By Anil Bains
In the last few years, businesses have witnessed an exponential increase in technological advancements, globalization, and multi-channel customer demands. To survive and thrive amidst this constantly changing environment, companies must deploy future-proofing business strategies that leverage predictive analytics, automation, and Artificial Intelligence.
Using AI predictive analytics to forecast consumer demand is becoming increasingly crucial for business decisions. AI-powered predictive analytics has enabled Marketing, Operations, and FP&A teams to understand consumer demand not only at the organization level but also at the business unit level. Businesses can identify demand trends, minimize risks, and maximize risk-adjusted ROI. AI predictive analytics employs a combination of statistical models, algorithms, and data mining techniques to interpret past events, identify trends, and recognize patterns in data. As AI predictive analytics has advanced, even smaller businesses can perform sophisticated analyses to understand customer preferences and engagement across customer segments, resulting in precise demand forecasting.
How AI works:
Predictive analytics help businesses plan for resources in the future by scanning historical data and real-time user activity. It requires collecting, integrating, and processing disparate data to derive granular insights. This data-driven statistical approach eliminates guesswork and empowers companies to make business-critical, revenue-optimizing decisions with clarity. AI predictive analytics utilizes algorithms and machine learning techniques to analyze data from multiple sources and find patterns and insights that would otherwise be difficult to detect.
This type of analysis can also help companies provide tailored recommendations to customers, ensuring that their money works for them in the best way possible. By utilizing AI predictive analytics, businesses can target consumers based on their financial objectives, leading to more specific and compelling marketing initiatives that reach their intended audience.
How AI is harnessed:
AI predictive analytics can be harnessed to forecast consumer demand by leveraging sophisticated algorithms, statistical models, and data mining techniques to analyze historical data, identify patterns, and make accurate predictions about future consumer behavior. Businesses can use multiple standard Machine Learning techniques to forecast demand. Some techniques involve historical data to identify patterns, while others involve real-time data such as current sentiments, consumer preferences, social media activity, and influencer activity to forecast consumer demand. AI-powered demand prediction models are powerful enough to consider multi-variate datasets and predict demand based on factors such as seasonality, promotional spending, and sentiments derived from textual data such as reviews. Here’s how the process typically works:
- Collecting and integrating diverse datasets: AI predictive analytics requires data from multiple sources such as revenue, social media engagement, traffic sources, clickstream, etc. By integrating these datasets, businesses can comprehensively understand customer behaviour and make accurate revenue predictions.
- Data Processing and Feature Engineering: Understanding consumer demand’s crucial features/variables will help businesses accurately build a reliable forecasting model.
- Continuous Model Refinement: With time, the consumer data changes due to multiple factors such as changing weightage of traffic sources, user preferences, evolving product selections, UI/UX improvements, etc. Continuously refining the models with changing data and emerging trends will ensure model accuracy and relevance.
- Real-time Data Incorporation: Combining historical data with real-time data feeds to adapt your consumer demand forecast continuously will help businesses minimize forecasting errors. Real-time data on social media mentions, website traffic, and competitor activities will materially improve the forecasts.
- Monitor Forecasting Accuracy: Continuously evaluate the accuracy of your demand forecasts against actual consumer demand. This will help you identify discrepancies or deviations, allowing refinement in forecasting.
AI predictive analytics is a powerful tool for businesses to forecast consumer demand accurately, enabling them to stay competitive, meet customer expectations, and adapt swiftly to the dynamic market environment.
The author is founder, CEO, Attryb