AI-powered customer, sales and inventory analytics

Optimise your retail operation, increase sales and improve customer experiences with big data analytics and real-time interactive dashboards. Create hyper-personalised customer engagements by using machine learning and AI to predict, forecast 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. Powered by Google Data Studio and Google Big Query, PredictRetail enables scalable analysis over petabytes of data.

Learn about Data Studio and Big Query

Customer business intelligence

Gain a deeper understanding of your customers and data with purpose-built dashboards and reports. Our flexible data templates support various customer-related properties, events and transactions.

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customer predictive analytics

Create hyper-personalised customer engagement and predict future behaviours using built-in machine learning models. Powered by Google Cloud AI, PredictRetail dynamically learns from your data to uncover trends and suggest optimal interventions.

Learn about Google Cloud AI

Customer
segmentation

Personalised
recommendations

Lifetime value
analysis

Propensity to
purchase

Inventory business-intelligence

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

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inventory predictive analytics

Reduce reliance on spreadsheets and gut-feel by using machine learning and AI to forecast, predict and segment more accurately. Powered by Google Cloud AI, PredictRetail dynamically learns from your data to uncover trends and patterns.

Learn about Google Cloud AI

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 Query, Google Dataflow, Google AI, and Kubeflow to handle any data transformation or machine learning workload with ease.

Frequently asked questions

What do I need to get started?

You need historical data, preferably lots of data. Predictive modelling is most accurate when there is sufficient data to establish strong trends and relationships.

What data do I need?

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.

Can you clean-up and merge my data?

Yes, our data engineers can clean and transform the data before importing it, if you do not have the capacity in-house.

What if I only need BI and not predictive analytics?

Machine learning and predictive models are optional and will be trained and activated when required.

How long does a typical implementation take?

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.

How secure is my data?

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.

What will it cost me to run?

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.

What else do I need to know from past implementations?

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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