Cracking “Open Data” To Rake In Profits
Picture this, a commuter on a suburban train typing furiously into his Smartphone ordering groceries from a local store, which get delivered by the time he reaches his doorstep! Situations like this are quite banal, with most transactions and purchases made online, as people scurry about doing what they do best.
However, the grocer has in his hands, a goldmine of data which includes the grocery items most often ordered, the time the consumer spends to place an order, brand preferences, how often specific items are ordered, which days of the week or month there is maximum traffic and current trends among consumers like organic food or fat free food. This is big “open data”, something which market analysts are focusing on keenly, which when carefully collated and pieced together get profits soaring.
Here are three amazing things companies are doing with such big data
#1. Companies understand you!
By analyzing the plethora of information associated with you, like the products you buy and how you pay and where you live etc, companies actually get to know you better. And, to know you better is to serve you better!
The huge online retailer, Amazon.com is doing just that. Every order placed by a customer is tracked, analysed and gathered to be used for better customer service. Thus, when a customer calls regarding a query, every detail of the customer is already available. This way, phone calls from or to customer care do no begin with monotonous droning about address details and contact numbers but are cheerful well informed conversations about the customer’s latest searches, knowing fully well the identity of the customer.
So these calls are targeted, specific and address the requirements of that particular customer which converts to a happy and satisfied buyer.
#2. Targeted marketing
Marketing using big data is not about taking calculated risks but about making informed and intelligent decisions based on data recovered from customers.
Automobile giant, GM motors did just that highly effectively. GM had to scale down the number of their service stations from 13,000 to 4,300 which they did along with satisfying their customers with their service. This was achieved by analyzing the data gathered from customers and then positioning service stations accordingly. According to the data they had with them, customers were willing to drive about 2 hours to a car dealer if the car was priced 500$ less than dealers who were situated nearby, however, they didn’t want to travel that far for service. Through careful positioning of the service stations, GM was able to cut costs by reducing the number of service stations while keeping the cash registers clinking with a steady stream of customers.
Strategies such as these are not merely limited to big companies like GM but can also be used competently by small businesses and start-ups. The crux of the issue for start ups is to source the data efficiently and to listen to customer’s needs without spending too much. Many start ups are now using Google Voice to interact with consumers during non-work hours, saving them money on a full time employee while ensuring that vital data is collected.
#3. Assessing customer risk
The vast amount of information available in the form of big data can be used to compare customers, letting them know where they stand as far as competition is concerned. A mighty piece of information, indeed, for anybody to receive!
With more than 5 million online customers, Commonwealth Bank of Australia(CBA) has developed a mobile App to determine customer risk. This way businesses banking with CBA know exactly where their businesses stand in comparison with a similar business or a business close to home. This mobile App already has about 2.9 million users, an indication of its popularity.
Businesses that understand their position in the market will be innovative, strategising adequately to outdo competitors and with a better focus on their goals.
The availability of open data is not just limited to one company or a set of companies. It is after all ‘open data’, which means that it can be accessed. Coming back to our example of a customer ordering groceries on the way home, apart from the data that the grocer collects from the consumer, he can also avail train schedules from the government and the time it will take the train to reach the various stations. Something that can be used for effective home deliveries or to deliver a quick snack midway!
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