Success Intelligence helps outbound contact centers turn conversation data into actionable revenue insights. It identifies customer objections, tracks how agents handle them, and highlights opportunities to improve conversion and sales performance.
Visual Design
Product Envisioning
MVP Design
UX/UI Design
Accessibility
Data Visualization
MaxContact is a UK-based company providing software for contact centers aimed at helping businesses to boost contact rates & build stronger customer relationships.
After the successful release of Spokn AI, MaxContact turned to sales performance aiming to further boost revenue of its customers within their outbound contact centre service.
Completely remove manual quality assurance and replace it with automatic QA based on scorecards (a series of questions/checkpoints that QAs go through to ensure the interaction and agent performance).
While AutoQA by Scorecards would certainly further decrease operational cost, higher conversion rates in sales, booking, follow-up etc. could drive revenue much further.
Outbound Contact Centre Leaders (such as Sales Director, Dialer Manager), responsible for strategy, seek deeper insights into “revenue intelligence” - the notion we further explored in terms of user needs and data required for it.
After a short design sprint, we used co-design to quickly explore ideas, align priorities, and define the most valuable direction for the next release.
Crazy 8 helped us generate low-fidelity solution ideas around sales conversation analytics. Ideas were mapped on a prioritization map by value and feasibility with stakeholders.
Once objections handling analytics became the selected priority, we focused on building the foundation for detecting, structuring, and turning objection data into actionable insights.
Data analysts proceeded with training the existing AI-model on a huge dataset of transcripts to define what an objection is.All found objections were reviewed with customer representatives and split into 4 categories based on:
The design team started work on data representation of objections and sales managers performance.
Once the basis for objections analytics has been covered, we've moved on to generating insights using AI. Same as with Spokn AI our goal all along was to reduce manual scanning and empower sales managers with a smart tool.
Data analysts proceeded with training the existing AI-model on a huge dataset of transcripts to define what an objection is. All found objections were reviewed with customer representatives and split into 4 categories based on:
The design team started work on data representation of objections and sales managers performance.
Once the basis for objections analytics has been covered, we've moved on to generating insights using AI. Same as with Spokn AI our goal all along was to reduce manual scanning and empower sales managers with a smart tool.
We've split objections analytics into 2 key focus areas to cater to different data needs:
(what objections happen in sales process)
(how sales managers handle them)
All found objections were displayed on a timeline to give a quick pick into the dynamics and objections. Compare objection trends by category or by specific reason.
Gives a detailed overview of each objection, its occurrence and outcome as well as conversion rate, level of calls with this objection turned into a sale.
The same objections metrics were used to display sales managers work, i.e. how well employees handle specific objections and how it affects conversion.
Each objection or employee record could be further reviewed in a detailed view as well as allowed access to conversation playback in Spokn AI
A quick digest of recent findings and trends delivered to sales managers every morning split into 3 categories.
Optimized for low-light environments while keeping data tables, filters, and actions easy to scan.
AutoQA delivered a measurable impact on launch and established the groundwork for what’s next.
200+
Agent licenses
secured upon release
AutoQA →
Conversation Analytics
foundation for a future analytics product
A cross-functional collaboration between design, engineering, and the MaxContact product team.
UX Design Team
Research · UX/UI · Product Design
Cross-functional team
Design · Development · Delivery
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Product team
Product expertise · Stakeholders · Feedback
