How to use data analytics to grow your jewellery business?

The business of jewellery is based on trust and relationships. Manufacturers enjoy relationships with retailers and retailers in turn with the end consumers. The industry is slowly realizing that harnessing data that can exponentially grow their business. There are some concepts that need to be understood and internalized by the fraternity to benefit from Data Analytics. Have you wondered if concepts like Big Data Analytics, Artificial Intelligence,

and Machine Learning are all mere buzz words, or do they have any real-life application? Are these at all relevant for your business or are these merely meant for big companies like Microsoft, Amazon, Netflix, Google? Can these be used by your jewellery business and can make it more profitable? Or maybe if a competition is already using and getting an edge over your business? In this article, we would simplify these concepts and explain how these technologies are being used in Jewelry Industry today.

Manishi Sanwal is the Managing Director of Voiceback Analytics Pvt. Ltd;

He is an expert on luxury retail and an authority on travel business. His past association with LVMH group includes stints such as the Managing Director of Mumbai Dury Free and General Manager, Indian subcontinent with their watch and Jewellery division.

So, what is Big Data?

Data is measured in Volume (sheer size of collected data), Velocity (the rate at which new data flows into the system) and Variety (various kinds of data like numbers, images, videos, audios, tweets, postsetc.). Big Data is when we are looking at situations where we have large volume of data flowing in at a large velocity and having a large variety. Like most things in life, complications multiply when things become large. These three are often referred to as the 3V of Data Analytics. Most of big data analytics today is about having a capability to handle large volume, velocity & varietyof data simultaneously. “If you have dat and a business problem, come to us. We will help you arrive at a Solution”.... Manish Sanwal

And Big Data Analytics?

The analytics of this big data collected above is very often classified based onmaturity or complexity of work being done.

Descriptive Analytics combine techniques which are used to describe the data. These include graphs, tables, charts, variousaverages etc. of the data.We have done Descriptive Analytics for our business but the current business intelligence platforms like Power BI are much more powerful and interactive.These platforms can analyze huge data sets in real time and throw dynamic graphs and charts. Highly interactive visuals with complicated analysison always-live data sets would make your data speak. PredictiveAnalytics

includes predicting or forecasting the future. Significant advancements have happened with Machine Learning and Artificial Intelligence which are terms given to various algorithms which look at independent variable and try to predict the future of these variables.

Prescriptive Analyticsis a step ahead wherein the analytics process would not

only predict a future scenario but also prescribe a solution or the best possible way-forward. This could be done at an aggregate or strategic level or an individual process level.

“Somewhere in your Data. Something incredible is waiting to be known”. Carl Sagan

Machine Learning / Artificial Intelligence

This is the modern-day avatar of age-old statistics and the strongest pillar of predictive & prescriptive analytics mentioned above. Vast amount of computing power harnessed by algorithms can run models with hundreds of variables over millions of data points to predict or classify behavior. Many of statistical techniques which few of us studied growing up (Regression, Correlation, Analysis of Variation, Clustering etc.)have evolved to grow into algorithms like Random Forest, Decision Jungle, ARIMA, Market Basket analytics, Sequential pattern mining and many more which deliver a highly productive view of the data for decision making.Deep Learning or Neural Networks are another line of models which mimic human brain. These take more data and more time to train and can be highly precise in select situations.

Jewelry Retail & Data Analytics

There are many reasons that Jewelry Retail can derive immense benefits from the modern-day data analytics practices. a) Jewelry Retail is organized and has data either for control reasons or for commercial reasons.The transactions are recorded, the inventory is recorded, the designs are recorded. This generates high quality and reliable data streams which can be used for improved business understanding and growth. b) Jewelry is an expensive and luxury item. There is substantial investment in stocks and inventory. Use of Advance analytics for inventory optimization can save huge amount of investments and a jeweler can open more stores with lesser investments.

c) Jewelry Sourcing is complicated with multiple vendors supplying multiple designs. Often this network of suppliers is combined with your own backward integrated production or combined with trusted job workers. This creates multiple coexisting supply chains – all behaving differently. This may lead to lead times, wait times, ordering issues, excess inventory, incorrect inventory, loss of sales etc. Modern day analytics can increase the understanding of these issues, analyze the impact of these