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AI-powered customer and sales analytics

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Gain a deeper understanding of your customer behaviours with real-time interactive dashboards and big data analytics. PredictCustomer empowers you to create hyper-personalised customer engagements by leveraging AI and machine learning capabilities that pro-actively predict shifts in customer behaviours and sales trends.

Interactive dashboards and reports

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

Descriptive analytics empower your team to learn from historical data and understand how it may influence the future.

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

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

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Customer Business Intelligence

Customer business intelligence

Gain a deeper understanding of your customer behaviours and sales with purpose-built dashboards and reports.

Build an effective, data-driven marketing strategy by leveraging customer-focused data analytics and business intelligence. 

Our flexible data templates support a rich dataset of customer and sales related properties, events and transactions that are easy to extend and customise. 

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predictive analytics & machine learning

Create hyper-personalised customer engagement by leveraging AI and machine learning capabilities that pro-actively predict shifts in customer behaviours.

Predictive analytics empower your team to anticipate what is most likely to happen, giving you an opportunity to act on the insight and improve your customer and product strategies.

Powered by Google Cloud AI and machine learning, PredictCustomer dynamically learns from your data to uncover trends and suggest optimal interventions.

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Customer
segmentation

Personalised
recommendations

Lifetime value
analysis

Propensity to
purchase

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

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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

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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