How AI can improve services revenue and customer success – Jahanagahi
Artificial intelligence

How AI can improve services revenue and customer success

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The revenue and margin leaks that services businesses never see coming tend to hit their financials the hardest. When there are gaps in customer success coverage combined with little visibility and control of services revenue, leaks can grow. Turbulent economic conditions make the leaks grow faster, giving customers a reason to question if they’re getting the value they expect.

Sure signs a customer success organization is experiencing revenue leaks include not knowing the costs of upsells, cross-sells, not having a financially based view of every customer engagement, growing churn rates and more. The costs of not getting cross-sells and upsells right – or worse, missing a commitment to a customer – all eventually roll up into financials and the metrics services that CFOs use to analyze them. Having real-time data that can be modeled using AI and machine learning is helping to solve these and other customer success challenges today.

Getting customer success right is hard

Not knowing where the data-blind spots are before they affect revenue makes it hard for customer success management (CSM). Blind spots happen when customer success is siloed and valuable insights are lost. Any CSM initiative to provide customer value must be integrated across CRM, financials, customer service and channel selling systems. It’s a prerequisite for driving services revenue at scale while contributing to customer lifecycle growth. There needs to be a continual source of real-time customer and financial data for CSM platforms to drive a true 360-degree view of all customers.

During a recent interview, Dan Brown, chief product and strategy officer at FinancialForce, told VentureBeat that investing in devops in collaboration with the Salesforce platform provides FinancialForce with an application and analytics platform-as-a-service they can use to take on these challenges . “Salesforce has invested in technologies like Einstein Prediction Builder, and discovery; we’ve been able to take advantage of those,” Dan said. “Salesforce Einstein is also a key lever for us, and we’ve progressed with how we integrate their AI services into our platform.”

“Applying machine learning is not terribly different from applying any other technology or tool to a problem a customer is encountering and trying to solve,” Brown continued “We want to use the technology that solves the problem in the most expeditious way with the best unit economics. The Einstein platform provides a suite of tools that is outstanding on both accounts for a class of problems that our customers are looking to solve, and in those cases, it’s an easy decision to leverage it as we do the rest of the Salesforce platform.” An article by Brown provides insights into the many challenges B2B enterprises face in connecting commercial arrangements with prospects and clients and the value that needs to be delivered once they become a customer.

Devops teams across the Salesforce partner ecosystem rely on the Salesforce platform’s core configurable enterprise microservices and Einstein application microservices to customize their specific solutions for CSM and revenue management.

The second challenge that makes getting customer success right is the tendency of service organizations to initially – and sometimes only – focus on the top 10-20% of customers. Instead, the goal is to concentrate on upsells and cross-sells to drive revenue with the largest accounts. As a result, CSM needs to have an integrated platform supporting it because of how much each cross-sells and upsell costs and its impact on Customer Lifetime Value (CLV).

The third challenge is getting CSM self-service right while providing customization at scale. Many service organizations define customer segmentation levels by contract value, with customers below a certain value assigned to digital self-service. Today, hundreds of bot providers are looking to sell their technologies to services organizations to automate digital self-service. Unfortunately, it’s rare to find a bot provider who can get contextual intelligence and personalization right. Leading providers of AI-powered bots for CSM include ChurnZero, Gainsight, Totango, Salesforce and many others.

How AI is helping today

Capitalizing on AI’s strengths to create and fine-tune intelligent experience engines is helping close the gap between customer success and service revenue through improved personalization. A recent article in the Harvard Business Review provides insights into how AI can help close the gaps between improved customer experiences and greater revenue. The authors explore how companies who built intelligent experience engines improve service revenue while attaining greater customer success. They analyzed over 100 leading global companies’ personalization efforts related to customer success and revenue. Their results show that over the past five years, the top 100 companies have seen increases in their revenue of 6% to 10% and an increase in net incremental revenue attributable to personalization anywhere from 40% to 100%.

Getting digital self-service right needs to successfully blend personalization, empathy and intuitive digital experiences that scale. SAP’s efforts at blending both deliver results in service businesses. Its SAP for Me digital companion guides customers through useful training information while providing incident data and licensing suggestions. In addition, SAP designed it to provide support for onboarding, renewals and go-live dates. It’s an approach that balances the need for empathy and efficiency, as SAP also provides online help with trained customer service representatives.

Knowing which metrics best identify if AI and machine learning are making a difference in CSM strategies. Having an integrated CSM platform capable of tracking handoffs between sales and service. “Machine learning helps provide last mile automation or prescription on how to take the next step in a process,” Dan Brown told VentureBeat. “The metrics commonly found in PSA solutions will measure the proper application of machine learning to professional services business processes.” Dan said the metrics he and his team see with services customers most often include utilization, billable rate, revenue leakage, on-time %, on-budget % and others. “In addition to these, because machine learning can help move a process forward faster – either directly or through a “next-best action” approach, cycle time is a good machine learning measure of success for any step in a process,” he said.

FinancialForce’s dashboard of management team metrics can capture the impact of machine learning on process cycle times, customer success effectiveness, and their combined impacts on revenue.

Next steps

Closing the gaps between customer success and services revenue takes an integrated CSM platform combined with technologies that excel at efficiency and empathy. Getting customer success right is hard because it takes patience in building customer lifetime value versus being too opportunistic with upsells, cross-sells, promotional offers and aggressive renewal pricing to drive revenue. Balancing efficiency and empathy with digital self-service drives down operating costs, yet needs to be balanced, providing a virtual high-touch environment for all customers.

CSM strategies need to first concentrate on preserving Annual Recurring Revenue (ARR). Next, the goal is to streamline customer onboarding and ensure customers are delighted and will return for renewal. FinancialForce is taking the next step in this progress by providing services to companies with the costs of upselling and cross-selling, which is a must-have for building a successful business case for any Customer Success program in the future.

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