We build your B2B target customer machine in 14 days - or you pay nothing.
200 ready-to-buy companies per month. Individually calibrated to your offer. Set up by our sales architects - not pulled from a generic database.
Start with 3 free analyses.
As you read this text, your sales staff are burning €208 per hour on leads that never buy.
The honest calculation that nobody in the SME sector likes to look at:
| What you are currently doing | What it costs you |
|---|---|
| Salesperson researches manually on Google/LinkedIn | 5,000€/month salary |
| 80% of your leads do not match your offer | 4,000€/month wasted |
| Generic outreach = 2% response rate | ~15 lost deals/month |
| Long sales cycles due to poor targeting | Ø 90 days to close |
| Annual damage: | ≥ 48,000€ + opportunity cost |
⚠️ Every month you wait costs you more than 5 years of bloo.research.
How bloo.research works: 3 steps. Zero risk at the beginning.
You test first. Then you decide. We invest in you before you invest in us.
3 free analyses
Test for 7 days. No credit card required.
- ✓ You define industry + product
- ✓ You receive ~25 companies per analysis
- ✓ You check the quality yourself
- ✓ No obligation, no direct debit
Onboarding & ICP setup
We build your target customer machine.
- ✓ 2× strategy workshop (90 min each)
- ✓ ICP definition with architects
- ✓ Criteria calibration for your industry
- ✓ Prompt engineering of your filters
- ✓ First 200 companies in 48h after go-live
Monthly scaling
Your sales machine is running.
- ✓ 200 companies + contact person/month
- ✓ Unlimited rebooking at €2/company
- ✓ Dashboard with all analyses
- ✓ CRM export (HubSpot/Salesforce)
- ✓ Ongoing ICP optimization
"4,000€ setup? Why not just log in and get started?"
Because generic lead tools fail. Our approach is different - before you see the first company, our sales architects build your personal target customer definition with you.
Let's briefly calculate whether this is worthwhile for you.
No marketing figures. Real benchmarks from the German SME sector.
If you acquire just 5 new customers from 2,400 companies per year and your average customer value is only €10,000: €50,000 turnover from €8,788 investment. ROI: 5.7×.
Typical bloola customers reach 15-40 new customers/year.
Questions? Objections? Here are the facts.
Do you have a question that is not answered here? Write to us at cs@bloola.com or call us on +49 2332 9169-01. We will reply within 24 hours - usually in less than 2 working days.
Because the setup is not software access, but consulting work. Two of our sales architects work with you for 14 days to define your target customers: Kick-off workshop, ICP definition over 2 days, criteria calibration with 5-10 test runs, validation workshop, go-live.
With a traditional sales consultancy, the same service would cost 8,000-15,000€ . We offer it for €4,000 because we then continue to work with you on a monthly basis. The result: a target customer definition that no one else in your company has - and that belongs to you forever.
Yes, we offer2 monthly installments of €2,000 each at no extra charge. Talk to us about it in the demo meeting - we'll do it easily by invoice.
No, the setup result is yours. The ICP definition, the filter criteria, the cold mail playbook, the template sequences - everything stays with you, even if you cancel our monthly model.
The term of the monthly subscription is freely selectable and can be canceled at any time at the end of the month. No minimum term, no annual contract, no gag clauses. If bloo.research doesn't work for you, you can leave - without giving a reason.
Honest answer: Not for you, if any of these apply:
- Your average customer value is less than €2,000. Then the setup costs will not amortize quickly enough.
- You only need 1-2 new customers per year. Then a classic referral strategy is sufficient.
- You don't have a sales department that actively works with leads. We deliver target customers, not acquisition calls.
- Your product is suitable for every company in Germany. Then precision targeting is superfluous for you - Google Ads is enough.
We tell you this openly in the initial meeting - and reject customers for whom bloo.research is not the right solution.
The difference lies in the depth of the matching. Classic lead tools work with industry codes (WZ/NACE) and company data from databases. This provides "Mechanical engineering companies in NRW" - hundreds of hits, very few of which actually match your offer.
bloo.research goes deeper: We identify companies based on their actual products, manufacturing processes and technologies - for example "Companies that offer 5-axis milling for medical technology and accept contract manufacturing in the Stuttgart area". And that’s not all: we also identify the right contact person for your offer.
The result:
AI Market Fit Engine for B2B SMEs and Industry
You don't pay for quantity, but for match. Classic lead tool: €80-200 per qualified company. bloo.research: €3.66.
Yes, 100%. bloo.research is developed and hosted in accordance with GDPR standards. All data is stored on servers in the EU. On request, we conclude a data processing agreement (DPA) in accordance with Art. 28 GDPR.
The researched contact persons come from publicly accessible sources (imprint, company websites, business directories) - no gray area data sources, no purchased databases of dubious origin. Made in Germany.
You operate bloo.research yourself. The application runs 100% in the browser - on a PC, tablet or smartphone. No installation, no server, no IT integration required.
After onboarding, you can start new analyses with a click, filter results and export contact persons as a CSV. You can then import the CSV file into HubSpot, Salesforce, Pipedrive or your CRM of choice - any sales employee will do.
The process is clearly structured:
- Day 1-7: You test 3 analyses free of charge. No credit card, no obligation.
- Day 7-8: Decision meeting (30 min). You tell us whether the quality is right. If not: We leave without any follow-up.
- Day 8-10: Contract + scheduling of the kick-off workshop.
- Day 10-24: 14-day onboarding (setup phase).
- Day 24-26: First 200 companies with contact persons go live in your dashboard.
After day 26, the monthly subscription runs - can be canceled at any time at the end of the month.
No. The monthly price of €399 includes 200 companies and contact persons. You can add more companies at any time - for just €2 per additional company including contact person.
Many customers start with 200/month and scale up to 500-1,000 companies per month after 3-6 months.
Because €4,000 demands trust.
You go in at each stage with no financial risk. We take the risk until we deliver.
The free trial guarantee
3 free analyzes before you spend 1€. If the quality doesn't convince you, walk away - no follow up, no drama.
The ICP clarity guarantee
After the kick-off workshop, you decide whether to go ahead. If the ICP definition is not clear and feasible: 100% of the €4,000 back - even if we have already done the work.
The 10-qualified guarantee
If one of your first 3 analyses after go-live does not deliver at least 10 qualified companies: We will optimize free of charge. If that doesn't help either: Refund of the first 3 months.
Bottom line: You risk a maximum of one week of your time. We risk 4,000€ of work. That tells you how safe we are.
Every month without bloo.research costs you
4.000€.
Just as much as the complete setup.
This is not sales drama. This is the honest math from your own cost calculation above.
WASTED SALARY
LOST DEALS
PIPELINE DELAY
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START TODAY
Day 26
Your target customer machine is running.
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vs
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START IN 30 DAYS
Day 56
Same machine, 30 days later.
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Those 30 days aren 't just time - they're deals your competition is closing in the meantime. And €4,000 in salary that you spend on manual research for the second time. |
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The free trial costs you €0 and 2 minutes.
Waiting costs you €4,000 per month.
You now have three options.
The decision you make in the next 90 seconds will determine your sales output for the next 12 months.
Contents of the online tutorials
- Basics of AI
- Definition and history of AI.
- Differences between AI, machine learning and deep learning.
- Areas of application
- Examples of AI
- AI in the industry.
- Basic concepts
- Algorithms and models
- Training data and procedure
- What is generative AI?
- Differences between generative and discriminative AI.
- Neural networks
- Neural networks explained simply
- How our brain forms the building instructions for AI models.
- What GPTs are
- Introduction to GPT models
- GPTs for experts
- GPT models explained at expert level
- GANs = creative AI
- Understanding and using GANs - from theory to practice
- AI tools
- Types of AI tools
- 6 common tools
- ChatGPT
- What is ChatGPT?
- What is the focus of ChatGPT and why is it an alrounder
- MidJourney
- Generate optimal images with MidJourney
- How to add a personal touch to images.
- HeyGen
- How to create a digital clone
- Shoot your very own story with HeyGen
- Elevenlabs
- Realistic speech with the AI audio platform from elevenlabs
- How talking to AI is changing the world
- Langdock
- AI operating system for companies.
- Use multiple AI platforms with one tool
- Microsoft CoPilot
- How to use in Outlook, Word, Excel.
- Advantages and limitations of Microsoft CoPilot
- Good prompting
- Practical examples of prompting
- Tips and tricks for optimal results
- Quality control
- Hallucinating and how to avoid it
- Review and validation of AI-generated content
- Collaboration with AI
- How optimal human-AI collaboration works
- When human intervention is necessary.
- Brainstorming and innovation
- How optimal human-Ki collaboration works
- When human intervention is necessary.
- Visual creation with AI tools
- Use cases in marketing and design
- Use of tools such as Midjourney and Canva.
- Bias and fairness in AI systems
- Bias and fairness
- How biases can arise in data and models.
- Guidelines for secure handling
- Basics of data protection in the AI context
- Rights of data subjects and obligations of the company
- A milestone in AI regulation
- Overview of relevant laws and regulations (esp. EU AI Act)
- Risk-based approach and classification of AI systems
- Risks and challenges of using AI
- Technical limitations: Understanding the limitations of AI systems.
- Errors and misinformation: Dealing with inaccurate or incorrect AI outputs.
- Current trends and developments:
- Overview of the latest advances in AI.
- Outlook for future technologies:
- Potential impact on the company and the industry.
- Training opportunities:
- Sources and resources for independent learning.

