AI and Machine Learning are completely transforming the retail industry these days. Our purchase journey is becoming shorter and more personalised than ever. We see it happening but do we understand the technology behind it? Here are a few examples for how it is done.
Build self-learning models that predict sales, help increase sales revenue, and reduce storage costs. Using Linear Latent Variable Models (LAVA) and/or Elastic Nets to estimate the latent factors that highlight customers purchasing behaviour.
Big Data Analytics and Visualisation
Systematic analysis of big data is crucial when exploring under-performing streams of sales revenue. By deploying a combination of large-scale analytics and data visualisation we can illuminate hidden campaign strategies, such as cross-sales, which will alleviate such poorly performing SKUs.
Implement statistical models with demand and supply uncertainty features that are inherent to the supply chain process. The perturbation of these model treat hidden externalities and generate a robust toolkit for modelling supply chain. Some additional areas where machine learning could help you’re your business is planning group problems, optimising stock levels, and warehouse automation.
Backtesting Campaign Strategies
Campaigns can be costly if they are not implemented correctly, and thoroughly backtested. Finely tuning tune back-testing models will help build a well-constructed cost-effective campaign strategies, giving management at all levels the details and implications for deployment.
Targeted Campaign and Retail Segmentation
Have a nuanced view of public opinion and target customers more accurately with Multi-level Regression and Poststratification (MRP). Create retail segmentation with artificial neural networks (ANNs) giving you a better understanding of your customers' shopping habits.
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