> ## Documentation Index
> Fetch the complete documentation index at: https://afrizon-admin.tunzaa.co.tz/llms.txt
> Use this file to discover all available pages before exploring further.
# Analytics and performance reports in Meneja dashboard
> Explore GMV trends, average order value, new vs returning buyer cohorts, and cart abandonment reports to understand your marketplace performance over time.
The analytics section of Meneja gives you time-series reports on the metrics that matter most to your marketplace. Where the main dashboard shows you a snapshot, the analytics reports let you track how each metric is moving over time, compare periods, and identify patterns that a single number cannot reveal. Each report described below corresponds directly to the data your dashboard fetches — nothing here is estimated or approximated.
## GMV performance
The GMV performance chart plots your Gross Merchandise Value over time. You can switch between three time granularities using the **Daily**, **Weekly**, and **Monthly** toggle above the chart.
The daily view gives you the most granular picture of revenue flow. For each day you can see:
* `daily_gmv` — the total GMV generated that day
* `daily_orders` — the number of orders placed
* `daily_aov` — the average order value for that day
* `daily_subtotal`, `daily_tax`, and `daily_shipping` — a breakdown of where revenue came from
* `prev_day_gmv` and `daily_gmv_growth_percent` — the previous day's GMV and the percentage change, so you can immediately see whether you are trending up or down
Use the daily view when you need to diagnose a sudden drop or spike — for example, after a promotion ends or a payment gateway issue occurs.
The weekly view aggregates data into entries anchored to the start of each week. The key fields are:
* `weekly_gmv` — total GMV for the week
* `weekly_orders` — total orders placed that week
* `weekly_aov` — average order value across the week
* `prev_week_gmv` and `weekly_gmv_growth_percent` — the prior week's GMV and the week-over-week growth rate
Use the weekly view to smooth out day-to-day noise and spot consistent upward or downward trends across several weeks.
The monthly view shows the broadest trend, anchored to the first day of each month. Fields include:
* `monthly_gmv` — total GMV for the month
* `monthly_orders` — total orders placed
* `monthly_aov` — average order value for the month
* `prev_month_gmv` and `monthly_gmv_growth_percent` — the prior month's GMV and the month-over-month growth rate
Use the monthly view for reporting cycles, business reviews, or any time you need to present performance over a quarter or longer.
When a growth rate field shows no value, it means there is no prior period to compare against — typically the first data point in your history.
## Average order value
The average order value report tracks how much buyers are spending per order each day. Unlike the single AOV figure on the stat card, this report shows you the full distribution and daily movement.
Each data point represents a single day and includes:
* `daily_aov` — the mean order value for that day
* `median_order_value` — the median, which is less sensitive to outliers caused by a single very large or very small order
* `min_order` and `max_order` — the lowest and highest order values placed that day
* `order_count` — how many orders contributed to these figures
* `prev_day_aov` and `aov_growth_percent` — the prior day's AOV and the percentage change
Comparing `daily_aov` against `median_order_value` helps you tell whether a high or low average is driven by a few extreme orders or reflects what typical buyers actually spend.
A rising AOV can indicate that promotions are working, upsells are landing, or higher-value buyers are returning. A falling AOV alongside growing order counts may mean buyers are splitting purchases into smaller baskets or that lower-price products are gaining share.
Look at the `order_count` alongside the AOV figures. A very high AOV on a day with only a handful of orders may be a statistical artefact rather than a real trend.
## New vs returning buyers
The new vs returning buyers report shows you the daily composition of your buyer base. It answers the question: are you growing through new customer acquisition, repeat purchases, or both?
For each day, the report surfaces:
* `new_buyers` — the count of buyers placing their first-ever order on that day
* `returning_buyers` — the count of buyers who have ordered before
* `total_buyers` — the combined count
* `new_buyer_revenue` and `returning_buyer_revenue` — the revenue generated by each cohort
* `new_buyer_ratio` and `returning_buyer_ratio` — the proportional share of each cohort as a decimal
The chart stacks new and returning buyers as proportional areas, so you can see at a glance whether one cohort is growing relative to the other.
A high `new_buyer_ratio` means most of your orders are coming from first-time customers. This is healthy if you are in a growth phase, but it can be a warning sign if your business relies on repeat purchases — it may mean retention is weak.
A high `returning_buyer_ratio` signals strong loyalty and repeat behaviour. Monitor `returning_buyer_revenue` alongside this: if returning buyers generate a disproportionately high share of revenue, they are your most valuable segment and worth protecting with loyalty incentives.
The revenue fields (`new_buyer_revenue` and `returning_buyer_revenue`) let you calculate the lifetime value contribution of each cohort. If returning buyers consistently generate more revenue per head, that is a signal to invest more in retention programmes.
## Cart abandonment rate
The cart abandonment report shows you how many shopping sessions end without a completed purchase. You can filter the chart to the last **7**, **30**, or **90 days** using the toggle above it.
For each day, the report provides:
* `total_carts` — all cart sessions started that day
* `carts_with_items` — sessions where at least one item was added
* `converted_carts` — sessions that resulted in a completed order
* `abandoned_carts` — sessions where items were added but no order was placed
* `conversion_rate` — the percentage of carts that converted to orders
* `abandonment_rate` — the percentage of carts that were abandoned
* `avg_cart_session_hours` — the average duration of a cart session in hours
A rising abandonment rate is a signal that something in your checkout flow is causing buyers to drop off. Common causes include unexpected shipping costs appearing late in the checkout, friction in the payment step, or a confusing UI.
Use `avg_cart_session_hours` to understand buyer intent. Very short sessions paired with high abandonment suggest buyers are leaving quickly — possibly before they reach checkout. Longer sessions with high abandonment suggest buyers are reaching late stages but not completing, which may point to payment or pricing issues.
Compare `conversion_rate` over consecutive periods after any checkout changes you make to measure the impact of your improvements.
Cart abandonment rates vary significantly by industry and buyer segment. Focus on your own trend over time rather than absolute benchmarks — a consistent downward trend in `abandonment_rate` is a reliable signal that your checkout experience is improving.
## Back to the dashboard overview
Return to the main dashboard to see headline stats, order status, payment health, and top products at a glance.