Driving sustainable growth amid changing market conditions: How leading companies like Klaviyo stay competitive with AI.

Predictability and profitability are musts for any company hoping to mature. But with changes in the market, revenue teams can no longer rely on traditional approaches; AI has become a necessity. 

At least, it was when Klaviyo went public. As the Partner at Simon-Kucher (ex-VP GTM Strategy & Ops – Klaviyo, Atlassian, Intercom), I found technology to be the catalyst to sustainable growth – here’s why:

With higher interest rates, the era of cheap capital is fading and tech companies have to change their go-to-market approach. Instead of focusing on rapid expansion fueled by easy access to capital, companies are building sustainable business models that prioritize long-term value creation. 

This transition involves a strategic re-evaluation of investment priorities and emphasizes the importance of operational efficiency, predictability, and profitability.

The integration of AI into business operations represents huge opportunities for transformation across go-to-market functions. 

Today, I’m seeing GTM teams use the technology to:

Enhance products and services: With AI capabilities woven into the fabric of product offerings, companies are creating more innovative business models and new revenue streams.

Uncovering trends and correlation in data: By surfacing new patterns, AI now empowers businesses to anticipate market shifts, customer preferences, and competitive dynamics, helping teams stay ahead of the curve.

Transform internal decisions and operations with better insights: Teams are using advanced analytics and machine learning algorithms to extract actionable intelligence from large amounts of data. This leads to informed decision-making characterized by greater accuracy and confidence.

Automate low-value tasks and optimize workflow efficiency: By leveraging automation technologies, such as robotic process automation (RPA) and natural language processing (NLP), teams are streamlining routine administrative tasks, data entry, and customer service inquiries. This reduces operational costs and human errors, freeing up time for team members to focus on strategic initiatives.

AI’s potential – for both product innovation and operational efficiency – can drive enormous value, but only if revenue leaders can re-tool and adapt their playbooks to take advantage of it.

Predictability is the crux of a stable business. In dynamic and competitive markets like we’re witnessing today, it’s crucial for GTM teams to have a clear understanding in order to make informed decisions and mitigate risks. Predictability helps business leaders enhance their competitiveness, adaptability, and resilience, ultimately driving long-term growth in an ever-evolving marketplace.

One way to achieve predictability is through accurate forecasting. But, the typical approach to forecasting is inefficient and often inaccurate. 

78% of RevOps professionals say they don’t have the data they need to forecast accurately.

Historically, most teams have relied on manually inputted CRM data to forecast revenue, but this approach leaves folks with more questions than answers. 

Data entered by sales reps is often subjective and unreliable. The process of filling in CRM fields is also inefficient, diverting time away from revenue-generating activities. And CRM data often lacks accuracy due to limited signals and, historically, the fact that customer interactions couldn’t be incorporated into forecasts in a predictable way.   

The advent of AI-driven forecasting is revolutionizing the game by leveraging advanced technologies to overcome these challenges.

Using AI, teams can tap into customer interaction data at scale to anticipate future revenue outcomes.

We use revenue intelligence technology to gather actionable insights across all customer interactions, including emails, calls, and meetings. NLP technology then analyzes these interactions, allowing for a more comprehensive understanding of customer sentiment, summarizing key takeaways, and highlighting potential red flags early in the deal and renewal lifecycle. 

With the ability to extract actionable insights from unstructured data, we can now identify opportunities and risks more effectively, improve forecast accuracy, and ultimately introduce a customer-centric approach to forecasting. And the prediction doesn’t stop there. Learning algorithms can actually analyze historical deal data and past rep behavior, so we can predict close rates with greater precision. 

By identifying patterns and trends in deal progression, these algorithms equip our sales organization with the necessary insights to make data-driven decisions and adapt strategies in real time. AI-based forecasting can help augment and provide a more objective view of the business.

Profitability is a cornerstone of sustainable business growth. It stems from productivity across the revenue organization – the more efficient teams are with their resources, the more revenue the company makes. By optimizing effectiveness and collaboration, GTM teams can unlock alignment that will directly impact the bottom line.

GTM leaders can improve productivity across the revenue organization with automation, better pipeline management, and enhanced enablement. These initiatives empower sales teams to focus their efforts on high-value activities, such as building relationships with prospects and closing deals, leading to increased win rates.

Of equal importance: Post-sales activities are critical to driving profitability, as they contribute to customer satisfaction, retention, and upsell opportunities. By implementing proactive customer success initiatives to deliver exceptional customer experiences, companies can maximize customers’ lifetime value and drive incremental revenue growth.

As Klaviyo has grown, I’ve learned how teams can use AI to increase profitability. Here’s my advice:

Typical decision-making cycles aren’t straightforward and leaders often deal with fragmented feedback collection, resulting in a disjointed understanding of customer needs and market trends. We’ve all been there – a seller has one piece of information, a CSM has another, and communicating that intel with other teams is like playing a game of telephone. Because of this fragmentation, teams struggle to make informed decisions and optimize strategies to drive revenue growth.

AI transforms decision-making cycles by providing unique insights into customer behavior, market trends, and sales performance. By leveraging advanced analytics and machine learning algorithms, we can use AI to access a wealth of data in real time, making informed decisions quickly and effectively. In fact, Gong Labs found that sellers who use AI to guide their deals increase win rate by 35%.

These insights also benefit R&D teams, by quantifying opportunities and creating feedback cycles. Product and R&D teams can develop solutions that better meet market demands. 

Typical win-loss analysis comes directly from customer input. But this approach creates a limited set of qualitative insights, and can lead to subjective decision-making. With revenue intelligence, GTM teams can easily uncover sentiment analysis across thousands of conversations to reveal implicit information otherwise hard to capture.

We put revenue intelligence to the test to validate the effectiveness of its win-loss analysis capabilities. At Klaviyo, we have two distinct sales motions – high-velocity SMB and complex enterprise. Like many companies, we traditionally excelled at gathering information on big deals due to the high visibility nature of the deals. But we struggled with feedback on smaller ones, as CRM inputs are cumbersome for high-velocity teams. With revenue intelligence, we have direct data and insights from large volumes of small deals. 

This has improved our ability to learn about why we lose deals and who we lose to. It gives a roadmap for where we need to focus our efforts to optimize conversions and support the needs of both sales motions. Win data is equally important so we can share best practices with the rest of the team and provide feedback to R&D and marketing teams on product features and positioning.  

Rep coaching is typically reactive, backward-looking, and one-size-fits-all, but this approach isn’t scalable or effective. Getting coaching right is a must; it has a huge impact on the trajectory of the rep and can be a differentiator for GTM teams. 

By leveraging advanced analytics and AI, our team at Klaviyo uncovers best practices, identifies coaching opportunities, and enhances sales effectiveness. This data-driven approach enables continuous improvement, empowering our sales reps to self-coach and learn from their interactions. 

We also use AI to aggregate best practices from all team members and historical interactions, allowing managers to customize feedback and coaching based on individual needs. 

Revenue teams that want to demonstrate repeatable and successful results must use customer interaction data to their advantage. A truly data-backed approach provides insights that scale a business and make it profitable. I’ve seen it work at Klaviyo and know it’s possible for others. 

The opportunities for AI are endless, and we’ve just scratched the surface to unleash a RevOps revolution where RevOps will play an even more strategic role. Read here on how RevOps leaders can stay above the noise and drive impact through prioritization.

Want more revenue leadership advice and data-driven insights? Subscribe to The Edge.