Mathematical Ideas for Marketers

March 18, 2013 by

Posted by willcritchlow I’ve been hiding from my natural geekiness recently. My last few blog posts and my most recent presentations have all been about broad marketing ideas, things that play out well in the boardroom, and big picture “future of the industry” stuff. Although those topics are all well and good, sometimes I need to feed the geek. And my geek lives on logic and maths (yes, I’m going to use the *s* throughout – it’s how we roll in the UK and that’s where I studied). One of our most recent hires in our London office is a fellow maths graduate and I’ve been enjoying the little discussions and puzzles. (The last one we worked on together: in how many number bases does the number 2013 end in a “3″? Feel free to share your answers and workings in the comments.) Rather than just purely geek out over pointless things , I have been casting my mind over the ways that mathematical ideas can help us out as marketers; either by making us better at our jobs, or by helping us understand more advanced or abstract concepts. Obviously a post like this can only scratch the surface, so I’ve designed it to link out to a bunch of resources and further reading. In approximate ascending order of difficulty and prerequisites, here are some of my favourite mathematical ideas for marketers : Averaging averages The first and simplest idea is really a correction of a common misconception. We were talking about it here in the context of some data we were visualising for a client. The problem goes like this: Our client had data for average income broken down by all combinations of age, location, and gender (details changed to protect the innocent). We wanted to get the average income by gender. It’s tempting to think that you can do this from the data provided by averaging all the female values and averaging all the male values, but that would be incorrect. If the age or geographic distribution is not perfectly uniform by gender, then we will get the wrong answer. Consider the following entirely made up example: Female, 25, London –  Average: 30,000 (10,000 people) Female, 26, London – Average: 31,000 (11,000 people) It’s tempting to say that the average for the whole group is 30,500. In fact, it’s 30,524 (because of the hidden variable that there are more in the second group than the first). You will often encounter this in marketing when presented with percentages. Suppose you have a campaign that made 200% ROI in month one and 250% ROI in month two. What’s the ROI of the campaign to date? Answer: anywhere in the range 200-250%. You have no idea where. Try it out on this brainteaser hat-tip @ tomanthonyseo : If I drive at 30mph for 60 miles, how fast do I have drive the next 60 to average 60mph for the whole trip? Correlation coefficients Although the mathematical background can look scary , linear regression and correlation coefficients represent a relatively simple concept. The idea is to measure how closely related two variables are; think about trying to draw a “line of best fit” through an X-Y scatter chart of the two variables. The summary of how it works is that it finds the line through the scatter chart that minimises the sum of the distances of the points of the scatter plot away from the line. The great part is that you don’t even need to dig into the mathematical details to use this technique. Excel has built in functions to help you do it – check out this YouTube video showing how to do it: Bayes Thomas Bayes was a mathematician who lived in the early 1700s. The break-through he made was to come up with a way of analysing probability statements of the form: “What’s the probability of event A given that event B happened?” Mathematicians write that as P(A|B). Bayes discovered that this = P(A and B) / P(B) In plain English, that means: “The probability of both event A and B happening divided by the probability of B happening.” And also that P(A|B) = P(B|A) * P(A) / P(B) Which means: “The probability of B happening given A happened, times the probability of A happening, divided by the probability of B happening” Why is this important? It’s critical to understanding the results of all kinds of tests – ranging from medical trials to conversion rate. Here’s a challenge from this great explanation of Bayesian thinking : “1% of women at age forty who participate in routine screening have breast cancer. 80% of women with breast cancer will get positive mammographies. 9.6% of women without breast cancer will also get positive mammographies. A woman in this age group had a positive mammography in a routine screening. What is the probability that she actually has breast cancer?” If you want to dig deeper into the marketing implications, I really like this article . O(n) and o(n) One of the things I did during my maths degree was write really bad code. My lecturers suggested using either Pascal or C. C sounded like “real programming,” so I chose that. It’s incredibly easy to write horrible programs in C because you manage your own memory (reminding me of this programming joke ). When you think of programs failing, you tend to think of crashes or bugs that return the wrong answer. But one of the most common failings when you start hacking on real world problems is writing programs that run for ever and never give you an answer at all. As we get easy access to more and more data, it’s becoming ever easier accidentally to write programs that would take hours, days, weeks, or even longer to run. Computer scientists use what is known as “big O notation” to describe the characteristics of how long an algorithm will take to run. Suppose you are running over a data set of “n” entries. Big O notation is the computer scientists’ way of describing how long the algorithm will run in terms of “n.” In very rough terms, O(n^2) for example means that as the size of the dataset grows, the algorithm run-time will grow more like the square of the size of the dataset. For example, an O(n) algorithm on 100 things might take 100 seconds but an O(n^2) would take 100*100 =10,000 seconds. If you’re interested in digging deeper into this concept, this is a really good primer . At a basic level, if you are writing data analysis programs, what I’m really recommending here is that you spend some time thinking about how long your program will take to run expressed in terms of the size of the dataset. Watch out for things like nested loops or evaluations of arrays. This article shows some simple algorithms that grow in different ways as the data size grows. Nash equilibria Using words like equilibria makes this sound scary, but it was explained in layman’s terms in the film A Beautiful Mind: “Games” are defined in all kinds of formal ways, but you can think of them as just being two people in competition, then: “A Nash equilibrium occurs when both players can’t do any better by changing their strategies, given the likely response of their opponent.” The reason I include this bit of game theory is that it’s critical to all kinds of business and marketing success; in particular, it’s huge in pricing theory . If you want a more pop culture example of game theory, this is incredible: Time series Time series is the wonkish mathematical name for data on a timeline. The most common time series data in online marketing comes from analytics. This branch of maths covers the tools and methodologies for analysing data that comes in this form. Much like the regression analysis functions in Excel, the nice thing with time series analysis is that there is software and tools to apply the hard maths for you. One of the most direct applications of time series analysis to marketing is decomposing analytics data into the different seasonality effects and real underlying trends. I covered how you do this using software called R in a presentation a few years ago – see slides 39+: Prime numbers/RSA OK. I’m getting a little tenuous now. It’s not so much that you actually need to know the maths behind factoring large numbers or the technical details of public key cryptography . What I do  think is useful to us as technical marketers is to have some idea of how HTTPS/SSL secure connections work. The best resources I know of for this are: Entry-level and very readable introduction to codes and cryptography A surprisingly accessible technical overview of SSL Markov chains You might have come across the concept of Markov chains in relation to machine-generated content (this is a great overview ). If you want to dive deep into the underlying maths, this is a great primer [PDF] The general concept of Markov chains is an interesting one – the mathematical description is that a Markov chain is a sequence of random variables where each variable depends only on the previous one (or, more generally, previous “n”). Google Scholar has a bunch of results for the use of Markov Chains in marketing . It turns out that there are a bunch of great mathematical properties of Markov Chains. By removing any possibility of the outcome of the next step being dependent on arbitrary inputs (allowing only the outcomes of the most recent entries in the sequence), we get results like conditions for stationary distributions  [PDF]. A stationary distribution is one that converges to a fixed probability distribution – i.e. one that *isn’t* based on previous elements in the sequence. This leads me neatly into my final topic: Eigenvectors/Eigenvalues OK. Now we’re talking real maths. This is at least undergraduate stuff and quickly gets into graduate territory. There is a branch of maths called linear algebra. It deals with matrix and vector computations (see MIT opencourseware if you want to dig into the details). To follow the rest of my analogy, all you really need to know is how to multiply a matrix and a vector . The result of multiplying appropriate vectors and matrices is another vector. When that vector is a fixed (scalar) multiple of the original vector, the vector is called an “eigenvector” of the matrix and the scalar multiplier is called an “eigenvalue” of the matrix. Why are we talking about matrices? And what do they have to do with stationary distributions of Markov chains? Well, remember PageRank ? From a mathematical perspective, there are two models of PageRank: The random surfer model – where you imagine a web visitor who randomly clicks on outbound links (and randomly “jumps” to another arbitrary page with a fixed probability) The (dominant) eigenvector of the link matrix You’ll notice that the random surfer model is a Markov model (the probability of moving from page A to page B is dependent *only* on A). It turns out that the eigenvector is actually the stationary distribution of the random surfer Markov chain. And not only that. The random jump factor? Turns out that is necessary to (a) make sure that the Markov chain has a stationary distribution AND (b) make sure that the link matrix has an eigenvector. Things like this are the the things that make mathematicians excited. I appreciate that this post has been something a bit different. Thanks for bearing with me. I’d love to hear your geek-out tips and tricks in the comments. Sign up for The Moz Top 10 , a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don’t have time to hunt down but want to read!

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Mathematical Ideas for Marketers

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Tips for Real-World Marketing from SearchLove and LinkLove

March 4, 2013 by

Posted by willcritchlow I want to tell you a story about one of our favourite sessions – Let’s Get Real – where we have all our speakers on stage at once. In this post, I’m going to: Highlight some of the incredible tips and tricks our speakers gave away at our conferences at the end of last year. Give away free HD videos of Let’s Get Real  from the conferences at the end of 2012 [ skip to the video giveaway ]. Share all the details of our upcoming conferences in London and Boston, along with the video deal we have running for SEOmoz PRO members [ skip to the conference details ]. Some of the earliest conferences I travelled to the US to attend were SMX West and Advanced. Back then, Danny used to have a session called Give it Up  that was supposed to be more like the kind of tips, tricks and stories you would normally only hear at the bar (in exchange for delegates promising not to share the stories publicly for a month). Although the formatting is a bit broken, you can get a sense of the kind of topics covered in this Marketing Pilgrim write-up  from 2007. I particularly like Matt Cutts’ story: “Alright, I’ll tell you about my favorite spammer of 06 … When you buy a domain, you own it for a year. Usually you get hosting, or park the domain … You set name server to “lamedelegation.org.” Millions of domains are marked this way. But some are marked “lame-delegation.org” with a hyphen … This spammer … registered lame-delegation.org.” I really liked the personal, conversational tone of the sessions and the glimpse behind the curtain. When we started running conferences, we used to end with similar sessions. Over the years, we felt that the tips being shared weren’t helping our delegates improve their marketing skills (Danny has done some similar soul-searching ). They were still fun (and often funny), but they were increasingly unuseful; not something you could go back to the office and implement. As a result, we introduced the let’s get real  panel where we invite all of our speakers on stage for a rapid-fire round of tips and ideas with the crucial difference: all of the tips should be the kind of thing delegate can go back to the office and use for themselves or their clients . To give you an idea of the difference between a regular talk and let’s get real , check out what Wil looks like on stage giving a formal presentation: …and what he looks like rocking at  let’s get real : Anyway, in the run-up to our next set of conferences (in March in London and May in Boston ) I thought I’d go back to last year’s tips and share the most useful with all of you. Here we go! Let’s get real These tips come from our most recent SearchLove conferences in London and Boston. If you’d like to watch them for yourselves, I’m giving the entire videos away for free at the end of this post . The credit for the tips goes to the individual speakers – though I’ve generally rephrased the tips in my own words – I’ve credited them as I go along: Social Media Beware management tools for Facebook — Jen Lopez If you routinely use tools like Hootsuite for managing your social presence between multiple team members and across multiple platforms, beware of the potential effect on the visibility of your Facebook posts. There are two big things to be aware of: Posts made via external applications suffer in Edgerank terms and so have lower “natural” visibility. If you are unlucky enough to post at a similar time to others using the same application, the Facebook timeline will often group posts together under “updates from Hootsuite.” It’s an ongoing challenge to manage multiple contributors across multiple platforms and the tools are a huge part of making that possible but it’s worth experimenting to see how your reach is affected. Check out G+ ripples to find influencers — Jen Lopez If you do a keyword search in Google+, the default ordering of results is heavily skewed towards heavily-shared content. By drilling into the ripples , you can find the influencers who are sharing content in any given space and who are having a particular influence on which pieces of content get widely shared. Craig wrote an article  on the power of building your filter bubble influence, and Jen’s tip is a great place to get started working out who you need to influence. Technical SEO Get a sample of googlebot visits in log file format — Richard Baxter It can be tempting to spend all our time in graphical tools, but Richard pointed out one specific use-case that has brought old-school techniques back to prominence for him. As googlebot gets better at interpreting JavaScript and attempts to crawl more and more AJAX content, it also increasingly makes mistakes. He and his team saw a major publisher having huge numbers of non-existent URLs requested based on googlebot misidentifying slugs in the HTML as URLs [we’ve seen this as well] – and this led to him recommending that we get our client dev teams to provide us with samples of googlebot log file data. Split test your SEO — Mat Clayton Mat is in the luxurious position of having complete control and authority over a massive site that gets loads of search visits , but nevertheless, I thought his stories were interesting and useful even if you’re running smaller sites. He talked about applying the principles of conversion rate optimisation to SEO. Take user profile pages for example (they have millions of them over at mixcloud): split them into two buckets (A and B) and make a set of changes to B designed to improve their search visibility. Treat visitors from search as “conversions” in a CRO sense and test to see if A or B is statistically better. Create site speed videos — Annie Cushing A short-but-sweet tip from Annie – check out webpagetest  for creating videos of your website loading alongside those of your top competitors. If you have a speed problem, this is one of the most powerful tools for getting management on-side with the (often considerable) investment needed to achieve significant speed increases. Clean your sitemap with Screaming Frog — Annie Cushing Remember Duane Forrester talking about how clean your sitemap should be ? Annie suggests a simple way of checking (on small-to-medium-sized sites). Use the list mode  of Screaming Frog  to run through your XML sitemap and check the status code of the pages it contains. CRO – Conversion rate optimisation What nearly stopped you buying? — Stephen Pavlovich Stephen described a simple set of three questions they include on the confirmation page at his experience days startup : What’s the one thing that nearly stopped you buying from us today? How could we make our website better? Is there anything else you want to say? It’s important, he says, to make the answers free-form text areas. The freedom to write what they want is a critical part of the process of getting useful feedback. The idea then is that you can check in regularly and take actions to fix common issues. Rank for your [ voucher code] search — Dave Peiris Dave highlighted the example of Argos (a UK high-street retailer) who have a good example of an on-site page targeted to Argos voucher codes  (in the US, I think “coupon” or “coupon codes” would be a more common search term). People are increasingly interrupting the checkout process to go and search for discount codes  and the search results are typically terrible. If they fail to find anything relevant to your brand, they could easily be diverted to a competitor. By bringing them back to your own site, you reduce the drop-off of your checkout process. Give your FAQ and T&C pages some love — Hannah Smith Hannah pointed out how close to converting  someone is when they check out your FAQ or T&C pages. When was the last time you read those kinds of page for fun? And yet, so many of us make those pages impenetrable to humans, give them tiny font, even make the navigation non-standard so that it’s hard to get back to the money pages. Don’t do that,  says Hannah, quite rightly. (While we’re talking about it, I love the 500px terms and conditions  - lawyer and  human friendly.) Email marketing Encourage people to reply to your email marketing — Patrick McKenzie Not everyone knew Patrick at our conference – he’s the second-from-top-ranked  user on Hacker News  under the username patio11 . Although his presentation covered a wide range of tips for conversion improvement, it was his email tips that stuck with me and changed our campaigns – literally as soon as I got back to the office. His top tip was to encourage people to reply to your email marketing. There’s a temptation to think that this is a bad thing and some companies go so far as to send email marketing from a no-reply@  address. By simply ending with the line “Hit reply if you have any questions – I read them all”, you can increase engagement, sell more, get instant feedback and generally get closer to your community. I can vouch for this; we’ve been adding this to most of our emails since Patrick gave away this tip, and I can’t count the number of positive reactions it’s caused. It’s closely related to his second tip: to give customer services a name and a face. He relates the story of a specific customer services rep who has received three marriage proposals in the last year. No one’s gone that far for me, but they have certainly seemed to appreciate the ability to chat 1:1. Facebook retargeting with “dirty” lists — me Everyone who’s been kicking around for a while has a bunch of email addresses they can’t use. The better you are at observing best practices for email list growth, the more you will find yourself with lists of email addresses for people who haven’t opted in to hear from you. With Facebook retargeting , you can put those email addresses to good use. Use your list of “interested but not opted-in” to build your advertising presence. Start your subject lines with “RE:” — Paul Madden Paul’s tip overlapped email marketing and outreach with a suggestion to test different beginnings for your subject lines. In particular, “RE:” can garner much higher open rates by playing on the appearance of an ongoing conversation. Send your competitors’ email marketing to Evernote — Stephen Pavlovich Stephen has talked before about the power of Evernote for saving and browsing a swipe file. Since it offers the ability to add notes by email, he recommends subscribing to competitors’ email lists and using gmail filters to direct their emails into your Evernote account. Do this well in advance of needing it of course, and then when a particularly significant time of year is approaching (Valentine’s day for a flower retailer for example), you have a ready-made swipe file of all the things your competitors did this time last year. Online advertising Swap retargeting pixels — me When you have close partnerships with other companies whose audiences’ interests overlap closely with those of your customers and clients, you can quickly grow your retargeting pool by including your pixel on their site. Add them into their own group so that you can run dedicated advertising to draw them into your own site and content. Combine Facebook demographic targeting and retargeting — Guy Levine The demographic targeting options for Facebook advertising are well known. By running tightly-targeted adverts driving visitors to your own landing pages, you can cookie those visitors with dedicated retargeting pixels that group them into buckets of people with similar interests. This gives you a powerful weapon for future content marketing (particularly at the agency level where having this kind of retargeting pool can be reused across multiple clients). Drive reviews with retargeting — Guy Levine Don’t think only of retargeting being for driving conversions; it can be useful post-conversion, as well. Guy advocated adding a retargeting pixel to your confirmation page so that you have a bucket of people who have bought from you. What should you do with this information? One example use-case Guy mentioned was to ask for reviews of the product purchased to drive rich content on your site. Better content Use HARO to solicit content input — Wil Reynolds You’re all familiar with Help A Reporter Out  (HARO), right? Realising that the content his clients are producing is often journalistic, Wil realised that they could be the reporter as well as the user of HARO. He’s had success with soliciting content input from small business owners via HARO – especially photo / image-based content for inclusion in rich posts. Screencast your interactive infographics — Lexi Mills As the technology underpinning our creative work has become more modern, we occasionally trip up against news rooms stuck using outdated operating systems and browsers. In these cases, they sometimes can’t access fancy animated graphics, etc. Lexi recommended including a short screencast in your journalist pitches to make it easier to see on any platform. Management Individual contributor tracks — Rand Fishkin Rand decided to cover some areas that are closer to the things that have been taking up his personal time recently, particularly on the management front. One of the things that he talked about was also something he has written about  in the context of wider team structure; namely, the need for strong career opportunities in your company for “individual contributors.” He pointed out the need for there always to be progression opportunities for your best people other than forcing them into management if that isn’t their goal. Reach out to your employees’ heroes — Rand Fishkin Rand used the example of Avinash  as being someone that many of his team look up to. Rand’s relationship with Avinash means that he has a chance of getting him to share great things written by the SEOmoz team. By doing this with great content and in a transparent way (“it would mean the world to X to hear that you had read their stuff”), he cements both relationships. Some general marketing/web tips Build your personal brand by owning a topic — Justin Briggs Justin pointed out that, for the bigger conferences, if you pitch a session topic and that topic is chosen to be a panel, you are 99% certain to get asked to be involved. So pitch great topics with credibility. He ran through a personal example – from writing an epic blog post  and using it to pitch a competitor analysis panel at a major show. If you don’t know Justin’s background, you should read his personal post first time, every time , that explains just what an incredible journey his has been. It’ll definitely make you think you can up your own game. Run wpscan — Paul Madden In a lightning-quick tip, Paul recommended that if you run a WordPress site, you should run WPScan  against your own site to check for any vulnerabilities. With the increase in hacking for SEO alongside exploits generally for all kinds of other reasons, it’s going to be increasingly important to lock down your stuff. Take screenshots of your competitors every day — Mat Clayton Mat and his team built a simple script to take a screenshot of the main pages of their competitors every day. He told a story about how they actually found it easier than their competitors  to know which changes were working for them. I recommend reading about webkit2png  and PhantomJS  if you want to try this out for yourself. Put your best content on your about page — Mark Johnstone As we all get better at making “big content” that is closely on-brand rather than just classic “internet bait” (something I know Mark and his team have been working on a lot recently), it makes more and more sense to integrate that great content into your normal website. In particular, try putting your top-performing content on your about page for two reasons: you drive people to your about page where they learn about your company, and potential clients wanting to learn more about your company get treated to your absolute best content. Link building and PR Turn your link developers into content producers — Lisa Myers Lisa described the positive results they have seen from having link developers build out rich online profiles, with posts they’ve written, authorship information, photos, and biographical information. Outreach works so much better when it comes from people who are (and seem) real. Build hack day projects on APIs and tell the API owners — Rob Ousbey Rob described a hackday project he built called Get Out Call . Based on the Twilio API , it is designed to let you send a text scheduling a call to your cell phone to get you out of sticky situations. The power of the API means that this was phenomenally easy to hack together but a big part of the PR value comes from the fact that it is built on a service provided by a hot startup. By letting them know that he had built it, he got their PR team  to hook him (and Distilled) up with coverage . Video marketing Sign up for YouTube advertising — Phil Nottingham If you do any Google Display Network video advertising, you get to include overlay links on your YouTube videos directing people to your own website. If you have an active YouTube channel, you should sign up and spend a small amount before pausing your campaign; even after you have paused, you can continue to have a clickable area on your YouTube videos. You can see this in action on the Distilled YouTube channel where we have a DistilledU video  that we used to run advertising for. Even now that we’ve stopped, there is still a clickable link to the Distilled website. Local businesses Leave useful comments on attractions in your local area — David Mihm David expanded on a tip Will Scott  gives for businesses interacting on Facebook: where you can interact as a page (read: business) instead of as a person. Will talks about leaving useful comments on the stories of the local newspaper or other local entities. David expanded this tip to Google+. In the same way as with Facebook, an admin of a business page can choose to browse Google+ as that business . That means you can leave reviews as a business. This is even more useful than commenting on Facebook because it is less transient. Not only are there fewer reviews than comments, but they are on static pages and the most helpful reviews tend to rank towards the top all the time. The example he gave was that if you are a hotelier in Edinburgh, and you do a search for Edinburgh, you see Edinburgh Castle as one of the top places listed. By leaving a comment along the lines of “the top 5 things my guests love about the castle,” you gain permanent mind share on the most prominent points of interest in your town. Giving away the videos We record all the sessions at our conferences and make them available to buy (as well as bundling them with DistilledU subscriptions). Although I’ve included many of the tips from the let’s get real sessions above, I wanted to give you all the chance to see the whole sessions; I left out a few juicy tips for the interested reader to find and I think it’s always great to watch the dynamic of people on stage. So, I’m giving you all access to the videos of both London and Boston absolutely free. The way our video hosting is set up means that the only way I can get you access is by giving you 100% discount codes  to “buy” them on our store. Just a heads-up: (Free) registration is required on our site You will be presented with a credit card form – but if you enter the code MOZREAL2013  you won’t be charged anything, and you won’t have to enter any credit card information Incidentally, I’ve added full transcripts to both videos on our site thanks to SpeechPad . London Let’s Get Real Get London Let’s Get Real 2012  for free by registering for a free account and entering MOZREAL2013  at checkout. Boston Let’s Get Real Similarly, get Boston’s Let’s Get Real 2012  by registering for a free account and entering MOZREAL2013  at checkout. Get tickets to see us live in London or Boston At this point, I’m obviously hoping that you are all so excited about the great content getting shared at these conferences that you simply can’t wait to come to one. Luckily, we have two conferences coming up (again, in London  and Boston ), and SEOmoz PRO members can use a PRO perk to get free videos added to any ticket purchases  (see the bottom of that page). London LinkLove, 15th March 2013 Check out the schedule  and the speaker line-up  and book your place here . Boston SearchLove, 20th & 21st May 2013 Check out the speaker line-up  (the exact schedule will be announced soon) and book your place here . Interested in the west coast? First, don’t forget that Mozcon  is coming up soon (I’m speaking!). We are also hoping to bring SearchLove to the West Coast – you can register your interest here . Just in case there’s any lingering doubt in your mind, I’ll leave you with a party photo :) Sign up for The Moz Top 10 , a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don’t have time to hunt down but want to read!

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Tips for Real-World Marketing from SearchLove and LinkLove

Filed Under: Advertising, affiliate marketing, articles, Beginner, blogging, boost sales, conversion, conversions, email marketing, facebook, Featured, Google, Google: Analytics, How To, Interviews, Link Building, linkedin, location, Marketing, mitt, Money, online marketing, Paid Search, phil internet marketing and social media and seo, research, Sales, sea, Search Marketing, seo, social media, success, Top News, traffic, travel, twitter, Video, youtube Tagged With: , , , , , , ,

Meaningful SEO Metrics

February 18, 2013 by

Posted by willcritchlow This is a post of two halves. The first half runs through my thoughts on what makes for good metrics, while the second half focuses on a specific process for building appropriate reporting metrics for your individual situation. Now, I’m a very numbers-driven person. I studied math(s)  and once thought I was going to be an inventor until I realised that inventing = engineering, and I needed to be good at partial differential equations. I find, however, that I’m often on the side of less measurement and quantitative decision-making. I like using editorial discretion in our conference programming and often “disagree” with the audience feedback(*). I like running split tests, but spend a lot of time aiming for the big wins (that are easy to spot with the most rudimentary measurement) over small percentage gains gleaned from detailed analysis. (*) it’s interesting for me to think about what I mean by “disagreeing” with quantitative data from large groups of people. I think this may be called arrogance but at least I’m in good company . My typical way of working is to spend a lot of energy thinking about hard problems – often in the abstract and often diving deep into whatever data I have to hand – before making the best decision I can in the messy real world. I am not as good as I should be at looping back around on my decisions afterwards and sense-checking them against the resulting outcomes. I once asked a management consultant friend of mine if his company ever went back and checked their revenue/cost forecasts for companies that they did due diligence on. He looked at me as if I’d reordered his PowerPoint slides. When he got over the shock, he asked me what the point of that would be? Taking that charitably, I think he was saying “the value is in the planning, not the plans.” This all leads me into the first half of my post (note that, throughout, I’m going to use Distilled examples because I can be more transparent with our numbers than with those of any of our clients): 1. My views on good metrics There is no one metric to rule them all Of course, at a business level, cash rules everything. Run out of cash and you die. But as a marketer, you are so far removed from cash collection or burn rates (in most businesses) that this is not helpful. Understanding the correct metric to use in any given situation is a large part of the skill of a consultant or marketer. I constantly find myself recommending different metrics in different situations. The best metrics guide behaviour People like investors and boards care about KPIs (Key Performance Indicators) that demonstrate the health of a company at a glance. These high-level metrics are good for busy executives and remote investors because they guide behaviour for those people : A healthy KPI means “all is fine – go work on something else” A sickly KPI means “this is where your focus should be” An example of a company-level KPI for a profitable, growing company like Distilled that doesn’t have a bankroll of investor cash is a (conservative) projection of minimum cash levels over the coming months. Growing a company is cash-intensive, and one of the trickiest parts is funding growth out of operational cashflow. As long as the cash situation looks good, management time should be spent growing the company,  but if the cash situation were ever to be poor, there would be nothing more important than resolving that situation. When we get down into individual marketing campaigns, however, the kind of KPIs beloved of executives become useless. While an executive will focus on whether total online revenue is on-budget (which feeds into the operating model and cashflow mentioned above), knowing that we are above or below budget doesn’t change the day-to-day activities carried out by the marketing team. Two things go wrong in marketing projects: We don’t get as much done as we wanted to (blog posts shipped, contacts made, pages updated, development tickets completed). Our efforts are less effective than we predicted they would be (not enough people read our posts, too many people ignore our emails, updated pages don’t drive the traffic or conversions we hoped, bug fixes don’t move the needle like they should). So, for ourselves,  I always advocate measuring activity and outcomes. Add some KPIs into the mix to communicate effectively with the execs and you are well on your way to an effective reporting pack. But, connect to the money The reason  these metrics guide behaviour is that they are ultimately connected to the company’s financial objectives. It’s important that you can see the path from your metrics to those company-wide goals even if there are a bunch of assumptions needed to get there. At Distilled, we recently started working with an experienced finance guy – his last gig was CFO at a public company – and had a very interesting few days connecting together our various financial reporting. At the end of it, we had a model that connected top-line revenue and costs in the P&L through non-cash balance sheet movements to cashflow. Of course, it bakes in a variety of assumptions (some of which have a critical impact on the outcome – like debtor days). Even taking into consideration the importance of those key assumptions, it has revolutionised our management accounting to be able to see our financial data all connected together. Of course, we can then both run scenarios on the key assumptions and calibrate them against the real world. The equivalent assumptions in online marketing are things like conversion rate and churn rate. Of course, in some projects these aren’t assumptions but rather variables – if you are directly seeking to change user behaviour – I’ll talk about this in a little more detail below. Measure something even when you can’t measure what you’d like to As the usercycle guys  put it, “what happens here?”: The Lean Startup  is a methodology designed (as the name implies) for startups, but there are a lot of analogies to marketing campaigns that rely on earned media. Typically, there is a long period of time during which a lot of action generates precious little in the way of end results before (hopefully) the curve starts trending upwards and ultimately (again, hopefully) surpasses any of the ways you could buy new customers. Eric Ries talks about innovation accounting  as a way of defining, measuring, and communicating progress during these long, lonely months. If you are going to succeed as an online marketer, you are going to have to master a similar set of skills. Our goal during this phase is best described as “learning” – we want to find the things we should be doubling down on, the things to kill before they cost too much money and give ourselves enough evidence to quieten both our own inner demons and those hard-to-convince bosses and clients. For startups, I’m a big fan of Dave McClure’s pirate metrics  - so named after the acronym AARRR: Acquisition Activation Retention Referral Revenue Startup Metrics for Pirates from Dave McClure He argues that your metrics should carefully measure each of these stages in the lifecycle of a customer and, importantly, that you should track them over cohorts   of users  (we use two-week long cohorts for DistilledU). During the phase where you only have leading indicators, you may get executive pressure to forecast numbers. My approach here is to plug them into some simple assumptions that are easy to highlight and understand as being currently guess-work (“if these pages accrue search traffic at 80% of the average of similar pages, we will grow traffic X% year on year”). Only worry about costs when it matters Cost per acquisition (CPA) is a critical metric for paid marketing channels where the costs scale linearly (or super-linearly) with conversions. In particular, it passes the “actionability” test I described above. If your CPA is too high you can reduce bids, increase conversion rates or increase customer spend. When we are considering channels with non-linear relationships between cost and conversions, it’s not easy to work out actions from average CPAs. It could be that you need to do more  of what you were doing to benefit from flywheels and economies of scale. It could be that you need to throw away the plan and do something different  because there is fundamentally no path from here to a profitable campaign. Much of the artistry present in search and other earned marketing channels comes from the difficulties of working with this uncertainty. Some tips that I’ve found useful in practice: Look at the biggest picture you can Look at the biggest picture you can – earned channels perform best over longer horizons, when multi-touch conversion is considered and lifetime value is counted appropriately. I would far rather be working out whether a five or six figure spend brought a big enough total uplift than deciding if an individual blog post (or even bigger piece of creative) has earned its keep. Make sure you are considering the profit margin of an incremental  sale It is tempting to think about the marginal profit of a single conversion as: Total profit (for this product) / number of sales This is misleading any time you have fixed costs involved. Let’s take an extreme example from our business illustrate this: We can easily imagine a situation of a conference that just beat breaking even with (say) 110 paying delegates paying £500 per head and fixed costs of (say) £50,000. How much are another 10 delegates worth? If we have valued incremental delegates as average profit, we think each delegate is worth £45 [((110 * 500) - 50,000) / 110] and so 10 more are worth £450. In fact, in our example with only fixed costs, an extra 10 delegates would bring us additional profits of £5,000 – more than 10x more. In our own business, I tell anyone working with marketing to consider our whole business as 100% gross margin: In DistilledU, there are (essentially) no variable costs and so 100% gross margin is actually reasonable In conferences, variable costs are dwarfed by the fixed costs and there is a hard-to-quantify benefit to having more people at a conference (in lifetime value, in network effects, in “noise” the conference makes on Twitter etc) The way our consulting works – based almost entirely on full time permanent staff (many of whom have grown up with our business) – we either have no capacity to take on new work (hence no conversions) or there is little marginal cost to doing so In reality, what I’m doing here is conflating two hard-to-measure things – the marginal cost of an incremental sale and the lifetime value of a sale above and beyond the first transaction. 2. Putting it all into practice Here’s a step-by-step process to follow: Understand how the business makes money In order of increasing complexity: Sells (near-)100% gross margin products online (including pageviews) Sells fixed margin products online Sells variable margin products online (this includes many subscription products where LTV depends directly on churn rate) Micro-converts website visitors onto an easily-valued asset (e.g. an email list that sells advertising) Generates leads online that are converted into sales offline Micro-converts website visitors onto a less-easily-valued asset (e.g. an email list designed to generate consulting leads) My favourite approach here is to make some simplifying assumptions that we can return to later to sense-check. Let’s think about some assumptions we can make in the most complex situation of building an email list for a consulting business: Fixed micro-conversion rates over time (i.e. if I increase the number of visitors to a conversion page, sign-ups will go up in lockstep) List growth will continue to turn into leads at the same rate as the past The sales team will continue to close leads at the same rate and to the same size contracts as in the past The goal of all of this work is to come up with KPIs at the micro-conversion level that correlate with the bigger-picture business goals. You need to trade off “small” (benefits = easier to influence, quicker to change, quicker to measure) against “close to the money” (benefits = speaking the language of management, real business benefits). In the complex situations, I typically find that the sweet-spot is somewhere between visitor growth (to appropriate pages from a good channel) and micro-conversion growth (i.e. email list growth or contact form submissions). In the simpler businesses, you can typically get closer to the real business metrics and simply work directly with revenue growth. Build a simple model This step is probably overkill in many client engagements but it’s important for in-house teams (and those in-house teams should be sharing their models with external teams in my opinion). The output should be the simplest Excel model you can think of that captures the important business drivers. The important part here is really the planning process  rather than the specific plans  that come out of it. Here’s the majority of the inputs I created when I was building the DistilledU business model before it had launched to the public (i.e. while it was still in private beta): In our case, I pulled a bunch of numbers from Rand’s exceptionally transparent funding post  and used them to benchmark against our visitor numbers and conversion rates. The output was a single sheet Excel model that forecast revenue growth (most of which was to be driven by inbound means): In truth, pretty much every single assumption in my model is wrong – many of them by quite some distance (including our ability to generate conversions from paid advertising which has been even worse than we forecast). But the planning process was the valuable part, and although the real world is never as neat and tidy, we have ended up not a million miles away: And now we know where the levers are that we need to pull to get the business results we want (one of which is conversion rate – we’ve already had one successful A/B test that nearly doubled conversion rate thanks to Optimizely  - my favourite testing platform - but that’s a story for another day). Estimate LTV, model CPA Any serious discussion of measurement in marketing needs to understand the lifetime value (LTV) of a conversion. As discussed above, it can be hard enough to work out the immediate value of a micro-conversion, never mind the lifetime value. Here are some techniques I have used to get to workable LTV numbers: Assume static churn rates If you are working with a subscription business, you can estimate LTV as: monthly average revenue per user (ARPU) / monthly churn So if you make $35/month / user on average and have a churn rate of 9%/month you can estimate LTV as $389 Make the numbers work with first purchase only If you have an efficient enough marketing engine, you may get to beyond break-even on average on first purchase. In this case, you can build a reporting pack based on first purchase (sometimes with some hard-to-estimate factors in the other direction such as the variable margins mentioned above) and include notes of additional uplift available from subsequent / repeat purchases. This is the approach I’ve taken with big ticket / b2b services with long lead and delivery times – even if there are repeat purchases, they come so far in the future that they are irrelevant on the short-term planning horizon. Average everything together – assuming all users are similar If you have access to the right data, you can bucket together large sets of users and their purchases over some large time horizon (12-24 months is sensible for many online businesses), discount appropriately and get to a very rough average LTV. This misses many subtleties and variation in underlying LTV. It’s probably most effective with small-to-mid-ticket basket sizes that aren’t skewed by large repeat purchases in the way that, say, consulting services would be. I’ve even used this approach to bucket together the “LTV” of email subscribers – many of whom purchase nothing. Doing some back-of-the-envelope calculations led me to a rough value of $6 per email subscriber per year for Distilled, for example. If I were to rely on this for paid marketing, I would need to keep a very close eye on the trends in this value as I artificially added subscribers from a different channel mix than that which grew the list to its current size. Build a simple model For mid-ticket purchases where basket sizes (and repeat purchasing behaviour) can vary over a wide range, I’ve found it best to build an explicit model based on a simple “propensity to convert again” in each time period after initial purchase. For those working through this at home, you end up modelling a simple Poisson Process  but you can get 80+% of the way there with a simple “x% of prior customers buy again in the subsequent quarter and y% in the quarter after that”. I tend to move towards longer time-periods when modelling this kind of process as purchases that fall close together might as well be counted simply as a larger basket. Measure a combination of KPIs and lean metrics So you now have access to a bunch of (estimates / modelled versions of) metrics like LTV, churn rate, CPA and have picked AARRR metrics that correlate with future success. Finally (we’re nearly there, I promise) you need to put this together. I suggest that you think about building two different reporting packs: KPI pack with high level “total success” metrics that demonstrates the value of the work you are doing (this will need to contain leading indicators and extrapolations in the early days) Project steering pack with actionable metrics that helps you, your team and your immediate point of contact / boss build a more effective campaign KPI pack – most likely updated and reviewed quarterly Brings together big numbers over long time periods – for example, it might contain: Total LTV generated through your channels % growth for new products YoY traffic and revenue growth 2+ year projections You want to show graphs like this one (revenue from organic search to deep pages within DistilledU): Project steering pack – most likely reviewed monthly Based on time-boxed or cohort-based data, focusses on metrics that give you deep insight into what you need to change to do even better  in each of the key activities you are undertaking. This is likely to be highly custom to your specific business and marketing campaigns but here are some examples from campaigns I’ve been involved with recently: Conversion rate optimisation: Number of tests run Total traffic to each variation Conversions for each variation Number of successful tests run Total improvement in conversion rate Outreach: Total new contacts made Responses (segmented by approach / type of contact) Successful outcomes User satisfaction: % of new sign-ups who subsequently upgrade to paid % of new paying members who engage with the service churn rates The table below shows (a section of) our cohort analysis for DistilledU and the bump in conversion rates to paying and engaging with the content when we announced that we were including all of our conference videos within DistilledU subscriptions : This was a test – and initially one that we marketed only to our existing community. It wasn’t without its costs (we estimated that it would reduce video sales by $50-100k / year) but the success led to (a) keeping it in place and (b) a successful landing page A/B test that we’re going to write up on our own site soon. I hope this meander through meaningful metrics has been useful to you. I’d love to hear your experiences in the comments and any thoughts you have on how I can improve any part of my approach. Sign up for The Moz Top 10 , a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don’t have time to hunt down but want to read!

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