Most redemption programs suffer from the same challenge: delivering rewards that customers actually want. To make this possible, the programs offer ever-more rewards, which puts the onus on the customer to find desirable ways to spend their points. In the end, redeeming points can be more of a chore than a reward, ultimately diminishing the value of the very program that was supposed to create value and differentiation in a crowded space. But with millions of customers, no one (or even 100) reward(s) will meet the desires of everyone. So what are credit card issuers to do? How do they put the value back into these programs, so customers are incentivized to choose one card over another?
When to nudge a delinquent customer for a payment — that is the question. Or rather, that is one of many — many — questions that Signals are helping telecom companies (telcos) answer. Because when to call and ask for payment can actually help determine whether to call at all, and eliminating calls can lead to significant savings. So how do Signals do this? And more important, how can telcos, specifically, take advantage of these Signals to improve customer experience and maximize bottom-line impact?
We’ve all been there. That place in the airport where you’ve just learned your flight has been canceled. If the weather’s bad, you can understand and roll with it. But if it seems arbitrary and royally screws up your plans — well, that’s another story. As a customer, you wonder, “Why do airlines do this? And how do they decide that my flight is canceled when others are not?” But airlines are asking their own questions, namely: “We have to cancel X number of flights, but which flights should we cancel to minimize the loss of revenue and customer loyalty?” Here, we delve into both sides of the issue, and the answers should provide a little context — and hopefully quell the frustration for everyone.
Marketing automation's time has finally come, but it's been a long, tortuous road.
I’m a big fan of marketing automation. My experience goes back a few years. I was just getting settled at a new company when our CFO received a renewal invoice from a marketing automation vendor. She didn’t know how we were using the software, and neither did I.
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.
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.
Think you know English? Think again. See if you have what it takes to teach a computer how to understand humans.
Anyone who has tried to learn English as a second language is only too familiar with its many — many — challenges. In addition to idioms, sarcasm, and a wide array of meanings when combined with various prepositions (think: make up, make out, make it, and of course, makeup), there’s also pop culture, trends, products, and more to keep straight. Luckily for us, we’re human, and even those well established in their native languages will be able to speak and decipher English with enough practice and exposure. But what about machines? How do we even begin to program them in a way that they can read and understand sentiment? Answer: very carefully. The process requires machine learning data scientists to use Natural Language Process (NLP) techniques, a form of advanced analytics. They use these techniques to build models that can decipher sentiment and weed out the meaningful information among the noise.
A new report, featured in The New York Times, highlights the benefits of Big Data and machine learning in the airline industry and beyond.
The travel industry has always been a vast data collector. Every airline reservation, every hotel booking, every car rental ends up in a conventional database of structured data. But today Big Data — the unstructured data that includes ratings on blog sites, likes on social media, conversations with call centers, customer clickstreams, and more — is becoming increasingly important in determining how travel companies keep customers coming back.
The New 1:1 Marketing Approach in Four (Not-So-Easy) Steps
The concept of 1:1 marketing generated a lot of hype — ten years ago. It changed the way companies reached their customers, creating that illusion that personalized marketing was actually… well, personal. But it also raised expectations of customers. They became acutely aware that their actions — especially those online — were tracked and used for marketing purposes. Eventually, they ignored companies’ attempts to advertise based on a search term or status update. “Really? Just because I posted that I’m baking cookies with my son does not mean I want to buy Chips Ahoy right now.”