How ValueCo Centralized Thousands of Temu SKUs and Cut Spreadsheet Handling by 98% with Revenuealot

About ValueCo

Founded in 2018, ValueCo has grown into a South African lifestyle and value retailer with more than 20 stores across several provinces. Its broad product mix spans more than ten departments, bringing variety and accessible pricing together for customers in a single shopping environment.

The technical decision behind a growing Temu operation

As ValueCo’s Temu catalogue grew to thousands of SKUs, Kenny and the team faced a scaling problem that was less about individual files and more about the operating model around them. Products, orders and returns all needed to be prepared, reconciled and shared between technical and commercial colleagues.

The team could continue adding spreadsheets, formulas and one-off scripts, but every new layer would create another process to maintain. Adding administrative effort would make the weekly cycle more expensive without giving everyone a common operational picture.

ValueCo chose Revenuealot to create a structured data-operations layer: centralize the recurring datasets, make the preparation steps repeatable and reserve human attention for exceptions and decisions rather than file handling.

The challenge: operational data trapped in recurring exports

At a scale of thousands of SKUs, the recurring work around products, orders and returns became as important as the individual records themselves. The team needed to search and filter the product set, prepare order data for operational follow-up and connect return activity with the right item context.

When each review starts with another export, the same hours are spent downloading, cleaning, joining and formatting files. Different spreadsheet versions also make it harder for commercial and technical team members to work from the same view. The process could not keep growing at the same rate as the catalogue.

ValueCo wanted to reduce that handling without replacing the seller decisions and marketplace processes that still require human control.

How Revenuealot helped

1. Centralize the Temu product view

Revenuealot brought Temu product records into a searchable workspace. The team could review titles, identifiers, status, price, stock and related operational data with shared filters instead of recreating a master product file for every working session.

2. Make order preparation repeatable

Order data followed a consistent structure inside the workflow. Rather than rebuilding column mappings and formulas for each export, the team could start its daily review from an already organized data set. This shortened the gap between receiving marketplace data and acting on it.

3. Connect returns to exception handling

Returns and other exceptions were reviewed with their related product and order context. The team could prioritize the records requiring follow-up instead of scanning every row. Revenuealot supported the data-operations layer; listing creation and publication on Temu remained outside the scope of this workflow.

One view for product, order and returns operations

The workflow centralizes operational data and moves the team’s attention toward exceptions.

The Temu Products workspace reflects the same operating model: searchable records, useful filters and the fields needed for daily review. The visual below uses sanitized demonstration data and does not expose ValueCo account details.

A shared product view replaces repeated spreadsheet preparation while keeping status, price, stock, sales and returns context together.

The outcome: spreadsheet handling reduced by 98%

For a typical operating week, the combined product, order and returns workflow moved from around 21 hours of spreadsheet preparation and review to less than one hour of active handling in Revenuealot. The three recurring tasks now take about 20 minutes in total, returning more than 20 hours to the team.

ValueCo reduced spreadsheet handling by approximately 98% while preserving the reviews that matter. Technical and operations colleagues gained one shared picture, faster access to exceptions and a repeatable process that can support a larger SKU base without increasing administrative work at the same rate.

Instead of using technical capacity to rebuild the same datasets, the team can spend it on the decisions and improvements that move the operation forward.