How to Move from Basic to Advanced Marketing Analytics in Four Steps

Author: Snowflake Staff

Snowflake Thought Leadership

Advanced marketing analytics can improve campaign relevance, increase customer lifetime value, accelerate insights, reduce acquisition costs, and drive ROI. But moving to advanced analytics requires a thoughtful investment in the right infrastructure for storing, tracking, and analyzing customer data, which can be daunting to companies that only have basic analytics capabilities. 

Most companies have significant work to do to ascend the maturity curve from basic to advanced. Only 10% of business leaders surveyed in a 2018 HBR analytics report said their organizations had embedded data and analytics into all business processes and decision-making, while only 9% reported having predictive capabilities like scenario modeling and data-driven forecasting.

To become an advanced marketing analytics practitioner, organizations must pass through the four phases below. For more details and recommendations, download our ebook, Moving from Basic to Advanced Marketing Analytics.  

Create a 360-Degree View of Customer Data 

To have a true 360-degree view of customers, marketers need to have both structured and unstructured data in their data warehouse. (Structured data is what’s put into specific fields, such as what a salesperson might enter into Salesforce, while unstructured data is free-form, such as the content of customer reviews.) They also need a single source of truth in the form of clean, merged data sets that a variety of teams can use.

To avoid burdening engineers with ongoing data maintenance, which can be costly and slow, marketing organizations need an ETL tool with prebuilt connectors to data sources. These solutions extract data from the original source (for example, Facebook or Adobe), clean it or change it into a useful form for the company’s purposes (such as converting full addresses into zip codes), and load it into their data warehouse. 

Companies also need a single platform that can natively support semi-structured data (for example, JSON data from a website) and structured data. In addition, they need an elastically scalable platform that enables large numbers of users to run a variety of concurrent workloads from personalization to attribution to ad hoc analysis. 

Optimize the ROI of Each Touchpoint

Customer journeys have become increasingly complex over the last decade due to the number of devices people use and the wealth of information available online. This has led to a huge increase in the number of touchpoints along the path to purchase. According to Think with Google, even the purchase of a candy bar could involve over 20 touchpoints.

Because consumers no longer follow a straight path from awareness to consideration to purchase, the practice of attribution modeling, or determining how much credit for sales or conversions should be assigned to various touchpoints, is increasingly important. For example, marketers need to know how much ROI to credit to Facebook, Google AdWords, and other media channels to determine whether they should increase or decrease spend with each partner.

Marketing organizations can build their own attribution models or select an off-the-shelf solution from companies such as Looker, Tableau, and Power BI. From there, they can make spend decisions based on actual revenue impact (specifically, how much each touchpoint is contributing to conversions).

Push Data to Marketing Channels to Create Hyper-Targeted Campaigns

Unifying customer data in one location is just the beginning. From there, companies must be able to activate data across their marketing channels to enable personalization at scale. That means syncing customer data to channels like an ecommerce interface, Facebook, Salesforce Marketing Cloud, and other customer touchpoints to send personalized content, offers, and experiences that result in higher conversions and more revenue.

Out-of-the-box solutions like Simon, AgilOne, and Segment, which have prebuilt connectors to platforms like Google, Facebook, and popular email systems, can help eliminate the need for engineering resources. Marketing organizations may also opt to build their own customer data platform solution, which comes with great flexibility and power, but a higher integration cost.

Harness Machine Learning to Personalize Content at Scale

At the top of the maturity curve, sophisticated data practitioners can harness predictive analytics to do things such as target ads and offers to segments that are similar to high-value customers, or identify existing customers at risk of churn and proactively improve their experience. They can also dramatically improve product recommendations on their websites, apps, and other touchpoints by using affinity scoring models to gauge people’s interests based on what they’ve looked at in the past. 

Machine learning can also improve ad-bidding optimization, product upgrade propensity scores, lead scores, and next-best-offer recommendations. Cumulatively, these efforts will reduce costs and increase customer lifetime value.

To learn more about ascending the maturity curve from basic to advanced marketing analytics, download our ebook, Moving from Basic to Advanced Marketing Analytics.