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Hour × day grid
The canonical view — 24 hours by 7 days, coloured by volume or any metric you choose. Every cell is interactive: hover for exact values, click to see underlying conversations, right-click to compare with a baseline period.
Staffing, broadcast pacing and SLA windows all hinge on knowing when traffic happens — by hour, by day, by channel, by segment. SabNode Activity Heatmaps render the answer in a single glance. Decide your shift roster, your broadcast launch time and your SLA thresholds on real data instead of folk wisdom.
Every support and growth team has a "we think Tuesday afternoons are busy" theory. Most of the time it is wrong, or right only in aggregate, or right last year but wrong now because customer behaviour shifted after the festive season. The result is a roster that has too many agents at 11am and too few at 8pm, a broadcast launched at 10am because that is when the marketing lead arrives at the office, and an SLA of "respond within 4 hours" set without checking when customers are actually waiting.
The data is there — every inbound message, every reply, every resolution is timestamped — but it is locked inside a spreadsheet nobody runs. When the question comes up ("should we add a night-shift agent?", "when should we send the festival broadcast?"), the analysis takes a week and the decision is made before the spreadsheet is finished. The team falls back to gut feel, the gut feel turns out to be wrong in a way that costs money, and nobody learns anything because there is no closed loop.
A heatmap closes the loop. One glance shows traffic by hour and day, by channel, by segment. The decision — when to staff, when to broadcast, what SLA to set — gets made on Monday morning in two minutes instead of a week. The next week, the heatmap reflects the impact of the decision, so you can tell whether moving the broadcast from 10am to 8pm actually changed read rate. This is operations grounded in evidence, at the cadence operations actually moves.
SabNode Activity Heatmaps render every operational signal as an hour × day grid. The default view is inbound message volume by hour-of-day and day-of-week, across all channels, for the last 30 days. From there you slice — by channel (WhatsApp, Instagram, email, web chat), by segment (paid vs free, new vs returning, by country), by team or agent, by label, by language — and the grid updates in milliseconds. Every cell is interactive: click to see the underlying conversations, hover for the exact volume, right-click to drill into the same hour on the previous month for comparison.
Beyond inbound volume, the same heatmap surface answers the operational questions that volume alone cannot. Response time heatmaps show when your agents are slowest, by hour and day, so the SLA conversation is grounded in real numbers. Resolution-time heatmaps show when chats take the longest to close — usually correlated with shift handovers and weekend coverage. Broadcast-window heatmaps show when read rates peak by segment, so the launch time conversation is settled in seconds.
Comparison is built in. Every heatmap can be overlaid with a baseline period — last week vs the week before, this month vs the same month last year, festive period vs non-festive — and the delta is rendered as a divergent colour scale. A spike in inbound on Thursday evenings that did not exist last quarter is visible immediately, and the drill-down shows you which segment caused it. Operations becomes a closed loop: notice the shift, intervene, see the impact on the next heatmap update.
Heatmaps drive other systems. The broadcast scheduler can suggest a launch window based on the read-rate heatmap for the target segment. The shared inbox can recommend a staffing pattern based on the volume heatmap for each team. The SLA configuration UI can surface "your current SLA of 4 hours is breached 22 percent of the time at 8pm on weekdays" — turning a static target into a living number. The heatmap is not a chart; it is operational intelligence.
The canonical view — 24 hours by 7 days, coloured by volume or any metric you choose. Every cell is interactive: hover for exact values, click to see underlying conversations, right-click to compare with a baseline period.
Slice the heatmap by channel (WhatsApp, Instagram, email, web chat), segment (paid vs free, by country, by language), team, agent, label or campaign. Every dimension updates in milliseconds without page reloads.
Beyond inbound volume, render response time and resolution time as heatmaps. Surface when agents are slowest by hour and day, which usually correlates with handovers and weekend coverage gaps.
Read-rate heatmaps power broadcast-window suggestions. When you launch a marketing template to a segment, the scheduler suggests a window based on when that segment historically reads messages within the first hour.
Overlay any heatmap with a baseline — last week, last month, same period last year. Divergent colour scale renders the delta directly so spikes and dips are visible without doing the maths in your head.
Heatmaps feed the SLA configuration UI. Setting a 4-hour SLA shows you which hour-day cells would breach historically, so the SLA conversation is grounded in real coverage rather than aspiration.
Heatmaps respect the recipient's timezone when relevant (read-rate heatmaps) and the operator's timezone otherwise (agent staffing heatmaps). No more debates about whether 8pm IST equals 2:30pm UTC; the platform does it correctly.
An e-commerce support lead reads the volume heatmap and discovers a sustained spike at 9–11pm IST that was not on the roster. They add an evening-shift agent the next week and the queue-wait heatmap for that window drops from 12 minutes median to under 3.
A D2C beauty brand launches Marketing templates based on read-rate heatmaps per segment — 7pm IST for "metro women 25–34", 8am IST for "tier-2 men 35–45". Average read rate within the first hour improves 18 percent versus a one-size launch time.
A B2B SaaS resets its support SLA after the heatmap shows 4-hour breaches concentrated at 7am UTC. They split the SLA — 1 hour during business hours, 8 hours overnight — and breach rate falls below 3 percent across the week.
A retail chain compares Diwali week volume heatmap to the same week the previous year. The 200 percent surge informs how many seasonal agents to onboard, when to onboard them and when to ramp down. The post-Diwali debrief uses the same heatmap to evaluate.
A logistics operator running in India, UAE and Singapore reads heatmaps in each region's timezone. Staffing patterns and broadcast schedules differ per region but each is grounded in the same heatmap discipline, surfaced in a single workspace.
Activity Heatmaps is included on every SabNode workspace. No separate billing, no extra setup — flip it on from your workspace settings.
Pick a metric — inbound volume, response time, resolution time, read rate. The default 30-day hour-by-day grid renders immediately.
Filter by channel, segment, team, agent, label, campaign or language. Every dimension updates in milliseconds without page reloads.
Overlay last week, last month or same period last year. Divergent colour scale renders the delta so spikes and dips are visible at a glance.
Click a cell to see the underlying conversations, messages and contacts. Jump straight to the inbox or contact view for action.
Use the heatmap to set a roster, pick a broadcast launch window, define an SLA or schedule a campaign. The next heatmap update closes the loop.
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