Hadoop Solutions Make Frugal Living and Extreme Couponing Easier than Ever

Frugal living has become a major fad since the onset of the recession in 2008.  In particular, extreme couponing has become a hobby that is practiced by tens of thousands of people all over the United States. Consumers saved $3.1 billion with coupons in 2017. However, in the quest to save money through couponing, many people don’t get the value they are looking for. The good news is that a number of Hadoop solutions can be invaluable for people that are trying to get the most bang for their buck.

How does Hadoop technology help with key couponing and frugal living?

There are a couple of issues with the extreme couponing movement:

• Finding and collecting coupons is a massive amount of work. Some frugal living experts have said that it doesn’t always turn out to be an effective use of people’s time. The value that they realize from the savings may translate into around the equivalent of a job paying $3 an hour.
• A lot of the coupons that people find are not for products that they would normally purchase. They also are not substitutes for those products, either. Ironically, this actually gives people the temptation to lose the money. Extreme couponers might feel frustrated after spending several hours looking for deals. If they don’t find coupons for products that they would normally purchase, then they might try buying goods that they otherwise never consider. In other words, they are risking going broke trying to save money.
• Many coupons are limited to purchases in certain cities or states. Unfortunately, places that aggregate coupons don’t always sort by geography.

The third point listed above is one of the biggest struggles that people in the extreme couponing movement face. Gaurav Deshpande of the Big Data and Analytics Hub from IBM highlighted this.

“Capturing and using location data requires tools that are capable of handling large volumes of data at high velocity. When location data is tied to individual subscribers, other technical challenges are introduced as telecommunications companies need to give subscribers a way to opt-in to share their location data and to specify the types of offers they want to receive. Since CSPs have tens of millions or hundreds of millions of subscribers, managing and honoring consumer preferences can be an extremely complex task.”

Fortunately, Hadoop and other big data technologies are playing an important role in addressing all of these challenges. Here are some ways that big data technology is playing in important role in the frugal living movement.

Merging deals

An article from the Washington Post talked about ways that technology is changing couponing. They mentioned that one of the best ways that people can get the most out of coupons is by combining them for the best offers. Hadoop technology is helping with this.

Customers can use Hadoop search algorithms to compare different brands and find related coupons. This helps them make sure that they are getting the most bang for their buck.

Helping couponers find local deals

Since many brands limit coupons to certain jurisdictions, it can be very frustrating for people to trawl through hundreds of pages of coupons only to find that most of them are not accepted where they live. The good news is that new coupon databases have a number of different search features that people can use. This enables them to apply location-based search filters to find coupons that will be excepted in stores near where they live.

Helping couponers find deals on their favorite brands

Traditional coupon books are not organized very well. People might have to dig through dozens of pages of coupons for brands they have no interest in, before they find deals for brands that they actually want to make a purchase from.

This is another advantage of using new were coupon databases. They have Hadoop search filters that make it easier for them to find coupons for their favorite brands. This prevents them from wasting their time looking through coupons they have no interest in.