E-Commerce & Retail
Exacting customer expectations, narrow margins and technological disruption are creating headwinds and opportunities for retailers and e-commerce businesses. Hyper-efficient core retail operations and precise pricing strategy are more important than ever. But current machine learning systems fail to predict supply-and-demand dynamics and price elasticities.
Causal AI identifies the drivers of consumer demand, understands consumer preferences, and shapes buying behaviour, enabling retailers to supercharge processes and optimize revenue outcomes.
Customer expectations demand end-to-end optimization
E-commerce and retail are in a constant state of disruption by new market entrants and changing technologies across the supply chain. Businesses look to Machine Learning to adapt, but current AI techniques are often inadequate:
- Basic AutoML takes a long time to adapt to regime shifts, leading to potentially enormous losses, and at least significant opportunity costs
- Non-causal ML techniques are likely to include spurious correlations, making it difficult to enter new markets with confidence
Solutions
Forecasts based on standard machine learning techniques only extrapolate from historical trends, making them fragile to new market entrants, product line changes and evolving consumer behaviours such as demand for omni-channel fulfillment. causaLens uncovers the true structure behind the supply chain to create predictive causal models for any supply chain variable. These models autonomously adapt up to three times faster than current state-of-the-art machine learning under regime shifts. CausalNet, causaLens’ proprietary inference engine, delivers actionable insights that not only evaluate the factors impacting fulfillment performance down to individual SKU and node level, but go beyond predictions to provide dynamically updated optimal warehouse layouts. With causaLens, retailers can prioritize slow-moving or obsolete store inventory, and make use of it at the most profitable price point.
Data-driven approaches can optimize the advertising ROI and increase the customer conversion & profitability. causaLens enables marketing teams to autonomously discover the causal drivers of marketing performance. Distinguishing between statistical correlations and true causal drivers translates to vastly improved models and, as a result, optimized marketing spend and product recommendations. It also allows business users to automatically answer what-if questions and assess market interventions that happened before without the traditional, costly trial-and-error process
Current approaches rely purely on historical data and they fail to efficiently model market interventions as well as competitors’ behaviour, leading to suboptimal results. causaLens allows you to autonomously discover the most profitable pricing points based on true causal drivers and market interventions. It also allows you to conduct virtual A/B testing by modelling novel scenarios without relying on traditional, costly trial-and-error experimentation.
Why causaLens
Which level is your organisation in?
No Machine Learning
- Do you think it would take you years to become an AI-driven Enterprise?
- Is the value of your enterprise’s data asset largely unrealised?
- Are the predictions, insights and business decisions compromised due to human error?
- Are resources wasted improvising solutions to problems that could be better solved with ML?
Manual Machine Learning
- Is it hard to build and retain data science teams?
- Does it take months or years to achieve real business impact?
- Is the team mostly reactive, responding to requests from business functions for analytics?
- Does your team spend their time implementing repetitive & expensive processes?
Automated Machine Learning
- Do you find that generic Data Science Platforms overfit & underperform?
- Do you have trouble translating predictive models & predictions to real value for the business?
- Are generic platforms not flexible or transparent enough while failing to incorporate your domain expertise?
Reach the top levels of Enterprise AI for E-Commerce & Retail
Causal AI
- Goes beyond modelling correlations in historical data and understands true causal drivers
- Autonomous, from raw data directly to causal signals, predictive models & insights
- Inherently explainable
- Adaptable and works in regime shifts
- Intuitively incorporate domain expertise
- Works in low-signal-to-noise environments
Strategy Optimization AI
- Automated KPI Optimization.
- Includes all benefits of Automated Causal AI.
- Goes beyond predictive models and integrates the business context and the domain knowledge in a meaningful way.
