Many so-called “revenue technologies” are universally applicable and measurably increase revenue – regardless of the core business.
In nearly every company, there are recurring processes that can be made significantly more efficient using modern technologies:✔️ Customer communication
✔️ Pricing
✔️ Product and service recommendations
✔️ Customer acquisition
✔️ Data analysis
These areas often operate independently of the actual business model and offer enormous potential for value creation. When strategically optimised, they generate additional value – even without fundamental changes to the product or service.
Revenue Technology Significantly Increases EDITDA (Base Scenario)
Here’s how a company with €200,000 in EBITDA could benefit from technical solutions. Model assumptions and technical details can be found below. Implementation costs are not included in the model, but remain well under the additional EBITDA of €500,000 with a low-tech approach.
| EBITDA | Revenue | Customers (Year-End) | |
|---|---|---|---|
| Initial Situation | 200,000 | 1,400,000 | 2,800 |
| + CRM | 320,467 | 1,562,794 | 2,835 |
| + Dynamic Pricing | 412,741 | 1,655,766 | 2,861 |
| + Recommendation Engines | 506,297 | 1,738,554 | 2,861 |
| + Predictive Analytics | 591,948 | 1,798,380 | 2,887 |
| + Advanced Analytics | 679,493 | 1,905,908 | 2,914 |
| + LTV Prediction in Media Buying | 758,645 | 2,039,322 | 2,914 |
| Post-Optimisation Scenario | 758,645 | 2,039,322 | 2,914 |
The Author Is An Experienced Consultant And Manager
Michael Popp spent three years as a top management consultant for the Cint Group – one of the world’s largest market research platforms, operating in over 130 countries with €300 million in annual revenue.
Following a successful Series A funding round of €24 million, he supported the electric mobility company felyx GmbH in entering the German market.
As Head of Online Marketing at Juwelo – a leading direct-to-consumer brand with €50 million in annual revenue – he led the company’s digital transformation and customer acquisition strategy.
At the beginning of his career, he worked at agencies that serviced major enterprises such as Deutsche Telekom, MediaMarkt, Shopify, and the Migros Group.
The EBITDA can increase by 600+% (Optimistic Scenario)
Notably, EBITDA has increased eightfold without a significant rise in customer numbers.
This is made possible through improved, automated sales processes, acquisition of more profitable customers, and optimised pricing.
| EBITDA | Revenue | Customers (Year-End) | |
|---|---|---|---|
| Initial Situation | 200,000 | 1,400,000 | 2,800 |
| + CRM | 454,993 | 1,736,350 | 2,870 |
| + Dynamic Pricing | 676,679 | 1,944,924 | 2,923 |
| + Recommendation Engines | 859,800 | 2,090,793 | 2,923 |
| + Predictive Analytics | 1,077,457 | 2,236,742 | 2,978 |
| + Advanced Analytics | 1,307,864 | 2,508,243 | 3,036 |
| + LTV Prediction in Media Buying | 1,479,150 | 2,759,067 | 3,036 |
| Post-Optimisation Scenario | 1,479,150 | 2,759,067 | 3,036 |
Each Technology Can Increase Core Metrics by 2.5%+ – In Total Leading To A Significant Transformation (Base Scenario)
Most corporations have clear optimisation potential within existing processes.
In SMEs and start-ups, these opportunities are often even greater. The changes may appear small and realistic at first glance—optimisations of 2.5% to 5% are usually very feasible, as existing workflows are rarely fully optimised.
These small but targeted improvements create exponential impact – ultimately leading to a fourfold increase in EBITDA.
| Module | Cost Reduction | New Customers ↑ | Churn Rate ↓ | Purchases p.a. ↑ | Revenue per Purchase ↑ | Margin per Purchase ↑ |
|---|---|---|---|---|---|---|
| + CRM | – | +2.5% | −5.0% | +5.0% | +5.0% | +5.0% |
| + Dynamic Pricing | – | +2.5% | – | +5.0% | +5.0% | +5.0% |
| + Recommendation Engines | – | – | – | +5.0% | +5.0% | +5.0% |
| + Predictive Analytics | −5.0% | +2.5% | – | – | +2.5% | +2.5% |
| + Advanced Analytics | −5.0% | +2.5% | – | – | +5.0% | +5.0% |
| + LTV Prediction in Media Buying | – | – | – | +7.0% | – | – |
Improving Several Metrics By 10% Increases Performance Extremely (Optimistic Scenario)
In the optimistic scenario, we assume 10% improvements per lever – which is entirely realistic compared to today’s often manual and inefficient processes.
Implementing these technologies results in an eightfold increase in EBITDA – with nearly the same existing customer base.
Implementing these technologies results in an eightfold increase in EBITDA – with nearly the same existing customer base.
| Module | Cost Reduction | New Customers ↑ | Churn Rate ↓ | Purchases p.a. ↑ | Revenue per Purchase ↑ | Margin per Purchase ↑ |
|---|---|---|---|---|---|---|
| + CRM | – | +5.00% | −10.00% | +10.00% | +10.00% | +10.00% |
| + Dynamic Pricing | – | +5.00% | – | +10.00% | +10.00% | +10.00% |
| + Recommendation Engines | – | – | – | – | +7.50% | +7.50% |
| + Predictive Analytics | −10.00% | +5.00% | – | – | +5.00% | +5.00% |
| + Advanced Analytics | −10.00% | +5.00% | – | – | +10.00% | +10.00% |
| + LTV Prediction in Media Buying | – | – | – | +10.00% | – | – |
Proven, Established Technologies For Great Impact
Here is an overview of a few technologies that often deliver quick and measurable value.
Customer Relationship Management (CRM)
What is a CRM system?
A CRM is a software solution that centrally captures, organises, and analyses all interactions a company has with prospective and existing customers. It unifies sales, marketing, and customer support into a single platform – with the goal of measurably improving customer relationships and increasing customer lifetime value (i.e. the total value a customer brings to the company). Typical CRM features:✔️ Lead and contact management
✔️ Sales pipeline tracking
✔️ Automated follow-ups and email campaigns
✔️ Activity logs and communication tracking
✔️ Integration with marketing and support tools
✔️ Reporting and forecasting
How does CRM deliver measurable value?
✔️ Higher lead conversion: Structured follow-up and automation improve close rates
✔️ Higher revenue per customer: Through segmented communication, reminders, and follow-ups
✔️ Scalable processes: Automated, personalised communication independent of staff
✔️ Improved forecasts: Realistic, data-driven sales projections
Based on an analysis by Nutshell and Nucleus Research, companies using CRM achieve on average:
✔️ +29% revenue growth (Nutshell)
✔️ +27% higher customer retention (Nutshell)
✔️ +42% more accurate forecasts (Nutshell)
✔️ +30% sales productivity (Nutshell)
✔️ ROI of 8.71:1 on average (Nucleus Research)
An ROI of 8.71:1 might seem unrealistic at first glance – how can a single tool return nine times the investment? But on closer inspection, it makes sense:
✔️ Low costs: Some CRM systems are free or very affordable
✔️ Targeted communication: Focused on the most profitable segments – prospects and existing customers
✔️ Efficiency: Personalised, revenue-focused communication without wasted reach
CRM Value Quantified
| Base Revenue | Revenue Growth | New Revenue | CRM Cost | ROI |
|---|---|---|---|---|
| €10,000,000 | +29% | €12,900,000 | €382,900 | 8.71:1 |
Our Project Experience
✔️ CRM systems almost always deliver measurable value in practice
✔️ User segmentation significantly boosts performance
✔️ Automated sequences provide long-term value – e.g. onboarding, upselling, re-engagement
Dynamic Pricing
What is Dynamic Pricing?
Dynamic pricing refers to automated real-time price adjustments based on market data, demand, competition, availability, device type, time, or user behaviour. Prices adapt dynamically to optimise revenue and margin. This strategy is powered by algorithms, machine learning, or AI-driven platforms. Dynamic pricing is no longer exclusive to airlines or Amazon. SMEs, SaaS providers, and D2C brands also leverage dynamic pricing – for example, to test price thresholds, capitalise on seasonal demand, or monetise surplus inventory.How does dynamic pricing generate measurable value?
✔️ Optimal pricing by target group, product, and time slot
✔️ Higher margins when inventory is low; lower prices when overstocked
✔️ Testing price sensitivity and customer response (A/B or real-time ML)
✔️ Revenue growth through better conversion at the ideal price point
✔️ Greater efficiency compared to competitors
Studies show measurable impact:
✔️ +1–8% revenue growth from implementing dynamic pricing (Cranfield University)
✔️ +25% profit increase at Amazon via aggressive dynamic pricing (Onramp Funds)
✔️ +22% margin with AI-powered pricing models (FPA-Trends)
✔️ +12% revenue uplift via Airbnb’s Smart Pricing feature (HelloPM)
Dynamic Pricing Value Quantified
| Annual Revenue | Initial Profit | Margin Increase | New Profit |
|---|---|---|---|
| €10,000,000 | €3,000,000 | +25% | €3,750,000 |
Our Project Experience
✔️ Pricing algorithms vary widely and must be tailored to the business model
✔️ Implementation should be iterative – overly rapid rollouts can lead to side effects
✔️ The mathematically optimal price is not always the right one: pricing also influences brand perception
✔️ Many tasks traditionally handled by pricing teams can be automated – with significant savings potential
Predictive Analytics (PredAn)
What is PredAn?
PredAn refers to the use of statistical models and machine learning to predict future events or behavioural patterns. Companies use this technology to identify trends early, forecast customer behaviour, and make data-driven operational decisions. Typical application areas:✔️ Revenue forecasting and demand planning
✔️ Lead scoring and conversion probability analysis
✔️ Churn prediction and customer retention
✔️ LTV prediction for marketing optimisation
✔️ Product or basket analysis
How does PredAn create measurable value?
✔️ Better decisions through probability-based insights instead of intuition
✔️ Proactive actions, e.g. for customer retention or inventory optimisation
✔️ Higher conversion rates through more personalised targeting
✔️ More effective budget allocation in marketing
Studies show concrete effects:
✔️ +8–10% profit margin increase from advanced analytics (NumberAnalytics)
✔️ Up to 10% cost savings through better planning and forecasting (NumberAnalytics)
✔️ +20% conversion rate via personalised, prediction-based campaigns (Folio3)
PredAn Business Case Quantified
| Annual Revenue | Initial Costs | Initial Profit | Cost Savings | New Profit |
|---|---|---|---|---|
| €10,000,000 | €7,000,000 | €3,000,000 | −10% | €3,700,000 |
Our Project Experience
✔️ Companies without PredAn often head into problems blindly
✔️ Damage can be prevented before it occurs – sometimes years in advance
✔️ Forecasts without PredAn are often overly optimistic
✔️ Fraudulent activities can be detected much faster using PredAn
Recommendation Engines
What is a Recommendation Engine?
Recommendation engines analyse user behaviour to generate personalised suggestions for products, services, or content. The goal is to increase conversion rates, raise average cart size, and strengthen customer retention. They are used across nearly all digital business models – including e-commerce, SaaS, streaming platforms, media portals, and marketplaces. Technologically, they are based on algorithms like collaborative filtering, content-based filtering, or hybrid models (often AI-driven).How do recommendation engines deliver measurable value?
✔️ Higher conversion rates through relevant product suggestions
✔️ Increased basket value via cross-selling and upselling
✔️ More repeat purchases via dynamically tailored recommendations
✔️ Stronger customer retention through personalised user experiences
Studies show clear results:
✔️ +5–15% revenue increase from personalised recommendations (McKinsey)
✔️ +32% revenue uplift from AI-powered recommendations in e-commerce (Netcore Cloud)
✔️ +3–23% higher conversion rates through personalised recommendations (Algolia)
Recommendation Engines Business Case
| Annual Revenue | Revenue Increase | Resulting Revenue |
|---|---|---|
| €10,000,000 | +32% | €13,200,000 |
Our Project Experience
✔️ Recommendations almost always increase conversion rates – especially when personalised
✔️ Distinct recommendations for returning users (personalised) vs. new users (conversion-focused)
✔️ AI-driven systems perform significantly better than rule-based models
✔️ Business goals like launching new products, margin optimisation, or customer reactivation can be directly embedded in the recommendation logic
Customer Lifetime Value (LTV) Prediction in Media Buying
What is LTV Prediction?
LTV prediction estimates a customer’s long-term value from the first purchase—or even from the first click. Using machine learning and historical data, it forecasts how much revenue a customer will generate over months or years, based on channel, behaviour, product, or initial offer. In media buying (e.g. Meta, Google Ads, programmatic), LTV prediction enables optimisation for long-term revenue rather than short-term conversions. The result: algorithms are trained to acquire high-value customer types—not just low-cost acquisitions.How does LTV prediction deliver measurable value?
✔️ Ad budgets focus on audiences with high long-term value
✔️ Fewer low-quality leads – more high-value customers
✔️ Stronger signal for Meta CAPI, Google Conversion API & others
✔️ Higher ROAS (Return on Ad Spend) with the same budget
✔️ Less dependency on short-term ROAS metrics
Market analyses show clear results:
✔️ +15–25% ROAS improvement through LTV-driven bidding (Voyantis)
✔️ +11% higher customer lifetime value through high-value customer selection (Lachi Media)
LTV Prediction Case in Media Buying
| Ad Budget | New Customers | Avg. Customer Value | Total Value | Avg. Value +25% | New Total Value |
|---|---|---|---|---|---|
| €100,000 | 10,000 | €90 | €900,000 | €112.50 | €1,125,000 |
Our Project Experience
✔️ Low-value customers are a cost—high-value ones generate millions in revenue
✔️ “Maximise new customers” in ad platforms often means “maximise bad customers”
✔️ LTV-based bidding creates a structural advantage over competitors
High-Quality Analytics
What is High-Quality Analytics?
High-quality analytics means much more than just Google Analytics or basic dashboards. It’s about having a reliable and strategically usable data foundation: complete, consistent, quickly accessible, and usable across all departments—from marketing and sales to product, finance, and customer success.Typical Components of a High-Quality Data Stack
✔️ Central data architecture (e.g. BigQuery, Snowflake)
✔️ Event-based tracking (e.g. Segment, RudderStack, Google Tag Manager)
✔️ Data modelling and transformation (e.g. dbt, Airbyte, Python)
✔️ Self-service analytics tools (e.g. Looker, Metabase, Power BI)
✔️ Forecasting models and machine learning
✔️ Integrations with CRM, ad platforms, and automation systems
Why Are High-Quality Analytics a Growth Lever?
✔️ Foundation for CRM, pricing, product recommendations, and LTV models
✔️ Accurate attribution and more efficient budget allocation
✔️ Faster strategic and product decisions
✔️ Fewer poor decisions due to reliable data
✔️ Faster, more informed decision-making across all levels
Market studies show clear impact:
✔️ +20–30% more efficient budget usage thanks to better attribution (McKinsey)
✔️ +3–8% more conversions through data-driven optimisation (Harvard Business Review)
✔️ +5–10% EBITDA increase through company-wide data usage (Harvard Business Review)
Simple Business Case: +8% More Conversions
| Annual Revenue | Conversion Uplift | Revenue Increase |
|---|---|---|
| €1,000,000 | +8% | €80,000 |
Our Project Experience
✔️ Critical data is often not being tracked at all
✔️ Dashboards empower employees to be far more productive
✔️ Marketing budgets are often wasted—solution: better attribution
Three Implementation Approaches – Based on Your Goals and Context
1. Integration of External Providers
We identify and integrate proven tools that deliver immediate business value. Common solutions such as CRM or pricing systems often generate a strong return on investment very quickly.
Effort: low
Advantage: fast, effective, cost-efficient
2. Customised Integration of Existing Tools
When standard solutions don’t fit perfectly, we build tailored setups based on your current tools. This results in a solution that fits your company and integrates smoothly into existing systems – even by combining multiple tools.
Effort: medium
Advantage: tailored, integrable, scalable
3. Development of Custom Solutions
In special cases, we develop completely new solutions – for example, when requirements are highly specific or the potential is particularly high. We ensure that no unnecessary costs are incurred due to avoidable custom development.
Effort: high
Advantage: maximum fit and differentiation
Book a Free Initial Consultation
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