Propelling Data-Driven Decisions with Real-Time Analytics

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We are currently at the peak of the analytics revolution. While for the most part we still analyze backwards (observing user actions that already happened), the time has come to harness real-time (RT) analytics to drive immediate decisions and most importantly, instant actions.

Companies are using more real-time analytics, because of the pressure to increase the speed and accuracy of business processes — particularly for digital business and the Internet of Things (IoT). – W. Roy Schulte, VP Distinguished Analyst, Gartner

Not too long ago, the analysis of data feeds in real time was quite cumbersome and presented a technological challenge. Real-time data was feeding only a few highly dynamic time-critical industries such as cybersecurity and brokerage. A lot of time and effort (and a lot of money) went into building such real-time environments.

With everything going online, on mobile and on social media (along with the rise of IoT), the number of data sources is increasing by the day. The strain to do something with this data is intensifying. Analytics had to adapt quickly and technology needed to evolve, so RT analytics would be more available and help businesses be in the “now.”

Businesses today need to deal with their big data infrastructure and time series analysis to gain quick and deep insights on customer behavior. Any advanced analytics platform needs to support these requirements.

Additionally, businesses need to get immediate answers to their most complex business questions that go beyond just page views and session times. They need the answers to the most granular questions, such as which users came from a certain campaign, purchased a certain item, added it to their cart, came back, chose another item, and checked out or abandoned their cart altogether.

Gaming, ecommerce, travel, online trading and digital media are just a few industries that are quickly adopting the analytical abilities they need to make data-driven decisions and perform immediate actions.

Real-Time Data Illuminates the Online Industry Spectrum

When your goal is to monitor and optimize the placement of hot products in your top banner, wouldn’t it be effective to see the clicks and conversions in real time?

You don’t want to waste valuable time during the biggest holiday weekend sale if the most promoted product in your top banner isn’t converting. You want to optimize the copywriting and graphic content to maximize conversions.

Ecommerce companies can use real-time reports to understand customer responses to newly launched products. The suggestion engines and next best offers can quickly adjust to offer matching or complimentary items based on actual data from customers that purchased just one or two hours ago. This has proven to instill the “wow” effect, and good, timely suggestions have a tremendous impact on higher cart values.

You might recall that, after an online shopping session on Amazon, you went back to the homepage and you were offered recommended items based on your browsing history. Amazon has spent nearly a decade optimizing their algorithms so they can process all this behavioral data in real time. This enables their content delivery systems to make immediate decisions on what to offer their customers. Amazon has the ability to monitor and analyze every user’s journey as it’s happening, so their content systems can make data-driven decisions.

Let’s look at another scenario from the online travel industry. Travel business heavily depends on email marketing for offering last-minute travel deals. RT analytics must be the tool of choice for marketing managers to monitor click and open rates of currently running campaigns. It can also help them see the effectiveness of each campaign in real time, so they can optimize it on the fly instead of wasting 20,000 valuable email addresses with an underperforming email campaign.

Imagine if you could see in real time that numerous customers in Australia were all clicking on the same offer and checking out. You would realize that there is a trend. You would want to capitalize on this trend, segment those users and send them a limited time offer email with more suggestions immediately while the iron is hot.

In the gaming industry, marketers spend big bucks on campaigns, so they need to see the number of game installs and understand campaign impact in real time. This is critical in helping them understand the immediate value of a campaign. It allows them to perform data-driven optimization immediately and save money that could have been wasted if the data were to arrive just a few hours later. If marketers can see, in real time, that iPhone installs are exploding from a certain Facebook campaign, they will want to increase their ad budget to meet the need.

In digital media, content publishers rely on the immediate impact of their articles or videos. Who would keep the featured story up there if it’s not being highly viewed or shared? They need to understand the immediate impact of their digital content and optimize accordingly: change the headline or image to get the desired share and click rates. The point is, they want to know the results right away.

The Virtual Suggestion Paradigm

Before the Internet age, sellers and buyers were able to get feedback in real time — the old fashioned way. They asked for it right on the spot during the purchase. In an online world where there’s no real interpersonal communication, a relevant response to our digital actions provides the feeling that whatever is feeding that suggested offer, game promotion or suggested content, knows what we want. That feeling translates to conversion, engagement and loyalty — all of which lead to business growth.

RT data gives businesses what they need to optimize, cut costs and maximize profits. RT analytics and reporting functionality is paramount to business success.

Guy Greenberg is the co-founder and president at CoolaData, a leading behavioral analytics platform. With more than 20 years of experience in big data and startups, he is an active angel investor and advisor of several big data startups.

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