Asana’s ‘Task’onomy Activating Data for Marketing Excellence

Summary

In this insightful conversation, Grace Lou from Asana discusses the company's shift from a product-led growth model to a hybrid approach, leading to more complex data management needs. She emphasizes how Asana uses a customer data platform (CDP) like Segment and integrates it with Iterable to personalize marketing campaigns and automate audience segmentation. Grace shares valuable lessons learned, including the importance of prioritizing impactful data, establishing data governance, and leveraging predictive models to improve customer engagement. The discussion also touches on the future of AI and data-driven marketing strategies.

Takeaways

• 😀 The speaker reflects on their experience working at Iterable and their excitement about how the company has evolved over time.
• 😀 Segment, a customer data platform (CDP), helps unify first-party customer data to enable better marketing efforts by creating robust, unified customer profiles.
• 😀 Iterable, as a long-standing partner of Segment, enables bi-directional data flow, allowing companies to activate customer data in marketing campaigns and feed results back into Segment for further enhancement.
• 😀 Grace Lou from Asana shares how their company’s journey transitioned from product-led growth (PLG) to a hybrid model that also incorporated sales-led growth (SLG). This shift led to challenges in data consistency and integration across teams.
• 😀 Asana faced challenges with data access for marketing teams, leading to reliance on engineering teams for building audience groups. This process was slow and costly, prompting the decision to implement a customer data platform (CDP).
• 😀 Asana adopted Segment's CDP to centralize customer data, enabling marketers to access the data they need without relying on engineering teams, thus speeding up the process of audience building for marketing campaigns.
• 😀 Asana prioritized data value over volume when selecting data points for marketing, focusing on the most impactful data that could unlock multiple use cases and drive engagement.
• 😀 The integration of Segment into Asana's marketing tech stack allowed for a unified profile view, making it easier for marketers to build audiences and activate them in downstream tools like Iterable.
• 😀 Asana's team used a structured approach to implement Segment, identifying valuable use cases, connecting data sources, and prioritizing data governance to ensure consistency and clarity across teams.
• 😀 Looking ahead, Asana is excited about leveraging predictive models within Segment's platform to proactively understand user behavior and deliver more personalized experiences, such as recommending new features like Asana AI based on user actions.

Q & A

What is Segment, and how does it support marketers?

Segment is a customer data platform (CDP) that helps businesses collect, unify, and activate first-party customer data. It centralizes and organizes customer data to create robust unified profiles, which can then be activated in downstream marketing tools, such as Iterable, to deliver personalized marketing campaigns.

How does Segment integrate with Iterable?

Segment integrates with Iterable in a bi-directional manner. Data from unified customer profiles in Segment can be sent to Iterable for activation and analysis. Similarly, any interactions from marketing campaigns in Iterable can be sent back into Segment to enhance and further refine the customer profiles.

What challenge did Asana face with its customer data before implementing a customer data platform?

Before implementing a customer data platform, Asana faced challenges with inconsistent data definitions between teams. For example, the sales team and marketing team defined highly active users differently, leading to discrepancies and inefficiencies in managing customer data and audience targeting.

How did Asana address the challenge of inconsistent data across teams?

Asana created a centralized layer to consolidate data from different warehouses into a single location. This helped them standardize their data and ensure consistency. However, they also recognized that their marketing teams still faced challenges in accessing and using this data effectively.

What role did Segment play in improving Asana's data access and marketing operations?

Segment helped Asana by providing a centralized platform for managing customer data. It enabled the marketing team to easily access data without relying on engineering or data science teams for manual requests. Segment also facilitated the creation of audience groups and data activation, significantly improving marketing efficiency.

What was the main goal of Asana in implementing Segment?

The main goal of implementing Segment was to empower marketers by giving them easy access to valuable customer data. By leveraging Segment, Asana aimed to improve data-driven decision-making and enhance personalization for marketing campaigns, which ultimately contributed to a better customer experience.

What is the importance of prioritizing the value of data over its volume, according to Asana's approach?

Asana prioritized the value of data over its volume to ensure that the most relevant data points were being used for marketing use cases. This helped avoid the pitfalls of having too much data without actionable insights, focusing instead on data attributes that could unlock multiple use cases and maximize impact.

How did Asana implement Segment into its tech stack?

Asana implemented Segment by integrating various data sources such as demographic, product behavior, web activity, and enrichment data into Segment. They then used Segment to create unified customer profiles, which were passed on to activation channels like Iterable and other marketing tools for campaign execution.

What is the role of Segment's prediction model in Asana's customer data strategy?

Segment's prediction model allows Asana to proactively predict user behavior by analyzing customer events and providing insights into potential future actions. This helps Asana to personalize user experiences and cater information relevant to users before they explicitly request it, improving engagement and satisfaction.

What advice did Asana's team provide for other marketing and product teams looking to leverage their data?

Asana's team advised focusing on the value of data over its volume, ensuring data is well-organized and structured, and maintaining data governance. They also recommended using data attributes that could unlock multiple use cases and avoiding unnecessary complexity by prioritizing the most impactful data points for marketing efforts.