The power of AI rests in data. If you’ve done any research on AI solutions, you’ve likely run into the phrase, “garbage in, garbage out.” The core of that message is true: All companies should strive towards clean, accurate, and organized data. But, no company will have perfect data maintenance, especially for a tool they don’t yet use.
Perfection should not stop progress, particularly when it comes to helping Customer Success (CS) teams build relationships with customers. In fact, the only way to achieve perfection is to constantly make progress. To help CS teams kickstart their AI journey, we’ve put together common reasons companies have bad data. More importantly, what you can do to overcome perfection paralysis and start using AI to grow revenue and reduce churn.
Limited Visibility Into What Data Exists Already
The most common obstacle companies find when assessing the state of their data is knowing how much of it exists—and where. In today’s world, where companies average at least 100 software programs across organizations, it’s almost guaranteed that there is customer data CS teams aren’t using to the entire company’s advantage.
Finding, consolidating, and managing all that data could take months. Time you can’t afford when you have revenue goals to meet and customers to support. To make progress towards a more intelligent CS organization, use AI to make sense of what exists. Staircase AI, for example, can synthesize data you already have to provide insights on how to best move forward. The more informed your decisions are about how to integrate AI into your customer journey, the more results you will see immediately.
Data From Inconsistent Sources
Another obstacle that prevents companies from adopting AI solutions is they believe their most important and useful data comes from sources inconsistent with the functions of AI. In other words, companies want actionable insights from emails, meetings, and other conversations, but worry that without consistency, they won’t get helpful suggestions from AI tools.
If this sounds like an issue for your company, consider the kinds of AI tools you employ. AI agents are different from generative AI tools, and those are different from process automation tools. Generative AI tools can take all the disparate data from meetings and emails to create helpful insights. Process automation tools may need a bit more prompting before they can be helpful. But remember, progress over perfection. Start using generative AI now to get immediate value and continue refining your data sets to prepare for other tools.
Fragmented Data Across the Customer Journey
Finally, companies find that their data, even within an organization, can be fragmented. As teams grow and evolve, processes often change. The kinds of data that are kept or ignored usually evolve with them. The result can be incomplete or inconsistent data throughout an account’s history.
For example, the notes taken during EBRs and QBRs could shift based on which Customer Success Manager (CSM) owns the account. Sales teams may change the information they add to their platforms during renewal and expansion conversations. Even the data collected by different people in the same meeting can leave gaps.
But hope is not lost. Powerful AI tools, like Staircase AI, are built with the purpose of making predictions despite those gaps. The models that power them provide valuable insights based on past data and current objectives—insights that, if ignored, could lead to churn.
The sooner teams start using AI, the more impactful AI will be in the future. Delaying adoption will not make the change easier; it will instead push your team further behind competitors.
Get Started With Staircase AI
Making progress today is the only way to ensure your CS teams can make the biggest impact on revenue tomorrow. To learn more about how Staircase AI can provide value, even with imperfect data, contact us. We can help prepare you with the solutions and roadmap necessary to meet your goals.
