Businesses have been collecting data for years and have been seeking valuable insights within the data for just as long. However, traditional data sets are limited both in the amount and the type of data that they can store and process.
Businesses have been collecting data for years and have been seeking valuable insights within the data for just as long. However, traditional data sets are limited both in the amount and the type of data that they can store and process. This means companies that use traditional data sets are missing out on valuable insights that they could find if they were using a more advanced system like Hadoop that can handle big data. Here’s a look at five types of insights traditional data can’t identify and how big data can help.
1. How to Create the Next Best Product
What if every business leader was able to predict what product the consumer will want in the future? Is this a gift limited to the Steve Jobs of the world? While a traditional data set may provide limited insights in this area, its ability to accurately predict whether a product will be successful is questionable. This is why many companies rely on product testing, but even this has flaws and is expensive. Netflix, on the other hand, used big data to predict the success of its series “House of Cards.” Netflix, as you probably know, has a huge store of data on its customers’ preferences as well as data such as, did the viewer pause this movie when he or she needed more popcorn? This information combined with outside data allowed the movie streaming giant to accurately predict the success of its new show.
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2. How to Improve Your Timing
Timing is a critical component of marketing, but marketers are generally shooting in the dark when it comes to timing because a broad demographic tends to have different eating, shopping and working habits. Thus, marketing campaigns tend to employ a variety of channels from online advertising to TV commercials that play throughout the day in the hopes of catching a customer at the right moment. In addition, just because women between the age of 20 and 40 are the most likely to have children doesn’t mean they are all pregnant or planning on it anytime soon. Target started using big data tools to overcome this conundrum. It did this by looking at the buying habits of mom’s-to-be to find out which customers might be pregnant and even when they are likely to be due. Target was then better able to segment its advertising of baby products.
3. How to Reduce Churn
A lifetime customer is worth a lot to a company, so churn is always a concern. Unfortunately, knowing why customers are leaving can be a mystery. While some companies may collect some data on this by asking why a customer is unsubscribing or canceling a service, many never get this chance because a customer simply stops walking in the door. Big Data as a service can help companies to answer questions such as, what are signs that a customer may leave, and what can we do to stop them from leaving? T-Mobile was able to use big data to identify patterns of consumers that would be leaving, and due to intervention efforts cut the churn rate in half.
4. How to Prepare for Events Beyond Your Control
The weather, national tragedies, big events—all of these things are beyond a company’s control. While no kind of data, big or otherwise, can make it snow at a ski resort, it can help businesses better adjust to these events. For example, a phone company might experience service issues because of increased activity around events like the Super Bowl. By using big data to predict these events, the company can better prepare to handle the surge of activity. Likewise a ski resort could use customer patterns cross-referenced with average snowfall to adjust employee schedules and reduce costs on less profitable days.
5. How to Adapt a Campaign in Real Time
Imagine being able to adapt a marketing campaign not only based on whether sales increase or decrease, but also to meet the individual needs of each customer. Many companies have already started doing this with big data. Amazon has a customized landing page for each customer based on their browsing history complete with product recommendations. Mobile Apps have also become more customizable based on location and social data. For example, a company may send an offer to a customer for the shoes they just pinned on Pinterest when they approach one of the business’ stores.
These types of valuable insights are what will determine the difference between a successful company and an unsuccessful one in the near future. Customers are hard to reach and are even more demanding when it comes to catering to their individual needs. Without access to big data, companies will struggle to keep up and soon see that their traditional data sets are no longer meeting their business needs.
