AI-Powered Pricing, Inventory, Customer and Sales Analytics

Predictive and prescriptive analytics for retail, supply chain, manufacturers and brands
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Increase sales, improve margins, reduce working capital and enhance customer experience with big data analytics and real-time interactive dashboards.

Proactively predict a shift in customer behaviour and sales trends; pre-determine optimal pricing and promotional strategies by leveraging AI and machine learning capabilities.

PredictRetail gives you a competitive edge through predictive analytics, without the hassle.
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Analytics Maturity Curve

PredictRetail-Value-Proposition-Update
* Gartner ascendancy model

PredictRetail and Predictive Analytics

Powered by Google looker Studio, Google Big Query and Google Vertex AI.

PredictRetail enables scalable analysis over petabytes of data that dynamically learns from itself to uncover relationships and trends in order to suggest optimal interventions, i.e. convert raw data into valuable insight.

The variety of pre-built models allows for a staggered AI adoption process based on your business needs, allowing you to have the right solution at the time. These predictive and statistical models will then customise themselves to your environment giving you real-time, actionable insights into corporate performance.
Predictive analytics empowers your team to anticipate what is most likely to happen, giving you an opportunity to:
  • Take action on the insight
  • Analyse selling prices and market share against competitors
  • Improve margins and market share by optimising regular and promo prices
  • Forecast, predict and segment more accurately than ever before
  • Deliver more precise and personalised marketing campaigns
  • Increase sales by ensuring you always have the right product at the right time, in the right location for the right customer

Self-learning Pre-built Analytics

PredictRetail uses a combination of data analytics types and models to give your business value decreasing the complexity normally associated with predictive and prescriptive analytics adoption.
Machine Learning & Statistical Models

The models are the HERO of the PredictRetail solution

Statistical-Models
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about the PredictRetail machine learning and statistical models

Analytics Data, Models & Results
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about the PredictRetail machine learning and statistical models

Start taking the guesswork out of your future planning now

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PredictRetail Solutions

Taking the guesswork out of future-planning
With Argility’s solution, you can take the guesswork out of future-planning, price optimisation, stock management and your customers’ behaviour. Predicting the future is how we can invent it, whether your business focus is on Inventory, Pricing or Customers – Argility’s got you covered, charting that path to the future your business deserves.

PredictRetail

Pricing, inventory, customer and sales analytics

PredictInventory

Inventory analytics for products, pricing, promotions, suppliers and sales

PredictPrice

Pricing analytics for product, categories, brands and stores

PredictCustomer

Customer analytics to improve customer retention and ROI on marketing spend

business intelligence

Your decision support system
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.
Optimise your entire retail operation, increase sales, reduce working capital and improve customer experience with big data analytics and real-time interactive dashboards.
Explore our various models
Inventory decision support
Gain a deeper understanding of your inventory, sales, pricing and promotions using real-time interactive dashboards and reports, designed to help your team make effective data-driven decisions.

Move away from spreadsheets and manual processes with near real-time sales and a merchandise decision support system.

Customer decision support
Gain a deeper understanding of your customer behaviour and sales with purpose-built dashboards and reports that support a rich and flexible dataset of customer and sales related properties, events and transactions.

Build an effective, data-driven marketing strategy by leveraging customer-focused data analytics and a decision support system.

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With PredictRetail you can determine optimal pricing strategies plus improve your customer & product strategies. We predict many great things in that kind of future.

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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 model, we need data points about customers, sales, inventory, pricing, 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 results will be visible within three to six months. The accessibility, quality, volume, variety, and velocity of the data will impact the effort required for ETL and cleanup.
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 models you select; the volume of data; and the processing power required every month. All models run on Google Cloud which lowers infrastructure complexity and cost. 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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