Why Join a Start-Up?

I had historically only worked at larger companies, and had often built products on top of what existed prior to me joining. I had for a long time been ok with this, in that I learned best practices in the process. But I also wanted the challenge & ownership that came with building something new; I also felt I had a finite window to pursue / trial that ambition before starting a family.

Working at a startup has certainly delivered on those hopes, but it has also required that I grow in areas I did not expect. Specifically with regards to adaptation.

Optimizing for Growth

Things moved fast. While pragmatic planning, and wieldy deliverables are necessary at larger companies, they can be detractors when working in a fast paced environment like a startup. To that point, I optimized for growth… so what does that mean?

At Loom, we had to iterate fast, and that required asking questions, providing answers, and building for the medium-term. Processes were still being defined and I had to apply data with the context that processes were flexible & people were still figuring things out. It also meant that things didn’t need to be perfect, they just need to be correct.

Building a Foundation

I was able to bring my domain knowledge from larger companies to build a strong foundation for how to measure and guide the business. This included defining efficiency, efficacy, and top-line KPIs as well as introducing project management structures to push key decisions.

Data Portfolio

Insights and Improvements

I took an active role in surfacing insights related to brand-equity transfer across LinkedIn products. By examining existing attribution, Return on Advertising Spend (ROAS), and Customer Lifetime Value (LTV) calculations, I identified areas for improvement. Subsequently, I implemented enhancements to these metrics, ensuring more accurate and comprehensive evaluations of our marketing activities. This led to a deeper understanding of LinkedIn's brand strength and enabled data-driven decision-making.

Stakeholder Support

Recognizing recurring stakeholder use-cases, I led the implementation of self-serve reports and forecast models, empowering stakeholders to access vital information independently. As a result, we were able to reallocate 60% of our team's bandwidth towards more strategic analyses, further enhancing our value to the organization.

I additionally l led transition of over 40 reports from LinkedIn’s marketing agency to in-house dashboards.

Program Management

To optimize LinkedIn's marketing strategy, I skillfully managed marketing initiatives with Google and Agency teams. Through close collaboration, we not only improved campaign measurement but also gained valuable insights into industry trends. By leveraging this knowledge, we were able to enhance the operational efficacy of LinkedIn's marketing efforts, driving greater success and impact.

From Consulting to Industry

Atlassian was my first venture into industry, having transitioned from analytics consulting. This shift brought a heightened sense of fulfillment as I directly saw the impact my work had on the broader business. During my tenure, I held served as the primary point of contact for Enterprise Demand Gen Analytics, and played a part in driving the company's success in B2B sales.

Revenue Generation

One of my key achievements was supporting the attainment of approximately $52 million in quarterly pipeline. Through analysis and strategic insights, I contributed to the company's revenue generation by identifying and capitalizing on opportunities within the enterprise market. This experience allowed me to develop a deep understanding of demand generation dynamics and the ability to navigate complex enterprise environments.

Scaled Reporting

I spearheaded the expansion of support for sales and marketing operations by implementing performance dashboards. These dashboards played a key role in providing actionable insights to stakeholders, enabling them to track campaign engagement, monitor lead conversions, and measure pipeline generation across all product offerings. By establishing comprehensive metrics and visualizations, I facilitated data-driven decision-making and empowered the sales and marketing teams to optimize their strategies effectively.

Long Term Skills

The skills I acquired and honed during my time at Atlassian have proven invaluable throughout my subsequent roles. The experience of being closely involved in driving revenue growth, implementing performance metrics, and leveraging data to inform decision-making has been instrumental in my professional development. It has equipped me with a strong foundation and a keen understanding of the crucial interplay between analytics, business strategy, and organizational success.

Ad-Hoc Reporting

My daily responsibilities included  processing adhoc data requests for iTunes Video. We supplied Business Development, Marketing, Sales. and Corporate Leadership with reports & data analysis as it pertains to their business needs. We employed an Agile methodology to create and work through a backlog of reports (with a typical lifecycle taking ~2 weeks). For each report, we would collect adequate requirements, build, test, and validate our findings. Upon completion, we provided a holistic summary of what the data indicated as well as supplemental reports to support our claims. 

Exploratory Analysis

Some requests were more ambiguous and required that we explore data trends to better understand customer insights. Such cases included identifying inflection points or key indicators that lent customers to engage with the product more. While we often had a solid understanding of what perspectives to start from, for some cases, we simply did not know what we did not know. 

We largely relied on Tableau and other visual data tools in conjunction with Teradata to quickly test hypotheses and coherently present results.

Report Automation

Some reports were needed on a regular basis to help drive key business decisions. We automated such reports through the use of Django & Python. By doing so, we could dynamically pass through the parameters we needed and update stakeholders all through a predefined script.. Report automation enabled us to open up our time to other requests & improve work efficiency. 

Big Data/Unstructured Data Sets

We investigated clickstream data to better understand user behavior & product navigation. From this, we could identify triggers for purchasing content, potential causes for customer drop-off, and the overall conversion for marketing campaigns. This data was unstructured for which we would rely on Hadoop and/or Splunk.