Online shoppers are getting antsy. They’re spending less time thinking about purchases before buying — or leaving a site — and drastically shrinking marketers’ opportunity time. We explore the latest trends in online personalization — and how to keep up.

We spend a lot of time talking about personalization in marketing. That’s because it’s both the most profitable way for our customers to leverage artificial intelligence and the most challenging. A new paper by Seth Earley, published in IEEE’s November/December issue of IT Professional, lays out some of these challenges and addresses how best to overcome them.“AI-Driven Analytics at Scale: The Personalization Problem” touches on many of the issues our customers face every day, including maximizing data scientists’ time, developing use cases in a timely fashion, and even using a Signal Layer to help expedite the process. It also cites a new study that examines exactly how much time companies must apply their personalization insights in a real-time setting.

In our new podcast series, The Data-Driven CMO, Opera Solutions Senior Vice President John Kelly interviews Julie Cary, CMO of La Quinta Inns and Suites, who has developed a strong reputation as both a traditional and digital marketer in her dde-long tenure with the company. She discusses the steps taken to integrate Big Data and data science into La Quinta’s everyday marketing operations.

Julie shares how she balanced marketing, technology, data science, and IT and secured the necessary budgets to develop a robust digital marketing environment with tangible marketing results.

Opera Solutions is pleased to announce its new podcast series, the Data-Driven CMO. With an increasing focus on technology and Big Data, the marketing profession must rapidly learn how to use such technologies as predictive analytics, machine learning, and artificial intelligence. 

At Opera Solutions, we have helped several global companies transform their marketing using our predictive analytics software. However, technology and products are only part of the story, and we wanted to bring the organizational and human perspective on this transformative shift to light. Our new podcast series opens the discourse through direct conversations with some of the most technically adept marketing leaders. 

What’s the big deal about Millennials? For starters, they’re now the biggest demographic in America. But their needs are unlike any generation before them. Here’s what retailers must know to succeed in marketing to this 80-million–strong population.

Millennials are now the largest living generation in the United States, according to the US Census Bureau. Defined loosely as those born from the early 80s to the early 2000s, Millennials account for roughly 25% of the US population at 83.1 million, exceeding the 75.4 million Baby Boomers in the country. They grew up with household computers, cell phones, and myriad other forms of technology, and they entered the work force during the Great Recession, which has made them price-conscious, tech-savvy shoppers — and robust savers. About half of them still live at home, and they’ve taken over the work force. While most are holding off on marriage, nearly 40% have children and 9% live with domestic partners, indicating that they’re starting families and establishing households (i.e. shopping and spending). Perhaps the most telling stats come from a study from FutureCast, which says that Millennials’ purchases are most influenced by their social circles and that they’re the first generation to influence older generations.

Everyone wants to belong. But how can that basic human need coexist with the commercial needs of a business so that both the customer and the business find the relationship beneficial? Big Data analytics makes it possible while also opening new possibilities.

History is replete with examples of human beings finding ways to connect with one another. We form tribes, congregations, clubs, and entire societies. We develop communications channels and pass specialized content through those channels. Even efforts to divide these groups and disrupt these channels simply engender new ones and actually help strengthen our identity as individuals. This pattern has continued to evolve with the advent of the digital age and extends to peoples’ relationships with products and services. Are you loyal to Mac or PC? Do you use Facebook or Instagram? Are you enrolled in Amazon Prime? Are you a Netflix subscriber? This need to identify one’s self with a larger group is a primal human instinct no matter how contemporary the group.

Calculating customer lifetime value has been a critical part of the marketing process for years, but new technologies are changing consumer behavior, and marketing professionals need to catch up.

Ever have a weekend where you just can’t stop spending money? Maybe you’re using some free time to stock up, or you’re decorating a room or planning a party. Or maybe you’re not spending more but just consuming more — a good television or movie series or, for example, plowing through a good book on your Kindle. This kind of behavior is called “clumpiness.” And while it’s not necessarily new, new technologies — particularly in the world of digital content — have shed light on this type of behavior for all industries. The problem is that it hasn’t been included in the formula marketers have been using for decades.

PART 4 of 4: Grow Revenue from Your Existing Customers: How Big Data Analytics Can Help

This post is the fourth in a four-part series. The third installment, “Existing Customers vs. New Customers — Exploring the Road Less Traveled,” discussed aspirational value and Big Data analytics’ role in attaining it. Here, we’ll discuss how all the elements described in this series fit together to drive revenue growth.

Businesses that overemphasize or exclusively focus on new business development to drive revenue growth are missing a substantial opportunity: their existing customers. Information about a business’ existing customers already resides in multiple areas of the overall corporate database, not just on a business development list. These customers’ profile information, consisting of demographic and psychographic details, preferences, and behaviors is there, offering a data picture that is far richer than the picture associated with prospective customers.

With 2016 already halfway over, retail marketers are deep into their 2016 campaigns, which are intended to acquire and retain customers, drive sales, and improve overall customer loyalty. But what are marketers doing differently to make 2016 better than 2015? Last year, the Commerce Department recorded only a 2.1% increase in retail sales (excluding automotive) over 2014 — marking the worst such performance since 2009¹ and a far cry from the 4.1% increase that the NRF projected². If retailers haven’t changed the way they approach marketing, are we in for more of the same?

Part 3 of 4: Grow Revenue from Your Existing Customers: How Big Data Analytics Can Help

This post is the third in a four-part series. The second installment, “Big Data Analytics: Necessary but Not Sufficient,” discussed three new ways companies can use Big Data analytics to improve a business’ ability to consistently improve revenue growth from existing customers. Here, we’ll discuss aspirational value and Big Data analytics’ role in attaining it.

People sometimes fail to notice opportunities that later seem obvious. Even when we do identify such opportunities, we may neglect to pursue them, or we may take an ineffective approach to pursuing them. One example of this phenomenon is how businesses think about driving revenue growth.

Go Beyond the Symptoms: How to Overcome Revenue Growth Challenges

Part 1 of 4: Grow Revenue from Your Existing Customers: How Big Data Analytics Can Help

This post is the first in a four-part series. Here, we discuss the key signs of slowing revenue growth and the first steps organizations can take to turn it around.

It isn't hard to Identify the symptoms of declining revenue growth. Often, more than one of the classic signs will be evident at any given time. A declining growth rate or a shortfall in revenue vs. the target level are perhaps the most obvious indicators. Operating expenses growing faster than revenue can be yet another strong harbinger of revenue growth challenges, as well as a potential indicator of a cost structure that is no longer aligned with the business’ sales capabilities. While they require more research and calculation to derive, declining market share or shrinking revenue growth rates vs. competitors are other telltale signs.