Category: Marketing

  • How To Think About Content Marketing Strategy

    How To Think About Content Marketing Strategy

    Stephanie asks, "Which comes first, content or the distribution strategy?"

    Neither comes first. What comes first is your goal, your objective. If you’re not clear on the objective, then you’ll create content poorly aligned to your goal, and then you’ll distribute content in the wrong places. Goals are everything for marketing, but in any situation where there’s substantial risk of wasting time, money, and resources, goals are doubly important.

    Content Marketing Goals

    What are some of the different content marketing goals you might be trying to achieve? One look at the average customer journey shows you multiple locations where content could have an impact.

    • Awareness. You need content to help people to become aware of a problem.
    • Consideration. You need content to help people to know you can solve the general problem.
    • Evaluation. You need content to show people to explictly understand how you solve their problem.
    • Purchase. You need content to convince or enable people to buy your product or service.
    • Ownership. You need content to make ownership easier.
    • Loyalty. You need content to reinforce brand loyalty.
    • Evangelism. You need content for evangelists to share with friends and colleagues.

    What are you creating content for? Which of these problems along the customer journey will you be using content marketing for?

    Bear in mind, of course, that content is inherently heterogenous. You can make content to fit different goals; one blog post could be explicitly a customer support piece, reinforcing ownership and encouraging loyalty. One YouTube video could be on problem awareness.

    Thus, you probably won’t have a singular content creation strategy or a singular content distribution strategy. Instead, you’re likely to have multiple, simultaneous content marketing strategies at work, all along your customer journey. That also means different owners of the different types of content and different parts of the journey. How do you keep it all straight?

    Content Marketing Strategy

    Given the disparate goals you could be working to achieve, you’ll have three layers of content marketing strategies.

    • Organizational strategy. What role does content marketing play in your overall organizational marketing strategy? For example, if your company is struggling with demand generation, then a good chunk of your content marketing focus will be on awareness and consideration.
    • Campaign strategy. In any given campaign, what role does content marketing play? For example, if you’re running Facebook ads, where do those ads go?
    • Discrete strategy. For any given piece of content marketing, apply Bob Stone’s direct marketing framework to it. Who is the audience? What’s the importance of that particular piece to the audience, the reason they’d consume it? What’s the creative, the contents?

    When we think about our content marketing this way, the question of content or distribution coming first fades away; both questions are part of a larger series of questions about our overall content marketing governance.

    If this sounds overwhelming, it’s only because it’s abstract and difficult to visualize. Let’s put it into concrete terms that make logical sense.

    • Organizational strategy is like having a restaurant. What’s the theme of the restaurant? Italian? Sushi? High-end steakhouse? This is where the big questions are asked and answered, like marketing budget and the overall audience you serve.
    • Campaign strategy is like the menu at a restaurant. What dishes go on the menu, and in what order? Tirimisu might be nice, but not as an appetizer. If you think about the customer journey as the courses of a meal, you’re going to have some campaigns for each – or you should.
    • Discrete strategy is like the individual dishes and recipes at a restaurant. How do you cook the dish? How quickly? With what ingredients? If scallops take four minutes to cook and bacon takes seventeen, you’d better time it right to have both done at the same time. With content marketing, this is where you assemble your content, as well as the timing, distribution, and placement.

    Like a restaurant’s theme, you rarely change the organizational strategy.

    You might change the campaign strategy infrequently, but like a restaurant’s menu, there can be substantial costs to doing so.

    And like a restaurant’s dishes, you’ll make frequent changes to how you cook a dish as you work ever harder to be more efficient and effective, while still delivering what’s promised on the menu.

    Content marketing strategy is much more than just cranking out content and hoping someone consumes it. When you do it right, you’re building an entire product line, because content marketing is a product. Treat it as such.


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  • Who To Follow on Social Media For Professional Development

    Who To Follow on Social Media For Professional Development

    A friend recently asked what sorts of great B2B content marketing stuff I’ve read lately that I could recommend and I realized… I haven’t read any in a really long time. Not because there isn’t any to be had per se, but because who and what I read has diverged very far from marketing over the past few years.

    What I study now isn’t marketing, but almost entirely pure data science and machine learning. I take what I learn and I apply it to the discipline of marketing, but the people and material I need to learn from in order to keep growing aren’t people in the marketing realm for the most part.

    Why? Because I’ve changed over the years. I’ve learned what I need to know to be competent in marketing – and yes, there’s always more to learn – but in terms of professional development, the area where I have the most growth potential isn’t in marketing, but data science and machine learning.

    This is a critical point for all of us. Who you follow on social media for professional purposes, what you follow, what you read should always be changing. You shouldn’t be following the same people all the time; what you consume should change as your skills continually upgrade.

    Obviously, this excludes those folks you follow as friends and acquaintances. By no means does this advice apply to people you actually like and enjoy hearing from. It’s exclusive to professional development and training.

    How To Know You’re Following The Right People on Social Media

    Here’s a simple test to determine if you’re following the right people on social media for professional development purposes. Look at the last 20 posts from your social media feed of choice. How many of them contain something you don’t fully understand, something that you need to investigate?

    If the answer is none – if nothing surprised you, confused you, or intrigued you – then it’s time to change things up.

    When I look at my feed, probably 75% of it is stuff that I don’t fully understand. Over the last weekend, I was watching videos from Max Kuhn, reading tutorials from Julia Silge, and hacking away alongside Shirbi Ish-Shalom. I was confused most of the time, and it took more than a few watches to get what they were saying, to understand the topics they were showing, but when I was done, I had a much better idea of what they were trying to share.

    How do you find those people? Start at the major hubs for your domain of expertise until you find some content that is new to you, and then start following those authors.

    What If You Can’t Find the Right People?

    The more advanced a practitioner you are, the harder it becomes to find people who are talking about and sharing stuff that you don’t know on social media. The pool of eligible candidates grows thinner and thinner every day as you advance your skills, because most people stop learning and growing substantially at a certain point.

    When that happens – when you reach a point where almost nothing is new in your field or domain, what do you do?

    You have two straightforward choices: blending or research.

    Blending is the practice of taking up a second, parallel domain and learning from it, then blending it with your primary domain of expertise. This is what I do – combining marketing with coding, marketing with analytics, marketing with data science, marketing with machine learning and AI. It opens up tons of new possibilities for you. The downside is that you have to be able to think in parallel across domains, which is challenging. Imagine being a sushi chef and learning plumbing – the lessons you can port from one domain to another may require considerable mental gymnastics to make them work (but it can be done).

    The second choice is research. At a certain point, the best stuff in your domain of choice isn’t going to be on social media posts. It’s going to be in academic research, or primary research you perform yourself. You won’t be consuming other people’s data and content as much as producing your own, to answer the burning questions that can’t be answered otherwise. The downside of this approach is that it’s a lonely one – it’s lonely at the top, especially if you’re researching things that are proprietary and you’re limited in who you can ask for help.

    Either approach is how you develop new capabilities when you’ve tapped out your usual sources, when you’ve hit diminishing returns on what your peers and juniors in your field have to share.

    It’s Always Lonelier At The Top

    No matter what, recognize that as you become more skilled, the pool of people you can confer with and be challenged by always gets smaller. Too many people stop making serious efforts at learning after a certain point in their careers, their lives. We see this all the time – people parroting old SEO advice, people recycling the same content with nothing new, people reciting the same platitudes ("just create great content!" and "be more human!") with nothing for the advanced practitioner.

    Recognize that your journey will grow tougher with every step, but if you’re dedicated to being the best you could be, then that toughness is what makes it possible for you to be the best. It filters everyone else out that isn’t striving for the same professional heights as you are.

    See you at the top when we both get there someday.


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  • How to Build a Data-Driven Social Media Strategy Plan

    How to Build a Data Driven Social Media Strategy Plan

    Emily asks, "What are your favorite resources for social media strategy planning?"

    The overall strategy for social media planning is and should be tightly coupled to your overall digital marketing goals. What are you trying to achieve as a department or organizational unit? Demand generation? Lead generation? Ecommerce sales? That’s the starting point.

    On my website, I care about four things – consulting opportunities, public speaking opportunities, book sales, and newsletter subscriptions.

    Determine Channel Effectiveness

    Once you have a clear idea of what goal you’re measuring towards, build and run an attribution model. If you’re dealing with mostly digital data, I recommend a machine learning model based on techniques like Markov chain modeling or Shapley values (I prefer Markov). What this achieves is an analysis of which channels already help to drive those conversions.

    For example, I recently changed how I publish content – I’m back to traditional blogging. (the reasons are detailed in this post) When I look at my attribution model for how my site is doing, it’s definitely the right choice. Here’s my model for January 2020:

    2020 Attribution Model

    We see that organic search was the primary driver of conversions, impacting 58.9% of conversions and yielding 184 conversions, followed by email at 16.5%, yielding 52 conversions.

    Now, let’s look at how January went, with my new strategy:

    2021 Attribution Model

    That’s a big difference. Organic search is now 51.2% of conversions impacted, but the absolute number is more than double at 387 conversions. That’s a huge jump, to double the number of conversions from organic search year over year.

    Email continues to bring up second place with 36.2% of conversions impacted, with 273 conversions, also a nice gain over the previous year.

    What about social media? After all, we’re trying to build a social media strategy plan here. In both models, Twitter has driven the most social conversions; Facebook was doing okay in 2020 but has dropped down to the middle of the pack in 2021.

    So, I have a good sense of which social media channel is working. Now the question is, how do I get more out of that channel?

    Determine Channel Tactics

    Once we know what channel’s working, it’s time to figure out WHY it’s working. If we can crack the code about what makes a certain channel tick, then we can do more of what works. To accomplish this, we first need to know what data is available.

    For this exercise, I’m looking at Twitter. It’s consistently a winner, year-over-year, in terms of social media channels for me. Why? What am I doing on Twitter that’s resonating?

    To answer this question, I’ve got to get Twtiter data. Right from the platform interface, I can export tweet-level data. What kinds of data?

    Twitter platform data

    While this spreadsheet is good – with all the major features like likes, retweets, impressions, etc. it’s not very good for understanding what’s working and what’s not. A big part of a social media strategy plan is understanding the content itself, and that’s not included in here (and never will be).

    What is included is the text of my tweets. Using basic text statistics, I can look for the words and phrases I use the most, then engineer new numeric fields based on those topics. For example, I’d count how many times I use the phrase "data science" or "social media".

    Once I have that information, it’s time to figure out if any of these metrics have a mathematical relationship to the outcome I care about, which in this case is clicks on the URLs in my tweets. Why? In order for someone to do business with my website, they need to get to my website. How do they do that? By clicking on links in the content I share that goes to my website.

    To build a model of what predicts my desired outcome, I use a machine learning technique called XGBoost to identify which participating variables have the strongest mathematical relationship to my target outcome, clicks. Bear in mind, I have to remove things like linear dependencies – metrics like engagements include clicks, so those variables shouldn’t be used in this kind of prediction. I also have to remove things like zero values – metrics which are always zero, like all the promoted tweets metrics (because I don’t promote my tweets).

    What’s the result?

    Twitter Channel Tactics

    Impressions is the greatest predictor of whether a tweet will get URL clicks, followed by retweets and detail expands. This is where domain expertise now comes into play. As a social media marketer, what things can you do to generate more impressions, more retweets, more detail expands?

    Impressions is pretty straightforward – you could pay to promote tweets. You could tag people in your tweet. You could use more popular or more niche hashtags. You could cross-promote your tweets on other social networks. You could include tweets in email marketing.

    Retweets would follow similar tactics, plus the very obvious asking people to retweet more important/valuable tweets.

    Detail expands is even easier – this feature isn’t available on tweets that don’t require it, like links and basic text tweets. Images and video do – so this would be encouragement to upload more media tweets.

    Is this the answer? Not quite. This is the testing plan, because regression analysis of any kind (which is what XGBoost is) only ever proves correlation and you learn in stats 101 that correlation is not causation. So the next step in our process would be to build a testing plan, testing each of these predictors – impressions, retweets, detail expands – individually and seeing if URL clicks increase. I’d set up a three week plan and tweet the same amount about the same topics, to the same audience, but with a focus on the tactics above for each of the predictors, one per week.

    After three weeks, I’d examine my results and identify whether or not the test was successful in generating more URL clicks than previously, and for the winning tactics, I’d now have a recipe for my social media strategy on that channel.

    Finally, I look at the analysis in terms of content and topics above. Why aren’t the topics I’ve identified a good predictor of URL clicks? Why aren’t people clicking on a specific topic more than others? The lack of predictive value for specific topics hints that I might not be sharing the kind of content my audience wants from me, so I should consider collecting data on a very common marketing topic and seeing what topics are most popular, then test that as well.

    Assemble the Final Plan

    After doing the above evaluation for as many channels as my attribution model suggested, I’ve got a social media strategy plan fully baked:

    • I’ve identified whether social media is working for me or not
    • I’ve identified what channels are working for me
    • I’ve identified what tactics on those channels get results
    • I’ve built a testing and action plan for those tactics to prove their effectiveness

    The final piece of the plan is something known as perturbation – introduction of randomness. One of the dangers of rigidly following a data-driven plan is over-optimizing, focusing only on what works with no testing of new things, even if those new things deliver less-than-great results. Anywhere from 5-20% of your marketing should always be perturbation, always be testing something new and different for a period of time to find out whether it’s appropriate or not.

    Perturbation testing could be within a set of tactics or channels, such as trying out Tiktok or Clubhouse, or it could be very granular, such as testing out new content or themes. Whatever it is, you always want to be testing new things to make sure you don’t miss the boat on an important sea change in your audience.

    So, that’s the data-driven answer on how to build a social media strategy plan. It will take time, resources, and expertise to put one together, but once you have it, you will stand behind it confidently.

    Shameless Plug

    If this sort of planning is something you’re interested in but you don’t have the time or knowledge base to do it, my company, Trust Insights, can build a data-driven social media strategy plan for you.


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  • How To Diagnose Marketing Conversion Funnels

    How To Diagnose Marketing Conversion Funnels

    Shannon asks, "What can I do differently to increase my conversions between home page —> contact page —> contact forms filled out?"

    This is a more complicated question than you might initially think, because there are several factors at play here. Let’s think back to 1968 and Bob Stone’s direct marketing framework. In that framework, there are three key factors that yield conversions to sales (remembering that this is in the era of physical direct postal mail and the Internet wasn’t a thing):

    1. List
    2. Offer
    3. Creative

    Stone posited that most direct mail campaigns fail because of one or more failures in this process. Do you have the right mailing list, do you have the right offer, and do you have the right creative?

    Direct Website Marketing

    Fast forward 53 years and the same logic still works for our marketing. In the context of your website and improving conversions, we have to ask the same three questions, albeit slightly more abstract.

    1. Do we have the right audience? This is a critical question, especially as it relates to things like SEO. If the traffic coming to your website is interested in, say, marketing books and we’re selling coffee, it doesn’t matter how good your website is, how clever the design is, how many A/B tests you run. You will never increase conversions because you’ve got the wrong people on your site.
    2. Do we have the right offer? Are we selling what our audience wants to buy? If our audience is interested in coffee, and our coffee is twice the price and half the quality of our competitors, we’re not going to do very well. If our audience is interested in regular coffee and we’re selling decaf, we’re going to have an uphill battle. Our offer to our audience has to be competitive.
    3. Do we have the right creative? This is where user interface and user experience come into play. Does our website’s design, navigation, and customer experience help or hinder conversion? Do people get lost on our site? Do we distract them from the things we want them to do?

    How do we know if we’ve got the right audience, the right offer, the right creative? This is where analytics and marketing data science come into play.

    The Right Audience

    Determining whether we have the right audience is a blend of several different metrics. First, we determine where our traffic comes from. If it’s from a source that has audience data, like organic search or Facebook, we make the determination about our audience’s interests. Go into Facebook Page Insights or Google Search Console and see what your audience is interested in. What words or phrases do they find your site for?

    Search Console Query Results

    Are these search terms I want to be known for? Will this attract the right audience to my site? If this isn’t right, if this isn’t the audience I want, I need to change my demand generation strategies.

    The Right Offer

    There is no substitute for asking your audience what they want. I do this frequently, in things like email surveys, on-site surveys, etc. Every website owner should have a continuous survey in place asking users what they came for and whether they found what they were looking for. There is no analytical substitute for asking people what they want, why they’re at your place, and what you can help them with.

    If your audience is the correct one but they’re not responding to your current offers, then ask them what they would respond to, what they want. You will either find you need to change your offers, or change audiences (because your audience is in fact not the correct one if no one wants to buy what you have to sell).

    The Right Creative

    This is where tools like Google Optimize, Microsoft Clarity, and many others help us determine the user experience on our sites. What things do people respond to? What creative choices are working for or against us? We check things like Google Analytics Site Search data (assuming we have site search set up and turned on) to find out what people are searching for on our website. If critical, core offerings are being searched for, it means our navigation isn’t leading them there naturally.

    Microsoft Clarity
    Microsoft Clarity dashboard of UI issues

    This is where we do extensive A/B testing of layouts, text, images, the works – anything that affects the customer’s path to purchase. Running tests with a tool like the Trust Insights Most Valuable Pages assessment will help here.

    The Wrong Order

    Here’s where so many marketers go wrong: the order of operations. Too many marketers start with creative; they rush out and start doing A/B tests, they commission a website redesign, they hire a new product photography shoot. Creative is last in priority! Do the steps in order.

    If you have the wrong audience, nothing else matters. You won’t sell anything.

    If you have the right audience and the wrong offer, it doesn’t matter how your site looks – people still won’t buy.

    If you have the right audience and the right offer, only then do you need to fix up your creative and optimize it.

    Conclusion

    Fixing up a conversion funnel isn’t rocket surgery. The challenge is to identify what’s broken and fix it, in order of importance. Make sure you’ve attracted buyers, presented them with a compelling offer, and gotten out of the way of the purchase process as much as possible.


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  • Can You Trust Historical Marketing Data?

    Can You Trust Historical Marketing Data?

    Shannon asks, "Are there identifiable website trends / metrics for speakers? I did some analysis on my website traffic for the last three years… can I trust the historical data and try to replicate it to increase my number of incoming leads?"

    As a fellow public speaker, you absolutely CANNOT build forecasts with any data about the speaking industry prior to the start of the pandemic. Period, end of story. Data from before the pandemic reflects a world, a reality that no longer exists. We are still in the middle of a pandemic, and speaking opportunities are radically different now. We’re on podcasts, we’re hosting digital events, we’re doing masterminds… anything except standing in front of a room of hundreds or thousands of people.

    This is going to be the situation still for months to come. At the time of this writing, many projections show the pandemic slowly receding around mid-year, barring any selection pressure which forces a mutation that evades vaccines and immune responses, thus prolonging the pandemic.

    Recent Marketing Data

    So, what does this mean for professional public speakers? It means the "digital only" era is going to stick around for a while. Probably by Q4 2021, barring those mutations, we’ll start to see hybrid events – a combination of digital and physical – emerge, and there will be a call for speakers to show up in person.

    In the meantime, we should be paying attention to recent data – data after April 1, 2020. That’s the data that reflects the world as it is. How different is that world? The number of people searching for keynote speakers is half of what it used to be at its peak before the pandemic.

    Keynote speaker searches

    Data sources: Google Trends and AHREFS. Chart shows average number of searches for keynote speaker terms by week for the past 5 years.

    In fact, when you look at just the 2021 data thus far compared to 2020, the number of searches is down 15.2% year-over-year – and that’s compounded on top of double-digit declines the previous two years. Now, is there seasonality to the search trends? Yes. There’s clearly a very seasonal, very cyclical ebb and flow to the search terms, a seasonality that you can still use to predict relative interest. But in terms of absolute interest, it’s been on the decline for years and the pandemic has only made that worse.

    If you were to extrapolate the current trends forward, you’d assume interest in keynote speakers would eventually go to zero. That’s not realistic, either. We don’t know what will happen with the events industry in the next two years, but whatever does happen, events will still need to have people presenting information at them in some fashion.

    What’s a Keynote Speaker to Do?

    If you’re in the speaking business, the time to diversify is now. Well, the time to diversify was a year ago, but there’s no time like the present. Search intent is down substantially, audiences have Zoom fatigue, and online events pay substantially less well than in-person events.

    What kinds of things should we be looking at? New books. Webinars. Podcasts. Subscription models. Masterminds. You name it – anything you can do to leverage the power of your brand and derive revenue directly from the audience, because events aren’t willing or able to pay as well as they used to – and even after the pandemic, it’s a big question mark whether fees will rise to pre-pandemic levels quickly or slowly.

    And get your vaccination as soon as you’re eligible. I foresee that it will be required by in-person events, at least to satisfy event insurance companies.

    The Lessons for All Marketers

    The most important lesson in all this is that your pre-pandemic data has to be taken with a large grain of salt for forecasting if your world has changed – which almost everyone’s has. Some businesses have flourished. Others have shuttered. No matter what your line of business, the fundamental changes in your audience have made pre-pandemic data unreliable for forecasting. Instead, be looking at recent data and discern your forecasts from that. Look for seasonality and cyclicality to provide a sense of when interest will ebb and flow, but glue your eyes to recent data, rather than looking at data from a world that’s gone.

    If you’ve been negatively impacted by the pandemic, diversify as much and as quickly as practical. The world we once knew isn’t coming back any time soon – and even when the pandemic is over, there will be large, permanent changes as a consequence. What those are, it’s too soon to tell, but we know they will be there.


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  • Does That Really Work?

    Does That Really Work?

    Sit down with any group of marketers who are self-aware enough to be able to think as both marketers and consumers, and you’ll inevitably hear this expression at some point in the conversation: "Does that really work?"

    Usually, it’s in reference to a specific tactic of some kind, particularly one that seems far-fetched or distasteful. Cold emails with rude or punishing subject lines. Social media tactics like mass-tagging people on LinkedIn or cramming your post full of hashtags on Instagram. Outright cold-calling.

    Why We Wonder

    Do these things work? For some, we do have data to demonstrate the efficacy – or lack thereof – of a given tactic. For others, we don’t necessarily have a lot of visibility. Even for those tactics that have questionable efficacy, we see other marketers persist in using them. Why?

    The answer could be one of a few reasons.

    Stale knowledge. This is most common in fast-changing disciplines like SEO. We see some marketers – really terrible ones – using tactics that are ancient, like keyword stuffing a web page. In cases like this, it’s old knowledge causing the problem.

    No knowledge. This is most common in very new marketing channels and disciplines where very little data is available; what tends to happen is early adopters set the tone of what "works" with no credibly researched data. What knowledge is "best practice" tends to be anecdotal. Examples of this include the early days of software like Tiktok or Clubhouse, where no data on efficacy exists.

    Cost-benefit ratio. This is most common in mass-scale disciplines like email marketing and cold calling, where the risk of doing something ineffective or distasteful are low compared to the relative rewards. Spam is the ultimate cost-benefit example; the costs of spamming are so low that even the slightest reward is worthwhile.

    Growth at any cost. This is most common not in tactics, but in specific kinds of companies. When management dictates unrealistic targets and goals, marketers have to pull out every possible option – even terrible ones – to make those numbers. In this case, no one cares what the costs are, just hitting their numbers by any means necessary.

    Do You Want to Know If It Works?

    Here’s the big question: of all the things we wonder about in terms of efficacy, which ones do we want to know really work or not? The answer here is shaded by nuance as well.

    There are some strategies and tactics which go against our values; for example, we as consumers might dislike being the recipients of cold calls. To find out they were highly effective could put us in a values-based bind – do we implement something we know we dislike, even if it’s legal? In those cases, it’s almost better we don’t know whether they work or not, because if we find ourselves coming up short on a key metric, we might be tempted to use the technique despite our objections.

    For other strategies and tactics, we need to test, to assess, as close to the format of a clinical trial as possible. Does the tactic work? What does it impact? What impacts it? Statistical modeling will help us understand the overall impact of a new strategy or tactic as long as we design the testing conditions well.

    In terms of professional development, make a list of all the strategies, tactics, and techniques for which you have questions about effectiveness. Prioritize them based on importance to you, and whether or not they go against your values. Remove the ones that are objectionable, and then start digging in. Get skilled up on them, understand them, talk to other marketers who use them, and use that list to build out your toolkit to become a better, more effective marketer.


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  • Measuring Social Media Engagement

    Measuring Social Media Engagement

    Alexei asks, "What are the most essential steps to analyzing social media data with ‘engagement’ in mind?"

    The steps for measuring social media engagement are the same as any other digital marketing metric, and follow the 3 Whats:

    • What happened?
    • So what?
    • Now what?

    Let’s dig into each of these steps.

    Social Media Engagement: What Happened?

    Our first step in dealing with social media data has to be around extracting data to understand what happened. Based on the channels and platforms we’re dealing with, that could be as simple as exporting the data straight from our social platform’s analytics to as complex as having to write custom code to extract individual post-level data.

    For example, with a platform like Agorapulse, I can export all my engagement data as a simple CSV file for analysis. For this example, I’ll focus on Twitter, but this works for any channel managed in the platform:

    Agorapulse

    And to focus on a meaningful objective, let’s export traffic data to my website from Twitter as a source in Google Analytics:

    Google Analytics Twitter traffic

    We’ve now got the basic ingredients for doing our analysis. Let’s take a quick peek first:

    Simple line chart of Twitter engagement data

    We’ve got some messy but usable data here.

    Social Media Engagement: So What?

    Here’s the key question. We’ve got Twitter engagement data, and we see the different types of engagement. So what? Does engagement mean anything, or is it just a bunch of noise? The answer to this question is something we find with basic correlation analysis. We’ll benchmark all our engagement variables against site traffic from Twitter. Does anything I do with Twitter from an engagement perspective result in traffic to my site?

    Let’s take a look:

    Spearman correlation

    What we find, using a Spearman correlation, is that there are three variables that have weak but detectable correlations to Twitter traffic: replies made to me, replies I made, and brand mentions.

    The variables that we would expect to yield traffic – like new followers, or published links, or likes – don’t. None of these have a statistically significant correlation in this example to traffic from Twitter to my website.

    It’s important to point out that this is not the answer. This is a hypothesis we need to test, and it’s unique to my data. Your data will inevitably be different, so you can’t take this answer and run with it on your own Twitter account. You must run this analysis with your own data.

    Social Media Engagement: Now What?

    So we’ve answered the key "so what" question: does any of our engagement data matter? We know three variables do. The question we must ask next is, what do we do about it? Of those variables, I have control over only one of them. I can’t control how often people reply to me. I can’t control how often people mention me. But I can absolutely control how often I reply to people, how often I jump into relevant conversations and participate.

    If unpaid traffic from Twitter was a priority, then my answer to "now what" would be to develop a plan for fostering lots of engagement. Identify Twitter chats I could participate in and jump into those. Start following relevant hashtags and jump into those conversations. Find questions to answer. I’d do this for 30 days and then measure again – did I achieve the goal of more traffic to my site from Twitter?

    If the answer is yes, then I’ve established that there’s not only a correlation, but a causative effect between engagement and website traffic. I now have a battle plan I can move forward with.

    If the answer is no, then I’ve established that there isn’t a causal link, and I need to go back into my data and dig deeper for why.

    Engagement Might Not Mean Anything

    The value of this analysis is that it provides us with a direction to go, working towards a meaningful goal further down our marketing operations funnel. We have to be prepared for an answer we don’t like: all the social media engagement might not mean anything if it’s not generating business results, so be prepared for that possibility.


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  • Options for Constructing Customer Monitoring Systems

    Options for Constructing Customer Monitoring Systems

    One of the pieces of advice I give frequently is to keep an eye on, to listen to your best customers. What are they saying? What’s on their minds? What do they care about, and how well aligned are you to it?

    For example, during contentious times, you may want to pause some social media activity as part of your crisis communications response plan. How do you know when to turn things back on? When your monitoring system lets you know that the crisis period has abated, you know it’s time to resume business as usual.

    So, what are some options for building customer monitoring systems? Let’s look at five solutions, in order of increasing complexity and cost.

    Simplest Monitoring Solution: Twitter Lists

    By far the easiest, a Twitter list helps you get a snapshot of what your customers are saying in bite-sized chunks.

    Twitter Lists

    It’s straightforward and simple, with a couple of catches.

    First, you have to manually check it. There are no reminders or alerts. Second, your best customers may not be on Twitter, or may not be active on it. Bear in mind as well that you’re monitoring for both individual people and brands, so be sure to add both to your lists.

    Basic Monitoring Solution: Talkwalker Alerts

    Want something more comprehensive than Twitter? Talkwalker’s free Alerts service (an improvement over the once-useful Google Alerts) provides exactly that – news and alert monitoring.

    Talkwalker Alerts

    Talkwalker Alerts are sent either by email or RSS feed, and you construct them using the same Boolean search syntax you use for Google searches. You’re limited to 10 clauses/operators in your searches, so if you’ve got a lot of customers and brands to monitor, you’ll need to set up multiple searches.

    If you want to see how I set this up integrating it with Slack and/or Feedly, watch this video.

    Inexpensive Monitoring Solution: Brand24

    Need something with an interface and some basic analytics? Brand24 is a news and monitoring system that has a simple, easy-to-use interface and solid monitoring capabilities.

    Brand24 monitoring system

    Featuring multiple media types and the ability to both isolate and download data, Brand24 provides you a reasonably thorough view of what your customers have going on. Monitoring systems like this give you the ability to dig in, set up multiple alert types, and export data for analysis.

    Brand24 pricing begins at US$49/month and gives you a rolling 30-day window of data.

    Robust Monitoring Solution: Talkwalker Pro

    Talkwalker offers a pro version of its monitoring service that to me is best-in-class. They support robust, rich data downloads and have tons of pre-built apps and systems you can use to monitor customers.

    Talkwalker Pro

    Powerful visualization and analysis tools help you dig into not only what your customers are saying or doing, but also identifying who is amplifying and spreading their messages. For monitoring what your best customers are doing, saying, and interacting with, there’s no better solution.

    Talkwalker Pro pricing begins at US$750/month and gives you up to 13 months of back data.

    Most Complex Monitoring Solution: Custom Build

    Depending on your needs, you may have to build your dream solution. For example, all monitoring services place restrictions on how much Twitter data you’re permitted to download each day. If you need every and any Tweet on a specific topic, you’ll have to build your own server and extract the data from Twitter’s Streaming API. Almost all services are restricted in the kinds and types of data you’re allowed to download from Facebook, so you may have to custom-code your own solutions there as well if there are very specific types of data you need access to.

    The advantage of custom builds is your software does exactly what you want it to, subject to the technical and policy limits of your data sources. The disadvantage is that you have to write and maintain your own software.

    Customer Monitoring Systems Roundup

    So, there you have it – five different solutions based on your needs, complexity, and cost. Which one is right for you depends on how much you’re willing to invest and what your data needs are.

    FTC Disclosures

    Talkwalker provided me with a free account for evaluation and review purposes. Talkwalker is also a business partner of my company, Trust Insights.

    Brand24 provided me with a free account for evaluation and review purposes. Brand24 is an affiliate of my company, Trust Insights, and a portion of any purchase is paid to Trust Insights as a commission.


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  • What Does It Take To Destroy a Digital Community?

    What Does It Take To Destroy a Digital Community?

    What does it take to kill a community, especially an online one? I’ve been watching a community I’ve been part of slowly fade away, and one of the most striking things about this community’s decline is how little it took to disrupt it. Out of a crowd of almost a thousand, the thing that began the community’s descent was… a single person.

    What happened? The community – let’s anonymize it to Digital Cockroach Marketing – started out as a tribe of like-minded folks discussing their favorite subjects. For months, the community grew and grew. Sure, every so often you’d get the inevitable spammer who would join, run in and spam their pitch everywhere, and then be unceremoniously booted out, but that’s par for the course on the Internet.

    Digital Cockroach Marketing’s managers, in their best attempts to keep engagement high, would run things like contests, talent showcases, and other fun activities, and for the most part, a decent crowd would participate. Until one day, the community managers decided to start a livestream and made Bob (also anonymized) the host.

    There were two problems with this decision. First, Bob was a close friend of the community managers. Second, Bob was a jerk. In a very short period of time, Bob’s new role went to his head and he started offering scathing commentary about other members’ contributions. Bob made sure to only invite the people who were his fans onto the Digital Cockroach Marketing livestream.

    Other prominent members of Digital Cockroach Marketing complained to the managers that Bob was acting like a jerk with a superiority complex, but because Bob was friends with them, they didn’t or couldn’t see it. And so, within a month, Digital Cockroach Marketing lost almost a dozen of its highest-profile, most active members. It’s no surprise what happened after – other members who weren’t Bob’s fans became less engaged, and Digital Cockroach Marketing began to wither, transforming into basically the Bob fan club.

    What could Digital Cockroach Marketing have done differently? Up until the livestream, most of the decisions about the community were made democratically, with members voting for changes and improvements. I suspect behind the scenes, Bob was the one who lobbied for a livestream to begin with. So, having and enforcing strict policies about how community management worked would probably have slowed the problem.

    What wrote the demise of Digital Cockroach Marketing wasn’t an individual decision so much as a series of failures, a governance problem:

    First, the community managers didn’t have clear policies on things like favoritism. That alone would have at least made the problem visible sooner. To this day, I’m still not sure Digital Cockroach Marketing’s community managers understand what the problem is, even after a raft of complaints.

    Second, Digital Cockroach Marketing didn’t have clear policies on member behavior enforcement. What behaviors are acceptable and unacceptable – and how rigorously are the rules enforced? When a complaint is filed by one member against another, how is the complaint evaluated?

    Third, the favoritism problem is one that occurs in many communities. It’s part of who we are as human beings, so we need checks and balances to counteract our natural biases. Digital Cockroach Marketing needed a member-led council specifically dedicated to community rule enforcement, separate from the community managers. That kind of oversight, particularly for larger communities, is essential.

    Finally, Digital Cockroach Marketing’s community managers had no analytics or data informing them, else they would have seen a fairly obvious decline in activity over less than a month’s time. That data should have alarmed them, made them ask what was happening that activity, especially among long-time, valued members was down.

    Had Digital Cockroach Marketing had any of the above precautions in place, the community managers might have been able to interrupt a significant problem, one that led to the decline of their community and its value to its members and stakeholders.

    I’ll leave you with one last thought, especially for digital community managers. Communities, especially online ones, are very fragile constructs. It doesn’t take much to destroy a digital community, just like it doesn’t take much poison to kill you. You don’t need to drink a gallon of cyanide to hurt yourself – a tiny bit will do. And a digital community doesn’t need more than one unchecked bad actor to poison the entire community.

    The reasons users tend to join a community – education and connection to others – are found all over the Internet, so if your community suddenly stops providing them, or creates a less hospitable environment, the switching cost to leave your community is low. If you want to keep a community vibrant and alive, you must create and rigorously enforce community standards absent of any kind of bias or favoritism. That’s hard to do – we are all human, and we all have biases towards people we like. But without it, our communities will just be shallow reflections of ourselves at best, and wither away at worst – taking any value the community provided with it.


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  • Consumer Alternatives to Avoid Marketing in Social Media

    Consumer Alternatives to Avoid Marketing in Social Media

    Oz asks, "Might some any of the major platforms ever go to a paid service where consumers can get some peace from ads? It’d be another thing that’s not good for marketers but it’d make me very very happy as a consumer."

    I don’t foresee the current ad-driven social networks going that direction, but that’s been the secret of success for networks like Slack and Discord. They make their money on the users instead; Slack charges for the instance overall once you’re past 10,000 messages (you can keep using it for free, you just get nagged a lot to buy stuff, and you lose message history) and Discord has paid upgrades to make your server better (improved voice chat, more emoji, etc.).

    Will either of the two supplement their main revenue streams with ads? Possibly. I can’t say for sure, but so far they’ve both been successful in the marketplace of ideas. Discord in particular has emerged as a real powerhouse, with tens of thousands of servers for nearly every interest.

    You raised an interesting point about Clubhouse in the comment you left – it’s unclear what their revenue model is, so there’s every chance that it will become an ad-driven network, in which case all the data folks have provided there will be leveraged at some point for more ads. That’s the key to understanding which social networks are more or less likely to have privacy issues: the revenue model. If it’s not clear how a company is making money, then chances are we are the product, not the customer.

    What’s the bigger picture solution? In the end, the centralization of social networks will inevitably give way to a decentralized solution of some kind. Every successful technology we’ve put on the Internet has eventually decentralized for one simple reason: scale. After a certain point, scale becomes unmanageable, or the company has to do some crazy stuff just to remain operational. When you look at how Facebook operates behind the scenes, they’re having to use massive amounts of AI just to manage their code, never mind adding new features.

    The challenge is that no decentralized solution for social media has become available yet that mirrors the ease of setting up a website or opening up an email account. Networks like Mastodon have made a good effort, but their complexity and lack of user friendliness have been substantial barriers to adoption. Another reason Slack and Discord have become so popular is that the hosting companies, which is what they effectively are, don’t have to moderate or manage the communities under their banner, whereas centralized networks do.

    Twitter has made slow progress on its Bluesky initiative, which aims to do this. Once decentralized, like email or the Web, then there’s no central authority collecting our data. That opens up a whole host of other problems, but there’s a tradeoff for everything – and in general, decentralization increases the opportunities for privacy. And marketers will still be able to operate in those environments; marketers run websites and send emails all the time. It’s just easier to tune out than a centralized location like Facebook.

    So for now, if you want more privacy and fewer ads, look to networks like Discord and Slack, eschewing the Facebook ecosystem. Encourage your friends and family to join you on your own server, and give yourself a breather from the watchful eyes of Zuckerberg and company.


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