Summary of Part 1: Challenges and Limitations of Traditional Retail Analytics In Part 1 of this blog, we addressed the challenge retailers face in translating vast amounts of data into actionable insights, highlighting the paradox of being "drowning in data but starving for insight." We explored the limitations of traditional Business Intelligence (BI) tools, which primarily report historical customer data rather than uncovering the underlying patterns and drivers of customer behaviors and customer interactions. The section further details why standard approaches to personalization, often relying on broad demographic or RFM segmentation, fail to meet consumer expectations for tailored experiences. Finally, Part 1 examines existing loyalty suites and platforms, both for enterprises and mid-market companies, highlighting their challenges with complex data, multiple data sources, hypothesis-driven approaches, and limitations in identifying time-sensitive or non-obvious drivers that influence retail sales performance, thereby hindering effective personalization and customer satisfaction improvement. The Missing Link: Discovery…
Drowning in Data, Starving for Insights Why Deep Analytics in Retail is No Longer Optional Retailers possess a wealth of data from multiple data sources, such as point-of-sale systems (POS), loyalty programs, inventory management systems, order management systems (OMS), online shopping carts, and mobile apps. The list is nearly endless. Despite the abundance of data, challenges persist. Many retailers struggle to translate raw data into a meaningful understanding of customer behavior and actionable insights. The volume and complexity of available customer data render manual analysis impractical. The result is a common paradox in the retail landscape: retailers are often "drowning in data but starving for insight." Traditional Business Intelligence (BI) tools have been the primary focus of retail analytics for years. They excel at identifying trends in the business by reporting on historical performance, allowing managers to track Key Performance Indicators (KPIs) such as sales volume, inventory turnover, and gross…
Introduction Time-series modeling is a statistical technique used to analyze and predict the patterns and behavior of data that change over time. Part 1 of this blog series explained standard time-series models such as AR models, ARIMA, LTSM, and Prophet and discussed their advantages and disadvantages. Part 2, on the other hand, introduced an alternative approach - feature engineering from temporal datasets, that provides numerous benefits over standard time-series modeling. In Part 3, the last of this blog series, we will examine ARIMA and Prophet models, compare them with an alternative feature engineering approach, and demonstrate the advantages of the feature engineering approaches. Dataset In this blog, we utilized the dataset taken from the Prophet quick start demo guide. The data is a time series based on the log of daily page views for the Wikipedia page for Peyton Manning. The data is a periodic time series spanning eight…
AI automation can solve a variety of problems and address multiple Insurance use cases The insurance industry is one of the earlier pioneers of making data-driven decisions and adopting Machine Learning. Over the last few years, the amount of data has exploded and insurance companies are turning a lot more to AI and ML to help process large quantities of data to drive innovative business decisions keeping the customer at the top of their minds.With the increase in the volume and variety of data and the needs of the insurance industry, the types of problems that are aimed to be solved with AI and ML have increased tremendously. The below visual gives a sneak preview into the Insurance business areas where machine learning can be leveraged and how often insurance companies are considering incorporating ML into that business area. Figure: % of Insurance companies that are considering incorporating ML into…
