Pierre Schramm on the role of the Chief Revenue Officer

The arrival of cloud platforms – Infrastructure as a Service – in the early 2000s democratised the software business. For a modest monthly fee, developers got access to the same capabilities as large corporations without a massive investment in hardware. Anyone with a good idea could start a software company.

By accessing software ‘as a Service’ rather than installing it, customers got frequent incremental updates across all platforms, with less maintenance effort. At the same time, the concepts of a ‘free base model’ (freemium) and a recurring subscription replaced one-time licence fees, reducing barriers for new buyers.

In this conversation, Pierre Schramm talks about the impact of SaaS, the rise of the CRO and the potential of AI.

“If you want to scale a business, start by creating a scalable product. SaaS software fits that description perfectly.”

How did you get started in SaaS?

The SaaS market in Germany was taking shape in 2012 when Ricco Deutscher, co-founder of Pactas (later billwerk) invited me to join his new venture: billwerk. The idea was to provide customers with a flexible billing and invoicing software to manage subscription business models.

Although later we were competing against SAP in financial accounting and billing, which were traditionally ERP domains, we gained early successes. One was a customer called waipu.tv (Exaring AG). They grew from being a small streaming provider to amarket leader with 1,25 mio. subscribers.

For five years we grew the company bootstrapped without investors, just family & friends, despite competing against well-funded giants like Zuora, Chargebee, and Paddle. At times, it was like Asterix and Obelix standing firm in Gaul.

What were the key insights from that experience?

If you sell one-time software licences, growth comes from customer acquisition and tends to be linear. SaaS companies, by contrast, have three revenue curves – acquisition, retention, expansion.

Growth by recurring revenue from from initial subscriptions (acquisition) tends to be modest. There’s a higher impact from retention revenue, when a lot of customers use the solution more intensively over time (retention). But the biggest part of the growth comes out of new capabilities or additional consumption-based pricing features because you’re up-selling additional functionality to a known customer base (expansion). Add them together and you get exponential growth.

The downside is that fast growth of a company puts internal processes under enormous stress. You start small with four colleagues who collaborate closely. The initial process looks like a relay race: marketing generates leads, hands them to sales, a sale is closed, then customer success takes over. The founders still generate and close deals themselves (the Founder-Led Growth phase). Rapid growth means you implement dedicated structures & departments fast. You have skilled individuals, but teams that act like silos. There’s a breakdown in communication and the result is misalignment. The teams are no longer working cohesively.

How does the Chief Revenue Officer fit into this picture?

Ultimately, the goal of the CRO is to ensure that revenue flows consistently by providing excellent service, so that you get at least one dollar back for every dollar you invest. It’s crucial that all teams understand the subscription business model: that revenue growth comes primarily from recurring revenue after the initial sale. They need that shared understanding, so that they all pull in the same direction, and work towards the same goals.

In practice however, reporting structures can make that difficult. For example: some SaaS companies see marketing and sales as business development issues, so those teams report to the CEO; but they treat customer success and onboarding as a technical issue, so they report to the CTO. The teams pull in different directions.

The key objective of the CRO is to achieve operational excellence across silos to get the most out of the scaling processes, technology used and the skills of the team. That means aligning all the teams in the value chain, from marketing through customer acquisition, onboarding, success and renewals, all the way to pricing. The CRO develops frameworks and principles that will unite all those teams under a single strategy.

What are the weaknesses or problems with the SaaS model?

Churn is a significant concern for SaaS companies. Losing a customer not only affects current revenue but also impacts future growth potential. SaaS companies should focus on where new sales opportunities arise, as these often lead to additional workload.

Over the last ten years, I've learned that incentives are necessary to ensure co-operation between teams. For instance, improving the quality of marketing leads requires customer success teams to share valuable insights, and they should be compensated for this contribution. Similarly, successful customer growth should benefit the entire team, not just sales.

The CRO role is an integral part of the growth strategy framework. It involves balancing sustainable growth, aligning customer journey stages, and integrating teams with product management and finance.

How do you make the CRO role work in practice?

Scalability is a key responsibility of the CRO, and that means aligning capabilities, processes and technology, as well as people. You can't do this job alone; a CRO needs a team. The team members are not traditional parts of the organization but are crucial for supporting the CRO's efforts.

A revenue operations manager identifies opportunities along the value chain to enhance revenue growth. This involves both improving processes and eliminating critical errors that hinder quality, such as issues in the CRM. Sometimes this role is filled by one person, and other times by a small team.

Another essential role is the marketing technology manager, responsible for the tech stack. This isn't just about CRM systems; there are many tools for various tasks that need to be aligned and integrated to ensure seamless operation.

In my team, I always include a revenue operations manager and a marketing technology manager. I avoid getting too involved in the day-to-day operations of sales or marketing; these teams handle their own operational tasks.

My role as CRO is to set goals, Not just revenue targets but also qualitative goals, KPIs, OKRs, project priorities, and resource allocation. I also focus on creating initiatives that add value.

Tell me about your recent collaboration with collect.ai

Collect.a is a SaaS solution based on AI principles such as Reinforcement Learning and Natural Language Processing (NLP) that creates and executes out of the box tailored collection strategies for individual debtors of large organisations with millions of consumers when it comes to payment disruptions. Typical collect.ai customers are in sectors like utilities, insurance, banking or also housing. The solution reduces the capital costs, accelerates cashflow and liquidity. This helps to keep the relationship between provider and consumer healthy. The impact is to minimise the need for debt management structures and to rationalise operations.

As the name suggests, the solution uses artificial intelligence to design the optimal payment collection process. The solution starts by offering the right payment methods or tariffs, then reduces barriers by simplifying the process. Various AI assistants collaborate. One determines the best communication method, another the optimal payment option, and a third assesses failure probability. Together, they craft personalised collection strategies.

What is the effect of AI on Go to Market for SaaS?

The approach we follow with collect.ai – using AI to determine the next best actions and touchpoints - can also be applied to marketing and sales. These systems can evaluate leads or prospects, determine the match to the ideal customer profile (ICP) and whether to pursue them. This helps avoid unnecessary effort in serving the wrong customers and streamlines onboarding processes. The AI can also inform marketing decisions, suggesting which campaigns to run based on past successes or failures with similar customer groups.

The go-to-market (GTM) strategies vary significantly across different business models, from fully automated no-touch to dedicated sales approaches. Each approach has unique requirements and demands. The choice between no-touch or high-touch is increasingly determined by the customer's profile, rather than the solution itself.

Currently, no-touch and low-touch sales are expanding. More services and SaaS applications now offer self-onboarding and self-service options. Think of companies like HubSpot and Slack, which do not require a sales representative for purchase in the SMB and MM segments.

More solutions are moving towards mid-touch and low-touch environments, allowing companies to purchase services without interacting with a sales representative, often using credit card payments. For example, many companies invest a substantial amount in AWS services monthly without ever speaking to a sales rep.

“Change is accelerating, driven by current technological innovations, so we must transform and innovate faster than ever before.”

The role of B2B sales is evolving …

Sales roles are most definitely changing. Traditional methods, where sales reps follow a script and make numerous calls daily, are becoming less effective. Sales is adapting by focusing on providing expertise. This particularly true for dedicated and high-touch sales, where large corporations seek long-term business partnership rather than just a product sale.

In no-touch and low-touch environments, I believe the need for personal contact will diminish significantly, potentially within 18 to 36 months. Buyers in large corporations are already moving towards a fully automated technology environment for tendering. They no longer need to speak with sales representatives. They have a system that evaluates suppliers and provides the best options via a high-frequency stock exchange. They can submit a request and get five or six proposals within an hour - all in the required quality and format. And those proposals can then be directly integrated into their procurement software. We see the rising relevance of autonomous commerce in B2B sales as a confirmation of this.

In this scenario, sellers have two options: leverage the freed-up capabilities to increase scale; or reduce the workforce. I see this as an opportunity for SaaS companies to invest in better training and development to improve the quality of their sales reps. My experience of over 500 conversations with sales development representatives (SDRs) in the past five years, is that they lack industry knowledge. This issue stems from companies focusing on the quantity of calls rather than the maturity of their SDRs. They replace under-performing reps rather than invest in their development. My hope is that this mindset will change, and companies will enhance the skills and effectiveness of their sales teams.

It's a pivotal time for the SaaS industry. The last three years have seen significant changes in a short period. First, we experienced the COVID-19 crisis, which drove rapid scaling across industries. Then, we saw a major market downturn, with companies like Alphabet and Facebook laying off about 10% of their workforce. Following this, Sam Altman introduced advances in AI technology.

Now, as we enter a new phase of growth, it is becoming increasingly difficult to target and create awareness among the right audiences. With the rise of AI, customers become more and more informed, independent and sovereign. They often already know more about solutions than the sales reps themselves. That shift is leading to significant changes.

“AI technology is enormously powerful, and its potential impact on business environments is still largely unknown.”

What is your view on the reliability of AI?

This is a process, too. Think of ten tasks an SDR typically performs. There are six or seven tasks that I wouldn't currently entrust to AI, because human intelligence excels in those areas. However, over time, this may change.

An AI requires training, much like a football player. With more training data, an AI becomes more accurate. An AI system trained on just five examples won't be reliable, but with 50,000, or 500,000 examples, it can achieve near-perfect outcomes. It’s important to remember this when critics claim that AI ‘hallucinates’ or makes errors—accuracy is a matter of time and training.

Anyone can input incorrect data into systems like ChatGPT, which makes it difficult for the machine to discern right from wrong. But specialised systems, such as those for receivables management, are different. These systems are designed for specific tasks and can be highly reliable, often achieving accuracy rates around 89% to 90%, though not always 100%.

How do you see AI being used in Sales?

I talked earlier about go-to-market (GTM) strategies: that there’s a spectrum from no-touch and low-touch models through to high-touch and dedicated approaches. When you identify a large number of similar customers with similar needs, communication doesn't need to be highly customised, and AI can effectively handle responses.

On the other hand, when customers make significant investments, like € 150,000 euros or more, a high-touch, dedicated approach is necessary. These customers expect personalised interaction and thorough analysis to help them find the perfect solution. In such cases, human involvement is crucial for building trust, acting as a consultant, and closing deals successfully.

Currently, account executives spend about 30% of their time actually selling, with the remaining 70% on administration and documentation. AI can assist sales representatives by managing administrative tasks, such as organising calendars, allowing them to focus on the core aspects of their job.

What can AI do for the CRO?

Let’s give me you an example, I'm currently working with an AI solution called Lerno that aggregates all customer interactions and conversations—from the first contact to closing and customer success—into a comprehensive view. It tracks every call, email, and support ticket, ranking the value of these interactions throughout the customer journey.

This system explores pattern and provides relevant information to all stakeholders, including sales, marketing, customer success, product and finance ?teams. It’s not just documenting and tracking every customer interaction. The solution detects patterns in a prospect or customer, or across all customers. For example, problems with the product or with onboarding that keep recurring and lead to churn. Or arguments in and requirements for sales that repeatedly lead to the deal not being closed. Or calculating the ICP pass grade, or the deal closure probability based on interaction and communication. Imagine the change that this would bring to forecasting and reporting.

Generally speaking, AI has made data management and interpretation more accessible. Previously, working with large data warehouses was cumbersome, often delaying real-time analysis. Now, the process is streamlined, allowing for timely decision-making. This approach provides a clear, practical view of data. Modern SaaS solutions allow us to monitor application usage, assess user engagement, and calculate churn probability in real time.

A data-driven approach enables you to make informed decisions daily, not just based on large datasets but also real-time insights. In many organisations, people are not aware of the connections between different contacts. For example, purchasing does not recognise that the same person represents different suppliers in various countries. Or Sales does not see that multiple small accounts in the CRM belong to the same customer organisation and deserve to be treated as a high-touch key account. Where older systems struggle to identify these links, AI can already help manage and organise digital workspaces. Recognising connections is crucial because these relationships influence business outcomes

Although AIs can produce content, calculate data, and create visuals, their true potential is deeper, like an iceberg. This technology acts as a smart assistant, similar to a technical team that supports or takes over daily business tasks. However, just as with human teams, the ultimate responsibility and supervision must remain with managers. While technology offers valuable assistance, final decisions and oversight should always be led by people.

Thank you, Pierre!