Ever wonder how services like Netflix or Pandora choose media to suggest to you? If you’ve been reading this blog for a while, you’re familiar — at least a little bit — with recommender engines. In our post “How Machine Learning Will Affect Your Next Vacation,” we talked about the impact machine-learning recommender engines have on regular consumers. But here, we want to dive deeper and talk about the math and science behind recommender engines.
Corporate executives make dozens of business decisions every day — most of which are invisible to the general population. But one business decision of late stands out as a stark exception: CNN’s decision to focus on missing Malaysian flight 370 (MH370) long after other news sources moved on. Some CNN watchers grew tired of the endless coverage, especially as other big stories fought for attention elsewhere. Yet CNN seemed to be stubbornly obsessed with the missing flight. For the first time in a long time — possibly ever — people were questioning why an entire network was ignoring major human interest stories — including a sunken ferry with nearly 300 teenage casualties in South Korea and 200 kidnapped schoolgirls in Nigeria — in favor of one human interest story that was no longer news. CNN even became the butt of jokes for its coverage.
As a relatively new term, “data science” can mean different things to different people due in part to all the hype surrounding the field. Often used in the same breath, we also hear a lot about “big data” and how it is changing the way that companies interact with their customers. This begs the question — how are these two technologies related? Unfortunately, the hype often masks reality and worsens the Signal-to-noise ratio when it comes to our increasingly data-driven society. Rest assured, there truly is something deep and profound representing a paradigm shift in our society surrounding data, but the hype isn’t helping to clarify data science’s exact role in Big Data. In this article, we strive to put to rest many of the misunderstandings surrounding data science.
Conversion rates for online retailers are generally considered pretty dismal by most common measures. Imagine if only 1–3 percent of shoppers entering your store ended up making a purchase. Maybe you’d think of trying a new strategy. The new strategy employed by many online retailers is called retargeting — the use of search and display campaigns to target the 97 percent of visitors who came to your e-commerce site but didn’t convert, meaning they did not make a purchase, fill out a form, or request a demo or call. Retargeting works by keeping track of people who visit your site and displaying your retargeting ads to them as they visit other sites online.
Be careful what you wish for. Most of us probably heard that phrase at some point during our childhood, or perhaps even more recently. The point is valid. When wishing for something we tend to focus on the positives while ignoring the potential negatives. After all, who would wish for something that had a downside?
This beginner’s guide to retail analytics will help you get the most out of the data you’re probably already collecting.
When people shop online, they know they’re sharing personal data to be collected and used for marketing purposes. And yet we’re surprised by how many companies fail to do even the bare minimum of analysis with the data they’ve painstakingly gathered. By implementing just a few basic analytics techniques into your marketing practices, you can quickly gain insight into your site’s performance, your customers’ satisfaction, and the effectiveness of your marketing campaigns. And if you take your analytics a few steps further, you can actually predict what your customers want and gain specific recommendations about how best to market to them — whether you have hundreds of customers or millions of them.
Way back in 1982, Thomas Dolby famously quipped “She blinded me with science and hit me with technology.” While musical tastes have since changed, the sentiment is stronger than ever. Science, especially data science, can be an intimidating subject for marketers and other non-mathematicians.
It’s no longer enough to simply manage suppliers. Without a strong handle on everything that’s happening in your company’s supply chain, you will miss big savings opportunities.
In a recovering economy, procurement managers need to know more about how their organization's total supply chain works than ever before. As business starts to fire on all cylinders again, it’s clear things have changed. Not only are we experiencing the truly global, challengingly mercurial commercial landscape everyone talked about before the bust, but the need to keep a close eye on exactly what’s going on day to day has shattered old supply chain and procurement best practices — even from just a few years ago. But now, new spend analytics solutions offer impressive capabilities that allow supply chain managers both deep and wide views of their spend. With these tools, it’s possible to shine a spotlight on problem areas that were previously hidden in siloed expense reports, forgotten contracts, and operations with little or no oversight.
Shipping costs can easily get away from managers, but help is on the way.
Have you ever shipped a package across town and accidentally paid for overnight air to get it there? Or negotiated a contract with a shipping company only to have the contract ignored or forgotten about a week later? Or paid to have something arrive by 10:00am only to find out that it didn’t arrive until 3:00pm?