Push analytics

Why push analytics is becoming a category

For two decades, "analytics" meant "dashboards." You built one, you sent the URL, and you assumed people would open it. The numbers were right. The behaviour change rarely followed.

Three things changed in the last few years. First, work moved into Slack and Teams — people stopped switching tabs to check anything. Second, generative AI made it cheap to write the one-paragraph "here's what happened and why" that used to be an analyst's manual job. Third, integration coverage expanded enough that the data sources you need are reachable from a single product instead of a warehouse project.

The result: the cycle of pulling numbers, pasting into Notion, summarizing in Slack — the most expensive ritual in most companies — can be automated end to end. Push analytics is the name for that shape.

Push vs. pull, in concrete terms

Both approaches have a role. Push analytics replaces the routine reporting layer. Dashboards remain the right surface for unstructured exploration.

Five principles of push analytics

1. Principle 1

   Right metric, not all metrics

   A dashboard shows everything. A push shows the one number that should change behaviour today. The harder discipline is the editing.

2. Principle 2

   Right person, not the channel-of-everyone

   Aim at the human who will act, not the team-wide channel where the message will get scrolled past. If the message goes to 200 people, no one is responsible for it.

3. Principle 3

   Right cadence, not real-time

   Most metrics do not need to be real-time. Pick the slowest cadence that still lets the recipient act in time. Hourly is rare, daily is common, weekly is underused.

4. Principle 4

   Right place, not the place that exists

   Where does the recipient already look? Slack DM, a specific channel, email, Teams, a phone notification? Send it there. Forcing them to open a new tool is the failure mode of every dashboard product.

5. Principle 5

   Right context, not just the number

   A number on its own is not analysis. A useful push includes the delta vs the relevant prior, a one-sentence "why", and (if known) the action implied. AI commentary is the unlock here — it removes the burden of writing the same paragraph every Monday.

Anti-patterns

Push analytics fails in predictable ways. If you are seeing any of these in your team, the fix is structural, not cosmetic.

• Dashboard-in-Slack

  Sending a screenshot of a dashboard to Slack is not push analytics — it is push *delivery* of pull *content*. The reader still has to scan the whole image. Send the one number that matters with an explanation.

• Alert fatigue

  Sending every change notification trains the team to mute the channel. Push analytics is selective by definition. If your alerts fire constantly, the thresholds are wrong.

• Same message to everyone

  A finance daily and a product daily should be different messages with different recipients. One-size-fits-all push devolves into pull (people scroll up looking for "their" line).

• No actionability

  If the recipient cannot do anything with the message, the message should not exist. Test: is there a decision or follow-up implied? If not, kill the pulse.

How to start

Pick one ritual you already do every week — the Monday revenue update, the daily AR call, the client weekly — and replace the manual half of it with a scheduled pulse. Then add the next one. Push analytics rewards focus; the temptation to wire up everything at once is the most common reason teams give up.