Drop-off-point fix

Drop-off-point fix is an implementation project: we take a specific problem step (from funnel analysis, session recordings, usability — if there is no diagnosis, it is needed first), analyze the cause of the drop-off and remove the barrier: fix what is broken, simplify the step, clarify wording, remove extra friction, fix errors and dead ends. Honestly about the prerequisite, this is key: to fix a drop-off point you need to know WHERE and ideally WHY it is — without diagnosis it would be a 'random fix'; if there is no data, funnel analysis/recordings come first (separate). Honestly about the result: we remove a specific barrier, and that is a reasoned improvement hypothesis, but NOT a conversion-growth guarantee — a drop-off at a step may be caused by more than friction (price, readiness to buy, traffic source), and the fix's effect should be confirmed with a test (A/B) if traffic allows. Honestly about the essence: this is fixing a barrier, NOT comprehensive CRO and NOT a promise of '+X%' — we make a step better, while measurable growth depends on many factors. Honestly about data: fixes and priorities rely on correct analytics (garbage in, garbage out). An important boundary: this is a targeted drop-off-point fix, not funnel analysis (diagnosis — separate/prerequisite), not full form/checkout optimization (separate services) and not redesign. If there are many problem spots, it is a series of tasks, not one. Picture this: instead of 'we lose half at the payment step but change nothing' you remove the specific barrier at that step. The base price starts from 18,000 ₽ per point; it depends on the step's complexity and cause.

• You know you lose people at a step, but the barrier is not removed.
• Broken or confusing spots on the way to the goal remain.
• You fix at random, without understanding the drop-off cause.
• There is no one to take and rework a specific problem spot.

• Analyzing a specific problem step (by data/research)
• Determining the drop-off cause (or noting that diagnosis is needed)
• Removing the barrier: fix, simplify, clarify
• Fixing errors, dead ends and extra friction
• A recommendation to validate the effect with a test (where traffic allows)
• Checking correctness after the fix
• Handover and a short change description
• Reviewing with you

• A specific barrier at the problem step is removed
• The step is simpler and clearer
• An improvement hypothesis ready for validation (confirmation — by a test)
• Less obvious friction on the way to the goal

• We take the problem step from data (or recommend diagnosis first)
• We determine the cause and remove the barrier
• We check correctness, recommend a test to confirm the effect

• Does a drop-off-point fix guarantee a conversion increase?

  No. We remove an identified barrier — a reasoned improvement hypothesis, but a drop-off may be caused by more than friction (price, readiness, traffic). The effect should be confirmed with a test (A/B) if traffic is enough; we do not promise '+X%'.

• What if it is unclear exactly where we lose?

  Then diagnosis is needed first — funnel analysis, session recordings or usability (separate services). Fixing without understanding the cause is guesswork; we honestly suggest finding the problem first, then fixing it.

• Is this full optimization?

  No, it is a targeted fix of a specific point. Full form, checkout or comprehensive CRO optimization are separate, larger services. If there are several problem spots, it is a series of tasks.