Alex Landa: Writer, Editor, and Content Strategist

10+ years of experience in storytelling and content management.

  • 4 Reasons You’re Getting Bad Leads From PPC

    You need leads for your newest product or service. You’ve already written blogs, posted organic copy on social media, sent out a press release, and so on. After all that, you finally tried setting up some pay-per-click (PPC) ads on search engines. You thought you did everything right, but you’re not getting any good leads – or any leads at all. You’re putting all of this money into something that should be a passive win as long as they’re up, but it seems like you’re only getting bots or weak leads subscribing to your newsletter or checking out your site. Hope isn’t lost forever. While there may be some reasons why you’re getting bad leads from PPC, there are also some easy fixes you can do to make the most out of your ads. Here are four reasons why you may be getting bad leads from PPC and how to fix it.

    Reason 1: You’re not using the right keywords

    Focusing on keywords should be one of – if not the first – things you consider when setting up a PPC ad campaign. If you’re getting bad leads from PPC, it may be because the keywords you chose aren’t relevant to your target audience, you’re thinking too broadly or too narrowly, or maybe you’re using negative keywords. Look at what’s doing well across the board already. Are certain blogs doing well? Are previous campaigns getting the right leads? Take the time to look through your analytics and see what your audience is already looking at and hop on that momentum. For example, if you want to promote a concert in Dallas, you might want to say “concerts in Dallas this weekend,” “Dallas concerts,” “rock music Dallas,” or similar keywords. Don’t go too broad and say “Dallas events” or too narrow like “Dallas rock concerts Saturday night.”

    Reason 2: Your copy isn’t up to par

    PPC ads aren’t magic posts that work by themselves by just using keywords. The copy you put out there can be a huge difference maker. Most importantly, you need to consider the attitude of your audience. Do they like comedy, puns, satire, or memes? Or are they straightforward, ROI-minded, and just want to be presented with the facts? An ad has to stand out, but standing out is subjective. For example, Converse used Google PPC ads to create a dialogue with potential customers using out-of-the-box keywords like “how to talk to girls” and used clever ad copy within the results. The ultimate goal is to stand out, get a conversation going, and make the reader want to learn more.

    Reason 3: You’re targeting the wrong audience

    Whoops. This entire time, you’ve been trying to connect with a particular group of people, let’s say IT decision-makers in the Boston area, but you went too broad and just targeted Boston as a whole. You might want to set your targeting preferences towards people already searching for competitors, who have an interest in technology, and live in the greater Boston area. Make sure you’re getting specific, but not too specific. You’d want to avoid going too general like only targeting people interested in technology or only people who live in Boston. A safe way to start is to target a lookalike audience and pick out the traits that work best. Consider interests, habits, devices used, areas, and so on.

    Reason 4: Your ads are working, but your website isn’t

    The ultimate goal of PPC ads is to get people interested in your project or service. You can have the flashiest ads, the right targeting, and the perfect set of keywords, but none of it matters if your website doesn’t get the job done. All parts of a marketing campaign have to be synergistic, so your ad copy should reflect the tone of your website, the site shouldn’t have broken links, and the CTA in the ad should reflect and exemplify what you’re trying to sell. If you create an ad saying how much money your product will save their company, then your landing page should be loaded with stats, figures, and examples to prove it. You got them on your site, now really hit it home.

    The takeaway

    PPC isn’t easy to perfect. It takes lots of experimenting with copy, targeting, bidding, and so on to get the right leads for your business. With so many variables to consider, changing one thing at a time will help you measure results in real time without having to play a guessing game to see what works and what doesn’t. Try changing your copy but leave your targeting as-is and see how that goes. By working with a PPC expert or a team of PPC experts, you can rest easy knowing that all of the above worries can be taken off your plate so you can focus more on what you do best and grow your company in your own way. 

  • Letter from the Editor – ODSC Guide to Deep Learning

    In one of my projects to obtain more email signups for my role at ODSC, I create a downloadable PDF, the “ODSC Guide to Deep Learning,” which contained highlighted blogs and recorded ODSC talks related to machine learning, among other pieces of content. At the end, I wrote a letter from the editor with my thoughts on machine learning. 

    Graphic design was done by Paxtyn Merten and Ava Burcham.
    Content compilation and descriptions by me.

    My first exposure to the world of machine learning and deep learning was a few years ago during my time at MIT Professional Education, where I worked in marketing to promote many of their short programs. The breadth of their offerings was impressive, but it was their courses with Vivienne Sze and Regina Barzilay that intrigued me the most.

    Machine learning and deep learning were both completely new to me. My only knowledge of data science was being able to do some basic sentiment analysis in R during my time as a graduate student at Boston University. Aside from that, I knew the terms, but that was it — they were terms under the AI umbrella to me. Now that I’ve been with ODSC for over a year, however, I see that “Machine Learning” and “Deep Learning” are umbrellas in their own right.

    Deep learning fascinates me. As a former psychology major and almost-psychologist in the past, the concept of neural networks is compelling. Although I find that there’s an art to content curation that makes understanding the brain quite useful, such as contemplating human motivation or the repercussions of media use, my (albeit now-limited) psych knowledge is only applicable at that level. This emerging field of study, rapidly growing in breadth and depth, is called deep learning for a reason — just like the brain, AI has layers and priorities. We’re only going deeper from here!

    Additionally, deep learning is incredibly reflective of how far we’ve come with technology. The amount of computational power required to run DL initiatives is impressive, and thus we need specialized computers and data centers to handle it. In my place of work, I sit near another company that uses ML and DL on a regular basis, and I’ll hear their computers from across the office when they’re running an overnight algorithm. If my laptop had emotions, it would feel inadequate in comparison.

    I appreciate how involved many DL engineers are with academia — even the working data scientist still reads papers for fun. I’ve spoken to a few practitioners who will print out papers and leave them in their bags to read on public transport or during lunch breaks. I suppose it’s expected in a field that’s so research-oriented. Now, if only us marketing professionals could be so engaged in research and academia.

    I hope that the resources provided in this guide prove to be helpful to you, your organization, your friends, and whoever else’s interest might be piqued. Each writer and speaker in this guide is an expert, and I’m sure that the insights they provide will enhance your knowledge and practice of deep learning. Feel free to let us know what you’re up to at work, in academia, or just for fun — we’d love to share it!

  • Letter from the Editor – ODSC Guide to Natural Language Processing

    In one of my projects to obtain more email signups for my role at ODSC, I create a downloadable PDF, the “ODSC Guide to Natural Language Processing,” which contained highlighted blogs and recorded ODSC talks related to machine learning, among other pieces of content. At the end, I wrote a letter from the editor with my thoughts on machine learning. 

    Graphic design was done by Paxtyn Merten and Ava Burcham. 
    Content compilation and descriptions by me.

    Maybe I’m biased, but I think language is pretty cool. But hey, I’m just a writer.

    Really though, I find NLP to be one of the most important developments under the data science and AI umbrella. Humans speak with words, not numbers, and it’s important that we do all we can to teach our machines to understand words and expression.

    NLP was probably my first foray into data science. When I was earning my master’s degree, I had to take a course on social data analysis, and in the world of media studies, that meant a lot of Twitter data. Considering my background has always been in writing, learning R wasn’t the easiest – but I found the results fascinating.

    In said course, my team and I examined (an at the time trending piece of) research on the consumption of red meat and cancer. Using sentiment analysis, we wanted to see if the public perception of the term “red meat” had any notable change before and after the World Health Organization (WHO) report on red meat emerged. 

    As you can imagine, not only did more people discuss “red meat” on Twitter (in reference to food), but also the tone of the discussion became more negative. The results didn’t really surprise us, but the process itself was exciting.

    Call me crazy, but I loved manual labeling. To teach our machine what to look for, we hand-labeled 200 tweets each, flagging for spam and other non-related content. We performed this research in the summer of 2016, so there were plenty of posts related to “red meat” in the context of politics. It felt oddly therapeutic to clean out all of the unrelated Twitter posts and just leave the relevant ones.

    I thank my time at Boston University for making me interested in data science. Since joining ODSC, I’ve only grown to appreciate it more. It’s not just red meat and hand-coding anymore. 

    When I first learned about NLP, I thought it was just a tool for social media, but I was clearly quite wrong. One of my favorite applications of NLP is definitely in the healthcare setting; being able to develop systems that can diagnose diseases, predict potential risks, and even help with treatment is quite literally a life-saver.

    NLP – and of course other AI techniques – will likely never replace humans completely. Though humans must learn to use AI to the best of our abilities. AI is becoming a powerful tool, and with 500 million+ tweets sent daily, we could use all the help we can with interpreting the vast amount of words that we write every day.

  • Letter from the Editor – ODSC Guide to Machine Learning

    In one of my projects to obtain more email signups for my role at ODSC, I create a downloadable PDF, the “ODSC Guide to Machine Learning,” which contained highlighted blogs and recorded ODSC talks related to machine learning, among other pieces of content. At the end, I wrote a letter from the editor with my thoughts on machine learning. 

    Graphic design was done by Paxtyn Merten.
    Content compilation and descriptions by me.

    One thing common thread I’ve seen at every stop in my career is the ever-increasing focus on using data to make informed decisions.

    Now, perched in a place to see day-to-day developments in the field, I’ve learned so much about various frameworks and languages, the unique ways that different industries are using AI, and of course, how strong the open data science community is in sharing its knowledge.

    There are definitely a few topics in particular that I’m paying extra attention to heading into 2019. Apache Spark’s MLlib is a frequent topic of conversation experts when I speak with data science experts, largely thanks to MLib’s remarkable scaling ability in implementing multiple ML algorithms. At this rate, I can see the scikit-learn library for Python becoming a common job requirement for ML experts – it’s appearing in social feeds, featured blogs, and job listings that cross my desk more and more. The TensorFlow framework is popping up in countless ML tutorials, so I’m hoping people stay active with it, even with newer libraries releasing frequently. This should be a no-brainer, but start looking into automated machine learning if you haven’t already, as it will help to increase the pace in which you can create more complex ML processes and algorithms. Let the machines work for you!

    Data scientists need to make 2019 a strong year to address the common “black box” problem. As Daniel Gutierrez, a data science consultant and frequent author for OpenDataScience.com put it, “I hope the trend of ‘explainability’ or ‘interpretability’ of AI will continue to be seen as critical to the continued acceptance of the technology.” Since machine learning may open up more chances for vulnerabilities to appear, developers need to be careful with their applications and to avoid leaving entryways for malicious attacks, and to be well-prepared to defend against potential adversarial attacks. It’s better to spend the time building up your defenses rather than going through the headache of resolving issues from hackers and malware.

    Whether you use the information provided in this anthology to learn a new tool, framework, or you’re now more invested in security and defense, I hope that all of the videos, blogs, and insights from the experts in this anthology prove useful for you. Whether you’re new to data science and machine learning or a seasoned vet, already working in applied data science or academia, or you’re just a fan of new technology, the open data science community is a good place to share knowledge and expand understanding of the most exciting topics in applied data science