Peach Analytics

analytics overview in peach the analytics tab in peach provides you with powerful insights into all the activities happening across your account it allows you to monitor performance, track engagement, and evaluate both automated and human led interactions this section serves as the entry point to understanding how your users, broadcasts, apps, and agents are performing below is a breakdown of the key areas available within analytics 1\ source view how users initiate chats through different channels connected to your whatsapp numbers helps identify which sources are driving the most engagement 2\ broadcasts provides detailed information about all the broadcasts you have sent includes delivery and engagement status, helping you measure campaign performance 3\ apps similar to broadcasts, this section highlights user engagement with the apps you’ve built in peach useful for understanding app adoption and interaction trends 4\ chat track activities of your human agents connected to your account insights are available at both the aggregate level and individual agent level if you have created agent teams, the data is separated and reported by team 4 1 assignments displays the number of contacts assigned to your human agents useful for workload distribution analysis 4 2 funnel shows the funnel flow assignment to human agents → responses provided by human agents includes insights into assignments that occurred due to ai agent escalations available at both aggregate and individual levels 4 3 response time track three key metrics across all human agents average first contact time average first response time average resolution time drill down to see these metrics at the individual agent level the specific contact level helps measure efficiency and identify areas for improvement in handling conversations 4 4 activities provides a detailed view of all activities for each contact useful for exploring individual customer journeys and touchpoints 🎥 watch the video below to learn about analytics section in peach source the source section in the analytics tab gives you an overview of how chats are initiated on the whatsapp numbers connected to your account it helps you understand whether conversations are starting from the user’s side or from the business, and through which channels when you open analytics, the source section is the first view you will see types of initiations there are two types of chat initiations visible in this section 1\ user initiated these are conversations triggered directly by the customer total sessions see the overall number of sessions that began with a user initiation channel breakdown sessions are grouped into three channels direct a user sends a message directly to your whatsapp business number you can view the exact keyword used by the customer the dashboard also shows how many sessions were initiated by a specific keyword if a keyword field is empty, it means the initiation was through an audio file or image instead of text paid sessions triggered when a user clicks through to whatsapp from an active meta ad campaign referral sessions initiated when a user starts a chat from a referral text configured for a specific agent in peach 2\ business initiated these are conversations triggered by your business campaign sessions started by broadcasts you send through peach others sessions that begin when a template is triggered via api a human agent manually shares a template from the shared chat inbox example source table why source matters the source section helps you answer key questions like how are most users starting conversations with my business? which campaigns or referral setups are driving the highest engagement? are users interacting more through direct messages, paid ads, or referrals? this makes it easier to measure channel performance and optimize your user acquisition strategies 🎥 watch the video walkthrough below to see how to navigate the source section in real time broadcasts the broadcasts section in analytics gives you a complete view of the messages you’ve sent to your users through peach it helps you track delivery, engagement, and user opt outs for every broadcast campaign key metrics when reviewing your broadcast performance, you’ll see the following metrics queued the number of messages waiting to be sent to users sent total number of messages successfully sent from your account delivered messages that reached the user’s whatsapp inbox read messages that were opened and read by the user engaged refers to interactions with your broadcast messages through cta buttons ctas may redirect users to a website connect users with an ai agent in peach (based on your campaign setup) failed messages that could not be delivered to the user failures can occur for multiple reasons example for marketing messages, meta may cap delivery to maintain a healthy user ecosystem, resulting in restricted delivery unsubscribed the number of users who opted out of receiving further marketing messages from your business campaign level data beneath the overall metrics, you’ll see a list of all campaigns you’ve run each campaign includes its own breakdown of performance by clicking on a campaign, you can drill down into a detailed view of that specific broadcast, including engagement, failures, and unsubscribes example broadcast performance table why broadcast analytics matter this section allows you to monitor message delivery health track user engagement with cta buttons understand unsubscribe patterns to refine future campaigns compare performance across different broadcast campaigns 🎥 watch the video walkthrough below for a guided explanation of broadcast analytics apps the apps section in analytics gives you a detailed overview of how users are engaging with the apps you have built in peach it helps you track the progress of users through each app journey — from starting it, to completing and submitting it key metrics in this section, you’ll find the following insights for each app started number of users who have started interacting with the app in progress number of users currently filling out the app but have not yet completed it dropped off number of users who abandoned the app before completion completed number of users who successfully finished and submitted the app completion rate (%) percentage of users who completed the app, calculated against total users who started it example app engagement table why app analytics matter tracking app engagement helps you identify where users are dropping off understand which apps have higher completion rates optimize app flows to improve user experience measure the effectiveness of apps in achieving business goals (e g , lead collection, surveys, eligibility checks) 🎥 watch the video walkthrough below to learn how to analyze app performance in real time chat (agent productivity) the chat (agent productivity) section in analytics allows you to track the performance of all your agents in one place it helps you understand how effectively agents are handling customer conversations over time key metrics chats started number of conversations initiated between customers and agents chats closed number of conversations marked as resolved/closed by agents chats dropped number of conversations that were abandoned or dropped without resolution total vs unique chats total the overall number of conversation sessions handled by agents unique the number of individual customers agents interacted with example if a single customer had two separate conversations (one yesterday and one today), it will count as 2 total chats but only 1 unique chat first response time the average time taken by agents to respond to the first message from a customer resolution time the average time taken by agents to fully resolve a customer’s issue example agent productivity table why agent productivity analytics matter this section helps you track the balance between total workload and unique customers served measure how quickly agents are responding to new chats monitor resolution times to improve customer satisfaction identify inefficiencies, such as high drop rates or long resolution times 🎥 watch the video walkthrough below for a step by step guide on analyzing chat (agent productivity) metrics by agent the by agent section in analytics allows you to view performance and activity data at an individual agent level or for a group of agents this helps you drill down into productivity, workload, and conversation outcomes key metrics for each agent (or group of agents), you can track assigned chats total total number of chats assigned to the agent unique unique customers whose chats were assigned customer outreach contacted number of customers the agent has actively contacted yet to be contacted number of assigned customers not yet contacted active 24 hour windows the number of open 24 hour messaging windows currently active for the agent chats closed and dropped total vs unique breakdown of closed and dropped chats other metrics messages sent total messages sent by the agent escalated chats number of chats escalated from the agent (or to the agent, depending on role) example agent performance table roles and access the data you see depends on your role in peach manager can view analytics only for their assigned team(s) admin has access to analytics for all agents across all teams why “by agent” analytics matter this section helps you monitor workload and productivity of individual agents track team performance and identify bottlenecks ensure fair distribution of chats across agents gain role based visibility into agent activities 🎥 watch the video walkthrough below for a detailed guide on the by agent analytics section assignments the assignments section in analytics helps you understand how contacts are being distributed to your human agents it combines daily trends, key metrics, and detailed breakdowns so you can track workload balance and ai escalations effectively daily assignment chart at the top of the section, you’ll see a chart showing how many contacts were assigned each day you can adjust the date range to zoom in on a specific time period for closer analysis key metrics just below the chart, three core metrics provide insights into assignment quality total assignments overall volume of contacts distributed to your agents reassignments cases where contacts were moved from one agent to another this can indicate workload balancing issues or escalations escalations from ai number of contacts that ai agents escalated to human agents shows when automation required human intervention table view scroll down to view a table of assignments by agent or team this allows you to see exactly who is receiving contacts and how many data can also be downloaded as a csv file for use outside peach example assignments table escalation breakdown on the right hand side, you’ll find details of which ai agents are escalating chats which human agents those chats are being assigned to if you’ve enabled round robin logic , escalated chats will be distributed evenly across agents if not, assignments will follow the specific agent mappings you’ve set up how to use assignments analytics start with the daily chart to spot trends in contact distribution review the key metrics to identify patterns in reassignments and escalations check the table view for distribution across agents or teams confirm your ai escalation logic by reviewing the agent breakdown 🎥 watch the video walkthrough below for a step by step explanation of assignments analytics funnel the funnel section helps you understand how conversations progress from being assigned to agents to being contacted and finally responded to by subscribers it includes a top level funnel overview and a deeper agent level funnel analysis so you can spot bottlenecks and optimize both human and automated engagement date range & global controls date range selector (top right) defaults to today change to last 7 days , last 30 days , or set a custom range applying a date range refreshes every number on the screen for the selected period summary metrics (top) assigned to agent total conversations assigned to agents or teams contacted by agent how many assigned conversations were actually messaged by agents ( conversion rate shown below ) responded by subscriber how many customers replied ( conversion percentage shown ) overall response rate percentage of assigned conversations that ended with a subscriber response these four numbers give you a quick health check for the funnel conversation funnel flow (visual) a three stage visual that makes drop offs easy to spot assigned to agent contacted by agent responded by subscriber use this view to quickly identify bottlenecks between stages funnel table (stage detail) breaks the funnel into three stages— assigned , contacted , responded —with count volume at each stage conversion rate % moving from one stage to the next drop off how many fell off; color coded (e g , red/green) for instant visibility download csv (top right) to analyze in excel or share with other teams example funnel table agent level funnel analysis (drill down) use this view to compare performance by agent or team controls search find a specific agent or team filters refine data further (e g , team, timeframe) csv export download for deeper analysis columns / insights agent/group name & team identify who you’re reviewing assigned / contacted / responded stage counts per agent/group overall rate effectiveness converting assignments into responses ( color coded for easy comparison) messages sent outbound effort from the agent/group escalated by bot how often the system bot had to step in how to use funnel analytics set the date range to the timeframe you want to review (e g , last 7/30 days) scan the summary metrics for a quick health check—especially overall response rate use the funnel flow visual to spot stage drop offs at a glance review the funnel table to quantify conversion and drop off by stage drill into agent level analysis to see who’s performing well, who needs support, and where the bot is intervening export csv if you need to share, audit, or run custom pivots 🎥 watch the video walkthrough below for a guided tour of the funnel dashboard and the agent level drill down response time the response time section in analytics helps you measure how quickly your human agents are handling conversations it provides both an overall view across all agents and the ability to drill down to individual agents or even specific customer contacts key metrics you can track three important time based metrics first contact time average time taken for an agent to first contact or acknowledge the customer after chat assignment first response time average time taken by an agent to respond to the customer’s first message resolution time average time taken by an agent to fully resolve the customer’s query or issue example response time table drill down options by agent view these metrics for an individual agent to evaluate their efficiency by customer contact analyze response times for a specific customer to understand their support experience why response time analytics matter tracking response times helps you measure the speed and efficiency of your agents identify potential bottlenecks in handling customer conversations improve customer satisfaction by reducing wait and resolution times benchmark team performance at the aggregate and individual level 🎥 watch the video walkthrough below for a step by step explanation of response time analytics chat activities the chat activities section in analytics provides a detailed log of all chat interactions it lets you both view high level overviews and dive into specific chat details to understand customer journeys and agent actions filters & search options you can refine the chat activity view in several ways by agent or group focus on chats handled by a specific agent or an entire team by status filter by whether chats are open , closed , or pending by activity type narrow down by specific actions, such as when a chat was assigned to an agent when a journey or flow was completed search bar directly look up a contact by name or phone number for fast access chat details view once filters are applied, you can see detailed information for each chat, including contact details (e g , name, phone number) activity type (assignment, flow completion, escalation, etc ) time of conversation source of conversation (e g , direct, paid, referral, campaign) other contextual activity logs for a full conversation history date range & export apply a date range to view chat activities for a specific period download the filtered data instantly as a csv file for reporting, auditing, or sharing with other teams example chat activites table why chat activities matter this section helps you trace the complete journey of individual customers audit agent performance and activity timelines analyze chat patterns by status, type, or source export raw data for further analysis outside peach 🎥 watch the video walkthrough below for a demonstration of how to filter, search, and export chat activities