G2

Alexandre L.

Senior Program Manager

Mid-Market (51-1000 emp.)

5/3/2024

"Proactive Services Insights Powered by AI"

5/5

What do you like best about Frame AI?

We have been working with Frame AI for more than a year now, and we really appreciate their collaboration, professionalism, thorough follow-ups and support. So far, we have calibrated their product for 3 main use cases: Cost to Serve, Escalation Risk and Predicted CSAT, and are very satisfied with the product, which we use on a daily basis. Review collected by and hosted on G2.com.

What do you dislike about Frame AI?

Being a very configurable environment, the initial lift requires some time and collaboration with employees to assess the amount of time spent on different tasks. Fortunately, Frame AI CSM was of great help and guidance throughout this process. Review collected by and hosted on G2.com.

What problems is Frame AI solving and how is that benefiting you?

Since their product allows us to bring data points into other systems such as Salesforce CRM, we were able to build custom alerts in our system of choice to proactively review Support cases based on the above use cases. It also allows us to provide additional visibility to Support agents directly on their cases, allowing them to adjust their customer interactions quickly if needed.

In terms of Cost to Serve, their highly customizable product factors allowed us to start looking into outliers quickly; cases that cost us more time and money to support than the average can now quickly trigger a collaboration discussion with other teams such as R&D, including actual data points from Frame AI to add weight to the conversation. Relying on the signals they detect is much more reliable than estimates from the Support agents themselves in the end. Within a year, this approach allowed us to lower our cost to serve by 24% (so far!)

Escalation Risk alerts combined with other minor adjustments allowed us to reduce escalation requests by almost 50%. Proactively reviewing cases with experts and helping Support agents proactively also has a positive impact on the overall customer satisfaction, which went up by 4.2% in the last year.

Predictive CSAT is based on signals collected on ALL Support cases received, versus the 15% of formal transactional survey responses we receive from customers. Both scores are aligned within 1% difference, which means we can trust the value predicted by Frame AI and be more confident in showing up this value to leadership as it is based on much larger scale than actual surveys.

They are introducing Case Summary capabilities through AI, which we trialing at the moment and are very satisfied with the results, which also include coaching tips for the agents based on the case interactions. Once again, those data points are made available to us, so we can reuse them in our Reporting, automations, continuous improvement initiatives and transparently display it to Support agents as well. Review collected by and hosted on G2.com.