AI Analytics
The backbone of everything Sift does is the intelligence layered on top of the unstructured conversations across your social ecosystem and community. That's how Sift decides what needs action, what's useful information, and what can be filtered out. AI Analytics is where you read that signal back: the topics people discuss, emerging trends, sentiment shifts, and how effective your support is.
At a glance
| Taxonomy: AI topic hierarchy with sentiment, mentions, response rate | ✅ |
| Taxonomy Trends: topic momentum with a 7-day delta | ✅ |
| Tag Insights: sentiment over time and by topic per tag | ✅ |
| Relevancy: relevance, actionability, and Hours Saved | ✅ |
| Moderation: spam caught, confidence, rule-type breakdown | ✅ |
| Drill from any topic, tag, or rule into the underlying records | ✅ |
What you're looking at
AI Analytics is the first tab in the Analytics section and the default view when you open it. It organizes the content of every interaction into five dashboards: Taxonomy, Taxonomy Trends, Tag Insights, Relevancy, and Moderation. Each one answers a different "what are people saying, and is it changing?" question.
Taxonomy
Taxonomy lets you understand the topics and themes being discussed across your social channels through an AI-generated hierarchy of categories you can drill into. Sift provides sentiment, mention counts, and response rates for each topic, with a trend chart showing how the top themes shift over time. Click a topic to see the conversations and records that belong to it.
How to read it: scan for topics with high mention counts paired with negative sentiment: that combination is where customer pain is loudest. A high mention count with a low response rate flags a topic your team may be under-serving.
Taxonomy Trends
Taxonomy Trends tracks how topics shift over time across your channels, helping you spot emerging conversations, seasonal patterns, and sudden spikes in interest. The trend chart plots the volume of the top themes over your selected period, with each topic as its own line so you can compare momentum at a glance. The topics table includes a 7-day delta column showing whether each topic is trending up, down, or holding steady versus the previous week. Filter by time period and source along the top.
How to read it: a sharp upward delta on a topic that was quiet last week is the early warning you want to catch; a steady decline often means an issue is resolving. Compare the slope of two lines rather than their absolute height to judge which topic is gaining ground.
Tag Insights
Tag Insights lets you analyze the tags applied across your conversations and understand the sentiment patterns within each tag. Three metric cards summarize your data: Total Tags, Total Tag Groups, and Total Mentions across all tags. The chart view shows Sentiment Over Time and Sentiment by Topic, so you can see how sentiment shifts day by day and how it varies across tags. Click any tag to drill into the records associated with it, or switch to Table View in the top right to see individual records.
How to read it: use Sentiment by Topic to find the tags dragging your overall sentiment down, then drill in to read the actual records behind them before drawing conclusions.
Relevancy
Sift uses AI to evaluate each message for relevance to your brand and whether it requires action. These evaluations triage interactions for your support team, typically filtering out non-relevant and non-actionable items before they reach a queue. Four metric cards summarize the impact: Total Items processed, Irrelevant Items, Non-Actionable Items, and Hours Saved by filtering out noise. The chart view shows Relevance and Actionability trends over time with percentage-change indicators, alongside breakdown charts splitting relevant versus irrelevant and actionable versus non-actionable items.
How to read it: Hours Saved quantifies the noise Sift is keeping off your team's plate. If the irrelevant or non-actionable share suddenly shifts, it's worth checking whether a new source or campaign changed the mix of what's coming in.
Moderation
Sift automatically detects and handles spam across your channels using a combination of AI detection, pattern matching, and custom rules. The Moderation tab tracks how effectively those rules protect your community. Four metric cards summarize activity: Total Spam Caught, Caught Today, Average Confidence score, and an Action Breakdown of how many items were suppressed, auto-closed, or tagged for review. The chart view shows Spam Volume Over Time, a Rule Type Breakdown (AI Spam, Stock Spam, Pattern Match, Regex), Top Spammers ranked by detection count, and Top Channels where spam is most prevalent.
How to read it: a low Average Confidence alongside a high catch count can mean your rules are being aggressive: spot-check the Action Breakdown to confirm legitimate messages aren't being auto-closed. Top Channels tells you where to tighten rules next.