01 · Area
Customer Support
for all the Metricool users. Chat on the Metricool platform and Email conversations, with 2 levels of expertise to solve and scale tickets as needed.
Team Blue meets Bruno
Customer Experience,
scaled with care.
How the Metricool CX team turned a support function into a revenue engine — and built Bruno to do it again, automatically, across every account.
Customer Experience
“On Metricool as a SaaS Platform, Support is a Product Failure”
— Juan Pablo Tejela
CX in Metricool
Metricool is a Platform for Marketing Specialists that look for Specialized Support, not only with platform knowledge, technical abilities, but digital marketing and social media specialists.
Meet
The Team
A team that owns the entire customer lifecycle.
CX at Metricool isn’t a cost center. It’s the revenue-protection layer between the product and every account we touch.
What started as a 1 person Chat Support based in Spain is now a 24/5 team of 28 Specialists located in Spain, Guatemala and El Salvador and divided in 4 areas:
02 · Area
Customer Success
for the White Label integrators, that need a more technical, personal support and follow up.
03 · Area
Customer Knowledge
to create the Help Center for the Customers and Internal usage and our SOPs with internal procedures.
04 · Area
Customer Engineering
(the newest) to provide the team our own tools and solutions.
As we need to be Expert to Expert Support, we certified the team and the Company for Meta Certified Company, as part of the Specialization process.
Growth
Metricool’s Growth <> CX Team Growth
The Metricool growth impacts directly in the Support volume, then it affects directly on the HeadCount and Scalability of the Operation.
As Metricool grows the number of tickets increases proportionally. We used to solve it by increasing the headcount. Yet we were always behind the growth trend.
but we had...
The Challenge
Compared to our Main Competitors, we are the only ones that offer a Live Support Chat. This became a Product Value and lead product reviews to a Top Position, and we felt AI Resistance from the users, that appreciated a Human to Human contact.
But we had the challenge to deliver hyperpersonalized attention to our Customers, and AI was the solution to both create a powerful context for users and control scalability.
so we built an
AI Enabled CX Team
AI had to take an important role in Customer Experience, so we worked together to create an AI Enabled team, without any fears of being replaced but to take the opportunity to boost their capabilities using AI. We created our own CX AI Laboratory to diagnose, test and implement AI solutions for the CX operation within the 4 areas. We are constantly exploring how AI can provide the best solutions for our operation.
Phase 01
Diagnose
Phase 02
Test
Phase 03
Implement
the Solution
Zentesimal
As we didn’t have the internal capabilities to develop our own platform, we came to a Partner to provide a solution for our AI needs and how to solve the challenges about knowledge and scalability, reducing the hallucination dangers and with key features for deeper training.
Key Features
Feature 01
Zerebro
The knowledge hub of Zentesimal, where all of our company information lives, structured and connected. It is where Bruno gets the context for every conversation, so answers stay accurate and on-brand. It centralizes internal procedures, Help Center content and product knowledge in one single source of truth.
Feature 02
Integrations
Using our current tools and platforms, we connect Zentesimal to the systems the team already works with. The AI workers operate inside the same channels our customers and agents use, so adoption is frictionless and context is shared in real time.
Feature 03
Workers
Then Knowledge becomes action. Workers are the specialized AI agents that apply Zerebro’s knowledge to real tasks, each tuned to a stage of the customer journey. They resolve, escalate and act across the four CX areas with consistent context.
- Resolve customer queries with immediate, contextual answers.
- Escalate to the right specialist with full conversation context.
- Generate trials and apply offers inside the support flow.
- Follow up on incidents with empathic, detailed notifications.
- Answer internal team questions on demand from Zerebro.
Feature 04
Talk to Zentesimal
The conversational layer that connects people, knowledge and workers into one experience.
Then...
Why Bruno?
Everytime, the team had an ease time with no incidents coming on the platform and from Social Media APIs, we said we didn’t have to talk about it, because when mentioned, something happened. That’s why we don’t talk about Bruno, and now Bruno is the one helping us navigate our work and manage our incidences.
how it works...
Our AI Workers
We have designed and created the following AI workers for Support / Success and Lifecycle within Customer Experience
From reactive tickets to proactive intelligence.
AI doesn’t replace the CX team. It absorbs the repetitive tasks, surfaces the signals nobody has time to read, and routes only does that genuinely need a human.
*all results are displayed from June 2026 as reference.
1. Support and Success · Customer Facing
A · Chat Worker
80% of our conversations happen in the Chat. Users expect an immediate one-time resolution and we engage in conversations on average for 15 minutes.
01 · Automated chat — Automated chat performance dashboard
- Resolution, Escalation and CSAT, with an average of 3,5 / 5 when solved by Bruno.
- Resolution Type (AI analyzed: Certained, Inferred, Uncertain, Escalado)
- CSAT — AI estimated based on Context (Voice of Customer)
- Revenue Generator: Bruno is also acting as a Sales person inside Support and we have given him the ability to generate trials for users with its own discount code: “BRUNO” and results are very relevant, generating a 70% conversion and generating 31,134 € ARR for new customers.
B · Email Worker
Handles the other 20% of conversations, with a higher escalation rate since most of them don’t come from the actual account and lack of context. We are working to improve response and experience here.
C · Success Worker · Work in Progress (WIP)
This worker has more complex context based on Sales conversations, deeper product analytics and technical knowledge upon integration settings and account setup. Each customer has a completely different usage case.
D · Incident Worker · Work in Progress (WIP)
Incidents affect from internal and external reasons, and create a high volume of conversations with a very low Satisfaction upon the incidence. Managing conversations this conversation need to include a specific and immediate understanding of the incidence and specially an empathic tone and detailed follow up and resolution notifications, that require special attention.
Internal usage
E · Q&A Worker
Helps the Metricool team directly on Slack, to resolve any questions or doubts like Customer would have based on a slack channel and responding based on the Zerebro information.
2. LifeCycle
Worker · LifeCycle
Churn Recovery Worker
Churn Recovery Worker: monitors usage and engagement signals to identify at-risk accounts and proactively starts a personalized recovery conversation, surfacing the right offer and next step for each customer.
WORKER · LIFECYCLE
Voice of Customer Worker
Voice of Customer Worker: aggregates feedback across every conversation and channel to estimate CSAT and surface recurring themes, giving the team a real-time read on customer sentiment and insights.
Worker · LifeCycle · WIP
Onboarding for White Label for Agencies
Onboarding for White Label for Agencies - WIP: guides agencies through setup and activation of their white label instances, adapting content and steps to each integration and usage case.
and we were not expecting...
Bruno Results
3 monthsLive
Based on market standards and operational metrics, we’ve worked on a full performance dashboard to measure OPEX and Revenue results impact on the ARR of each worker. Monitoring the overall performance allows us to have a strategic vision to make decisions based on results available, measuring every step and impact. We can monitor the impact of each new version and validate results in the behaivoir.
For example: by June 22nd we introduced an improved version for Bruno with performance updates and conversational changes and resolution scaled from 60% to 70% on resolution rate.
60% → 70%
Resolution rate after June 22 update
3,5/5
Average CSAT when solved by Bruno
31,134 €
ARR generated for new customers
70%
Conversion on trials
