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<\/figure>\n\n\n\nHere\u2019s an actual backlog I prioritized using <\/em>PXL<\/em><\/a> for a specific page experience<\/em>.<\/p>\n\n\n\n In theory, prioritization is a function, or even an algorithm, that takes in a variety of inputs (including constraints, historical data, qualitative insights, stakeholder wishlists, and subjective experience) and delivers a clear and simple output: an ordered list of actions that maximizes return and minimizes investment. <\/p>\n\n\n\n In practice, however, prioritization models are often used as cursory tools to get your boss or client off your back.<\/p>\n\n\n\n >\u201cHow are you prioritizing initiatives?\u201d<\/em><\/p>\n\n\n\n >\u201cWe have this scientific formula that stack ranks initiatives based on an expected value computation of impact, ease, and business relevance.\u201d<\/em><\/p>\n\n\n\n >\u201cOh very nice – carry on, then!\u201d <\/em><\/p>\n\n\n\n But do you really live and die by three dimensions, summed up and then divided by three, on your cloud-hosted collaborative Google Sheet? Do you really equally weigh keyword difficulty, keyword search volume, and business relevance, and then write content based on their composite score?<\/p>\n\n\n\n If you do, congratulations – you\u2019re Lawful Neutral<\/a>. But this rigidity will probably result in lower than optimal returns. Why? <\/p>\n\n\n\n I\u2019ll be honest with you: there\u2019s no plug-and-play template at the end of this essay that will solve all of your prioritization problems. <\/p>\n\n\n\n I\u2019ve been working on this problem for years, and I\u2019ve always run into the same roadblock: templated models are underfit<\/a>. They lose utility almost immediately with real world data and decision making. <\/p>\n\n\n\n So I\u2019ve surrendered to this, humbled myself to the complex organic marketing universe that refuses to be reduced to three static dimensions on a spreadsheet. <\/p>\n\n\n\n And if you, too, can accept that no such model exists, then we can move forward and elevate the concept of prioritization above a static artifact, and into a dynamic ritual that actually improves decision making and program outcomes. <\/p>\n\n\n\n Every single model you\u2019ve ever used or heard of is trying to approximate what is known as Expected Value<\/a>. <\/p>\n\n\n\n At an extremely high level, expected value is a calculation that predicts the average value you can expect to get if you repeat an action many times.<\/p>\n\n\n\n Basically, it takes into account the upside, the probability of the upside, and the cost of taking an action (as well as, sometimes, the downside potential and the probability of the downside). <\/p>\n\n\n\n It\u2019s a simple concept (in a casino) and a very complex one in the real world. <\/p>\n\n\n\n Basically, if you decide to pull a slot machine arm, you can calculate the expected value if you know:<\/p>\n\n\n\n Technically, you could complicate the formula if there is also a range of negative<\/em> outcomes (i.e., if you short a stock, you can win X, but you can also lose Y). <\/p>\n\n\n\n Where this gets harder is, well, the real world, where cost includes the freelancer you pay, but also the subjective difficulty of publishing a given article (is it 1k words or 5k, technical or breezy, images or no?), as well as the unpredictable difficulty of ranking and achieving a reward. <\/p>\n\n\n\n Then there\u2019s the opaque range of rewards. Sure, you can model out MSV, but do you know that you can also rank for 100s of other variants of that keyword, effectively 10Xing your estimate? <\/p>\n\n\n\n Okay, so impact and ease are both pretty tricky to nail down, but we can be pragmatic – we don\u2019t need 100% accurate inputs to get directional utility. <\/p>\n\n\n\n But then we get into interaction effects – in SEO, it\u2019s not 1 page that makes a program, but rather the cumulative benefit of 100s or 1000s of pages. Due to many factors outside the focus of this essay (topical authority, internal linking, backlinks, etc.), choosing 50 pages with maximum impact and ease across 50 different topic verticals may have a much lower composite outcome than choosing 50 pages with lower volume and higher keyword difficulty, but in the same topic cluster that is related to your core product. <\/p>\n\n\n\n We could spend all day discussing the limitations of expected value calculations<\/a> in SEO. For now, let\u2019s agree that there is some<\/strong> utility, but expected value sorting alone doesn\u2019t give you much information.<\/p>\n\n\n\n I\u2019ll return later in this essay to show you where you can use these calculations, but for now, let\u2019s move onto some more useful frameworks. <\/p>\n\n\n\n First order of business: there are multiple, hierarchical layers of prioritization. <\/p>\n\n\n\n Every business is unique, its own country. Some are complex multinational companies with hundreds of products, features, and personas. Some are startups with 1-2 personas and a very narrow search TAM.<\/p>\n\n\n\n So you probably shouldn\u2019t just dump ALL POSSIBLE keywords into a single spreadsheet and sort them by the same formula. <\/p>\n\n\n\n Let\u2019s use a concept from machine learning here – hierarchical clustering<\/a>.<\/p>\n\n\n\n Image Source<\/a><\/p>\n\n\n\n Before we sort keywords and stack rank their order, we first need to prioritize higher order dimensions. This depends on this business but could include:<\/p>\n\n\n\n As a simple example, we can imagine a MarTech company with 3 core product lines, one for marketers, one for sales, and one for customer support professionals.<\/p>\n\n\n\n Within each of those product lines, there are several distinct products and features to prioritize. For instance, their product suite for marketers includes landing pages, email marketing, marketing automation, and lead capture. <\/p>\n\n\n\n And within each of those product features, there are distinct keyword or topic clusters. For example, we could break down email marketing into several smaller groups:<\/p>\n\n\n\n So our hierarchical prioritization may look something like this:<\/p>\n\n\n\n So every business is unique. We can\u2019t bypass the strategic layers of prioritization, which is why it\u2019s so frustrating when some SEO professionals just sort a big ol\u2019 list of keywords. <\/p>\n\n\n\n Some of these dimensions have\u2026unscientific constraints. Team A has no budget, team B is flush with money. The CEO adamantly wants to grow customer segment C, even though LTV and remaining TAM in customer segment B is most opportune. Deal with it. This is where SEO strategists need to become, or at least communicate with, business strategists. <\/p>\n\n\n\n (Side note: we offer an \u201cOrganic Growth Blueprint\u201d service we\u2019ve used to help Fortune 500 companies identify their growth opportunities, index their current status on various dimensions and products, and build a roadmap towards leadership statu<\/em>s). <\/p>\n\n\n\n There are two core types of prioritization: filtering and sorting. <\/p>\n\n\n\n Imagine I have an open afternoon and want to decide how to spend my time. It\u2019s not until I filter out activities I don\u2019t want to do, leaving what I would be interested in remaining, that I can sort among similar options. <\/p>\n\n\n\n So I don\u2019t want to play sports, drink alcohol, or work, and it’s not evening yet, so there are no concerts I could attend. But I would be interested in reading books or playing board games. That\u2019s a filtering exercise. Then, I can sort which books I\u2019d most be interested in or which board games I\u2019d most like to play. <\/p>\n\n\n\n Most of the higher order priorities are chosen by way of filtering. And most lower order priorities, say keyword selections or technical SEO fixes, are chosen by way of sorting. <\/p>\n\n\n\n Before you sort, you need to filter. Filtering and segmenting lets you compare apples to apples. It wouldn\u2019t make much sense to compare sports (pickleball, skiing, tennis) to books (Catcher in the Rye, Infinite Jest, Good Strategy Bad Strategy) to board games (Cards Against Humanity, Settlers of Catan). So first we filter. <\/p>\n\n\n\n SEO research can help with filtering, but you have to understand much of it goes beyond and above SEO metrics. Where it can help is opportunity sizing and cost estimates. <\/p>\n\n\n\n You can help business stakeholders understand that, yes, you may want to drive adoption of product A, but product B represents a 20X total addressable search market, and you\u2019ve already got strong topical authority in this area. So, perhaps consider allocating a significant percentage of your investment in product B, and we can stake maybe 10-20% of our portfolio in product A to start building out that growth horizon<\/a>.<\/p>\n\n\n\n This is the juncture where SEO strategists become business strategists, by the way. Time to dust off that old growth model.<\/a> <\/p>\n\n\n\n Summary: map out the hierarchy of categories that matter to your business and filter through top priorities. Use SEO and market data to assist in opportunity sizing, and agree upon top business priorities, product and persona priorities, geographic priorities, before sorting keywords based on formula. <\/strong><\/p>\n\n\n\n Alright, let\u2019s assume we\u2019ve gotten to the point where we agreed upon which persona, product, and business unit we\u2019re investing in. <\/p>\n\n\n\n We\u2019ve also done our cursory market and competitive research, and we have a handy little SWOT analysis that tells us our comparative advantages, weaknesses, and opportunities. This helps us allocate our content portfolio appropriately among emerging topics, product-led content, buzzworthy link bait, and programmatic template pages.<\/p>\n\n\n\n Now it\u2019s time to break out the spreadsheet, with one small change: add custom weights to the variables. <\/p>\n\n\n\n The variables we typically use to stack rank content topics are impact (search volume), ease (composite score – topic complexity, SERP competition by domain rating, SERP competition by quality), product relevance, and conversion intent \/ customer journey stage.<\/p>\n\n\n\n We can then apply a weight to each of these from 1-10 that gives it a lower or higher effect in the overall composite score. These are chosen based on our strategic direction and the goals of the client. <\/p>\n\n\n\n For example, a leading company with a complex sales cycle may want to saturate their topic surface area without worrying too much about direct conversions. So we\u2019ll heavily weigh impact and product relevance while deprioritizing ease and conversion intent. <\/p>\n\n\n\n For an early stage startup, we may index heavily on ease and product relevance with medium weight on conversion intent and low weight on search volume. Then we\u2019ll complement this with linkable assets to drive authority and hit \u201cescape velocity.\u201d <\/a><\/p>\n\n\n\n This framework requires a lot more critical thinking than your typical prioritization model, which is a good thing. We\u2019ve spent too many years as an industry using the same keyword research tools, content brief tools, and simplistic sorting models, which has resulted in a sea of sameness and impenetrable competition. <\/p>\n\n\n\n Applying basic logic gives you a fighting chance to drive unique value and real ROI through SEO. <\/p>\n\n\n\n Models are only as good as their inputs, and sometimes you don\u2019t have access to predictive historical data.<\/p>\n\n\n\n For instance, linkable assets \/ passive link magnets. <\/p>\n\n\n\n Sure, you can reverse engineer content that has already been linked to a lot, and you can even assume that higher search volume may equate to higher link building potential (because more potential searchers will see it). This is how most people do \u201cpassive link building.\u201d <\/p>\n\n\n\n But that doesn\u2019t tell the full story, because most historical links don\u2019t always equate to future links, and reverse engineering a bunch of \u201c[industry] statistics\u201d blog posts and \u201c[keyword] calculator\u201d interactive tools (which are always the most linked) isn\u2019t the bedrock of a forward thinking strategy. <\/p>\n\n\n\n So what do you do? <\/p>\n\n\n\n Change the rules. <\/p>\n\n\n\n Only apply your model to pages and actions you can actually predict expected value with and be intellectually humble (and courageous) enough to invest in things with unpredictable upside. <\/p>\n\n\n\n If you, for example, find through customer research that a TON of people want a certain free interactive quiz or calculator, and there\u2019s no search volume; or if you have amazing 1st party product data that you think would get picked up by real journalists at top publications\u2026.<\/p>\n\n\n\n Just f*cking create it, regardless of what your model says.<\/p>\n\n\n\n Because your model would and should never account for the potential that a piece of buzzworthy content could garner 1000 referring domains. This is why the \u201cbarbell strategy\u2019 <\/a>concept is so important. Some percentage of your portfolio should, in fact, be incompatible with standardized models based on normal distribution outcomes. <\/p>\n\n\n\n Live a little, eh? <\/p>\n\n\n\n I made you a template. <\/p>\n\n\n\n It\u2019s based on a scoring rubric Sam Lund created for hiring in our editorial team. <\/p>\n\n\n\n Click here to get it. <\/a><\/p>\n\n\n\n You\u2019ll still have to customize it. For example, maybe you want different variables. We typically use Impact, Ease, Product \/ Business Relevance, Conversion Intent. <\/p>\n\n\n\n You\u2019ll also have to come up with a scaling system to fit your scores into a 1-10 scale. I use percentiles and deviations from the mean for impact (e.g. take all keywords and their volumes, run some summary statistics, and then build a 1-10 scoring system based on which percentile they fall into). <\/p>\n\n\n\n Then choose how to weigh each variable.<\/p>\n\n\n\n As in, maybe ease is your most important value. You can publish like 100 articles per month with AI, so you\u2019re doing a volume play. Very smart<\/a>. We\u2019ll be talking to you in a few months to a year when you need to fix that mess.<\/p>\n\n\n\n Snark aside, you\u2019d just choose \u201c10\u201d for ease and lower the weight of the other variables:<\/p>\n\n\n\n While I do think the above model is more useful than the industry standard, the main point of this essay is this: prioritization can\u2019t actually be boiled down to a formula. <\/p>\n\n\n\n You and I can both easily think of cases that fall outside prioritization models.<\/p>\n\n\n\n Imagine you\u2019ve got a piece at the top of your model. It\u2019s delectable – 10,000 monthly searches, no difficulty or competition, pretty strong conversion intent. Very likely to have a good payback.<\/p>\n\n\n\n But then an opportunity comes up. Your coworker is friends with Tim Ferriss, and if your team can spend a little extra time (and effort) mining some first party data your product collects on, I don\u2019t know, cortisol levels during extended fasting protocols, then there\u2019s a good chance that your company will be featured on the Tim Ferriss podcast as well as syndicated among his collection of friends and fellow podcasters. <\/p>\n\n\n\n Well, you don\u2019t have the search volume to attach to that opportunity. And it’s a higher effort content piece. But it seems like you should just throw the model aside for a second and take the opportunity, no? <\/p>\n\n\n\n Maybe, analogously, you had an itemized list of traits you want in an ideal romantic partner, but then you meet someone who doesn’t match many of them, particularly the superficial preferences (you like The Beatles, they like Blink 182, you like reading, they like adventure sports). What are you going to do, not fall in love because they didn’t match your little model?<\/p>\n\n\n\n So I\u2019m advocating for critical thinking over templates, rituals over artifacts, and a bit of common sense to adjudicate the numbers your very simple spreadsheet model spits out. <\/p>\n\n\n\n <\/p>\n\n\n\n Want more insights like this? Subscribe to our <\/a>Field <\/a>Notes<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":" Prioritization is the apex artifact bridging strategy and execution. I\u2019ve…<\/p>\n","protected":false},"author":3,"featured_media":6631,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[24,184],"tags":[],"wf_post_folders":[],"class_list":{"0":"post-4696","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-content-strategy","8":"category-field-notes"},"yoast_head":"\n\n
You Need to Understand Expected Value <\/strong><\/h2>\n\n\n\n
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Prioritization Should be Hierarchical <\/strong><\/h2>\n\n\n\n
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<\/figure>\n\n\n\n![\"\"](\"https:\/\/beomniscient.com\/wp-content\/uploads\/image-47.png\")
<\/figure>\n\n\n\nFiltering Versus Sorting<\/strong><\/h2>\n\n\n\n
Use Dynamic Variables to Customize Sorting Models<\/strong><\/h2>\n\n\n\n
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<\/figure>\n\n\n\nBeyond Apples and Oranges: Portfolio Thinking<\/strong><\/h2>\n\n\n\n
Okay fine, here\u2019s a spreadsheet template<\/strong><\/h2>\n\n\n\n
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<\/figure>\n\n\n\nEverybody has a plan until they get punched in the mouth<\/strong><\/h2>\n\n\n\n