Frame AI Reviews & Product Details

Frame is assisting in the following for our organization:

- Informing stakeholders regarding account sentiment outliers

- Guiding strategic product decisions using cost and sentiment data on key features

- Keeping escalation ratios at an industry low .56%while holding Support team to high CSAT standards

With the data Frame provides, we're able to proactively start and drive discussions with our PDE teams that lead to a deeper understanding of true customer sentiment and pain points regarding our most adopted features. With Frame's Predictive CSAT and Pre-Escalation scores, we're able to identify customer escalations before they happen and get the appropriate resources in the room quicker to resolve high level items of concern for our clients. Review collected by and hosted on G2.com.

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.

Frame AI analyzes all of our tickets and allows us to learn of problems quickly. Review collected by and hosted on G2.com.

This has been very helpful in letting us know when a client is unhappy and we need to step in to figure out how we can better serve our client. Review collected by and hosted on G2.com.

Helping us identify where the risk and gaps with our end users. Review collected by and hosted on G2.com.

The main problem Frame AI solves is the manual effort of reading each and every case to establish a trend or to find that needle in the haystack. Finding out how many customers experienced log4j issues for example, or how often are customers getting stuck performing a particular task. Typically the keyword search combined with some custom tags gets you the information you need - quickly. Review collected by and hosted on G2.com.

Frame is helping us find and articulate narratives, pains, and patterns that we could only speak to anecdotally before. In combination with their powerful ability to plug directly into systems and processes, it really feels like the sky is the limit! Review collected by and hosted on G2.com.

Helping us understand where the pain is and how we can adjust our systems and processes to better target self-service. Review collected by and hosted on G2.com.

Tagging and acting on product feedback across a number of feedback channels, at scale. Benchmarking and baselining complex areas / features within our product and ensuring we act on those causing the most user friction. Review collected by and hosted on G2.com.

We were handling support requests in Slack, and starting to get overwhelmed and worried that we'd drop issues and lose valuable insights. The normal path is to move away from Slack and into Zendesk or other ticketing systems. Our team didn't want to lose the close contact and responsiveness that we had with Slack, but needed to get some of the reporting and escalation policies that a ticketing system had.

We discovered Frame AI and integrated it, and it's done just that. As our support workload has grown, Frame has let our support team scale and hand issues off between different engineers who are on call. We have a way to know all open issues and transfer ownership of them. The best part is that our customers still interact with us using Slack, and don't need to learn anything new. They are still just using Slack and communicating with our team. Review collected by and hosted on G2.com.