By: Mehul Agrawal, MD & Co-founder, Fabfurnish.com
Lakhs of customers, stupendous amount of traffic, information coming from sensors, cameras, microphones, devices, networks, log files, applications, web, and social media - the volume of data coming into an organisation is endless. What comes out of all this data? Insights? And how useful are those insights? That depends on your ‘big judgement’. The name ‘Big Data’ itself suggests that it is related to size and hence volume is a major characteristic.
However, analysts need to figure out the category to which the data belongs. A lot of data is generated from multiple sources, what’s required is to understand the co-relation and link between different types of data. The process of gathering, shaping and examining these large sets of data to determine informative patterns is known as Big Data Analytics.
Corporations churn out a great amount of data on a daily basis. Researchers, analysts, and business users now make faster decisions by analysing the data which was not obtainable earlier. Statistics, machine learning, data mining, etc. are the modern techniques which help the businesses gain noteworthy insights.
Making a headway with Big Data
The hype around ‘Big Data’ is justified because it’s a bus that any eCommerce player cannot afford to miss. Data analytics help eRetailers in pinning down customer preferences. This data can make available personalised and relevant content and promotions based on the Internet behaviour, purchase history and other details like age and location of the consumer. To ensure constant renewal of the marketing strategy, it is important to study the data regularly, understand customer behaviour, and then use it to retain the existing customer base.
As per a NASSCOM survey, one skill that 62 per cent of the employers would be looking at while hiring talent would be Web analytics. In case of digital marketing, the data generated from social media lets one target the users better. The parameters involved are gender, age, preferences; click-through rate and past 12 months’ performance. In order to set dynamic pricing and generate interesting offers, separate profiles have to be created for each customer out of the processed data and each profile will help a firm make personalised offers and encourage the shopper to make repeated purchases. Recommending relevant products to customers, displaying ads with higher click-through rates, etc are some techniques used for the same. Understanding your customers’ concern and solving it by judiciously assigning the right resources within a shorter timeline is the biggest achievement for any organisation.
Come Up with Predictive Analytics based on Past Purchase Behaviour
With the help of Big Data, retailers can determine patterns which can be useful to predict any potential blow to the back-end process. For example: Every time we come up with a Diwali offer, we analyse past 6 months’ sales. We use data analytics and data crunching to ensure there isn't under stocking or overstocking. We forecast on the basis of marketing spend on each individual category and also ensure that we pre-pack the products which are expected to sell the most in advance.
Figure Out What's Important and What Isn't – Making sense of it all!
Spotting the difference between fad and trend would be a company’s biggest challenge when it comes to large sets of data. Ever heard of the term Apophenia? It is the tendency to perceive a connection or a meaningful pattern between unrelated or random things. It’s the skill of the data scientists that matters and not the data itself. It is equally important for decision makers to make evidence-based choices. Given the reliance on analysts, it's scary how decisions taken from data analysis often seem to be acted upon in an obedient way. One has to be questioning! Don’t let the data mislead you.
A Harvard University professor, Gary King, quoted an example- a big data project used social media to forecast the US unemployment rate. They picked out tweets related to ‘Jobs’, which increased for a separate reason. "What they hadn't noticed was Steve Jobs died," said King. It brings us to the point that there can never be a better alternative to human judgement.
Personally, I feel Big Data is not just a fad; it’s a reality we have to accept and inculcate in our business (if we still haven’t). I would stress on its judicious use. If used correctly, it is the biggest blessing for any eCommerce firm.
Big Data needs high-performance analytics so it is important to hire analysts who can find correct patterns in very large sets of data and translate them into useful insights
Big Data Analytics helps the sellers by giving them their target group at hand and through these sellers, it enables a more user-friendly and personalised web world for the buyers and to a large extent, it improves the internal operations as well.