The practical use of AI and Loading...machine learning (ML) is coming of age. Today’s top sellers use AI/ML in retail for Loading...predictive analytics purposes, helping them move beyond simple reports and dashboards that describe what has happened, to predictive analytics that map out what will happen. This shift allows companies to optimize their decision-making using the following processes:
- Regression and classification
Also called “supervised learning”, where we know what we’re trying to predict. For example, this allows banks to calculate the risk of loan defaults, or IT departments to detect spam. - Clustering
Also called “unsupervised learning”, where we don’t know what we’re trying to predict. This identifies recurring patterns to connect different variables and improve supervised models with feedback loops. - Reinforcement learning
Also known as “Loading...deep learning”, this enables a new level of end-to-end automation, where models become more adaptable and use larger data volumes for increased accuracy. This is ideal for complex tasks like image and speech recognition, but requires dedicated programming and frameworks, combined with powerful hardware and computing.
The benefits for online retailers
Combined with access to larger data volumes, these functionalities give e-merchants a competitive advantage, enabling better customer experience (CX) through personalized recommendations. Models also drive loyalty by predicting customer churn and optimizing fraud detection to prevent loss of revenue.
Traffic and inventory management can be improved by identifying hot or cold pages or section on websites, or by forecasting potential stock shortages. It’s also possible to optimize social media management through text and sentiment analysis that helps companies respond to hot topics.
Analytics maturity and AI/ML in retail
Retailers embracing AI/ML e-commerce tend to be in the advanced stages of analytics maturity. This means they’ll have created thorough data strategies that directly link to their overall business objectives. Having this clear link between AI usage and company objectives, helps secure management buy-in and ensure analytical objectives match up with the companies wider goals.
Businesses with Loading...advanced analytics also tend to have agile, open data architectures that promote access to data – data democratization. But data teams shouldn’t be dependent on a single tool, as they’ll still need to understand incoming data from departments across the organization.
Adoption of AI is also correlated with technology planning, so companies should focus on:
- Automation that streamlines data validation at a time when data volumes are exploding.
- Industrialization for observation of use cases and potential AI innovation.
- Interoperability allowing use of specific technologies and guaranteeing transparency of the project.
- Operational governance assuring the simplicity and reliability of analysis while limiting costs.
- User-focused design ensuring both seasoned and novice users can grasp analysis tools.
How Exasol supports AI/ML projects – in two distinct ways
Our database features enhanced User Defined Functions (Loading...UDFs) to run R, Loading...Python or Loading...Java code directly in SQL, giving a boost to data manipulation for greater speed and volume. We’ve also operationalized real-time predictive analytics via faster analysis and immediate forecasts, without sending data to external systems for results.
The platform also includes “test and train” processes to identify the best models, despite intensive requirements around data and time, by featuring several integrations to automate and simplify model development, including ML tools like AWS Sagemaker and TurinTech.
Such functionalities optimized our work with of of the world’s leading sports manufacturers, where we deployed R-based UDFs to build their customer database. Our platform manages more than 250 million customers and covers over 8,000 time-based data points per entry, ranging from basic attributes like gender to more complex measures, like the propensity to buy specific items within a defined time period. These were modeled using combinations of data from multiple sources, including website browsing, social media, and sales information, with every data point being updated within 24 hours of each new digital interaction made by customers.
Putting AI/ML in retail and e-commerce into practice
One thing is certain: AI models only work with good data, which means organizations must focus on quality and governance. In-memory analytics databases will become the driver of creation, storage, and loading features in ML training tools given their analysis capabilities. These tools will benefit from closer integration with the company’s data stores, allowing them to run more effectively on larger data volumes to guarantee greater system scalability.
“Low Code” or “No Code” ML is continuing to improve and make development more accessible, while R and Python have become low-level fallback options when GUI falls short. We’ll also observe a gradual shift from vanilla solutions to specific, internally generated models.
AI/ML will continue to grow across all verticals, with the democratization of analytics allowing data professionals to focus on complex scenarios and making CX personalization the norm. The technology will expand beyond the front end toward all business processes, providing the best possible customer experience and boosting profitability.
For more on the transformation of retail analytics, click here.