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AI-powered inventory and sales analytics

Gain a deeper understanding of your inventory, sales, and promotions using real-time interactive dashboards and big data analytics. Reduce working capital and improve inventory distribution by leveraging machine learning and AI to forecast, predict and segment more accurately than ever before.

Interactive dashboards and reports

Turn your data into fully customisable reports and dashboards that are easy to read and share.

Flexible interactive controls enable your team to filter, export and share data at a click of a button.

Descriptive analytics empower your team to learn from past events and understand how they may influence the future.

Powered by Google Data Studio and Google Big Query, PredictInventory enables scalable analysis over petabytes of data

Learn more about Data Studio and Big Query

Interactive Dashboards And Reports
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Inventory business-intelligence

Improve inventory levels and distribution with purpose-built dashboard and reports, designed to help your team make effective data-driven decisions

PredictRetail utilises descriptive analytics to present historical data in a way that can easily be summarised and understood.

Flexible interactive controls empower your team to filter, drill-down, and export data by date, product, region, category, price, promotion, discount and many other properties.

PredictInventory empowers your team to move away from spreadsheets and manual processes with near real-time sales and merchandise business intelligence.

inventory predictive analytics & machine learning

Reduce reliance on spreadsheets and gut-feel by leveraging AI and machine learning capabilities to forecast, predict and segment more accurately than ever before.

Predictive analytics enables you to reduce working capital and increase sales by ensuring you always have the right product, at the right time, in the right location. 

Powered by Google Cloud AI and machine learning, PredictInventory dynamically learns from your data, uncovering relationships and trends to convert raw data into valuable insight.

Learn more about Google Cloud AI

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Sales and demand forecasting

Market basket
analysis

Product life-cycle analysis

SKU
supersession

While there is no crystal ball to see into the future, predictive analytics provide the means to make an educated guess

Data transformation and processing

ATG is a Google Cloud Build partner. We utilise a microservices architecture and best-of-breed Google Cloud Analytics tools including Google Big QueryGoogle DataflowGoogle AI, and Kubeflow to handle any data transformation or machine learning workload with ease.

Frequently asked questions

You need historical data, preferably lots of data. Predictive modelling is most accurate when there is sufficient data to establish strong trends and relationships.
Depending on the product and model, we need data points about customers, sales, inventory, promotions, stores, etc. We will provide an exact template when we understand the available data.
Yes, our data engineers can clean and transform the data before importing it, if you do not have the capacity in-house.
Machine learning and predictive models are optional and will be trained and activated when required.
If the data is clean and formatted correctly, then one to two weeks. If data requires ETL and cleanup, then it depends on data accessibility, quality, volume, variety, and velocity. A general ballpark estimate is three to six weeks.
We take the privacy and security of your data very seriously. We typically do not require sensitive data for analytics and ML, and we anonymise the data beforehand. Googles strict compliance and security policies apply to all data deployed into our cloud products.
It depends on what products and models you select; the volume of data; and the processing power required every month. Rest assured the pricing model is flexible and you only pay for what you use.
  • Access to data often takes longer than expected, and the quality of data can cause delays if extensive cleaning, merging, and transformation are needed. It’s essential to appoint a single business champion to ensure the free flow of data and business-specific information and nuances about the data.
  • Post-implementation the focus is on change management and ensuring that processes are in place to use the insights and create value. For example, if an ML model predicts what a customer is likely to purchase next, and you don’t offer him the product to convert, then the insight has no value.
  • Ensure that your team have a clear understanding of the business goal you are trying to achieve. How do you measure it currently? What is the “needle” you want to move, and by how much? When we can measure and benchmark uplift and ROI against past performance, then it becomes easy to build support and create value throughout the organisation.

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