Retail and consumer goodsBI, AI and ML for the business
A broken checkout, seen at once
When the online store's checkout jams, visits carry on and orders vanish, and it comes out the next day. In Muvia sessions, conversion and errors are read every 15 minutes: a condition finds the incidents and an alarm takes them to whoever picks them up.
Illustrative scenario: it describes a typical case, not a customer project.
Visits climb, orders collapse, and nobody notices.
One evening a TV ad goes out, visits to the online store soar, and right then payment starts throwing errors. The site looks packed, but orders do not come in. The next day's report shows an odd day; the month-end one, a dip nobody can explain any more.
Sessions, conversion and errors live in different tools, and on their own they say nothing: it is the combination, low conversion with repeated errors, that tells an incident from a quiet evening.
- Who it's for
- E-commerce and digital managers, the site's technical team, marketing teams that launch campaigns.
In Muvia, step by step
Real product screens, recorded on a project with sample data.
The video · When the online store's checkout jams, visits carry on and orders vanish, and it comes out the next day. In Muvia sessions, conversion and errors are read every 15 minutes: a condition finds the incidents and an alarm takes them to whoever picks them up.
What you get
- Incidents found, not guessed
- The condition tells an evening with a broken checkout from a quiet one, without eyeballing charts.
- Noticed during, not at month end
- The alarm opens the episode on the latest data and takes it to whoever has to act.
- Every incident has its story
- The register keeps the classification, the lost orders, the note and the action of whoever picked it up.
- Marketing and engineers on one page
- Campaigns, visits and errors sit in the same analysis: you see at once whether the problem came with the ad.
For the technical team
How it is built in Muvia
- 1
Bring the store's KPIs into Muvia
Sessions, orders and checkout errors become sources, each with its own table; a query puts them on the same 15-minute grid, along with the conversion rate.
- 2
Read them as signals in Analysis
Sessions, conversion, basket, returns and errors stacked over time, with campaigns marked by notes: a spike in visits and a collapse in orders at the same moment jump out.
- 3
Write the incident condition
Conversion below a threshold with errors repeated for at least 30 minutes: the condition marks every occurrence on the chart and lists them, with start and duration.
- 4
Read the evenings in the heatmaps
A day-by-hour map of sessions and one of errors: you see when the ad lights up the evenings and on which single evening the checkout gave way.
- 5
Set the alarm and the register
An alarm on conversion and errors opens an episode and notifies the chosen channel. In the register, whoever picks it up classifies it and notes the lost orders and the action taken.
- The data it needs
- Sessions, orders, average basket and returns of the online store per quarter hour, read through a REST API
- Checkout errors by type, per quarter hour, from CSV files
- Campaign and TV ad calendar, from Google Sheets
- Parts of Muvia used
More cases in this line
All use cases- Every store's sales on one pageTill sales, orders and store master data sit in different systems, and the picture of the network arrives after month end. In Muvia revenue, growth, units and customers for every store sit in one dashboard, week by week, from the grand total down to a single department.
- Bought together: market basket analysisReceipts show which products are bought together, but they stay in the till system. In Muvia a Python function computes the support, confidence and lift of every pair each night, and the dashboard says what to do with them: bundled promotions, neighbours on the shelf, suggestions at the till.
- Price elasticity and the price-list proposalA price rise can cost a third of the units or almost nothing, and you find out after the fact. In Muvia a model your team writes estimates every item's elasticity each week and prepares the price-list proposal: what to raise, what to cut and how much margin it brings.
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