Automate data management
Collecting, storing and managing retail customer data can be daunting, time-consuming and costly; that's why many organisations only attempt it weekly or monthly. Daily analysis of daily data vastly increases insight, understanding and your value to your retail customers.

How can I automate customer data collection, preparation and use?
Data-driven grocery relationships are fuelled by information; data collected, processed and organised ready for reporting, analysis and insight that enables every supplier and retailer to explore trading performance together and agree on actions to address issues and advance opportunities across the Flow-of-Goods.
However, the reality of collecting, processing and organising that data is often fraught with challenges; delays, costs and errors. For many grocery suppliers, customer data systems leave a lot to be desired - whilst they contain fantastic data they also present many frustrations:
- Daily reporting requires considerable time, effort and dedication - if you want a complete record of temporary data such as daily stock positions (for stock cover calculations) you need to collect that data every day; so someone needs to run those reports at weekends, over holidays etc.
- System outages make the data collection task even harder - whether it's a predictable Monday slowdown as a customer system creaks under the weight of supplier access, or a sporadic system outage several days over a week, the challenge is to dedicate time when the system is available
- Report data, once collected, needs to be stored somewhere - for many grocery suppliers this means saving Excel or CSV files onto a shared network drive; without careful planning and management, different teams use different areas and even download and store multiple copies of the same, or slightly different, data
- Data changes! Grocers report adjustments to sales and stock positions 2, 3 or even 4 days after they are first reported - whilst these are typically fairly small in aggregate, they can be material at the day/store/item level and explain discrepancies between weekly and daily report values
- Lucky Lakers - their IT colleagues have built them a Data Lake, at considerable effort and expense, and they can now drop reports there whenever they like; some even have automated agents which collect the data for them... but retrieving the right file at the right time for the right analysis can feel like a lucky dip
- Hassled Hoarders - in their pursuit of all data multiple teams collect the same data from the same systems and store it in different pockets of their corporate network - local folders, shared folders, back-ups on USB drives... all bases are covered by their hoarding but guaranteeing security, certainty and consistency proves impossible
- Weekly Wonders - these laid-back folk solve the problem by only attempting it once a week; their weekly summary enables them to understand useful long-term trends but they can't address the here and now - issues such as supply shortages, depot stock cover or day-one promotion insight... weekly is informative but rarely actionable
Thankfully, there is a better way. A way to automate data collection, manage a single copy of data, guarantee accuracy and completeness AND access data from your favourite reporting and analysis tools.
SKUtrak includes a fully managed data collection robot that retrieves report data from your grocery customer systems every day. What's more, our robot collects several days of recent history at a time so that all data changes are captured. If a retailer system is unavailable for a period, SKUtrak waits patiently - checking once or twice an hour - before recommending data collection as soon as the system is available again... even if it's in the middle of the night.
SKUtrak stores all of the collected data in its Demand Signal Repository (DSR) - a single, secured, accessible store for all your grocery customer data. What's more, the DSR automatically translates retailer format data into a single common format so that you can access, query, report and analyse any customer data set in a consistent manner - regardless of the source reporting system.
SKUtrak provides a suite of rich, visual reporting and analysis tools for you to explore your customer data and the trading performance that it represents, quickly and easily. The same DSR data is also available for you to query directly into your business systems, including:
- Reporting, analysis and BI tools such as Microsoft Excel, PowerBI, Looker, Qlik and Tableau
- Trade Promotion Management tools like UpClear BluePlanner
- ERP systems like SAP, Microsoft Dynamics and Oracle NetSuite
- On-premise and cloud databases like Microsoft SQL Server, Oracle, Amazon Redshift and Snowflake
SKUtrak provides the most efficient, reliable, complete, detailed and secure means of collecting, managing and using your customer trading data to manage the Flow-of-Goods from your organisation, through your grocery customers and into the hands of your shoppers.
On a Monday morning, I am able to know immediately how trading has been over the weekend. I’m not having to wait for someone to extract the data, interpret it and send it to me. The data is also in the same format across all retailers, which is so valuable helping me to compare apples with apples. For example, every retailer's weeks are different, SKUtrak enables me to select calendar months as opposed to retailer weeks. Therefore, I know I can do a straight comparison.
In this short video, Samantha John, Demand Planning Manager from Hain Daniels explains how SKUtrak has helped her:
- Condensed the time it takes to crunch data
- Present data to internal and external stakeholders
Do you pay your people to crunch data, or make decisions?
BLOG
Good businesses want their people to make complex decisions with confidence.
See what our customers say about SKUtrak
VIDEO
Our customers share their experiences of how SKUtrak has helped them.
Get Started
FIND THE RIGHT SOLUTION FOR YOU AND YOUR BUSINESS

- Arrange a product demonstration
- Discuss plans & pricing
- Want to know more?