Atticus Li has led marketing analytics work at Silicon Valley Bank (SVB) and NRG Energy across Google Analytics and Adobe Analytics. At SVB, his analytics supported a startup-banking business area whose internal pipeline reporting exceeded $1B; that is business context, not revenue personally generated by Atticus. NRG’s internal 2025 program readouts recorded 100+ experiments.
Two Companies, Two Completely Different Data Worlds
When I moved from SVB to NRG, one of the biggest adjustments wasn't the industry or the products — it was the analytics stack.
At SVB, we used Google Analytics. I was there through the Universal Analytics era and into the GA4 migration. At NRG, the entire measurement layer runs on Adobe Analytics, with Tealium as the CDP and Contentsquare for behavioral analytics.
These aren't just different tools. They're different philosophies.
Google Analytics gives you a standardized data model. Sessions, users, pageviews — they mean roughly the same thing across every GA implementation. The trade-off is flexibility. GA makes assumptions about how you want to measure things, and fighting those assumptions is painful.
Adobe Analytics gives you a blank canvas. You define your own variables, events, and data model. The trade-off is complexity. Two Adobe Analytics implementations can look completely different, because every company builds their own measurement schema.
Neither is better. But switching between them taught me something critical: the tool doesn't define your analytics capability. Your understanding of what the data means does.
The Data Dictionary Problem
Here's something nobody tells you about enterprise analytics: the hardest part isn't building dashboards or running queries. It's understanding what the data actually represents.
Across the enterprise implementations I worked with, familiar metric names often hid company-specific definitions.
A "user" in Google Analytics isn't necessarily a person — it's a device/browser combination. A "session" in Adobe Analytics might be defined differently than what Google calls a session, depending on how session timeout rules are configured. A "conversion" might mean one thing in the marketing team's dashboard and something completely different in the finance team's reporting.
At SVB, one of the first things I did was build a data dictionary that documented every metric, dimension, and event — not just what the tool called them, but what they actually measured in the context of SVB's business. Who was counted as a "new user"? What triggered a "lead" event? How was attribution assigned across marketing channels?
At NRG, the same challenge exists at larger scale. With five brands each running their own analytics implementations in Adobe, a "page view" on Reliant's site might not be counted the same way as a "page view" on Green Mountain Energy's site. Before any cross-brand analysis, I have to reconcile those differences.
This is unglamorous, tedious work. But it's the foundation of everything. Every insight, every test result, every revenue projection is only as good as your understanding of what the underlying data actually means.
Data Storytelling: The Skill Nobody Teaches
At both SVB and NRG, stakeholders needed a compressed decision view rather than the level of statistical detail an analyst uses every day. The communication job was to preserve uncertainty while translating it into the budget, customer, and operating decision in front of them.
This is where most analysts fail. They build a 40-slide deck packed with every metric they can find, overwhelm the room, and then wonder why nobody acts on the findings.
I've learned to do the opposite. My reporting follows three rules:
1. Surface what matters. If I have 50 data points, I'm showing three. The three that tell the story of what's happening, why it's happening, and what we should do about it. The other 47 live in an appendix for anyone who wants to dig deeper.
2. Always give recommendations. Data without recommendations is just trivia. Every analysis I present ends with "here's what I think we should do and why." Stakeholders don't want to be data analysts — they want to make decisions. My job is to make the decision easier, not harder.
3. You're a consultant, not just an analyst. This mindset shift changed my career. An analyst says "here's what the data shows." A consultant says "here's what the data shows, here's what it means for the business, here's what I recommend, and here's the risk if we do nothing." That second version is what gets you a seat at the strategy table instead of the reporting table.
SVB: What I Built
At Silicon Valley Bank, I was part of the marketing analytics team supporting a startup-banking business area whose internal pipeline reporting exceeded $1B. The figure describes the broader operation, not causal revenue attribution to my analytics work.
Campaign Landing Pages and Partner Experiments
SVB ran a significant volume of partner landing pages — co-branded pages with accelerators, venture firms, and tech ecosystem partners. I ran A/B tests on these landing pages, optimizing for lead form submissions and qualification rates.
Internal project readouts averaged a 32% conversion uplift across that set of landing-page experiments. That historical average is not externally audited, an industry benchmark, or a forecast for another company. The observed behavior—founders and CFOs reading more deeply and interacting with detailed proof—led us to hypothesize that credibility signals and specific program information mattered more for this audience than generic value propositions.
Email A/B testing was another significant channel. We tested subject lines, send times, content length, and CTA placement across SVB's email campaigns. The key learning: SVB's audience was so niche and high-intent that most generic email "best practices" (short subject lines, early-morning sends) didn't apply. We had to build our own playbook from scratch.
Customer Acquisition at Scale
The internal campaign-results artifact recorded 22% year-over-year growth in new customer acquisition across the broader initiative. My role was building the analytics infrastructure used to connect new customers with marketing channels and campaigns. The 22% figure is a historical internal operating readout—not a reusable benchmark or a claim that the analytics infrastructure alone caused the growth.
This sounds straightforward until you realize that enterprise banking customers don't convert in a single session. A founder might see an SVB ad at a conference, visit the website three months later, get referred by their VC six months after that, and finally open an account a year into the relationship. Attributing that conversion to a single touchpoint is meaningless. We built multi-touch attribution models that gave fractional credit across the entire journey.
Geo-Incrementality: The OOH Attribution Problem
One of the projects I'm most proud of at SVB was our geo-incrementality testing for out-of-home (OOH) advertising.
SVB wanted to measure whether billboard and physical advertising campaigns drove online outcomes. Individual-level attribution is incomplete for OOH, so the decision needed a market-level counterfactual rather than a simple pre/post chart.
We designed an incrementality experiment: Austin and Miami were test markets where OOH campaigns ran, and Seattle was the control market with no OOH advertising. The project combined a market-level comparison with tracked direct-response paths connecting selected OOH placements to web and CRM activity.
The result prevented an easy overclaim. Web traffic rose 97.8% in Miami and 94.4% in Austin, while the digital-only Seattle control rose 114.3%. Because the control grew faster, the aggregate comparison did not establish causal OOH lift. The project still gave SVB a repeatable framework for testing offline media against online outcomes.
Consolidating the Reporting Mess
When I arrived at SVB, there were six separate marketing reports going to different stakeholders, each with different metrics, different time frames, and different definitions of success. Some contradicted each other.
I consolidated them into a single Looker dashboard that served as the single source of truth for marketing performance. The internal campaign-results artifact recorded 691% web-traffic growth across key markets and a 196% increase in lead quality during the broader initiative. Those are observed program results, not proof that the dashboard alone caused the movement.
The consolidation process was politically delicate. Every report had an owner who felt ownership over "their" metrics. Replacing six reports with one meant six people who needed to be convinced that a unified view was better than their custom slice. It took months of stakeholder management, but the end result was transformative: fewer meetings, faster decisions, and no more "your numbers don't match my numbers" debates.
NRG: A Different Scale, The Same Principles
At NRG, the analytics challenges are different in specifics but identical in nature. Instead of one brand with one analytics implementation, I'm working across Reliant Energy, Direct Energy, Green Mountain Energy, Cirro Energy, and Discount Power — each with their own Adobe Analytics setup, their own data definitions, and their own stakeholder groups.
The experimentation program I built here ran 100+ experiments in 2025. Every test requires accurate measurement, an explicit decision rule, and an evidence label for any modeled revenue impact.
The tools changed. Google Analytics became Adobe Analytics. Looker became internal reporting tools. The startup banking world became retail energy. But the core principles stayed the same:
Understand your data before you trust your data. Build the data dictionary. Validate the event tracking. Question every metric until you understand exactly what it measures and what it doesn't.
Tell stories, not spreadsheets. Your stakeholders don't care about your SQL skills. They care about what's happening in their business and what they should do about it. Package insights as narratives with clear recommendations.
Be a consultant. Don't wait to be asked for data. Proactively surface insights that matter. Build relationships with stakeholders so they come to you with questions before making decisions, not after.
Connect the evidence to the financial decision. At SVB, the model used pipeline value and customer acquisition cost. At NRG, it used revenue per customer and modeled annual impact. The inputs change, but the principle is consistent: expose the assumptions that connect analytics evidence to a budget or operating decision.
What Enterprise Analytics Actually Looks Like
I want to be honest about something: most of what I've described isn't exciting. Building data dictionaries, reconciling metric definitions across brands, sitting in meetings to explain what a confidence interval means to a VP — this isn't the sexy side of analytics.
But it's the real work. It created the measurement foundation for five NRG tests whose internal models summed to $1.2M+ in projected annual impact. That is an assumption-labeled historical projection, not booked revenue.
If you're an analyst trying to level up, stop optimizing your SQL queries and start optimizing your stakeholder communication. Learn to tell stories with data. Learn to give recommendations, not just reports. Learn to be the person in the room who translates numbers into decisions.
That's what enterprise marketing analytics actually looks like. Not from the stage at a conference, but from the seat where the work gets done.
Want to discuss analytics strategy? Reach out at atticus@atticusli.com.
FAQ
What changed most between SVB and NRG?
The business model, data systems, decision cadence, and unit economics changed. That is why a metric definition or attribution model cannot be copied across companies without validation.
What should an analytics leader learn first?
Map the revenue model, decision owners, source systems, metric definitions, and evidence gaps before proposing a new dashboard or experiment roadmap.
Why does cross-channel measurement matter?
Nielsen reported that only 32% of surveyed marketers measured traditional and digital media holistically, and Gartner found that 52% of surveyed senior marketing leaders could prove marketing's value and receive credit. Neither survey establishes a universal waste rate.