Over the next two years, 75% of companies report that they will invest in technology that collects big data. The big data industry has quickly solidified itself as a growing and increasingly essential part of the business environment. But big data cannot help a company by itself; someone or something must analyze the data […]
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You’ll be aware of the recent explosion of interest in AI, and most likely have seen it in action too, in the form of smart assistants on phones, desktops (think Cortana) and elsewhere. Pretty soon, the ability of machines to achieve a greater understanding of the real world in all its messiness will become […]
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For forward thinking leaders, Big Data can lead to even bigger profits. So, partner with the best tools and people you can find, and dive in!
In 2013, an astounding statistic began circulating in the tech and business spheres: 90% of all global data was created over the last two years. It’s as if data had been dormant prior to 2015 and has only just begun to be produced.
In many ways, that’s true. Historically, companies have been able to track only a limited amount of information about their customers, such as names, phone numbers, and previous transactions. Thanks to advancements in smart phones, social media, and e-commerce, we’re now able to track almost every major moment of our lives and turn this information into data.
Businesses now have access to a whole slew of new information about their customer base across many decision points — including their reactions to different stimuli in social media and advertisements.
They have a massive opportunity to better understand how customers make decisions and predict their future behavior when presented with new stimuli. While the customer may not know exactly why he bought a new hat, the business that sold it to him will. New techniques allow businesses to analyze data points over a long lifespan of interactions in order to disaggregate how customers make decisions.
While this influx of data is exciting, it is also difficult to interpret.
Without the ability to interpret the data, it becomes meaningless. That’s where behavioral analytics come in. Much like applying complex root-cause analysis to weather patterns, with the right information, human behavior can be predicted too.
Behavioral analysis aims to understand how and why consumers behave the way they do in order to predict what they are likely to do when presented with new situations. By compiling and analyzing raw data points like clicks, tweets, and online purchases, behavioral analysts can uncover hidden inner drivers. These insights help businesses understand what marketing should be used for individual customers — at the right time and at the right place.
The adoption of IoT technology can provide companies with unprecedented opportunities to reduce operating costs, increase productivity and tap into new markets previously inaccessible. But that won’t happen when IoT data gathers dust in the cloud.
According to research by McKinsey Global Institute, of the IoT industry’s forecasted yearly value of $11.1 trillion by 2025, 60 percent is predicated on the ability to correctly integrate and analyze data. However, the research further finds that most of the IoT data being collected by companies is not being used, and the data that is being used is not fully exploited.
McKinsey’s findings are corroborated by those of other organizations, including a recent Forrester study and a global survey by the research firm Strategy Analytics.
With more and more firms and manufacturers climbing on the IoT bandwagon at a steadily increasing pace, and millions of new devices being connected to the internet every day, it’s time we change our perspective toward what we can do with all the IoT data that we’re producing and collecting.
If there’s any doubt as to why “big data” has become as ubiquitous in business as pens, chairs and coffee mugs, look no further than the margins. Since becoming the buzzword of the decade, big data has given countless businesses huge competitive advantages by redefining the quality of the information at their fingertips and the speed at which they can react. And profits have soared.
So why have apparel brands lagged in doing the same? In many cases, identifying the “next big thing” — what will sell, and the rate at which it will fly off the shelves — is still steeped in guesswork and unsupported instinct.
For brands, the use case is abundantly clear. The global apparel market is one of the world’s biggest sectors. It’s valued at $3 trillion and accounts for 2 percent of the world’s gross domestic product. This industry is trying to find its footing in a landscape of constant change, driven by new technologies and consumer spending that’s moving away from brick-and-mortar to online.
In the Unites States alone, there were more than 211 million digital shoppers in 2016 who browsed through mountains of information, from new products, pricing shifts, promotions and more. This data, when amalgamated, could be used to provide retailers with a new, in-depth way of exploring the opportunities hidden within the retail landscape.
That sort of on-demand knowledge could show you which dresses priced between $10 to $30 sold best last week — and could be the difference between blindly buying into a declining trend and avoiding it altogether. The possibilities are numerous. But even if you have access to this data, the question is: how can you take advantage of it to make critical decisions, increase customer loyalty and boost sales?
Big data, big opportunity
Big data is exactly what it sounds like: information on a large scale. But more commonly it means a large collection of structured or unstructured data that is pieced together by computers and organized in a way that makes it possible for humans to derive valuable insights rapidly. Used in this way, big data can be the key used to answer previously unanswerable retail questions about what people are buying, the prices they’re paying for them, and when are they buying.
As such, retailers can make better pricing and assortment decisions, reduce markdowns and decrease costs of dead stock by analyzing what’s happening in real time or over a specific period. The focus of the analysis can be as broad as the entire world or narrowed to a single category, sub-category or trend.