Sistava

Funnel and Conversion Analysis

AI Growth and Strategy

Find the step where people actually leave

Everyone has analytics and almost nobody has an answer to where the funnel leaks, because getting one means assembling several tools into a picture nobody has time to build.,Your employee builds it: visitor to signup to activation to paid, with the drop at each step and which of them is worth your attention.,The output names the step, the size of the leak, and what would plausibly fix it, rather than handing you a chart to interpret.

Benefits

How It Works

  1. Step 1:
  2. Step 2:
  3. Step 3:
  4. Step 4:
  5. Step 5:

At a Glance

One view
Across all your tools
Biggest
Leak named, not all of them
Segmented
Where the average lies
Scheduled
Regressions surface early

The Missing Activation Step

Most funnels are drawn as visitor, signup, paid, and that middle gap is where the actual answer lives. Between signing up and paying, a user either reached the moment where the product obviously works for them or they did not, and everything about whether they convert follows from it. Teams that do not define and measure that moment cannot explain their conversion rate and cannot improve it deliberately, because the only levers they can see are the landing page and the pricing, neither of which is usually the problem.

Every Funnel Leaks Everywhere

Any funnel examined closely shows loss at every step, which makes a list of leaks useless as a prioritisation tool. The step worth fixing is the one where the improvement multiplies through the most volume, and that combines drop rate with how many people reach it. A ninety percent drop at a step forty users reach matters less than a twenty percent drop at a step four thousand reach, and reports that rank purely by drop percentage send teams to work on the wrong thing with complete confidence.

Averages Hide Two Different Funnels

A mediocre overall conversion rate frequently turns out to be one healthy funnel and one badly broken one averaged together: one traffic source converting well and another converting near zero, or one segment activating easily while another never finds the value. The aggregate number describes neither and suggests broad, expensive changes to a product that works fine for half its users. Segmenting before concluding is what turns a vague conversion problem into a specific, fixable one.

FAQ

What tools does it need?

Whatever you have connected: web analytics, product events, and billing. More sources give a sharper picture, and it will state which steps it cannot see rather than estimating across a gap.

What if we do not track activation?

Then that is the first finding, and usually the most valuable one. Signup to paid with nothing in between is the most common reason a team cannot explain its conversion rate, because the step where users decide whether this works for them is invisible.

Can it tell me why people drop?

It can tell you where, how much, and which segments differ, and it can correlate a drop with what else changed. Why is a hypothesis, and it will be labelled as one. Session recordings and talking to churned users answer why; this tells you where to point them.

How often should this run?

Weekly for most, which is frequent enough to catch a release regression while the cause is still findable. Daily mostly reports noise on small volumes.