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Does your phone keep prompting you with the perfect jacket or the exact shoes you were thinking of getting? Does it send you discount offers on the book you were curious about? How does Amazon or Flipkart know exactly what you are thinking and exactly what you want? The answer is Big Data!
Data science has truly become a part of our life in unimaginable ways. With retail data increasing exponentially in volume, variety, velocity and value, retailers are aware that each of these interactions hold potential for profit. This creates a grand entry for the new-age hero, the data scientist!
Magical combination
Data analytics and retail create a magical combination. For example – Target’s analytics team sat down to figure out how to tell if a customer might be pregnant, even before any announcement was made. By analyzing purchasing trends, it turned out they could not only assign a level of likelihood that a customer was pregnant, but could even predict a probable due-date. The use of data science and big data analytics aids in the following ways:
A McKinsey report suggests that use of business analytics, could increase a retailer’s profit by almost 60%. Such studies are evidenced by many retailers making a beeline to tie up with big names in software to provide them with advanced analytics, business intelligence and data management.
Let’s explore some of the ways that data scientists are changing our online shopping experience.
1. Merchandising – Improved layouts and product placements, using heat sensors and image analysis to identify consumer behavior patterns. This in turn allows for identification of shopping trends and cross selling opportunities. These make way for higher profits due to the combination of both internal and external data. Post purchase usage information is also gathered using product sensors to determine further markets for the product.
2. Marketing – Timing is everything! Being at the right place at the right time with the right price could set you apart from your competition. Data Analytics creates an opportunity for location-based personalized offers with real time pricing using ‘second by second’ metrics. Campaigns can be targeted using business analytics that can segment customers, and identify the right channels to attain optimal ROI.
3. Customer Experience – Predictive Analytics creates personalized recommendations and multi-level reward programs based on purchase preference, search history, online data and sentiment analysis of social media streams. This leads to enhanced customer experience across all channels and devices, both online and offline.
Who would have thought there is so much captured in a simple click of a button to buy your favourite product? The opportunities stretch far and wide, and will be interesting to watch what the new slew of big data scientists will roll out in the future.
