The Argility Technology Group has announced the release of its latest innovation: PredictRetail, a Google Cloud native application offering predictive analytics to retailers.
PredictRetail enables retailers to make better business decisions, by using machine learning, data science and predictive modelling, to extract insights from historical data and translate it into business value.
The application is developed on top of the Google Cloud Platform (GCP) and uses a variety of GCP services from the building of data pipelines and training machine learning model’s, through to data visualisations and image recognition. Being a native GCP application, PredictRetail is able to scale to any size data sets or processing workload.
The future of business in a data driven world is integration, optimisation and automation of business processes across the enterprise. Typically driven by artificial intelligence (AI); machine learning (ML) and data science.
Marko Salic, CEO of the Argility Technology Group, says that whilst these terms are now popular buzz words not everyone fully understands what they mean.
“AI for example can be defined as the simulation of human thought processes by machines. ML is a subset of this as it aims to facilitate the ability of computers to learn and improve without the need for re-programming. Data science typically refers to the ability to extract useful insights from raw data,” he adds.
Salic notes that while core retail operations have remained the same for centuries, the consumer has changed. “We demand a personalised service and a flawless customer experience. If we don’t get what we expect, it’s very easy to switch to a competitor as we have more options than ever. Predictive analytics and Data Science are key to offering an improved customer experience, by predicting future outcomes and customer needs more accurately, you can act on them early and effectively.”
Most retailers have been collecting data about their customers, inventory, products etc. for decades, but little is done with this data outside of your typical BI-postmortem analysis.
“PredictRetail’s purpose is to help retailers monetise their historical data by using data science techniques to discover hidden trends and patterns in this data, then exposing those patterns as insights and assisting business users to make smarter business decisions.”
Salic says that PredictRetail benefits include:
* Increased sales by determining the most likely next-sell opportunity using an ML based recommendation engine.
* Improve marketing spend using an ML clustering algorithms to segment the customers more accurately and create tailored marketing campaigns for each segment.
* Improve customer retention by determining the customers propensity to buy, there by engaging them at the right time, with the right products.
* Realise more sales by using ML to predict demand more accurately, ensuring less capital is locked in under-performing stock, and more realised sales from reduction in out-of-stocks situations.
* Optimise customer acquisition costs by predicting historic and future lifetime value of a customer, focusing spend on the right audiences.
* Maximise margins through price elasticity modelling.
* Increase basket sizes using market basket analysis to determine most likely up-sell opportunities.
* Introduce image or voice-based searching on your e-commerce sites.
* Tracking of brand sentiment using social media sentiment analysis.