Smart Dashboards & Analytics

We use AI to turn business data into clearer insights and more useful decision support. That can include smart dashboards, automated insights, intelligent alerts and predictive models built around real business metrics. The goal is simple: help teams understand what is happening and act earlier.

We build dashboards that make data easier to understand and easier to use in day-to-day work. That can include: - automated insights - intelligent alerts - natural language interaction - performance views across sales, marketing and operations Instead of just showing raw numbers, these dashboards help teams understand what is changing and what needs attention.

We connect dashboards to the systems companies already use, so insights are based on real operational data. That can include integrations with: - GA4 - CRM systems - sales platforms - marketing tools The idea is simple: bring the right data into one place and make it easier to act on it.

We build predictive models to help companies anticipate what is likely to happen next. That can include forecasting: - churn - sales - leads We also use AI to support customer segmentation and identify patterns that are harder to detect manually.

Different parts of this solution create value in different ways: - Smart dashboards: clearer visibility, automated insights and better decision support based on AI-assisted analysis - Predictive analytics: earlier signals, better alerting and more timely decisions The result is better visibility, earlier decisions and stronger use of business data.

This solution is especially useful for companies that rely on data to manage performance, monitor risk or improve decision-making. Different parts of this solution are usually a better fit for different types of teams: - Smart dashboards: medium-sized and large companies, and enterprise teams that need visibility across business metrics - Predictive analytics: medium-sized companies, large organisations, enterprise teams and e-commerce businesses that want earlier signals around churn, sales or lead performance If business decisions depend on data from multiple systems, this solution helps turn that information into clearer insights and better decisions.

AI helps by spotting the early warning signs before a customer actually leaves — patterns in usage, engagement or support activity that tend to show up ahead of cancellations, which are hard to catch manually across a large customer base. Once those patterns are identified, they can feed into segmentation (grouping at-risk customers) and alerts, so teams know who to focus on and when, instead of reacting after the fact. This works best as part of a broader analytics setup — churn forecasting combined with customer segmentation and automated alerts — rather than a single standalone score, since the reasons behind churn usually vary across different customer segments.

A few things matter more than a long feature list: - Integration: does it connect to the systems you already use — GA4, CRM, sales and marketing platforms — or does it need a separate data pipeline? - Insight quality: does it just display raw numbers, or does it surface automated insights and intelligent alerts when something changes? - Interaction: can you explore the data through natural language, or only fixed charts and filters? - Prediction, not just reporting: does it forecast churn, sales and leads, and support customer segmentation, or only show historical performance? - Coverage: does it bring sales, marketing and operations into one view, or stay siloed by department? Yetiman's Smart Dashboards & Analytics service is built around these points — connecting to the data you already have and adding automated insights, alerts and predictive models on top of it.

Smart dashboards and predictive models are only as reliable as the data behind them. Before building anything, Yetiman's AI opportunity assessment reviews the data the business already has — where it lives, how usable it is, and what gaps exist — so dashboards and forecasts are built on a realistic picture of data quality, not an assumed one. Governance follows the same logic: defining who can access which data, how it's used, and what guardrails apply, so dashboards stay accurate and compliant as more data sources get connected over time.