What Ada users actually want
Mimir analyzed 15 public sources — app reviews, Reddit threads, forum posts — and surfaced 15 patterns with 7 actionable recommendations.
Top recommendation
AI-generated, ranked by impact and evidence strength
Build an interactive ROI calculator that surfaces personalized business impact projections based on customer's industry, channel mix, and current automation rate
Rationale
The strongest signal in the research is quantified business impact: 30 distinct sources show concrete metrics like 200% ROI, 83% autonomous resolution, and 2X agent productivity gains across 8+ industries. Yet this critical evidence lives scattered across case studies and marketing pages rather than being activated at decision points. Product managers and engineering leads evaluating Ada need to see how these outcomes translate to their specific context before they can justify investment to stakeholders.
Building an interactive calculator that ingests industry vertical, current resolution rate, ticket volume, and channel distribution would transform passive case study browsing into an active planning tool. Users could model scenarios, adjust variables, and export projections with supporting evidence from similar customers. This directly moves the engagement and retention metric by giving decision-makers a concrete artifact to champion Ada internally.
Without this, prospects rely on generic ROI claims that feel like marketing rather than evidence. The 943% ROI achieved by Ipsy or the £600K monthly revenue unlock by Simba Sleep become abstract rather than actionable. Given that Ada has served 350+ businesses across diverse industries, the platform has enough data density to make personalized projections credible and defensible.
More recommendations
6 additional recommendations generated from the same analysis
Theme 2 establishes that knowledge base quality directly determines AI agent effectiveness, yet 14 sources indicate customers struggle with proper HTML structure, overlapping categories, and content staleness. The research explicitly states AI and LLMs struggle to find information without proper H1/H2/H3 tags and clear information architecture, and that poor structure directly limits automation gains and customer satisfaction. This is a prerequisite for ROI, not a nice-to-have feature.
Theme 6 reveals a critical gap: AI agents require ongoing coaching to improve beyond baseline, yet most successful customers only invest 2-3 hours weekly because they lack structured guidance on where to focus. The research warns that AI agents won't improve without active feedback and that organizations treating AI as a one-time purchase risk stagnation. This directly threatens retention when customers fail to realize expected ROI improvements over time.
Theme 8 identifies voice as one of the most-used yet most neglected customer service channels, historically expensive to staff and easy to get wrong. The research shows Ada's unified Reasoning Engine enables consistent automation across voice alongside chat and email, with measurable CSAT increases on voice channels. Yet voice remains underinvested by most businesses despite its usage volume, creating a significant competitive opportunity for Ada to own this channel.
Theme 5 exposes a painful gap: 88% of support teams offer multilingual support but only 28% of end users actually receive support in their native language. The research quantifies the cost of this gap: 40% of online shoppers won't buy from non-native websites, 35% would switch products for native language support, and 29% of B2B businesses have lost customers due to lack of multilingual support. Ada supports 50+ languages, but customers need visibility into where language gaps are costing them revenue.
Theme 4 reveals a critical insight: each user complaint represents multiple unreported issues, yet organizations treat confusion as isolated support tickets rather than design flaws. The research explicitly states that treating user confusion as a bug for the design team results in far more interesting and better products, and that organizations should build feedback loops that move beyond solving immediate problems to proactively preventing future issues.
Theme 1 establishes that Ada's unified Reasoning Engine delivers centralized intelligence across all channels, with improvements in one channel automatically cascading to others. This is positioned as an industry-first capability across 14 sources. However, the research doesn't indicate whether customers can proactively validate that this consistency is maintained as they coach and optimize agents.
The full product behind this analysis
Mimir doesn't just analyze — it's a complete product management workflow from feedback to shipped feature.
Themes emerge from the noise.
Ranked by severity and frequency, with the original quotes inline so you can judge for yourself.
Talk to your research.
Ask questions, get answers grounded in what your users actually said.
What's the top churn signal?
Onboarding confusion appears in 12 of 16 sources. Users describe “not knowing where to start” [Interview #3, NPS]
A prioritized backlog, not a wall of sticky notes.
Ranked by impact and effort, with the reasoning you can actually defend in a roadmap review.
PRDs, briefs, emails — on demand.
Generate documents that reference your actual research, not generic templates.
Paste, upload, or connect.
Transcripts, CSVs, PDFs, screenshots, Slack, URLs.
This analysis used public data only. Imagine what Mimir finds with your customer interviews and product analytics.
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