This article was based on the interview with Guan Wang of Snowflake by Greg Kihlström, MarTech keynote speaker for The Agile Brand with Greg Kihlström podcast. Listen to the original episode here:
Real-time predictive marketing is a strategy that utilizes data analytics, artificial intelligence (AI), and machine learning to make real-time predictions and decisions to drive marketing actions. This approach allows marketers to develop personalized campaigns, content, and messages to engage customers and prospects based on real-time insights and intent, ultimately increasing the likelihood of marketing engagement.
To build a predictive real-time marketing strategy, marketers must first align with marketing executives and stakeholders to define key business objectives. This could include goals such as meeting and exceeding sales pipeline targets. Marketers then need to bring all data from third-party and first-party platforms and applications to one centralized data platform, such as Snowflake, to leverage modern tools and applications for data transformation and feature engineering.
Once the data is ready, marketers can choose to use a third-party predictive analytics tool or build an in-house predictive engine. At Snowflake, the team has chosen to build everything in-house from scratch, leveraging the power of Snowflake to create a framework for predictive real-time marketing.
From a marketer’s perspective, predicting and measuring real-time marketing ROI can be challenging but essential for success. Marketers need to bring together all available marketing, sales, finance, and customer data to a centralized platform to measure the impact of marketing campaigns and activities on investments. By connecting marketing data with finance data and building data models to measure marketing’s impact throughout the sales funnel, marketers can gain valuable insights into their ROI.
Applying advanced analytics and AI machine learning algorithms to predict the future is a crucial step in achieving success with predictive real-time marketing. For example, Snowflake’s team has been able to predict the next four quarters’ sales pipeline, a rare feat in the B2B industry. By utilizing predictive real-time marketing, brands can optimize ROI, drive engagement, and ultimately achieve sustainable success in today’s competitive marketplace.
Real-time predictive marketing drives engagement by enabling brands to anticipate customer needs, personalize marketing messages, and make data-driven decisions in real time. With the right strategy and resources in place, brands can unlock the full potential of predictive real-time marketing and achieve their business goals.
