Staircase AI, a fast-growing Customer Intelligence platform vendor, has successfully launched a first-of-its-kind ML customer sentiment analysis model for B2B businesses. This innovative post-sales solution is helping customer success teams understand sentiment fluctuations with a unique business approach that’s based on unbiased and objective human insights.
Post-sales teams are using this sentiment analysis to identify churn risks, uncover new growth opportunities, and fine-tune their playbooks based on specific use cases, consumer trends, and market turbulences to boost their book of business.
The news of this launch comes as music to the ears of B2B businesses that are struggling to maintain high growth rates and mitigate churn as the market is cooling down. With AI being injected into multiple software solutions, it was only a matter of time before it started boosting post-sales operations. As per a recent Metrigy study, 75% of B2B companies rank “customer satisfaction” in their top 3 business priorities.
Companies can now adopt a proactive approach to understanding sentiment, something that wasn’t possible with outdated methods like surveys and direct outreach, which couldn’t paint a complete real-time picture due to poor response rates.
“In uncertain times like these, where companies are missing their earnings and facing challenges with acquiring new business, feeling existing customers’ pulse is critical. We learned that most of the feedback is hidden in the communication flowing between vendors and customers. Following extensive research and breaking down of post-sales ecosystems, we learned that the language used in a B2B context has unique attributes that relate to how customer sentiment is interpreted,” said Ori Entis, co-founder and CEO at Staircase AI.
Staircase AI’s new solution eliminates human bias, another first in the customer-led post-sales world. This is because this intelligence platform focuses on customer insights and human signals, unlike traditional tools such as surveys and interviews that have a limited operational scope. Another key differentiator is the scale and precision that AI can offer.
“Most conversations in B2B are polite and positive in general, therefore, most analyzers are simply incapable of reading between the lines. Our ML team worked hard on the algorithm and the model training process was unique – we debated positive and negative scenarios and discarded all disputed factors while including only rules that were agreed upon by everyone,” co-founder Lior Harel continued. “B2B businesses can now enjoy industry leading technology with an accuracy of 90% and above, an unprecedented feat in this space, which is used to 55% accuracy.”