- Smart Processes
- Smart Systems
- Smart Devices
- Smart Data
Keep your ear to the ground with Advanced Analytics
~ Sandeep Arora - EVP & Head - Enterprise Data Management
Enterprises vie to maintain one-to-one relationship with customers. However, delving into the customer preferences is a challenge. Again the factors which influence the customer mind may vary across a wide spectrum, right from the quality of customer service to availing the latest technology on the block to being able to afford a particular product service. Though during customer interaction, there is a definite trail and a holy grail that is left behind which is hidden in enormous quantities of structured and unstructured data. This piece of intelligence is a treasure trove. Retrieving this intelligence is possible with the help of Advanced Analytics.
Earlier, customer actions and trends were hidden away beneath transactional data and enterprises drove their marketing campaigns plainly based on gut feeling. Customer segmentation was done based on traditional methods, which possessed definite drawbacks such as limited scope, small sample sizes, out-dated categories, incomplete picture, poor insights, siloed data sources, limited data lifespan. The result of the traditional methods was limited intelligence which again was obscured. Today, model-based segmentation, which is powered by Advanced Analytics, allows creating dynamic customer-segmentation based on diverse data points including customer’s web logs. Advanced Analytics, and more specifically Machine Learning, takes customer segmentation and research to a totally new level. A few pointers would help here:
- Analysis of Transaction Data:
Transactional data, which includes a beautiful array of customer-centric data, such as payment methods, discount values, purchased quantity, time, price, location, when analyzed and viewed holistically gives a complete picture of the customer’s purchase habits. Using this data, up-selling and cross-selling can be achieved with high precision of correctness.
- Analysis of Interaction Data:
The customer and prospect interaction data sourced from website, social media, mobile conversation, email, text messages can be analyzed in real-time to derive a global customer-view. Moving away from myopic views of limited sample data to a holistic analysis allows an enterprise to develop a strong, one-to-one relationship with the customer and prospect.
- Analysis of External Data:
The analysis of geographic as well as socio-demographic data can be super-imposed on Transactional Data and Interaction Data can add more meaning to the customer behavior and provide a global perspective. Instances such as weather changes on footfall at retail outlets can be used to immediately replenish stocks.
Machine Learning, which is a combined effort of Advanced Analytics, Mathematics, and Statistics, predicts outcome based on patterns in data. Machine Learning based Model Segmentation helps an enterprise predict a customer’s course of action in the form of TN (true negatives), TP (true positives), FN (false negatives), and FP (false positives). It means gut feeling based decision making is eliminated, customer segmentation, data recalibration, and decision making are based on real-time data, and alignment of customer segmentation with business objectives is enabled to provide a single version of truth in real time.
Such a data-driven market research helps the enterprise and its data scientists to relate to and drive towards the bigger picture, refresh the data sources in real-time, and keep on challenging and refining the single view of the customer from time-to-time. The adage ‘Listen to the customer’ holds worth as long as you are able to integrate a real-time customer view with customer feedback. Here Machine Learning based solutions are able to hive off the True Negatives and False Positives and derive real-time intelligence.
Vying to maintain a one-to-one relationship with customers is as challenging as trying to take a hit at a moving bull’s eye. Advanced Analytics and Machine Learning based solutions help enterprises analyze and perceive their customer preferences and their probable actions and thus leverage their marketing efforts accordingly.