Artificial intelligence (AI) is still more hype than reality in most businesses. However, given the explosive growth in the creation of digital data and the transition to web3 with more distributed data, it’s increasingly clear that artificial intelligence and machine learning (ML) are the best path forward. Tech providers, particularly software-as-a-service (SaaS) companies, are on the leading edge, and 86% of tech providers say AI is important for gaining market share and building customer loyalty. A growing wave of investment in tech infrastructure is also accelerating this capability. Over the past five years, venture capital invested more than $170 billion in AI hardware and software platforms and algorithms. That’s on top of investments made by the leading cloud service providers (Amazon Web Services, Google, Microsoft Azure), which are the prime innovators in this space.
A key area where we see AI making significant inroads is customer success—helping businesses deliver more personalized and productive experiences. The potential for AI in customer success is promising. With a complete view of the customer—from first contact through onboarding, ongoing service and monitoring, to replacement and renewal—AI systems can provide sharp insights and specific recommendations to build closer relationships with customers and increase usage. AI can improve and automate functions such as customer segmentation, reducing churn, upselling, tailoring features, coaching sales reps, suggesting next best actions, and targeting service. In our recent survey, two-thirds of chief information and chief technical officers said that using AI for customer success is among their top priorities over the next two to four years.
AI’s expansive role in customer success
The integration of AI into customer success applications signals an inflection point where it moves beyond offering insights and begins to modify and tailor product development and features for specific customers. AI enables a more comprehensive perspective on customer engagement, allowing for more bespoke product development and fine-grained predictive analytics that signal opportunities and risks.
To get the full potential, companies need a number of critical capabilities:
- an effective product architecture and infrastructure for AI-infused offers;
- feedback loops for data capture and ongoing learning;
- the ability to track customer engagement throughout the customer journey; and
- feedback that enables product development teams to personalize offers using AI.
To that end, fewer than 20% of companies have structured their products for AI with an integrated view of customer product usage, consistent data infrastructure with appropriate access rights, and effective feedback loops. Quite a long way to go.
We’re still in the early innings of AI, and few of even the most common AI uses are being deployed at scale (see Figure 1). The challenges to achieving the potential are both technical and organizational. Many organizations aren’t aware how rapidly it’s evolving and may not realize what it can bring to their products. Tech providers can invest more in educating their customers about these capabilities and showcasing AI’s growing applicability.
Many AI tools have been introduced, but few are being deployed at scale
Specifically, the next generation of AI tools differs from already familiar software in its ability to operate more autonomously, to better inform decisions, and to identify patterns that allow it to craft more specific actions or recommendations. AI’s ability to offer insights at a more fine-grained level offers the promise of radical personalization. In the business-to-consumer space, a North American media company uses AI to address the issue of customer churn on its streaming platform. Going beyond the well-known capabilities of a recommendation engine, this AI helps the company identify which portion of the content is critical for drawing in customers and gaining new subscriptions. By offering insights on what a customer’s current habits indicate about future viewing, the system can help the company make more informed decisions about where to invest in content development, acquisition, and marketing.
In the business-to-business space, AI systems are also increasingly adept at synthesizing the various stages of a customer’s journey, to guide customers to potential purchases and recognize when interventions may help prevent churn. One B2B software company built an AI model to identify and attribute customer acquisitions to specific products and services across their portfolio. The company found that while many products expand revenue from its existing customer base, only a few acquired new customers. This discovery prompted the company to focus its investments in marketing and sales. The same company uses another AI model to understand product usage patterns, in order to learn more about what’s popular and where the pain points are, helping to guide investments in product enhancements. Finally, AI helps this company reduce churn by showing that some customers are more sensitive to long turnaround times on ticket issues, while others are more bothered by reopened tickets, which helps identify which customers are most at risk of leaving.
The task ahead
To capitalize on this opportunity, leading technology providers are connecting the dots and building out the infrastructure, processes, organizations, and customer engagement necessary to turn the customer’s data signals into AI-driven recommendations that enhance success across the customer’s life cycle. Most are taking significant actions in three areas.
- Data and infrastructure. Companies are introducing converged data platforms (with access to user engagement data across the customer life cycle) with appropriate access rights that expedite product development. For many, this means multitenant cloud with shared databases. Analytics is no longer back-office IT, but rather the differentiator for winning through personalized customer success.
- Customer-led AI roadmap and processes. Product development is integrated closer into the customer support infrastructure, so that feedback from products and services is directly accessible to the product developers and can be considered in upgrades and redesigns. Another goal is improving the ability to track engagement with customers across the entire journey, including targeting, usage, retention, and expansion, and to identify barriers to success.
- People and processes. Integrating AI engineers with product teams, centralizing data centers of excellence, and providing technical sales and marketing with AI capabilities are all essential to successfully applying AI’s capabilities to customer success efforts. Continuous training in AI and ML tools and skills are essential, given how rapidly the technology is progressing.
Tech leaders are investing in AI because they see it as the path to taking customer-centricity to the next level. Although our research finds that these investments have yet to match executives’ aspirations, the direction of travel seems clear.