well as using data triggers to engage with them with timely and contextual campaigns (the marketing automation part) with the objective of changing an outcome that drives your business objectives forward while evolving your organization to become data driven and agile (the amazing part).

Data Science unlocks key customer insights that are hidden in unstructured datasets by sorting through signals, finding patterns, and applying machine learning algorithms.

Data science has enabled marketing organizations to collect, map, and analyze large amounts of customer and market data to derive insights that help understand large complex problems into simple and testable hypothesis. With the help of predictive models and machine learning, data scientists are able to tap into thousands of signals related to customer usage behaviors and user profiles and classify them into a set of customer cohorts that makes it easier to determine how to target and communicate with each customer in a more targeted and contextual way. For example, customers can be classified according to their lifecycle behavior stages they exhibit (newbies , dormant, hesitant, advanced, etc) or they can be classified by their specific usage patterns or activities (weekenders, texters, daylight callers, after dark callers, super-users). These segmentation models are useful to understand why certain customers are more likely to take an intentional action such as converting from free to paid or continue renewing their subscriptions or churn away and move to a competitor. It can also be used to help move customers from one segment to another by targeting specific product behaviors exhibited by one group vs the other. Machine learning can be applied to these models to help predict which customers are likely to fall into one of these segments or to take an intentional user action. With these customer segmentation and predictive models, marketers can gain insights that help formulate a hypothesis that aligns to business objectives such as how to increasing user acquisition, drive higher DAU/MAU, or improve renewal rates.

Marketing Automation enables organizations of any size to be able to execute data-driven hypothesis that aim to impact a key business objective at scale.

The analysis and insight that data science provides helps determine the set of experiments and campaigns that marketing automation can execute. Marketing automation comprises of a set of capabilities that enable marketers to track, target, engage, and measure interactions with end users across multiple channels (email, display, web, apps, mobile). These interactions rely heavily on data signals that determine which customers to target with specific actions. Big Data has expanded the amount of signals that are available to improve relevancy and timeliness of these interactions. Managing this amount of data at scale requires significant investment in automation as well as integration with end-points where you can communicate with users. Today’s marketers have access to modern channels such as email, social, display media, in-product messaging, mobile notifications, search, and blogs. For marketing to be effective, marketers needs to be able to engage with users across one or many of these channels and be able to track and measure the intentional interactions of users. These interactions go beyond traditional channel metrics such as open, clicks, and bounce metrics. Marketers work with data scientists to identify and measure key intentional user actions that are tied to each campaign’s call to action. For example, campaigns that focus on increasing customer retention would focus on measuring if a customer ended up renewing their subscription within a specified timeframe after receiving these interactions. These outcomes provide the ultimate measure of performance for each experiment or campaign that is executed. Marketing automation enables organizations of any size to be able to execute data-driven hypothesis that aim to impact a key business objective at scale.

Data scientists and marketing automation specialists provide marketers with the framework, tools, and solutions to ask and answer these key questions.

Combining forces these two disciplines provide marketing organizations with the ability to tap into the potential of data science by providing the deep analytical expertise to derive insights from data and join it with the scale that automation provides. But the truly amazing part of this fusion of disciplines is how it is starting to transform the way we think about marketing and the role of the modern marketer. Think about your traditional marketer, the Don Drapers of the world. They rely heavily on intuition and years of expertise to determine the tactics and success of marketing campaigns. Data for them is only meant to serve their preconceived vision, not to reshape it. The modern marketer instead is aware that data is the key to asking the right question and ultimately iterating through multiple experiments to find the right answer. Data scientists and marketing automation specialists provide marketers with the framework, tools, and solutions to ask and answer these key questions. More so, the marketing discipline is now evolving to include data scientists and marketing automation specialists as core roles that lead marketing organizations in the transformation to become more data driven and agile.

The pod model enabled the fusion of these modern marketing disciplines and provided accountability for driving results.

As part of our transformation in Office 365, we have created an operating model centered around pods. These pods are self-contained cross discipline teams that are comprised of data scientists/analysts, marketing managers, and marketing automation specialists/engineers. These pods are autonomous in driving a key business objective that aligns with investment priorities. The pod collectively decides which experiments and campaigns to prioritize based on balancing business impact with viability of each. By focusing on a less is more MVP approach, the pod is able to be agile and fail fast and learn through the many iterations of experiments to derive results. The pod model enabled the fusion of these modern marketing disciplines and provided accountability for driving results within our Office 365 marketing organization by tapping into the strengths of each respective discipline. The collective is greater than the sum of each of its components which makes the pod model an efficient way to drive impact and results. The model also allows for scale as we are able to augment additional pods as business priorities change or new investments are added. For a further detailed look at how we have built and scaled this operating model, take a look at this other post.

Marketing organizations that seek to take advantage of the insights and the execution scale that these two disciplines provide should look into operating models where data, tools, and people work together as one collective body that leverages each other’s strengths and insights. Investments where these resources operate independently will not be able to take full advantage of the capabilities that each provide. Data and insights without having the means to experiment and execute at scale will be limited in applications. Likewise, engaging customers with powerful automated interactions without having accurate segmentation and predictive models will potentially drive the wrong customer experiences and business outcomes. Unlocking the value of both is key to driving growth practices, gain customer insights, and ultimately drive business impact.

Interested in being part of the Office 365 team driving business impact using data and marketing automation practices? come join us, we are hiring!

Original post on LinkedIn

Image Credit: Volkan Olmez