Nukam

Everything your analystwould do by 9am

Ten screens, and the parts of each that do the work — the exact cards a performance team reads every day. On a sample account: the numbers are generated, the software is exactly what you get.

Overview

The whole account on one screen.

The screen a founder or growth lead opens when they want the answer to "are we fine?" without opening Ads Manager. Spend, revenue, blended ROAS and where the month is heading, in the time it takes to read four numbers.

What it fixes

  • Nobody can tell you today's ROAS without building a report first
  • Month-end overspend arrives as a surprise instead of a warning
  • Trends live in a spreadsheet somebody has to remember to update
  • Spend, revenue, blended ROAS, active campaigns
  • Budget pacing against your monthly target
  • Daily spend and ROAS trend, against your Cold benchmark
  • Top campaigns, with funnel stage and status

Month-to-date spend against your monthly target, with the expected-pace marker — so an overspend is a warning in week one, not a surprise at month end.

Daily spend beside daily ROAS, plotted against your own Cold benchmark rather than an industry average.

Today's flags — CTR fatigue, frequency, pacing — each naming the campaign and the threshold it crossed.

Daily Briefs

A dated briefing note, not another dashboard.

What a performance manager reads at 9am instead of rebuilding yesterday in Ads Manager. One day, measured against a named baseline, attributed to a named campaign — and written in sentences, so it can be forwarded as-is.

What it fixes

  • The first two hours of every day go into reassembling yesterday
  • "ROAS is down" with no answer to which campaign, or by how much against what
  • A campaign quietly stops spending and nobody notices for three days
  • Updates to the founder get retyped by hand every morning
  • A plain-English headline naming the campaigns that moved
  • Eight metrics, each with its delta and the baseline it is measured against
  • Why ROAS moved, split into order value, conversion rate, CTR and CPM
  • Delivery health, what changed, where the money went, best and worst ads

ROAS is order value × click-to-purchase × CTR ÷ CPM, so those four always account for the whole move — nothing is left unexplained.

Did anything quietly stop spending, measured against that campaign's own usual daily rate — the one question a list of top movers cannot answer.

Each flag measured against that campaign's own history, so a normally volatile campaign does not cry wolf every morning.

Alerts

What to do, ranked, with the number that triggered it.

The list a media buyer works through on a Monday. Each card names one entity, quotes the figure and the threshold behind it, and ends in a link to that exact object in Ads Manager.

What it fixes

  • Money burning on zero-conversion ads nobody has audited this week
  • Ad sets running past their own budget for days without anyone noticing
  • Winners nobody scaled because no one had time to find them
  • Recommendations you cannot check, from tools that will not show their working
  • Findings tiered red, amber and hygiene
  • Pause, alert, scale, fold, flag and restructure actions
  • The comparison behind every card — spend, budget, days, benchmark
  • A direct Ads Manager link on each finding

Red: money to stop burning today. Each card names the object, quotes the spend and the threshold, and links straight to it in Ads Manager.

Amber: budget moves, scored against that stage's own ROAS benchmark — a decrease is marked for your sign-off rather than assumed.

Performance

Every ad sorted into what to do with it.

Creative triage across the whole account. Each ad falls into Top, Scale, Monitor, Refresh or Remove on rules you write — so a weekly cull takes minutes instead of an afternoon of sorting a CSV.

What it fixes

  • Six thousand ads and no defensible way to choose which to pause
  • Every analyst applying their own private definition of "working"
  • Good creative left unscaled because it never surfaced
  • Rules living in someone's head instead of in the tool
  • Five buckets, each with its ad count and spend
  • The rule behind each bucket, editable by you
  • ROAS, spend, CTR, frequency and cost per purchase per ad
  • Filters by category, funnel stage and ad type, and a CSV export

Every ad in the account triaged into five buckets on rules you write — with the spend sitting in each one.

The Top bucket, open: the ads clearing your rule, with the ROAS, CTR and spend that put each one there.

And the Remove bucket — the weekly cull, already made, with the numbers to defend each one.

Category filters

Your catalogue, not Meta's campaign list.

Where you answer "how is sunscreen doing?" — a question Meta cannot answer, because Meta has campaigns and you have products. Drill from a category to a product to a single creative, at campaign, ad set or ad level, over any date range.

What it fixes

  • Category performance means exporting and pivoting by hand
  • Ads have to be renamed in Ads Manager before they can be grouped
  • One ₹40 ad with a fluke purchase drags the account average around
  • Nobody can explain why a given ad was counted under a given product
  • Category, product, ad type, funnel and format filters together
  • Campaign, ad set and ad levels on one screen
  • Spend-weighted ROAS, CTR and frequency, with low-spend rows excluded and counted
  • A per-row explanation of why each ad landed where it did, and a CSV export

Spend-weighted ROAS, CTR and frequency for whatever you filtered to — with low-spend outliers excluded and counted, so one ₹40 ad cannot move the account average.

Every ad in the filtered set carrying its format, parent ad set, funnel stage and ad type — the catalogue view Meta cannot give you.

Reach

How many real people, not how many impressions.

The number to quote when someone asks how many people you actually reached last quarter. Every figure is Meta's own deduplicated reach for that exact window — never added up across campaigns, days or months.

What it fixes

  • Reach numbers inflated by summing figures that were never additive
  • Age and gender mix that nobody tracks month to month
  • No defensible reach figure for a board deck or a brand review
  • An exact deduplicated total for the precise range you picked
  • Absolute reach per month, each bar its own independent figure
  • Gender and age mix over time
  • A reach by age and gender matrix

Age mix month by month, each bar a share of that month's own deduplicated reach — so a shift in who you are reaching is visible instead of buried in a total.

The full age × gender matrix and the gender split — the figures you quote in a brand review, and the ones a media plan is built from.

Spend

Which states pay you back.

Regional read for anyone planning where the next rupee goes — spend, purchases and ROAS per state, ranked, with each region graded against your own thresholds.

What it fixes

  • Regional performance sitting in an export nobody refreshes
  • Budget spread evenly across states that convert nothing alike
  • No view of where spend concentration and returns disagree
  • Every region ranked by spend, with share of total
  • Purchases and ROAS per region, colour-graded
  • A month range picker, stated as month-precision rather than implied as daily

Every state ranked by spend, with its share of the total, its purchases and a ROAS graded against your own thresholds.

Audience segments

Cold, remarketing and post-purchase, side by side.

The funnel review. Where your money sits versus where you want it, how each stage performs against its own benchmark, and exactly which step between impression and purchase is leaking.

What it fixes

  • Cold, remarketing and post-purchase judged against one blended ROAS target
  • Budget drifting out of balance with nothing on screen saying by how much
  • "The funnel is broken" with no answer to which step
  • Benchmarks that belong to the industry rather than to your account
  • Spend, ROAS, CTR, frequency, purchases and cost per purchase per stage
  • Actual budget share against your own ideal band
  • Where each stage leaks between impression, click, cart, checkout and purchase
  • A ranked findings list, each quoting its number and its threshold

Cold, remarketing and post-purchase side by side — each with its own ROAS target, frequency cap and ideal budget share, not one blended number for all three.

Where the money actually sits against the split you said you wanted, with the gap stated in rupees and percentage points.

Ranked findings, and no AI anywhere in them. Each one names the stage, quotes the number and states the threshold it failed.

Compare

Two periods, each with its own filters.

For the review where "ROAS fell" is not a good enough answer. Pick any two windows — each with its own level and its own filters — and it separates segments that genuinely got worse from spend that simply moved between them.

What it fixes

  • Month-on-month comparisons rebuilt by hand every time
  • Blended ROAS falling while every individual segment improved
  • No way to compare unlike things — cold in June against remarketing in July
  • "Is 5x good?" answered by instinct rather than by your own targets
  • Two independently filtered periods, each at its own level
  • Grouped metric deltas for revenue, cost and delivery
  • Why ROAS moved, and mix effect versus real performance
  • Winners and losers by name, and grades against your benchmarks

Two periods, each with its own level and its own filters — cold traffic in June against remarketing in July, if that is the question.

The one that catches people out: blended ROAS can fall while every segment improves, purely because spend moved between them. This splits the two apart.

The biggest revenue gains and losses by name, each split into how much was volume and how much was efficiency.

Detailed Report

The monthly review, already written.

What a growth lead assembles before a monthly review — did the extra money work, where the month lands, what revenue depends on, which categories are moving, and how formats compare. One date range, one copy button.

What it fixes

  • A monthly deck rebuilt from scratch every month
  • Extra budget approved without ever checking what the marginal rupee returned
  • Revenue concentrated in three campaigns and nobody has said so out loud
  • Video-versus-static argued from opinion rather than spend and revenue share
  • Marginal ROAS on the extra spend, against your own target
  • Where the month lands at the current run rate
  • Revenue concentration and category momentum, at ad level
  • Format breakdown, spend share versus revenue share, and format by funnel stage

What the extra spend actually returned — marginal ROAS on the increase, not the blended average that hides it.

How much of your revenue rests on how few campaigns — computed at ad level, where the product name actually lives.

Video against static against carousel on spend share versus revenue share — a format taking more than it returns has nowhere to hide.

See it on your own account

Thirty minutes on your real Meta data — your categories, your funnel, your thresholds.

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