Steinert Analytics

Elevating your brand’s story.

Serving businesses in Central Ohio and beyond with a focus in healthcare & roofing.

Skills: Keboola, Azure Data Factory, Etleap, Python 3, ELT, APIs, JSON, SQL, LookML, Snowflake, GCP/BigQuery, Amazon Redshift, Looker, Looker Studio, Tableau, Databox, Power BI

Steinert Analytics transforms the data of Central Ohio businesses into profit driving insights. 

- Currently working with small to large-sized businesses in the $10M - $2B revenue range with a focus on mid-sized companies.

Founder & Analytics Consultant

Lead Data Architect

Data & Impact - a Passion2Knowledge Experience

Voicing the power of data with business domain experts.

- Building this brand from the ground up, it has given me my first taste of entrepreneurship, branding, and self-starting 
- Between 300-700+ total unique organic listeners per month
- Develop social media marketing content with Canva
- Obtaining actionable insights from Libsyn's analytics dashboard
- Serves as a platform that has enabled me to tap into a best-in-class Columbus, OH Area business network & beyond
- 50+ episodes released

https://podcasts.apple.com/us/podcast/data-impact-a-passion2knowledge-experience/id1431897021

https://open.spotify.com/show/1iu8YvxEiEXG4Lnr9WI5jj?si=eljC2-RBQzO01FkSyHvzhw&utm_source=sms

Podcast Host

LaSalle Investment Management

JLL Technologies | LaSalle Business Technology Group

- Serving as lead data architect and SME for LaSalle's $80 B Global AUM data pipeline & model
- Spearheading project management, data leadership & strategy, data management and technical expertise for tool migration and cost reductions in 2023
- Presented LaSalle's self-service BI strategy road map to Digital Product and Engineering exec leadership that leverages Power BI Premium and Workspaces which served as the catalyst for successful implementation in early 2023
- Actively upskilling in Power BI, Azure Data Factory & Databricks

Senior Data Engineer

Skills: Looker, Power BI, SQL, ETL, Snowflake, Linux/DevOps, HTML, Liquid LookML (like Ruby), Talend, Azure, Databricks

- Managing LookML transformations for $82 B in Global AUM asset data, responsible for migrating the entire project & Looker data model to Snowflake for performance increases that tap into Snowflake's virtual warehousing and parallel processing
- Writing advanced SQL views for operational finance data in AM and Corporate Functions asset data for query optimization
   - Continually enhancing Global AUM reporting through reimagined data visualizations and automated the company's financial roll forward analysis utilizing advanced LookML modeling & dimension/measure code
- Troubleshooting semi-additive facts from raw data as it relates to quarterly AUM totals on a specific end of quarter date
- Implementing a standardized process for user security & management through documentation of Looker groups, roles, and model sets as they relate to JLL's AD groups connected up via SAML
- Driving a majority of design decisions on LaSalle's raw transactional data by extracting field meaning, table relationships, and join keys to utilize the construction of a data model in Microsoft SQL Server Management Studio
- Working with LaSalle's world-class Liferay app developers to ensure proper design considerations are discussed and implemented in respect to Looker's iframe & API embed capabilities for multiple portal application projects as part of LaSalle's Corporate Functions 
- Managing Looker project upkeep in a highly complex multi-stage development framework (3 instances Dev-Test-Production) 
   - project version control, connection configuration, access grants in Snowflake, and Github repository set-up w/ JLL-T
- Responsible for Looker on-prem server back ups, restarts, upgrades & patching using Linux command line and LaSalle's VPN via RDC
- Ensure ETL loads aligned across 3 databases Dev-Test-Prod

Data Engineer | Lead Developer

Updox

*Updox acquired by EverCommerce in Dec. 2020*

Updox, an EverCommerce company, is simplifying the business of healthcare to help providers, patients and partners have a happier, more satisfying experience.

- Working in SFDC, Zuora, Firetower, AWS, Matillion (ELT), Snowflake, Looker, Excel and Python 3 (Jupyter Notebook)
- Q1 2021 co-leading Updox-Databricks churn propensity & product usage predictive analysis using Python 3, Spark and SQL (facilitating/building machine learning models) 
- Performing ELT in Matillion to execute relevant data transformations for sales budgeting
- Descriptive, predictive & prescriptive data analysis on monthly financial/sales reports
- Writing LookML (SQL(DQL)) views and explores to provide better field optimization for data accuracy in visualizations through database relationship type assignment and performing left & full outer joins
- Coding advanced SnowSQL queries to generate persistent derived tables (PDT) for data accuracy and customized visualizations
- Conduct sales reporting breakdowns & reconciliation w/ Excel Pivot Tables, advanced formulas & VBA to slice and dice/format & match the data accordingly
- Data validation by crossing Snowflake DW with Looker visualizations using SQL (w/LookML) 
- Automating enterprise & channel sales reports using Looker and SQL 
- Defining business KPIs through internal (sales stakeholders) & external research[Gitlab] (ideal sales KPIs & telehealth industry standard) to create socialized/cross-functional/documented (Google Sheet Data Glossary) context/relevance when using Looker dashboards
- Q2 2020 I am project management lead (using Smartsheet Gantt chart) for the implementation of Updox Analytics product for customer enabled self-service analytics & reporting (partnered w/ Looker, a Google Cloud Platform) 
- Taking an agile methodology approach to SQL development and LookML for this embedded integration while co-developing with Looker/Google Team

Lead Business Intelligence Analyst 

Sigma Pi Fraternity, International

In April 2019 I joined the Sigma Pi Columbus Alumni Club to expand my involvement in the fraternity to give back and assist them in any projects they need me for. 
- Currently working on updating & optimizing sigmapicolumbus.org's site UI and upgrading to an HTTPS secure connection so we can streamline due payments for members directly on the website.

Member of the Columbus Alumni Club

Vertiv

- Perform MDF, Co-Op, and Rebate status updates, claim approvals, and budget tracking for Vertiv’s value-added partners using supply chain enterprise software (E2Open)
- Create, plan and manage marketing events with Vertiv’s diamond partners to ensure a strong brand presence, relevant promotions & product mixes, and value-added marketing collateral are present to showcase the differentiation of Vertiv’s critical infrastructure offerings
- Deploy through-channel marketing by updating and maintaining Vertiv’s enterprise channel management system (Zift) and reporting on adoption & performance of through-channel digital marketing campaigns
- Execute To-Partner digital marketing activities for on domain advertising with diamond partner CDW
- Utilizing Gantt Charts & other project management trackers in Excel & Wrike to streamline tracking & real time status updates for projects to create performance-based visibility in the channel marketing department

Channel Marketing Specialist

- Responsible for A+ content management, syndication (via web templates & HTML), and B2B digital optimization in efforts to take the U.S. Channel from $250 million to $1 billion in annual revenue
- Conduct web/content audits and situation analyses to give VERTIV a better understanding of the competitive digital landscape and how to improve our content
- Collaborate with the VERTIV Sales Analytics/Data Science department to build out an automated digital content audit using ScrapeHero and Microsoft SQL Server DW
- Work with digital B2B resellers such as Amazon, CDW, Graybar, & Anixter to better understand Search Engine Optimization potential on these mediums
- Inventory, sales (YoY deltas, run rate forecasting, and sales segment breakdowns), & pricing data analysis on all North American VERTIV SKUs through Microsoft Excel & Power BI
- Utilize digital marketing analytics to track performance, measure marketing ROI, obtain customer churn rate, and gain insight regarding how to best optimize and improve all VERTIV digital content
- Manage entire Vertiv Amazon Ads Campaigns & Digital Marketing Analytics tracking
- On the channel's Skunkworks agile team executing ad hoc digital content syndication & primary channel promotion management under c-suite executives and CEO Rob Johnson's lead 
- Currently conducting a market research basket analysis with the data science team on VERTIV product bundle sales using Power BI analytics and advanced statistical modeling in R (Apriori algorithm). 
- Coordinate, organize, and push out purchase orders for creative items such as VERTIV-branded magnets, flyers, and other promotional items
- Innovate & create channel marketing event plans for partner trade shows and training conferences to help VERTIV maximize brand presence, product knowledge deployment, and relationship building with partners and end customers

Associate Channel Marketing Analyst

- Produce semesterly budget in Excel to assure it results in a minimum 5% savings allocation
- Manage entire chapter funds and submit monthly reports to the international Executive Office
- Communicate weekly with account manager to set up billing cycles for 30 plus members with individual accounts and manage accounts receivables
- Maintain the chapter's OmegaFi billing management system through the roster and billing cycle adjustments
- Responsible for tracking expenses and writing checks to designated committees 
- File 990-N tax forms for the chapter's non-profit status
- Communicate with the Executive Office through monthly financial reporting and national bill payments
- Attended the fraternity's international Mid-Year Leadership Conference in St. Louis, MO to develop financial & organizational leadership skills. 
- Managing over $10,000 for the Spring & Fall 2017 semesters

Treasurer of the Gamma-Mu Chapter

- Managed and organized the chapter's member roster, workbook, and important forms
- Recorded detailed and accurate minutes during all weekly chapter meetings
- Submitted monthly, bi-annual, and annual reports to the Executive Office of Sigma Pi Fraternity regarding chapter status, committee progress, housing information, and improvements
- Took attendance at all meetings, philanthropy events, and Greek-related activities
- Coordinated service projects and annual chapter events
- Acted properly in difficult situations and decision making for the good of the chapter, while maintaining a high degree of leadership and rationality
- Attended the fraternity's international Mid-Year Leadership Conference in St. Louis, MO to develop presidential & organizational leadership skills. 
- Due to my organization and efficiency, I assisted the chapter in winning the UW Oshkosh Fraternity & Sorority Life's Chapter of Excellence and Most Improved Chapter of the Year for 2016 awards.

Secretary of the Gamma-Mu Chapter

Kohler Co.

Marketing analyst intern for the product side of Global Faucets - Performance Showering during summer 2017
- Execute comprehensive competitor research and pricing analysis to gain product insights 
- Analyze sales projections and product launch propositions to ensure marketing efforts are in line with company budget and KOHLER's target market
- In-depth data analysis via Excel & SAP studying sales trends, margins, and product specifications
- Presented a plan encompassing product marketing and supply chain that streamlines the direct distributive approach with Amazon, thus expanding KOHLER's Ecommerce initiatives while pushing social media marketing
- Constructed presentations on Microsoft PowerPoint illustrating the cutting-edge new features for the DTV+ Digital Showering interface
- Attended two intent workshops analyzing the Next Gen Rite-Temp and low flow showering systems to see how to strategically develop, market, and sell these industry leading products

Product Market Analyst Intern

University of Wisconsin Oshkosh

Phonathon caller at the UW Oshkosh Alumni Welcome and Conference Center

- Cold call, start a conversation, and build rapport with alumni of the university
- Sell the UW Oshkosh Foundation by delivering value-added propositions to successful alumni about the benefits of donating to the university
- Maintain accuracy and high attention to detail when recording alumni information and entering payment data

Sales Representative

Ansay & Associates

- Transferred client calls to personal and commercial lines advisors
- Effectively performed tasks in Applied EPIC (ERP) software
- Analyzed Excel documents containing reporting data for commercial lines agents' performance and sales projections. 
- Executed underwriting workshop exercises with a mock commercial lines sales meeting at CNA Insurance
- Created a presentation implementing Tableau, a BI software tool, that enhances the company's production and efficiency by emphasizing visual best practices in the workplace
  - 2016 Ansay Intern Business Challenge winner
- Sorted mail, printed labels, and scanned documents to other offices
- Created marketing items such as cover and pre-renewal letters, proposals, and carrier submissions
- Received first person experience with Ansay's sales process by riding along with and observing agents
- Created and saved MVR reports about individual driving history

Business Development Intern

A communicative analyst by nature. 📊

Founder at Steinert Analytics | steinertanalytics.com

Elevating your brand's story.

Book an initial consultation: https://calendly.com/steinertanalytics/initial-analytics-consultation

I am a data-driven business strategist and marketer with a passion for both the operational and technical components of data-driven strategy, blended with tactical execution. I have a broad array of experience across various industries, including an aptitude for turning your marketing and sales systems into insight-rich sources to drive revenue and save your team money.

As a full time employee, I had in-depth involvement in critical areas of business success. When I was in channel, product and event marketing analysis and project management at multibillion dollar companies, I enhanced the brand presence of my employer and optimized successful B2B marketing campaigns for their channel sales efforts leveraging data analysis and insights.


When I moved into data & analytics engineering at an innovative telehealth SaaS start-up and $80 B AUM commercial real estate investment management firm, it gave me the opportunity to strengthen the relationship these companies have with their clients by providing information that attracted and retained customers.

Additionally, I am a creative and hungry entrepreneur driven by the desire to help my clients in Central Ohio succeed with turning data into profitable action. Please feel free to reach out on LinkedIn or via email (christian@steinertanalytics.com) if you are interested in connecting with me.

Christian Steinert

Leadership wanted self-service analytics.
 
(So Finance built their own "Net Revenue" metric in Power BI DAX.)
 
Here's what happened:
 
The data team built a beautiful self-service dashboard.
 
Gave Finance the ability to slice data however they wanted.
 
Filters. Drill-downs. Full control.
 
Leadership loved it.
 
Two months later: chaos.
 
𝗧𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺:
 
Finance created their own calculated measures.
 
Operations did the same.
 
Marketing built their version in Excel connected to the dashboard.
 
Now "Net Revenue" has 5 definitions across the organization.
 
Nobody trusts any of them.
 
𝗪𝗵𝗮𝘁 𝘄𝗲𝗻𝘁 𝘄𝗿𝗼𝗻𝗴:
 
Self-service without governance is just chaos at scale.
 
You gave users tools without giving them governed data.
 
𝗛𝗼𝘄 𝘁𝗼 𝗱𝗼 𝘀𝗲𝗹𝗳-𝘀𝗲𝗿𝘃𝗶𝗰𝗲 𝗿𝗶𝗴𝗵𝘁:
 
𝗗𝗲𝗳𝗶𝗻𝗲 𝗺𝗲𝘁𝗿𝗶𝗰𝘀 𝗢𝗡𝗖𝗘 𝗶𝗻 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮 𝘄𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗲
 
Net Revenue gets calculated in the warehouse. Not in Power BI DAX. Not in Excel.
 
𝗟𝗼𝗰𝗸 𝗱𝗼𝘄𝗻 𝗰𝗮𝗹𝗰𝘂𝗹𝗮𝘁𝗲𝗱 𝗺𝗲𝗮𝘀𝘂𝗿𝗲𝘀
 
Users can filter and slice. They can't create their own revenue definitions.
 
𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴
 
Data glossary. Metric definitions. Where it comes from. Who owns it.
 
𝗧𝗿𝗮𝗶𝗻 𝘂𝘀𝗲𝗿𝘀
 
Show them how to use the governed metrics. Answer their questions.
 
𝗧𝗵𝗲 𝗿𝗲𝘀𝘂𝗹𝘁:
 
Finance self-serves their analysis.
 
But they're using the same Net Revenue definition as Operations.
 
Trust increases. Ad-hoc requests drop 80%.
 
𝗧𝗟;𝗗𝗥:
 
Self-service BI is powerful.
 
But without governance, it creates more chaos than value.
 
Govern the metrics. Then give users the tools.
 
♻️ Share this if you've given users self-service tools and watched them create chaos.
 
Follow me for real talk on self-service BI that actually works.

I help healthcare data leaders with inherited chaos fix broken definitions and build AI-ready foundations they can finally trust. | Host @ The Healthcare Growth Cycle Podcast

Your first week as a data leader.
 
(You just inherited 50 reports with 3 definitions of "Net Revenue.")
 
𝗗𝗮𝘆 𝟭: Excitement. You're modernizing this place.
 
𝗗𝗮𝘆 𝟯: Confusion. Net Revenue is calculated three different ways—two stored procedures and a Power BI DAX formula.
 
𝗗𝗮𝘆 𝟱: Concern. Nobody knows which is "correct." The DBA who built them left 2 years ago. Zero documentation.
 
𝗗𝗮𝘆 𝟳: Panic. Meeting with CFO and VP of Revenue Cycle.
 
CFO: "Net Revenue for Q3 was $23.4M."
 
VP: "I'm showing $22.1M."
 
Both pulling from reports your team built.
 
𝗧𝗵𝗶𝘀 𝗶𝘀 𝘄𝗵𝗮𝘁 𝘆𝗼𝘂 𝗶𝗻𝗵𝗲𝗿𝗶𝘁𝗲𝗱.
 
Not a modern platform. Not AI-ready infrastructure.
 
Chaos disguised as reporting.
 
𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗵𝗮𝗽𝗽𝗲𝗻𝘀 𝗶𝗻 𝗺𝗶𝗱-𝗺𝗮𝗿𝗸𝗲𝘁 𝗵𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲:
 
Healthcare moves slow. Decisions are political.
 
Finance built their Net Revenue 5 years ago. Operations built theirs 3 years ago with different logic.
 
IT inherited both. Never questioned them.
 
Nobody had time to align. Nobody had authority to force it.
 
Chaos compounded.
 
𝗬𝗼𝘂𝗿 𝗷𝗼𝗯 𝗻𝗼𝘄:
 
You can't deploy AI on this foundation.
 
You can't build models when Finance and Operations disagree on basic metrics.
 
Fix the definitions before doing anything strategic.
 
𝗪𝗵𝗲𝗿𝗲 𝘁𝗼 𝘀𝘁𝗮𝗿𝘁:
 
Pick ONE critical metric causing the most pain.
 
Get Finance, Operations, and IT in a room. Force the conversation. Document the agreed definition.
 
Update all reports to use it.
 
Build trust. One metric at a time.
 
𝗧𝗟;𝗗𝗥:
 
You inherited chaos. Fix definitions. Build trust. Then modernize.
 
That's the path to an AI-ready foundation.
 
♻️ Share this if you've inherited data chaos.
 
Follow me for real talk on cleaning up healthcare data.

Net Revenue is screwed up in 3 ways.
 
(The VP and CEO started arguing on the call.)
 
Last week I had coffee with a senior data architect at a Columbus insurance company.
 
20 years in data. Built their entire governance infrastructure from scratch.
 
He told me something that goes against the data mesh hype:
 
𝗖𝗲𝗻𝘁𝗿𝗮𝗹𝗶𝘇𝗲𝗱 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝘄𝗶𝗻𝘀. 𝗘𝘃𝗲𝗿𝘆 𝘁𝗶𝗺𝗲.
 
Here's why:
 
You're the first data engineer at a mid-sized healthcare company.
 
You find Net Revenue defined 3 different ways: 

→ Stored procedure #1 
→ Stored procedure #2 
→ Power BI DAX formula
 
You call the VP of Revenue Cycle and CEO: "How do we define Net Revenue?"
 
VP: "Deductions include write-offs."
 
CEO: "Deductions do NOT include write-offs."
 
They debate. You mediate.
 
𝗧𝗵𝗶𝘀 𝗶𝘀 𝘄𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝘀 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗰𝗲𝗻𝘁𝗿𝗮𝗹𝗶𝘇𝗲𝗱 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲.
 
𝗪𝗵𝘆 𝗱𝗲𝗰𝗲𝗻𝘁𝗿𝗮𝗹𝗶𝘇𝗲𝗱 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗳𝗮𝗶𝗹𝘀:
 
Finance defines metrics one way. Operations another. IT a third.
 
Nobody agrees. Nobody trusts the data. Decisions stall.
 
Data mesh sounds great: "Give control to teams closest to the data."
 
In healthcare? It creates chaos.
 
𝗪𝗵𝗮𝘁 𝘁𝗵𝗲 𝗖𝗼𝗹𝘂𝗺𝗯𝘂𝘀 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁 𝗱𝗶𝗱:
 
Centralized governance to the data team.
 
Built data dictionaries and glossaries.
 
Documented every metric with full lineage.
 
Got business sign-off on definitions.
 
Result? 20 years of trusted metrics and faster decisions.
 
𝗥𝗲𝗱 𝗳𝗹𝗮𝗴𝘀 𝘆𝗼𝘂 𝗻𝗲𝗲𝗱 𝗰𝗲𝗻𝘁𝗿𝗮𝗹𝗶𝘇𝗲𝗱 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲:
 
→ 3+ definitions for the same KPI 
→ Execs arguing about metrics 
→ No agreed "source of truth"
 
𝗧𝗟;𝗗𝗥:
 
Data mesh doesn't work for mid-market healthcare.
 
Data team owns definitions. Business signs off. Everyone sees the same numbers.
 
That's trust.
 
P.S. - Full breakdown in newsletter. Link in comments. 👇
 
♻️ Share this if execs ever argued about a metric.
 
Follow me for governance that works.

My data consulting predictions for 2026.

(These are 3 right from my head.)

𝟭. 𝗧𝗵𝗲 𝗺𝗶𝗱-𝗺𝗮𝗿𝗸𝗲𝘁 𝗹𝗲𝗮𝗻𝘀 𝗼𝗻 𝗰𝗼𝗻𝘀𝘂𝗹𝘁𝗮𝗻𝘁𝘀 𝘁𝗼 𝗱𝗲𝗹𝗶𝘃𝗲𝗿 𝗺𝗼𝗿𝗲, 𝗳𝗮𝘀𝘁𝗲𝗿.

Gone are data teams with 5-6 hires. No one wants to hire right now. Instead, the mid-market is squeezing consultants to get what they need faster.

𝟮. 𝗗𝗮𝘁𝗮 𝗰𝗼𝗻𝘀𝘂𝗹𝘁𝗮𝗻𝘁𝘀 𝗮𝗿𝗲 𝘂𝗽 𝗮𝗴𝗮𝗶𝗻𝘀𝘁 𝗮 𝗹𝗼𝘁 𝗺𝗼𝗿𝗲 𝗿𝗶𝘀𝗸 𝘄𝗵𝗲𝗻 𝗶𝘁 𝗰𝗼𝗺𝗲𝘀 𝘁𝗼 𝘁𝗵𝗲𝗶𝗿 𝗼𝗳𝗳𝗲𝗿𝘀.

Be prepared for clients that expect a lot more promise out of the gate. Large deals will not happen without offers that promise big returns in short time periods. 

This drives up risk for the consultant, and puts extreme pressure to deliver the highest quality work. We need to be ready to grind.

𝟯. 𝗧𝗵𝗲 𝗰𝗼𝗻𝘀𝘂𝗹𝘁𝗮𝗻𝘁𝘀 𝘁𝗵𝗮𝘁 𝗰𝗿𝗲𝗮𝘁𝗲 𝘂𝗻𝗶𝗾𝘂𝗲 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲𝘀 𝗳𝗼𝗿 𝘁𝗵𝗲𝗶𝗿 𝗰𝗹𝗶𝗲𝗻𝘁𝘀 𝘄𝗶𝗻.

I'm not just talking about innovative data products. I'm talking about the journey when building. Consultants need to lean into their saavy people skills to create an enjoyable working relationship. 

Take your clients out for drinks, crack jokes and post funny gifs in the Teams group chat. Talk about your love for comic books and movies. If you're all business, your clients won't thrive during the hard meetings too.

As we continue to provide our services in the Age of AI, the stakes are higher.

More efficiency, less time and intentional relationships are crucial.

♻️ Share with data leaders & consultants that are new or anxious about the new year.

Follow me for more insights on the realities of being a data consultant in 2026.

The CFO presented Q3 revenue to the board.
 
(The COO's numbers were $2M different. In the same meeting.)
 
Uncomfortable silence.
 
Board members looking at each other.
 
Someone finally asks: "Which number is correct?"
 
CFO: "Mine comes from the financial system."
 
COO: "Mine comes from operations reporting."
 
Both confident. Both wrong in different ways.
 
The CEO pulls you aside after: "We need to fix this. Now."
 
𝗧𝗵𝗶𝘀 𝗶𝘀 𝘁𝗵𝗲 𝗺𝗼𝗺𝗲𝗻𝘁 𝘁𝗿𝘂𝘀𝘁 𝗱𝗶𝗲𝘀.
 
Not because the data is bad.
 
Because leadership realizes they've been making strategic decisions on inconsistent numbers.
 
𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗵𝗮𝗽𝗽𝗲𝗻𝘀:
 
Finance defines revenue based on cash collected.
 
Operations defines it based on services delivered.
 
Both are technically "right" from their perspective.
 
But when they're both called "Q3 Revenue" in board presentations? Disaster.
 
𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝗰𝗼𝘀𝘁:
 
It's not just embarrassment.
 
Strategic decisions get delayed. Budgets get questioned. Leadership loses confidence in the data program.
 
The board starts asking: "If we can't agree on revenue, what else is wrong?"
 
𝗛𝗼𝘄 𝘁𝗼 𝗳𝗶𝘅 𝗶𝘁:
 
Stop blaming the departments. They're not wrong—they're just measuring different things.
 
Get everyone in a room. Define what "Q3 Revenue" means for board reporting.
 
Document it. Get sign-off. Update every report.
 
Then enforce it.
 
𝗧𝗟;𝗗𝗥:
 
Misaligned definitions don't just create confusion.
 
They kill strategic decisions and erode trust in your entire data program.
 
Fix the definitions. Restore the trust.
 
♻️ Share this if you've watched executives present conflicting numbers in the same meeting.
 
Follow me for real talk on fixing healthcare data chaos.

ParkBee

Interim Data & Analytics Engineer

Non Financials - Sustainability Reporting Agency | ESG data, GRI, SASB, TCFD, EU Taxonomy & CSRD

Als Associate Partner bij NonFinancials ben ik verantwoordelijk voor het begeleiden van bedrijven bij het realiseren van hun duurzaamheidsdoelen. NonFinancials is een reporting agency die organisaties verbindt met hooggekwalificeerde professionals op het gebied van duurzaamheid. Vanuit deze rol werk ik nauw samen met klanten in diverse sectoren – van vastgoed en financiële diensten tot retail en gezondheidszorg – om maatwerkoplossingen te ontwikkelen die hun duurzaamheidsstrategie versterken en toekomstbestendig maken.

Mijn verantwoordelijkheden omvatten:

-> Het implementeren van duurzame strategieën en het begeleiden van bedrijven bij de uitvoering hiervan.
-> Business development, ik help bedrijven hun duurzaamheidsbehoeften om te zetten in concrete acties.
-> Het coördineren en leiden van complexe klantprojecten, en het waarborgen van kwaliteit en impact.
-> Het faciliteren van workshops om strategische dialogen over duurzaamheid te stimuleren.
-> Het verder uitbouwen van het team van NonFinancials en het ontwikkelen van innovatieve klantoplossingen.

Met mijn ervaring op het gebied van datastrategie en -implementatie, breng ik nu diepgaande kennis van relevante frameworks zoals CSRD, GRI, en GHG Protocol. Mijn missie is om bedrijven te ondersteunen in hun transitie naar een duurzame en toekomstbestendige bedrijfsvoering met ESG data.

Co-founder

Are you ready to embrace the future of corporate sustainability reporting? 

We understand that the journey to CSRD compliance can seem complex, but fear not! Sustainability Intelligence is here to simplify the process, ensuring your company not only meets the new requirements but thrives within the evolving landscape of sustainability and climate tech 🚀 

Our expertise extends to ensuring the quality and accuracy of your sustainability data. With our guidance, you'll have the tools to gather, process, and present data that stands up to scrutiny. 
We'll help you transition seamlessly, enhancing the accessibility, visibility, and impact of your sustainability reporting. We can also help you arrange third-party assurance or verification, elevating your reporting to the next level of credibility. 

Our mission is to guide you toward sustainable practices that benefit your company, stakeholders, and the planet. The CSRD might be a requirement, but it's also an opportunity and we're here to help you seize it 💪

(We do not pretend to be a GENERATIVE AI startup, a Fintech, FinOps, RevOps, or not even an MLOps company using the GPT-4 model and DALL-E system. But if you are a VC, into data technology for sustainability reporting and reading this, please do send me a message and I'll tell you everything about our tool)

Sustainability Reporting Expert

Captured Stories

Investor

Onramper

Advisor & Investor

Worked with the founding team during pre-seed stage to built a data capability from scratch, onboarded the first hires for the data team

Data Lead

DataBright

DataBright helps companies transform their Data Platform, by implementing a Modern Analytics Stack, to improve their decision making, operations and monetize their data, as well as provide AI Development services to help companies develop new data-driven products and services.

Our Services Include:
- Data Strategy
- ETL (Data Pipelines)
- Cloud Data Warehousing
- Modelling Layers
- Data Visualisations
- Automation
- Data Science (AI Development, including Machine Learning, Natural Language Processing, Vision and Speech)

Co-founder (Acquired)

Doordat Data Science en Business Analytics steeds meer in elkaars verlengde komen te liggen, is er een nieuw soort professional nodig. Een professional die inzichten uit data kan vertalen in business value. Behalve dat je inzichten kunt creëren, wil je uiteindelijk dat op strategisch niveau besluiten onderbouwd worden met deze data. Dit vereist een vermogen om inzichten uit data te kunnen interpreteren, deze op waarde te kunnen schatten en dan ook nog eens te kunnen verkopen. Vanuit deze gedachte is DataBright ontstaan, wij werken met de “Brightest Minds” in data, die capabel zijn om deze vertaalslag te maken en daarom vanuit data, echte waarde toe kunnen voegen voor de business.

Gecertificeerd solution partner van Looker, Snowflake, Fivetran en Matillion

Zie jij kansen om een verbeterslag te maken op het gebied van Data Science, Enterprise Datawarehousing of Analytics? Twijfel dan niet om contact met mij op te nemen

Director of Partnerships

Flow Your Money

Built a modern data platform for Flow to leverage their data for growth 🚀

-Fivetran
-Snowflake
-dbt

Interim Data Lead

Drukwerkdeal.nl

Begeleiden migratie naar Looker in samenwerking met moederbedrijf Cimpress

Communicatie Team Data & Insights naar MT

Voor eindgebruikers data workshops en trainingen opgezet in NL en BE 

-Looker
-Snowflake
-dbt

PaymentGenes

We understand the core of your business. We are from the payments industry and we have experienced and implemented what data can bring to the industry. Our quick-scan has proven to increase the effectiveness of operational strategies by providing actionable insights on what you’re doing in the field of fintech. Many efforts have been put in developing analytical capabilities, we ensure that these efforts are executed in the right direction. By providing expert recommendations from different angles, we lay out the foundation for your organization to scale out.

Principal Consultant | Co-Founder PaymentGenes Consultancy

Syntrus Achmea Real Estate & Finance

Syntrus Achmea Real Estate & Finance is bezig met het ontwikkelen van een moderne cloud datahub die centraal de data uit de verschillende domeinen van Syntrus Achmea kan opslaan en distribueren.
Met DataBright heeft Syntrus Achmea een partner gevonden met de expertise, het inzicht en de ervaring met bewezen methodes, om deze moderne cloud datahub zo efficiënt mogelijk in te richten.

Technical Project Manager

Door het ontsluiten, transformeren en opnieuw modelleren van diverse interne relationele datasets, heeft het GVB kunnen voldoen aan de vereisten voor herkeuring van haar certificeringen. Hiervoor is een moderne cloud datawarehouse oplossing van Snowflake geïmplementeerd, om snel tot inzicht te kunnen komen en zo tijdig te kunnen voldoen aan de audit.

-Snowflake Datawarehouse
-Qlikview
-PowerBI

Cloud Datawarehouse Engineer

I’m an expert in Data & AI with a strong track record in modernizing data infrastructure. As a co-founder of a consulting firm (successfully exited), I’ve helped companies across fintech, e-commerce, and beyond adopt modern technologies—defining strategies, building teams, and leading technical implementations.

Always open to discussing new ventures and business opportunities.

Wesley Nitikromo

Most businesses don’t have a visibility problem.
They have a content production problem.

That’s why hiring a “content creator” — which is growing 36.8% YoY — works for almost no one. 
In fact, 73% of companies end up disappointed within 6 months.

Here’s what I see in Amsterdam every week:

The Fantasy:
→ Hire a content creator for €50k/year
→ They make viral TikToks
→ Brand explodes overnight
→ ROI justifies itself

The Reality:
→ Month 1: Onboarding
→ Month 2: Testing content → low engagement
→ Month 3: “We need better equipment”
→ Month 6: Burnout from juggling 5 roles
→ Month 12: Back to job boards

Why it fails:

1️⃣ Swiss Army knife, expecting a surgeon
Videography. Photography. Editing. Copywriting. Strategy. Analytics. Community. Design.
7 skills. 1 person. No wonder it breaks.

2️⃣ Viral success is a system, not a person
You need:
• Professional equipment (€10k+)
• Studio space
• Editing workflows
• Strategic planning
• Multiple formats
One person can’t scale this.

3️⃣ The hidden cost
Salary: €50k
Benefits: €15k
Equipment: €8k
Software: €2k
Training: 3 months
Total first year: €75k+

The smarter alternative:
Instead of hiring a single person, build a content production system:
→ Batch content creation
→ Work with specialists
→ Scale output based on needs

At Mira Studios, companies replace that €50k hire with a €4,000/month production package:
• Studio + pro equipment
• Video + photography
• Editing included
• Zero salary, zero onboarding

Most companies don’t need a content creator.
They need a system that works whether someone quits, gets sick, or goes on vacation.

What’s your experience?
Ever hired a content creator who actually delivered? Or is this exactly what you see? 👇

P.S. If you’re about to post that job listing, DM me. I’ll show you a smarter way.

I replaced expensive Data Scientists with Infrastructure in 2019. Now I’m doing it for Content Creators | Founder @ Mira Studios | Former Analytics Engineering Director | Data & Fintech Expert | Angel Investor

No alternative text description for this image

I made a mistake founders keep repeating:
I tried to use ads to fix what only a content engine can fix.

This morning I got this message in my inbox:

“Je betaalt €860 per conversie. Dit kan lager.”

And suddenly the Google Ads guilt trip begins.
The promise is always the same:

👉 “Book a call and we’ll optimise your ads into overnight success.”

Here’s the honest truth:

For nearly six years I’ve run a highly profitable business with Dwayne Gefferie without spending €1 on ads.

We landed big clients through:

• Showing up
• Posting authentic content
• Warm introductions
• Real relationships
• Consistent output

No magic funnel.
No CPC wizardry.
No four-figure “optimisation call.”

But recently, I decided to experiment.
I put a couple thousand euros into Google Ads to promote some new content.

And guess what?

Suddenly I’m responsible for “optimizing campaigns,” “maximizing ROI,” “keeping CPC down,” and “improving conversion flows.”

Meanwhile — every agency is in my inbox promising instant miracles.

It made me realize something important:

Who should REALLY be responsible for your growth?

A Google Ads agency trying to fix your strategy?
Or the content system that fuels everything you do?

Because if your content is unclear, inconsistent, or uninspiring…

No optimization will save you.
No agency will “hack” their way to ROI.
No budget will fix a broken message.

**The real engine of growth isn’t ads.

It’s repeatable, high-quality content.**

Ads amplify.
Content converts.

A solid content system — with strategy, production, and output you can scale — will outperform “ad optimisation” every single time.

So here’s my lesson learned:

Ads are gasoline.
Your content system is the engine.
Most businesses are trying to fuel a car that isn’t built yet.

Curious:
Has anyone else experienced this?
Did ads help you — or just expose that your content wasn’t ready yet?

graphical user interface, text, application, Word

You don’t need a podcast.
You need 50 LinkedIn posts first.

Here’s why:

If you can’t write 50 interesting posts, you won’t be able to talk for 50 episodes.

The medium doesn’t matter.

Your ideas do.

Test this:

Write 10 LinkedIn posts about your podcast topic.
	•	Can you generate 10 unique angles?
	•	Did anyone engage with them?
	•	Do you have more to say after 10?

If no → You don’t have a podcast yet
If yes → Now you’re ready to record

Why this works:

Writing forces clarity.
Talking allows rambling.

Most podcasts fail because people ramble for 20 minutes with 2 minutes of actual insight.

Writing first = you know what you’re trying to say.

The brutal truth:
90% of new podcasts shouldn’t exist.
Not because the audio quality is bad.

Because the creator had one good idea and stretched it into a “weekly show.”

One good idea = One good episode.
Not a podcast.

Before you buy equipment, or rent a studio:
	1.	Write 50 posts on your topic
	2.	See if people engage
	3.	See if you have more to say
	4.	See if you can stay consistent

Still interested after 50 posts?
Now start your podcast.

You’ll already have:
	•	✅ 50 episodes worth of ideas
	•	✅ Proven audience interest
	•	✅ Demonstrated consistency
	•	✅ Clear articulation of your ideas
And guess what?

You can repurpose those 50 posts into your first 50 episodes.

Equipment comes last, not first.
Ideas come first.

Validation comes second.
Consistency comes third.

Equipment comes fourth.

Most people do it backwards.

Don’t be most people.

Disagree with me?
Comment and tell me why.

Agree?
Send me a DM with “WRITE” and I’ll share my 50-post framework.

2 hours of recording. 15 short clips. 1 went viral.
Shared by Bas Smit the #1 most influential influencer in the Netherlands in 2023 (this year, runner-up behind Monica Geuze 😜)

Here's what most people get wrong about going viral:

They think it's luck
While it's a numbers game.

What actually happened:
We didn't chase one "perfect" video.

We recorded a 2-hour session and extracted: 
→ 15 different clips 
→ 15 different questions 
→ 15 different hooks

14 performed okay.
1 caught fire.

Bas Smit shared it.

But here's the truth:

We had no idea which one would be "the one."
And that's the whole point.

This is how viral actually works:
❌Record 1 video, pray it goes viral ➡️ Record 15 videos, let the data decide
❌Chase perfection in one take ➡️  Batch produce and scale what works
❌Hope for luck ➡️ Engineer probability

Viral isn't random. It's statistical.

The more high-quality shots you take, the higher the chance one lands.

Most companies do this backwards:
They spend:
-3 hours planning the "perfect" video
-2 hours shooting it
-4 hours editing it

Post it once
Get 47 views...
Give up...

What works:
30 min planning the shots
2 hours recording everything
3 hours cutting into 15 clips

Post all 15
See which one hits

Double down
Same time. 15x the probability.

The uncomfortable truth:
You're one systematic recording session away from content that reaches millions.

But you're treating it like a lottery ticket.

The Bas Smit moment wasn't the strategy.
The 2-hour batch recording session was.

Because if we had only recorded 1 video that day, the viral one wouldn't exist.
Viral content is born from volume, not magic.

What would change if you stopped shooting one video per week and started batch-producing 15 clips in a single session?

Bas Smit didn't share it because it was "perfect." He shared it because it resonated with him personally. However, it only had the chance to resonate because we created enough content for one to stand out.

Pardis Faqiri Youssef Louzati
#ContentStrategy #ViralContent #SystematicMarketing #Amsterdam

I’m going to lose followers for this but, here are 5 patterns that actually drive engagement:

1. Stop hiring people to solve system problems

Companies spend €50k/year on a content creator expecting them to be:
• Videographer
• Editor
• Strategist
• Designer
• Copywriter
• Analyst
• Community manager
7 roles. 1 salary. 100% burnout rate.

The fix isn’t a better hire.
It’s infrastructure that doesn’t quit when people do.

Takeaway: If your solution breaks when someone goes on vacation, you don’t have a solution, you have a dependency.

2. Test cheap before you invest expensive

My “you don’t need a podcast” post last week was the most controversial 🤓

The framework:
Write 50 LinkedIn posts about your topic first.
	•	Can you generate 50 unique angles?
	•	Did anyone engage?
	•	Do you still have more to say?

If no → You don’t have a podcast, you have one good idea
If yes → Now buy the equipment

Takeaway: Ideas are free to test. Equipment isn’t.

3. Ads amplify, they don’t fix

The brutal truth:
I paid €860 per conversion trying to use ads to fix what only content could fix.

Meanwhile, my other business ran 6 years profitably without spending €1 on ads.

Just:
• Consistent content
• Warm intros
• Real relationships
• Showing up

Ads are gasoline. Your content is the engine.
Most businesses are trying to fuel a car that isn’t built yet.

Takeaway: If your organic content doesn’t convert, your paid ads won’t either. Fix the foundation first.

4. Write to force clarity, talk to ramble

Most podcasts fail because people ramble for 20 minutes with 2 minutes of insight.

Writing forces you to know what you’re trying to say.

Talking lets you get away with not knowing.

That’s why:
• Bad LinkedIn posts = vague but short
• Bad podcasts = vague but long

50 written posts will sharpen your thinking more than 50 recorded episodes ever will.

Takeaway: The medium doesn’t matter if your ideas aren’t clear. Start where clarity is enforced.

5. The “Swiss Army knife” trap kills businesses

73% of companies are disappointed within 6 months of hiring a “content creator.”

Not because the person is bad.
Because the job is impossible.

You can’t expect one person to:
• Execute like a specialist
• Think like a strategist
• Scale like a system
• Cost like an intern

Takeaway: Stop looking for unicorns. Build systems that work with normal humans.

Here’s what nobody tells you about content 

ROI:
My 3 posts last week generated 59 profile visits.
In my Google Ads campaigns, I pay €2.86 per click.

That’s €168.74 worth of traffic.

Cost to create these posts: €0

Time investment: A couple hours in total
Shelf life: They’ll keep driving visits for months

Meanwhile, my Google Ads budget:
• Requires constant optimization
• Dies the moment I stop paying
• Costs €860 per actual conversion

The math isn’t even close.

What’s one system you wish you’d built sooner instead of hiring for?

#ContentStrategy #SystemsThinking #Ads #LinkedIn #BusinessGrowth #ContentCreator

The best content creators I know don’t own a camera.

They don’t own editing software either.
Or lighting equipment.
Or a studio.

And they’re outproducing everyone who does.

Here’s what they figured out:

The old playbook, like I once did myself:
Step 1: Buy a €3,000 camera
Step 2: Learn DaVinci Resolve
Step 3: Build a home studio
Step 4: Spend 6 months “getting good at production”
Step 5: Finally start creating content

Result:
You’re now a photographer, editor, and strategist.

Except you’re mediocre at all three.
And you’ve produced 12 videos in 6 months.

The new playbook:
Step 1: Develop 30 content ideas
Step 2: Book 2 hours of studio time
Step 3: Record everything
Step 4: Professional team handles production
Step 5: 30 videos published in 2 weeks

Result:
You stayed in your zone of genius: ideas and strategy.

The infrastructure handled the rest.

Here’s the uncomfortable truth:

Owning equipment doesn’t make you a better creator. It makes you a worse one.

Because now you’re thinking about:
• Lighting setups
• Audio levels
• Frame composition• Color grading
• Export settings
• File management

Instead of:
• What story am I telling?
• What angle will resonate?
• What insight drives action?
• How does this serve my audience?

The best creators I work with treat production like they treat accounting:
Essential. Professional. Outsourced.

They don’t say:
“I need to learn Exact so I can do my own bookkeeping.”

They say:
“I’ll hire an accountant so I can focus on growing the business.”

But for some reason, with content, we all think we need to be:
• The strategist (who comes up with ideas)
• The creator (who performs on camera)
• The cinematographer (who frames the shot)
• The editor (who cuts it together)
• The distributor (who posts and optimizes)

That’s 5 full-time jobs.
You’re doing it as a side task.
No wonder it’s not working.

The infrastructure model:
→ You show up with ideas
→ Professional space captures it
→ Professional team produces it
→ You focus on distribution and strategy

Outcome:
More content. Higher quality. Less time.

The infrastructure does the technical work.
You do the creative work.
That’s the division of labor that actually scales.

Most people think:
“Once I master the equipment, I’ll create great content.”

Reality:
“Once you stop worrying about equipment, you’ll create great content.”

The camera doesn’t make you interesting.
Your ideas do.

What would happen if you stopped trying to be a videographer and just focused on what you’re passionate about?

My business model depends on people using professional infrastructure. But this isn’t a pitch. It’s an observation: the creators who treat production like a utility (not a skill to master) consistently outperform those who own all the gear (like I did).

#ContentStrategy #CreatorEconomy #SystematicMarketing #ContentProduction

How Booking.com produces 10,000+ videos without an army of creators.

They use Cube’s creative automation solution to produce videos directly for social media platforms within minutes. 

While most companies hire individuals and hope for viral moments, Booking.com built a system:

→ 10,000+ targeted videos published across Facebook, Instagram, and YouTube 

→ Content reaches travelers in 40+ languages across 190+ countries 

→ Their 2024 TikTok campaign generated 40.6 million impressions 

→ They focus on “AI and creative automation, with a systems-thinking and business-first approach” 

Here’s the uncomfortable truth:
That “Content Creator” job posting you’re about to fill?

They’ll burn out in 6 months trying to:
	•	Shoot 20 posts per week
	•	Stay on top of every platform’s algorithm
	•	Manage their own production quality
	•	Edit everything themselves
	•	Come up with endless “fresh ideas”

What you actually need:

A production system that enables your team to create at scale:
	
1.	AI-powered prep workflows → Turn one idea into 30 pieces of content
	
2.	Professional infrastructure → Studio space when quality matters
	
3.	Batch production methodology → 30 videos in 2 days, not 2 per week
	
4.	Repeatable frameworks → Process over personality

At Mira Studios, I see this pattern weekly.
Companies spend €50k+ hiring a “creator” expecting magic.

What they get: 
Inconsistent quality. Format fatigue. Burnout.

What Booking.com figured out:
Infrastructure > Individuals.
Systems > Stars.
Process > Personality.

The future isn’t hiring more creators.
It’s building systems that turn anyone into a creator.

Your move:
Keep hiring €60k creators who max out at 10 posts/week.

Or build the infrastructure to produce thousands of pieces of content with your existing team.

Booking.com is actively hiring for roles with “broad social media platform knowledge and an understanding of content trends.”  But their competitive advantage comes from systematic creative production infrastructure, not just individual talent.

#ContentStrategy #ContentProduction #MarketingStrategy #Amsterdam

Everyone’s using AI wrong.
They sit in front of a camera asking it to “write me a script.”

I use AI to plan my entire content production day, and it lets me shoot 15 videos in 4 hours.

That’s why we shoot 30 videos in 2 days while most companies struggle to make 2 per week.

AI shouldn’t create your content.
AI should prepare your production day.

Below you can find my exact workflow:

48 hours before shooting, I run 3 prompts

THE RESULT:
	•	2.5 hours of AI prep
	•	15 videos shot in 4 hours
	•	Zero stress on production day
	•	Authentic delivery (your words, not AI’s)

THE DIFFERENCE:

Without this: 2 videos per week, high stress, inconsistent quality

With this: 15 videos in 4 hours, just show up and talk

AI = Your production assistant, not your creator.

Let it analyze patterns and structure logistics.
You handle the authentic delivery.

This is how engineers think about creative work:

Preparation > Improvisation
Systems > Spontaneity

Most “creatives” think this kills creativity.
I think it enables it.

Want the full 6-step workflow, including technical shot lists and repurposing strategy?

Comment “PREP” and I’ll send you the complete framework.

Content Production Workflow

Most companies aren’t creating content.
They’re accumulating content debt.

And just like technical debt, it compounds.

Here’s what content debt looks like:
Week 1: You skip posting because you’re “too busy”
Week 2: Your competitor posts 3 times
Week 3: They’re building authority while you’re still planning
Week 4: They show up in search results. You don’t.

52 weeks later:
They’ve published 156 pieces of content.

You’ve published 12.

That’s not a gap.
That’s a canyon.

The cost of content debt:
→ Every conversation you didn’t have with your audience
→ Every search term you don’t rank for
→ Every platform algorithm that forgot you exist
→ Every competitor building trust while you stayed silent

And here’s the brutal part:
Content debt doesn’t just sit there.
It grows.

Because while you’re stuck in “plan mode,” everyone else is:
• Testing what works
• Building distribution
• Capturing search traffic
• Creating compounding assets

Most companies fall into this cycle:
Month 1: “We need to start creating content”
Month 2: “Let’s plan our content strategy”
Month 3: “We should hire someone for this”
Month 4: “Still interviewing candidates”
Month 5: “Onboarding the new hire”
Month 6: “They need better equipment”

6 months. Zero content.
Meanwhile, your competitor posted 78 times.

How to fix content debt:
You can’t “catch up” by working harder.
You catch up by working systematically.

Option A: The slow way
Post 1 piece per week for 156 weeks (3 years)

Option B: The systematic way
Batch produce 30 pieces in one day

Repeat monthly
Hit 156 pieces in 5 months
Same endpoint. Different timeline.

The math that should scare you:
If your competitor publishes 3x per week and you publish 0…

After 1 year:
	•	They have 156 pieces of content working for them
	•	You have 0
After 2 years:
	•	They have 312 pieces ranking, getting shared, building authority
	•	You’re still “planning to start”
After 3 years:
	•	They’re the obvious category leader
	•	You’re explaining why you’re not visible

Content debt = invisible tax on growth.

Here’s the good news:
Content debt can be paid off faster than you think.

But only if you switch from:
❌ “We’ll post when we have time”
✅ Systematic batch production
❌ “One person posting sporadically”
✅ Infrastructure that produces regardless
❌ “Waiting for perfect”
✅ Publishing volume and iterating

The companies that win aren’t more creative.
They’re more systematic.

How much content debt are you carrying right now?

52 weeks × 3 posts = 156 pieces per year

How many did you actually publish this year?

That gap?
That’s your content debt.
And every week you don’t address it, your competitors pull further ahead.

What if one systematic change you could make to stop accumulating content debt?

Companies spend months “planning” while their competitors spend 2 hours recording and produce 30 pieces. Guess who wins? Not the better planner. The better executor.

#ContentStrategy #SystematicMarketing #ContentProduction #MarketingStrategy

You can’t personally interview 1,000 customers. But you can systematically record conversations that scale like content.

Here’s the shift nobody’s making:

The old way:
Interview customers → Take notes → Maybe use a quote

The new way:
Record customer conversations → Extract testimonials → Create case studies → Generate marketing copy → Build UGC library

Same conversation. 10x the output.

What I mean:

When you record a 15-minute customer conversation, you get:
→ 3-5 testimonial clips (social proof)
→ 10-15 direct quotes (website copy)
→ Their exact language (ad copy that converts)
→ Pain points you didn’t know existed (product roadmap)
→ The moment they almost quit (objection handling)
→ Why they chose you over competitors (positioning)

One conversation = your entire marketing strategy.

But here’s the problem:

You’re treating customer feedback like data.
It’s content.

The companies winning:
They’re not sending surveys.

They’re recording conversations and treating them as:
• Testimonial libraries
• Copy repositories
• Product insights
• Content assets
All at once.

This is how you scale “talking to customers”:
Make every conversation do triple duty:
	1.	Learn (insight)
	2.	Capture (testimonial)
	3.	Repurpose (content)

One call. Three assets.
That’s the system.

Stop taking notes. Start hitting record.

Who’s already doing this? 👇

#CustomerInsights #ContentStrategy #ScalingSystems

We’re testing an AI agent that schedules customer conversations, records them, and turns them into content.

Without me touching anything.

Here’s what it does:
→ Reaches out to customers who hit certain triggers
→ Runs the conversation automatically
→ Records and transcribes everything
→ Extracts testimonials, pain points, product insights
→ Suggests podcast episodes, social clips, case studies
→ Drafts the content

The agent does everything.

This is the shift:

Most companies:
• Manually schedule feedback conversations (never happens)
• Take notes during conversations (miss half the insights)
• Hope someone turns notes into content (they don’t)

Agentic approach:
• AI identifies who to talk to
• AI handles scheduling
• AI captures everything
• AI suggests what to create
• Humans approve and publish

One customer conversation becomes:
→ Video testimonial (marketing)
→ Podcast episode (content)
→ Product feedback (roadmap)
→ Social clips (distribution)
→ Case study quotes (sales)
Without manual work.

The future isn’t “AI creates content.”
It’s “AI harvests content from real conversations.”

Because your customers are already creating your best content.

You’re just not capturing it systematically.

Wants to know what we’re using? 
Send me a DM

#AgenticAI #AIAgents #ContentStrategy #CustomerInsights

I stopped sending customer surveys 3 years ago.
Now I just hit “record” and ask them to tell me their story.

Here’s what happened:

Before (the survey era):
“On a scale of 1-10, how likely are you to recommend us?” 
Response rate: 12%
Usable quotes: 0
Actual insights: “Our service is good 👍”

After (the conversation era):
“Hey, can I record a quick video with you about why you chose us?”
Response rate: 67%
Usable quotes: Every. Single. One.

Actual insights:
“I almost went with your competitor because their website looked more professional, but then I called you and realized you actually understood my needs. Their sales guy just read from a script.”

That’s not feedback.
That’s your entire marketing strategy handed to you.

Here’s what nobody tells you about surveys:
They’re designed for YOU, not for them.

You want data you can put in a spreadsheet.
They want to be heard.

You ask: “Rate our service (1-5)”
They think: “It was fine but honestly that’s not even the point”

The real issue is buried 6 questions down.
And they quit by question 3.

When I switched to actual conversations, everything changed:

What I learned about our business:
	•	The tool we thought was our differentiator? Nobody mentioned it.
	•	The jumpstart email we almost cut? 4 people called it “the moment they knew we were different.”
	•	Our pricing wasn’t “too high” - it was “confusing to explain to our CFO.”
What I got for marketing:
	•	4 video testimonials
	•	20+ direct quotes
	•	5 case studies (they told me the story, I just wrote it down)
	•	The exact language our customers use 

What I got as insights:
	•	The real reason people churn (it’s never what you think)
	•	Features they’re hacking together with workarounds
	•	The competitor they almost chose and why they didn’t

All from conversations.
Zero from surveys.

But here’s the problem:
This doesn’t scale.
I can’t personally record 100 customer conversations.

So most companies read this and think:
“Great idea, but we have 10,000 customers. Back to surveys.”
And that’s the gap I’m obsessed with right now.
Because the companies that figure out how to have conversations at scale are building an unfair advantage:

→ UGC content library that competitors can’t fake
→ Customer language that makes copy convert
→ Product insights that surveys will never surface
→ Testimonials that feel real because they ARE real
→ Employee feedback that actually drives retention

Surveys give you scores.
Conversations give you stories.

And stories are the only thing that still cuts through the noise. #CapturedStories 😂

So here’s my question:
What would change in your business if you had 100 recorded customer conversations this quarter?

Not survey responses.
Actual conversations.

I’m not saying I’ve solved the “at scale” problem yet. But I’m working on it. And I think the answer is closer than most people realize.

#CustomerExperience #ProductMarketing #ContentStrategy #CustomerFeedback  #B2B #Braindump

I asked 3 customers why they chose us.
Zero mentioned my “key differentiator.” 😅 

Welcome to my Friday reality check.

Here’s what our website said:
“Professional-grade studio equipment at accessible prices”

Here’s what customers actually said:
“I was tired of recording in my bedroom and feeling embarrassed to show clients the footage.”

“My business partner kept canceling podcast recordings because our home setup looked unprofessional.”

“I needed a space where I could just show up and record without spending 2 hours setting up equipment.”

Not one person mentioned price.
Not one person mentioned equipment specs.
They wanted to stop feeling amateur.

The problem:
We write marketing based on what WE think matters.

Customers buy based on what THEY actually feel.

The gap between those two = why your content doesn’t convert.

What actually happened in these conversations:
I stopped asking:
❌ “What features do you like?”
❌ “How would you rate our service?”
❌ “Would you recommend us?”

I started asking:
✅ “What almost stopped you from booking?”
✅ “What were you doing before you found us?”
✅ “What did you tell your colleague when you recommended us?”

The answers completely changed.

What I learned from those conversations:

We thought our differentiator was:
Professional equipment

Customers actually chose us because:
“I don’t have to explain to clients why my background looks messy”

We thought people cared about:
Acoustic treatment specifications

They actually cared about:
“Not worrying if the neighbor’s dog is barking”

We thought price was the objection:
“€60/hour is competitive”

The real objection was:
“I don’t know if I’ll use it enough to justify booking”

(That’s why attraction offers work, it creates psychological commitment, not price)

Here’s the trap:

What we advertised:
 • Features
 • Specifications
 • Comparisons
 • Pricing tiers

What actually converted them:
 • Emotional relief
 • Status anxiety
 • Peer validation
 • Removal of friction

Your marketing talks about you.
Their decision is about them.

The real value lies in the question asked.
Questions that never show up in a survey.

It only surfaces in a conversation.

What changed after these conversations:
Before:
“Professional podcast studio with premium equipment”
After:
“Show up. Hit record. Look professional. Zero setup required.”

Same service. Different language. 
Probably 10x better conversion.

The Friday lesson:
Your marketing is probably solving the wrong problem.
Not because you’re bad at marketing.
Because you never asked the right questions.

Stop surveying.
Start conversing.

Surveys ask what you want to know.
Conversations reveal what actually matters.

What’s one assumption you have about why customers choose you that you’ve never actually validated?

Let Me Know in the comments below 👇

#CustomerInsights 
#Marketing 
#ContentStrategy 
#StartupLessons
#FridayReflections

PostNL. what else? On a scale of 0 to 10, I would definitely recommend all of you to use PostNL (unless you’re a big fan of DHL?)

What’s the value of these generic questions; “On a scale of 1-10, how satisfied are you with our service?”

I gave you a 6.
Now what?

You never followed up.

You never asked why.

You never told me what changed.

You just… disappeared into your spreadsheet.

Here’s what’s broken:
You asked for my opinion.
I gave it to you.

Then you treated it like a data point instead of a conversation.

You reduced my experience to a number.
Then you ghosted me.

The real question isn’t “rate us 1-10.”
It’s “what almost made you leave?”

But you don’t ask that.

Because that requires a conversation.
And conversations don’t fit in dashboards.

Here’s what drives me crazy:

I’d give you feedback for free.
I’d tell you exactly what’s broken.
I’d explain the moment I almost quit.
I’d walk you through what your competitor does better.
For free. Right now. Just ask.

But instead you ask me to rate you on a scale.

And then you wonder why you get nothing useful.

The system is backwards:
Companies want feedback.
Customers want to be heard.

But we’ve built a system where:
→ You get a number
→ I feel ignored
→ Nothing changes
→ We both lose

What if it worked like this:
You: “Can I record a 5-minute conversation about your experience?”

Me: “Yes.”

You: Actually listens. Actually responds. Actually changes something.

Me: “That felt good. I’ll tell everyone.”

Instead we get:
“Rate us 1-10”
[Gives 6]
[Silence forever]

And here’s the really stupid part:
When you finally DO reach out 6 months later… It’s another survey.
Asking me to rate you again.

Still 1-10.
Still no conversation.
Still no follow-up.

So let me ask YOU something:
What’s the point of collecting feedback you never act on?

What’s the point of asking for my opinion if you treat it like a metric?

What’s the point of a “6” if you never ask me why?

The feedback system is broken.

Not because people won’t give feedback.
Because companies don’t want conversations.

They want scores.
And scores tell you nothing.

Here’s what I want:

Ask me real questions.

Record my answers.

Actually do something with it.
Tell me what changed.
That’s it.

That’s the whole system.

But instead we’re stuck with:
“On a scale of 1-10…”

Who else is tired of being reduced to a number?

Drop your worst survey experience below 👇
If you would actually call me, with something like “you gave us a 6, can we talk about it?” 
I’d give you an hour. For free. That’s how rare real conversation has become.

#CustomerExperience #Feedback #CustomerInsights #NPS #PostNL #DHL

Within 3 years, Content Creators will be obsolete, replaced by content infrastructure teams the same way Data Scientists were replaced by Data Engineers.

10 years ago, I told companies to stop hiring Data Scientists. Hire Data Engineers instead, I said. 

Build infrastructure, not dependency on unicorns. People thought I was crazy. 

By 2021, I was right. Every company was hiring Data Engineers to clean up the mess. 

Now I’m saying it again: Stop hiring Content Creators. You don’t need another person doing 7 jobs poorly. You need content production infrastructure. 

For the last decade, I’ve built systems that process millions of data points, serve hundreds of users, and run 24/7 without human intervention. 

I’ve seen the same pattern repeat over and over: 
1. Company identifies a need (we need to be data-driven / we need to go viral) 
2. HR posts a job for a “unicorn” (Data Scientist / Content Creator) 
3. Job description lists 7-10 different specialized skills 
4. Company hires someone who’s mediocre at most of them 
5. Expectations are impossibly high 
6. Individual can’t scale or deliver 
7. Company gets frustrated, person burns out 8. They realize they needed a system, not a person 

I’ve watched this happen with: 
• Data Scientists → Data Engineers (infrastructure won) 
• Full-Stack Engineers → DevOps teams (infrastructure won) 
• Growth Hackers → Marketing systems (infrastructure won) 

Now it’s happening with Content Creators Required skills: 
• Professional videography 
• Video editing 
• Photography 
• Graphic design 
• Copywriting 
• Social media strategy 
• Analytics and reporting 
• Community management 

You’re not hiring a person. You’re hiring a miracle. 

Here’s What Actually Happens: 
Month 1: Emma learns your brand, attends 15 alignment meetings, produces 6 pieces of content 
Month 2: Emma tries 3 different strategies because your CEO “saw something on LinkedIn” 
Month 3: Emma is now also managing Facebook, writing newsletters, and somehow responsible for the blog 
Month 4: Emma asks for €5,000 in equipment “to make professional content” 
Month 5: Emma spends 80% of time in meetings, 20% creating. Output drops. 
Month 6: Emma quits.

You’re back at square one. 

The Alternative I’m Building at Mira Studios: Content production infrastructure that works like data infrastructure: 
1. Batch Processing 
• Stop making 2 videos per week 
• Shoot 30 videos in 2 production days 
• Edit in parallel pipeline 
• Distribute on schedule for 3 months 
2. Professional Infrastructure 
• Studio with acoustic treatment (not office corner) 
• Multi-camera setup (not one iPhone) 
• Professional lighting and audio 
• Dedicated production space 
3. Measured Systems 
• Cost per asset: €36 (vs. €480 with a hire) 
• Output metrics: Videos per production day 
• ROI: Savings vs. full-time hire 

So I’m Calling It Now: By 2027, smart companies won’t be hiring Content Creators. They’ll be building content production infrastructure.

McDonald’s just proved why AI can’t replace your face on camera.

Their AI-generated Christmas ad got pulled after massive backlash.

“Zielloos.” (Soulless.)
“AI-rommel.” (No translation needed 😂)
“Ruined my Christmas spirit.”

Here’s what everyone’s missing:
10 people worked on this ad for 5 weeks.

That’s MORE time than a traditional shoot.
And it still felt fake.

The problem isn’t AI.
The problem is using AI to REPLACE humans instead of ENHANCE them.

We use AI every day at Mira Studios.
For research. For editing. For repurposing.

But the person on camera?
That’s always YOU.

In a real room.
With real lights.
Making real eye contact.

Because your audience doesn’t connect with prompts.

They connect with people.

McDonald’s learned this the hard way.
Don’t make the same mistake.

#Rommel #AI #ContentStrategy #CustomerFeedback

Your customers tell you exactly what content to make✋

Every week.

In support tickets🗒️ 

In sales calls📞 

In Slack threads💬 

The same questions. 
Over and over.

Most companies ignore this goldmine.
They brainstorm content ideas from scratch instead.

Here’s where it gets interesting:

Agentic AI can now harvest these insights automatically.

Pull topics from your CRM.

Flag patterns in customer conversations.

Build your content calendar while you sleep💤 

But here’s what AI can’t do:

Sit in front of a camera and be YOU.
Build trust with your face and voice.

Make someone feel like they know your company.

The winning formula for 2026:

AI extracts the insights.
Humans deliver the message.

Stop guessing what to talk about.

Start listening to what your customers already ask.

Have a great start of the week 😁

Turned out there are a lot of people ready to turn their content into a podcast. If you’re ready too, book your slot on mirastudios.nl ;)

taplio

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The Battle

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