A Take on Current Trends in Customer Success

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In an era where the software market is expanding rapidly, the integration of AI in customer success has become more than a trend; it's a strategic necessity. Predictive, prescriptive, and generative AI can bring about hyper-personalization at scale. This blog explores the growing software market, the potential of AI in revolutionizing customer success, and validates these advancements with the evolving needs of Customer Success Managers (CSMs), as detailed in Daphne Lopez's insightful report.

Incorporating the insights from Forrester and Gartner, the statistical growth predictions from Statista, and the practical research by Daphne Lopes, this blog paints a comprehensive picture of the current state and the future of customer success in the expanding software market. AI's role in this landscape is not just transformative but essential, providing the tools and insights necessary for CS teams to thrive in a rapidly evolving market.

1.1 The Growing Software Market: A Land of Opportunities

The software industry, with a valuation of $659 billion in 2023, is not just growing; it's evolving. Current estimations from Statista even predict that the global software market will be worth $858 billion in 2028.

As the software market expands, so does the volume of data generated. This volume of data offers both challenges and opportunities. Efficiently managing this data is crucial for companies to not only stay afloat but to thrive in an increasingly competitive landscape. The key lies in leveraging this data to gain insights, drive decision-making, and enhance customer engagement.

This expansion reflects a market responding to digital transformation, where companies are increasingly seeking innovative, AI-driven solutions to stay competitive.

1.2 The Rising Star: AI in Customer Success

AI's role in the software market is becoming indispensable. Tools like Churned.io utilize AI for predictive analytics, offering personalized customer experiences on a large scale. This is where AI shines - in understanding customer needs, predicting trends, and prescribing actions that drive customer engagement and retention.

CS Teams: Doing More With Less

Forrester highlights a significant challenge for CS teams: the need to "do more with less." CS teams are finding themselves stretched thin, needing to find alternatives to high-touch models and manual processes. The solution lies in
investing in digital-led programs, which can free up CSMs to focus on areas of greater impact.

Advancements in AI for Customer Success
CSP vendors are addressing these challenges by delivering advanced capabilities that enable CS teams to understand customer behavior, predict their needs, and proactively offer solutions. These solutions are not just about retaining customers but also about adding value and fostering growth and advocacy.

Refining the Customer Journey with AI
One of the key ways CSP vendors are leveraging AI is to refine the customer journey and scale the CS motion. AI and machine learning are used for predictive analysis, which optimizes resources and enhances the efficiency and effectiveness of customer success teams.

Forrester's recent insights reveal that AI advancements, robust reporting, and digital-led strategies are becoming crucial for CS teams. Faced with an expanding scope and the need to demonstrate the impact of their work, CS teams are turning towards AI-powered customer success platforms. These platforms enable understanding customer behavior, predicting their needs, and offering proactive solutions that deliver value and foster retention, growth, and advocacy.

2. Choosing the right CSP in 2024

In their comprehensive research, Gartner outlines several key recommendations for companies in the process of selecting a Customer Success Platform. These guidelines are crucial for ensuring that the chosen platform aligns with the company's goals, especially in the context of customer retention, CX enhancement, and digitizing customer processes.

2.1 Essential Considerations in CSP Selection

Review Past Performance and Roadmap: Include questions that focus on the vendor's past performance, lessons learned, and achievements, as well as their future roadmap.

Areas of focus should include:
• Deployment and scaling capabilities.
• Experience in data unification and types of data they can process.
• Ease of integration with existing systems.
• Alignment of vendor’s expansion goals with the company’s business goals.
• Track record of evolution and response times to major changes.
• Understanding AI Model Degradation: It’s vital to understand the AI model degradation cycle, especially with constant changes in customer data. Set up a unit with the vendor to focus on mitigating AI model degradation by updating the AI model or incorporating changed patterns.
• Identify Key Performance Indicators (KPIs): Include various business unit leaders to get their perspective and feedback on identifying KPIs associated with different functions.
• Involve End Customers in Trials/Implementation: To gauge reaction and experience, involve end customers extensively in trials or implementation projects associated with AI in customer journeys.

3. Daphne Costa Lopez’s Report: Validating AI's Relevance in CSM

From recent research conducted by Daphne Lopes, Director of Customer Success at HubSpot, we gain valuable insights into the evolving landscape of customer success management (CSM). Lopes' report, a comprehensive analysis of the trends and challenges in the field of customer success in 2024, offers a unique perspective that aligns closely with the ongoing expansion of the software market and the increasing integration of AI-driven strategies.

3.1 Prioritizing Automation and Customer Success Platforms

Daphne Lopes her research reveals a significant trend among CS teams: a focus on automating processes and adopting customer success platforms. This trend underscores the growing recognition of AI's potential in scaling customer success efforts. While companies are still in the early stages of fully embracing AI, they are increasingly recognizing its value in automating routine tasks, thereby freeing up CS teams to concentrate on more strategic, high-impact activities.

3.2 The Synergy of Reporting, Forecasting, and AI

In addition to automation and CSP adoption, Lopes' highlights reporting and forecasting as key areas of focus for CS teams. While these areas might seem distinct, they are intrinsically linked with AI's capabilities. AI excels in analyzing large datasets to provide comprehensive reporting insights and accurate forecasting predictions. This integration of AI can transform raw data into meaningful insights, aiding CS teams in making informed decisions and strategizing effectively for future customer interactions.

3.3 The Interconnected Role of AI Across CS Systems

The separate topics identified in Lopes' research - automation, CSPs, reporting, and forecasting - while treated independently, actually present a cohesive picture when viewed through the lens of AI integration. AI acts as the underlying thread that enhances each of these areas:

• In Automation: AI streamlines customer interactions and operational processes, increasing efficiency and accuracy.
• In CSPs: AI enhances the capabilities of these platforms, offering predictive insights and personalized customer experiences.
• In Reporting and Forecasting: AI's analytical strength provides deep insights and foresight, essential for strategic planning and decision-making in customer success.

Daphne Lopes' research clearly indicates that while the adoption of AI in customer success is still evolving, its potential to revolutionize CS systems is undeniable. By seamlessly integrating AI across various facets of customer success - from automation to predictive analytics - companies can not only enhance their operational efficiency but also provide more personalized and proactive customer experiences. As the software market continues to grow, leveraging AI in these key areas will be crucial for businesses looking to maintain a competitive edge and drive customer success.


The expanding software market and the evolution of customer success are increasingly intertwined with the adoption of AI. Tools like Churned.io are at the forefront of this revolution, offering AI-powered solutions that resonate with the current needs and future directions of CSMs, as highlighted by Daphne Costa Lopes. As the market grows, AI in customer success is not just an option; it's becoming an integral part of how companies understand, engage, and retain customers

Want to know more about AI in customer success? Learn more

Also, definitely check out Daphne her website for great customer success content, click here.