Over the last several years, a new breed of data-savvy marketer has emerged from the Silicon Valley start-up scene. Forced to make rapid revenue gains on a small budget to survive, these so-called “Growth Hackers” became experts in interpreting consumer behavior from the digital breadcrumbs left behind by their customers. By learning how to capture and read this data in real-time, these marketer/product manager hybrids were able to make frequent, incremental changes to both marketing and product, driving super-sized growth as a result.

Established companies trying to increase agility, accelerate growth, and expand their digital footprint should take heed. The term Growth Hacking may seem like just another buzzword, but it represents an important shift in the evolution of marketing and product management. This methodology has helped fuel the growth of hundreds of successful start-ups like Airbnb, Uber, Instagram, and LinkedIn, forcing traditional companies to take notice. In this article, we’ll illustrate how re-thinking your relationship with data can serve to unlock growth. We’ll also highlight some of the tools and technologies that can help facilitate this transformation.

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COVID-19 has created a business crisis that, paradoxically, can serve as a positive catalyst for marketers. The urgent need for both revenue growth and cost reduction provides a strong incentive for companies to unblock latent ROI by making changes that have historically been politically unpalatable. This starts with dismantling departmental silos that have long impeded growth initiatives aimed at unifying the customer experience. It also includes the rapid adoption of new technologies that can provide quick, direct ROI, like automation platforms. No/low-code automation technologies like Robotic Process Automation (RPA) and SaaS integration platforms like Segment can significantly streamline process-heavy marketing operations workflows. Marketing orgs that tear down silos and automate processes will be able to do more marketing with a smaller team while improving performance through better targeting. A win-win-win.

In this article, we’ll walk through some real-world patterns and technologies that illustrate how no/low-code automation can rapidly move the needle on reducing waste and improving marketing performance. But first we’ll answer a key foundational question: Why tackle the automation of marketing ops now?

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The demand for Customer Data Platforms (CDPs) has been rising steadily for the past several years. Yet, despite the growth of the CDP market, most people remain perplexed about what a CDP actually is. There are many reasons for this: CDPs are relatively new players in the MarTech world, the space is evolving rapidly, CDPs provide similar functionality to other enterprise technologies, and there are so many vendors that have adopted the CDP label. I pity the poor fool trying to learn about CDPs through the marketing materials from the vendors themselves. They all start to blur together and sound the same, even though they are all very different under the hood.

In this article, we’ll cut through the noise and answer some of the most commonly asked questions about CDPs. As a MarTech Practice Area Lead at Slalom, I’ve had the opportunity to get my hands dirty with several different CDP platforms, from strategy to evaluation to implementation, for companies across a broad range of industries. To help clear up the confusion, I’ve compiled a list of the 10 questions about CDPs that I get the most from clients, with answers to each.

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What if you could free yourself from legacy constraints and had the opportunity to build a modern marketing engine from scratch? How would you design it?

Despite the tectonic shifts in marketing technology that have occurred over the last two decades, day-to-day marketing operations for most organizations has remained largely the same. In part this is because the risk of making major alterations to a complex, revenue-driving engine while in-motion is high. But what if you could hit the reset button on your marketing engine and start over? What would it look like?

The Modern Marketing Manifesto is an attempt to answer this question. It is based on two decades of experience as a digital consultant working with large- and mid-sized companies across a wide range of industries to help them harness an exhausting stream of new technologies, including websites, intranets, online ads, emails, e-commerce sites, mobile apps, chatbots, virtual reality experiences, dashboards, data lakes and customer data platforms.

And although most companies don’t have the luxury of building a marketing engine from scratch, every organization can transform itself over time, given the right vision and leadership. For established enterprises like these, the Modern Marketing Manifesto provides a north star for transformation initiatives.

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This article was published on 7/12/2018 in The New Stack.

Cognito is AWS’s service for managing user authentication and access control. Although it was originally associated with AWS’s mobile backend-as-a-service offering (MBaaS), it has recently gained the attention of the serverless crowd, who are looking for ways to offload user management concerns to a service provider. Cognito solves this problem by providing a fully-managed, scalable and cost-effective sign-up/sign-in service—but at the cost of a steep learning curve. One of the reasons for this is because Cognito is actually comprised of two services—User Pools and Identity Pools (a.k.a. Federated Identities)—that are similar on the surface but different under-the-hood. These two services solve the same problem (i.e. authentication and authorization) but do so in very different ways. They can also be used separately or together, providing both flexibility and a source of confusion.

In this article, we’ll provide a gentle introduction to User Pools and Identity Pools, including the nuanced relationship between them. Before we dive into the explanation, however, we first need to explain two core security concepts: authentication vs. authorization and identity providers.

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The increasing prominence of machine learning in marketing is changing the way the industry operates. No doubt, this evolution is exciting, as it opens up new opportunities to connect with customers in ways that weren’t possible just a few years ago. But it is also terrifying, as the race between the data haves and have-nots intensifies, and marketing FOMO sets in. The good news is that while it may feel like everyone else has already become an AI marketing ninja, the truth is that the industry as a whole is still learning. According to a report by Salesforce, only 26% of business leaders have confidence in their organization’s ability to develop an AI strategy. And this statistic aligns with my own experience working with marketing clients across a range of industries: most companies are at the beginning stages of using data and machine learning in their marketing (but they are learning quickly).

The challenge for marketers right now is that the appetite for AI-infused marketing solutions is high, but the understanding of what AI actually does, is low. Unfortunately, this breeds an environment where vendors play fast-and-loose with the term “AI” and marketers are at-risk of being sold digital snake oil. As an engineer, this dynamic frustrates me. So, I’ve outlined five practical steps for getting smart about marketing with machine learning quickly, in an effort to help marketers become more informed buyers, whether they are evaluating AdTech solutions, interviewing agencies or building their own in-house data science teams.

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This article was published on 5/11/2018 in TechBeacon.

Serverless architecture has quickly emerged as the up-and-coming way to develop cloud-based applications. The benefits of serverless computing—zero server maintenance, out-of-the-box scalability, and ground-up support for asynchronous workflows—are just too good to ignore. It’s a developer’s dream.

But serverless architecture comes with a catch. These operational advantages carry a hefty price for developers: laborious configuration, immature frameworks, a lack of tooling, and a dearth of established design patterns. Until recently, going serverless meant throwing out decades of established tools and practices and starting over. In a nutshell, making serverless apps can be painful.

That’s why I selected the open-source Zappa as my serverless Python framework on a recent project for a dotcom start-up. I wanted to eliminate the burden of server maintenance for the young company and to design a cost-effective cloud architecture that was cheap when traffic was low, and would scale as the company grew. But I wanted these benefits without the unpleasant side effects that I’d encountered in the past when working on serverless apps. After doing the research, it became clear that Zappa embodied the culmination of the progress that serverless technology has made over the last 18 months. Here’s why.

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This article was published on 4/3/2018 in VentureBeat.

As a software engineer whose clients are marketing professionals, I’ve gained a great deal of empathy for marketers over the years. Not because marketing technology is evolving so rapidly (which it is), but because the proliferation and misuse of buzzwords is so rampant, with the term “AI” leading the way. This is unfortunate because AI — or more accurately, machine learning (ML), a subset of AI — has huge potential for marketers. Making matters worse, when vendors combine buzzwords like AI with other buzzy, ill-defined technologies like “marketing automation,” the result is a buzzword soup of confusion. How can marketers separate reality from the fake marketing news?

It’s important for marketers to gain a high-level understanding of how ML works. If you don’t understand the basic concepts, it’s much easier to be taken for a ride. For ML newbies, I recommend Microsoft’s Data Science for Beginners. This video series provides an excellent non-technical overview of ML.

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This article was published on 10/23/2017 in VentureBeat.

Since the invention of mass media, arguably, the primary focus of marketing has been to increase its level of personalization. Marketers constantly seek more targeted audiences, and strive to deliver messages that speak more directly to their diverse audiences. So, it's no surprise that AI and machine learning — with their ability to predict consumer behavior and make personalized recommendations on-the-fly — has captured the attention of the marketing world. But the elephant in the room is that advances in machine learning have far outpaced most marketers' ability to harness them

Unfortunately, this inability to personalize the customer experience is a huge missed opportunity. Customers now expect a tailored experience, including customized recommendations and a personal touch. And customers are willing to reward companies who provide it. According to Gartner, "By 2018, organizations that have fully invested in all types of personalization will outsell companies that have not by 20%." Even more alarming, customers are increasingly likely to dump brands that don't offer personalization. According to a 2016 Salesforce study: "… more than half (52%) of consumers are likely to switch brands if a company doesn't make an effort to personalize communications to them, 65% of business buyers say the same about vendor relationships."

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This article was published on 7/17/2017 in VentureBeat.

Advances in deep learning and other machine learning algorithms are currently causing a tectonic shift in the technology landscape. Technology behemoths like Google, Microsoft, Amazon, Facebook and Salesforce are engaged in an artificial intelligence (AI) arms race, gobbling up machine learning talent and start-ups at an alarming pace. They are building AI technology war chests in an effort to develop an insurmountable competitive advantage.

While AI and machine learning are not new, the current momentum behind AI is distinctly different today, for several reasons. First, advances in computing technology (GPU chips and cloud computing, in particular) are enabling engineers to solve problems in ways that weren’t possible before. These advances have a broader impact than just the development of faster, cheaper processors, however. The low cost of computation and the ease of accessing cloud-managed clusters have democratized AI in a way that we’ve never seen before. In the past, building a computer cluster to train a deep neural network would have required access to deep pockets or a university research facility. You would have also needed someone with a Ph.D. in mathematics who could understand the academic research papers on subjects like convolutional neural networks.

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This article was published on 4/3/2017 in VentureBeat.

The term “serverless architecture” is a recent addition to the technology lexicon, coming into common use within the last year or so, following the launch of AWS Lambda in 2014. The term is both quizzical and provocative. Case in point: while explaining the concept of serverless architecture to a seasoned systems engineer recently, he literally stopped me mid-sentence—worried that I had gone insane—and asked: “You realize there is actual hardware up there in the cloud, right?” Not wanting to sound crazy, I said yes. But secretly I thought to myself: “Yet, if my team doesn’t have to worry about server failures, then for all practical purposes, hardware doesn’t exist in the cloud—it might as well be unicorn fairy dust.” And that, in a nutshell, is the appeal of serverless architecture: the ability to write code on clouds of cotton candy, without a concern for the dark dungeons of server administration.

But is the reality as sweet as the magical promise? At POP, we put this question to the test when we recently deployed an app in production utilizing a serverless architecture for one of our clients. However, before we review the results, let’s dissect what serverless architecture is.

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