How Bipp.io Reached BI Leaders Across 12 U.S. Cities Using LinkedIn Ads
After working with hundreds of tech brands over the years, we got Bipp as a new kind of challenge.
New, because it was the first full-scale Business Intelligence software project we were taking on.
And unlike typical SaaS categories, BI audiences don’t respond to broad messaging or generic SaaS playbooks.
- They’re deeply technical,
- extremely specific in their needs,
- and notoriously selective about the tools they evaluate.
This meant our strategy had to be sharper, our ICP definition deeper, and our execution more disciplined than usual, and that’s exactly what we built for Bipp.
About Bipp.io
Bipp is a modern BI platform that enables teams to explore billions of records in real time, create and share dashboards instantly, and collaborate with SQL at scale.
01. The goal
Bipp.io wanted to grow its brand visibility and generate high-intent demo leads from BI professionals in major U.S. tech hubs.
While their product was powerful, their ideal customers were spread across multiple cities, industries, and company sizes.
Reaching the right decision-makers required a precise, compliant, and scalable LinkedIn Ads strategy.
That’s where Shivyaanchi stepped in.
02. The Challenge
Bipp.io needed a campaign that could:
- Pinpoint BI professionals who actively use BI tools like Power BI, Tableau, Looker, and Snowflake.
- Filter by specific job titles, skills, groups, and functions without narrowing the audience too much.
- Strictly target mid to large-sized companies (51–200, 201–500, 501–1000 employees).
- Engage senior BI professionals who often ignore generic SaaS ads.
- Run campaigns without relying on Google Analytics or third-party cookies (due to internal security policies).
03. Defining the ICP (Ideal Customer Profile)
After extensive workshops, we finalized a multi-layered ICP:
🎯 Target Locations
Austin, Boulder, Chicago, Dallas, Denver, Los Angeles, NYC, Portland, San Diego, SF Bay Area, Seattle.
👥 Target Job Titles
- BI Analyst / BI Developer / Business System Analyst
- Sr. BI Developer / Sr. BI Consultant
- Director of BI, Director of Analytics, Director Data Engineering
- Senior Manager BI/Analytics
- Senior Business Intelligence Engineer
- Solutions Architect (Data/BI/Analytics)
🧠 Key Skills
SQL, Data Warehousing, Snowflake, Tableau, Power BI, Looker, Dashboarding, BI Tools.
📌 LinkedIn Groups
Looker Developers, Power BI User Group, Cloud Data Platform Groups, BI & Analytics Professionals, Tableau Enthusiasts, and others.
🏢 Company Sizes
Mid → Large
51–200, 201–500, 501–1000 employees.
🧩 Functions
Business Intelligence, Data Engineering, Technology.
04. Getting Started: The Setup Checklist
We followed our standard 6-step setup before launching any ads:
1. Ads Account Access
Full Account Manager access + Company Page Content Admin.
2. Company Page Integration
Ensured sponsored content ads mapped correctly to Bipp’s company page.
3. LinkedIn Insight Tag
Installed and validated.
Bipp couldn’t use third-party cookies → aligned our tracking strategy using first-party cookies & Plausible Analytics.
4. AMO Strategy (Audience · Message · Offer)
The core of our execution:
We finalized a deep ICP, mapped pain points, motivations, and drafted positioning.
5. Imagery Requirements
Ad creatives set at 1200×628, avoiding blue tones.
Why avoid Blue color tones in LinkedIn ads?
LinkedIn’s brand color is a shade of blue. If you want to make your brand stand out, you should look at the opposite of blue on the color wheel. Opposite of blue is the color orange. So, sticking to oranges, greens, reds, and anything is going to make your ad stand out.
Image source: Canva
Bipp’s internal design team created all visuals while Shivyaanchi provided continuous consultation, insights, and feedback at every stage.
6. Billing i.e., Credit Card set up
No setup was required; their LinkedIn ads account was already configured.
05. Pain Points We Used in Messaging
After studying Bipp’s market proposition and ICP carefully, we did our due diligence and identified recurring frustrations among BI teams:
- Outdated extracts/cubes slowing down reporting
- BI tools not leveraging modern cloud warehouses
- Constant SQL re-writes
- Rigid enterprise pricing
- No collaboration/version control for BI code
- Lack of trust due to multiple data sources of truth
- Difficulty scheduling reports/alerts
- Limited custom visualizations
- Dashboards not built for real-time insights
- Heavy dependency on analysts for every small query
These directly formed the foundation of our ad copy and content offer.
06. Ad Copy Direction (Examples)
“Struggling with BI tools that still rely on outdated cubes and extracts?”
“Still rewriting the same SQL queries again and again?”
“BI tools not built for your modern cloud data warehouse?”
The messaging positioned Bipp as the modern alternative for BI teams experiencing bottlenecks with legacy tools.
07. Creative Assets & Content Offers
Below are a few examples of the ad graphics we used for Bipp 👇
These creatives were designed by Bipp.io’s internal team; however, we worked alongside them throughout the process, providing insight, feedback, and creative direction to ensure the visuals aligned with LinkedIn ad best practices.
Content Offers/Lead Magnets We Used In Bipp’s LinkedIn Ads
Below is a snapshot of the content offer e-book we had used in Bipp’s LinkedIn ads that brought the most traction.
Our team reviewed the structure and messaging and ensured it mapped to ICP pain points before launch.
08. Tracking & Analytics Setup
No Google Analytics allowed → No 3rd-party cookies.
Since Bipp had already said that they didn’t want to use Google Analytics, we built a privacy-friendly tracking framework:
- First-party cookie tracking inside Bipp.io
- Plausible Analytics conversion goals
- Clean UTM structure for post-click behavior
- Custom event triggers created internally by Bipp (scroll depth, engagement time)
- Detailed lead tracking via their demo Thank You page
This ensured attribution stayed intact despite technical limitations.
09. Budget & Execution
Budget: $2K–$3K/month
Campaign Type: Sponsored Content (Single Image Ads)
Landing Page: Lead Gen Form → with option to build dedicated LP later
Optimization Goal: Content Offer Download
10. The Results
Within the first phase of the campaign, Bipp.io saw:
🔹 12–20% Conversion Rate
High-quality leads through lead gen form and converting at impressive rates.
🔹 1%+ CTR
Consistently strong click-through rates across all BI persona variations.
🔹 2%+ Engagement Rate
Ad creatives and messaging resonated with BI professionals.
🔹 <$4 CPC
Significantly lower than standard BI industry benchmarks.
Overall Outcome:
A predictable pipeline of BI-qualified leads from top U.S. cities, validating that BI and data engineering audiences can be profitably targeted on LinkedIn with the right ICP and messaging.
11. What did the client say?
12. Final Takeaway
Bipp.io had the product.
LinkedIn had the audience.
Shivyaanchi built the bridge.
By narrowing down BI personas across 12 cities, leveraging role-specific pain points, and optimizing content offer + creative + messaging, we helped Bipp.io create a sustainable LinkedIn Ads engine built for scale.
LinkedIn ads are already expensive.
Avoid the additional learning tax.
It’s no surprise that LinkedIn ads are becoming more expensive with time.
Plus, one mistake can damage your brand reputation in the first impression itself.
It’s always more efficient and less risky to outsource it to someone who’s been doing it every day for years.
We, at Shivyaanchi, have been doing it every day for the past 11 years.
Let Shivyaanchi set up your BI, SaaS, or data-focused LinkedIn Ads engine- end to end.