March 2017 Summaries

Integrating Salesforce with Heap has empowered companies to conduct advanced customer analysis by combining Salesforce data such as customer roles, industry type, and account information with in-product behavioral data. This seamless integration enables businesses to develop a comprehensive understanding of customer personas, optimize marketing strategies, and refine outbound processes. For example, while product teams are the most active users of Heap, CEOs, founders, and directors are often the initial account creators, prompting targeted ad campaigns towards these roles. Additionally, the integration aids in converting trial users into paying customers by providing sales teams with crucial insights into lead behavior and facilitating targeted communication. Post-sale, Customer Success teams leverage this data to enhance customer education, retention, and engagement, using metrics like Health Scores to identify opportunities for re-engagement. Overall, the Salesforce→Heap integration simplifies the consolidation of sales and product data, allowing companies to iterate and refine their go-to-market strategies effectively.

Heap conducted a study on signup conversion rates among 79 software-as-a-service (SaaS) companies to determine what factors influence user signups. The study found that the average signup conversion rate was 36.2%, with significant variation influenced by factors such as the use of one-click third-party OAuth signups, which increased conversion rates by 8.2 percentage points, and the presence of additional form fields, which generally decreased rates. Significantly, companies employing A/B testing and experimentation software, such as Optimizely, achieved higher conversion rates, suggesting that iterative testing leads to optimization. The study also highlighted that splitting signup forms into multiple sections could modestly improve conversion rates, potentially due to a psychological "foot-in-the-door" effect. The findings suggest opportunities for businesses to enhance their conversion rates by adopting these practices and continuously analyzing user behavior through tools like Heap.

Amazon Redshift, a cost-effective cloud-based data warehouse, offers companies an affordable alternative for data warehousing, especially benefiting those with limited budgets. Heap, a company utilizing Redshift, developed Heap SQL to enable customers to sync their datasets with Redshift clusters, enhancing data analysis capabilities through SQL operations on historical data. The blog post discusses the challenges Heap faced in optimizing data sync processes due to Redshift's unique characteristics, such as its columnar storage format, lack of index support, and non-enforcement of constraints, which differ from traditional systems like Postgres. These differences necessitate careful query optimization, such as efficient use of distribution keys and sort keys, to improve performance and maintain data quality. Heap's ongoing efforts include re-architecting sync processes to increase speed and exploring real-time streaming architectures to reduce latency, highlighting the complexity and adaptability required in managing large-scale data operations.