APIs for Data-Driven Marketers

Posted by Dr. Pete Data is everywhere, and companies are virtually climbing over each other to give it away. If you’re a data-driven content marketer, data is opportunity, but accessing that data can take some technical know-how. This is a guide to APIs, one of the key methods for accessing 3rd-party data, and also a mini-directory of some of the most useful APIs currently available to marketers. What Is an API? Let’s start with the official definition – API stands for “Application Programming Interface”. Sorry, I’m not the one who lets engineers name things. Put simply, an API is a way to let you talk to a 3rd-party application, usually either to retrieve data or update that application. We’re going to focus primarily on the first use (retrieving data), and it looks something like this: The API itself isn’t really a box floating in space, so much as a chunk of code that acts as a gatekeeper. That code helps translate the third party’s data into something you can read, and it makes sure that only authorized users can access the data (a process called “authentication”). Why Should I Care? There are hundreds of applications on the market that collect useful data, and many of them are making that data available for free or very cheaply. You can use that data to do original research, create unique content or even build your own applications. If you’d rather stick to beet farming, well then that’s cool, too. Where Do I Start? Here’s the bad news – APIs are far from standardized, and you’re going to have to understand data structures and write some code. This is not a how-to manual so much as an overview of what’s out there that can help you decide if the world of APIs is right for you. There are some bright spots on the horizon – tools and sites that make programming APIs easier – and I’ll cover some of those at the end. Following is a list of hand-selected APIs (I’ll do my best not to play favorites, and our competitors are on the list), broken down into a few industry categories, and alphabetical within each category. For each API, I’ll provide a main link, a documentation link (documentation can be way too hard to find), a brief description of what’s available in that API, and whether or not there’s a free version. APIs are split into five sections: APIs for SEO APIs for PPC APIs for Social Miscellaneous APIs API Support Tools The last section covers sites and tools that can help you if you’re new to APIs, new to programming, or just are hunting for something that’s not on this list. (1) APIs for SEO This section contains APIs for organic SEO data, including keyword research and link profiling. Bing Search  ( Docs ) The Bing search API allows you to integrate Bing search results and search data directly into your applications, including web search, images, news, videos, related search, and spelling suggestions. Free Version?    YES , but rate-limited. Majestic SEO  ( Docs ) The Majestic API includes a wide range of link metrics, including full back-link lists, discovery dates for links, anchor text, redirection information, and ACRank. Some features are limited to the paid version. Free Version?    YES , but limited functionality. Raven Tools  ( Docs ) The Raven Tools API lets customers access and update account and campaign information. It can also be used to access link data from your Raven campaigns. Free Version?    NO , paid accounts only. SEOmoz Mozscape  ( Docs ) SEOmoz’s API has access to proprietary metrics, including MozRank, Domain Authority, and Page Authority, as well as link metrics such as linking root domains and anchor text data. Free Version?    YES , but rate-limited. WordStream Keyword Tool  ( Docs ) WordStream’s Keyword Tool API lets you access WordStream’s keyword volume metrics, along with related keywords and structured keyword suggestions. Free Version?    YES , but rate-limited. (2) APIs for PPC The following APIs provide access to major ad platforms, including Google, Bing, and Facebook. Bing Ads API ( Docs ) While primarily a campaign management platform, the Bing Ads API does have access to useful data, including keword volume and keyword suggestions/opportunities. Free Version?    YES , but authorization required. Facebook Ads API ( Docs ) The Facebook Ads API provides access to managing Facebook campaigns, as well as statistics about Facebook keyword searches and audience segments. Free Version?    YES , but authorization required. Google AdWords API ( Docs ) Like Bing, the Google AdWords API is mainly for campaign management and building AdWords apps, but it also the only portal to Google keyword volume data. Getting authorized can be a long process. Free Version?    YES , but authorization required. SEMRush API ( Docs ) The SEMRush API has a number of tools for both organic and paid search campaigns, but where it really shines is in competitive analysis, especially for paid search. Free Version?    NO , starts at $15/month. (3) APIs for Social These APIs can access a wealth of information from major social networks and social aggregators. Facebook Graph  ( Docs ) Facebook’s “Graph” API is the primariy interface to building Facebook-based apps, updating Facebook accounts, and accessing Facebook social graph data. There are other, secondary Facebook APIs. Free Version?    YES , but rate-limited. FollowerWonk ( Docs ) FollowerWonk’s Social Authority API scores Twitter users on a 1-100 scale, for simple influence scoring and comparisons (Note: FollowerWonk is a part of SEOmoz). Free Version?    YES , but rate-limited. Gnip ( Docs ) Gnip provides an enterprise-level API with “firehose” and filtered streams for Twitter, Facebook, Google+, YouTube, and more. Pricing is custom and is aimed at large-scale applications. Free Version?    YES , but trial only. Google+ ( Docs ) The official Google+ API allows you to manage accounts, build apps, and access to data from user profiles, posts, and comments. It includes some limited search capability. Free Version?    YES , but rate-limited. Klout  ( Docs ) The Klout API provides access to Klout’s aggregate social metrics, including Klout score, influencers, influence graphs, and topics of influence. Free Version?    YES , but rate-limited. PeerIndex  ( Docs ) PeerIndex is another social aggregator, and their API provides data on multiple influence metrics, including activity, authority, and audience scores. Free Version?    YES , but rate-limited. SharedCount ( Docs ) The SharedCount API lets you access sharing stats on a number of platforms, including Facebook, Twitter, Google+, Reddit, LinkedIn, Digg, Delicious, StumbleUpon, and Pinterest. Free Version?    YES , but rate-limited. Topsy ( Docs ) The Topsy Otter API is an alternative source for Twitter data, including a number of useful search functions – search by keyword, by links mentioned, by popluar stories on a domain, etc. Free Version?    YES , but rate-limited. Twitter ( Docs ) The official Twitter RESTful API includes many tools for account management and data gathering, including individual tweet and user data, follower stats, and a variety of search options. Free Version?    YES , but rate-limited. (4) Miscellaneous APIs Here are some other useful APIs, including Google products, analytics, and text processing. AlchemyAPI  ( Docs ) AlchemyAPI provides a Natural Language Processing engine to perform tasks such as sentiment analysis, named entity extraction, author extraction, and topic categorization. Free Version?    YES , but rate-limited. Google Analytics API ( Docs ) The Google Analytics API is a full-featured system to manage GA accounts and profiles, customize tracking codes, and to access and export analytics data. Free Version?    YES , but authorization required. Google Places API ( Docs ) The Google Places API allows you to access the entire family of Google local data, including Google Maps, Google+ Local, and Google Places search. Free Version?    YES , but authorization required. PageSpeed Insights  ( Docs ) PageSpeed Insights is a Google Developer tool for website performance analysis. The PageSpeed API allows access to PageSpeed scores and recommendations. Free Version?    YES , but authorization required. Repustate  ( Docs ) The Repustate API provides access to a number of advanced algorithms, including sentiment analysis, social media monitioring, and predictive analytics. Free Version?    YES , but rate-limited. (5) API Support Tools If you’re new to APIs, this section can help get you started or find APIs outside the scope of this post. CodeAcademy API Track CodeAcademy is a resource for learning programming concepts and languages. The API track has specific online courses designed to help you learn API coding. Free Version?    YES . Mashape ( Docs ) Mashape is an API marketplace that allows you to access over 2,000 APIs from a single account. Mashape also lets you distribute and monetize your own APIs. Free Version?    YES , depending on the API. ProgrammableWeb ProgrammableWeb is a directory of over 9,000 APIs on a wide variety of topics. ProgrammableWeb has its own API, that allows you to access their search database. Free Version?    YES. SEER Interactive SEO Toolbox ( Docs ) SEER’s all-in-one interactive toolbox lets you access multple APIs via Excel, including Google Analytics, SEOmoz, Majestic, Raven, Twitter, and Klout. Free Version?    YES , but rate-limited. SEOGadget Excel API Extensions ( Docs ) The SEOGadget API extension for Excel allows you to easily call link data from Excel spreadsheets, including SEOmoz, Majestic, and additional SEOGadget data. Free Version?    YES , but rate-limited. What Are Your Favorites? While I don’t intend this to be an exhaustive list of APIs, I’ll try to keep the post up to date with the most useful APIs for marketers (assuming that people are interested). So, feel free to share your favorite data-collection APIs 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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APIs for Data-Driven Marketers

Personalization and SEO – Whiteboard Friday

Posted by randfish Personalization usage data and user data give marketers deep insights into their users’ interests and actions. But how can you make the most out of these complex data sets to better serve your SEO campaigns? In this week’s Whiteboard Friday, Rand takes us through the intricate world of personalization and how it affects SEO. We’d love to hear your thoughts and tips in the comments below!  Video Transcription “Howdy, SEOmoz fans. Welcome to another edition of Whiteboard Friday. This week I’m wearing a hoodie and a T-shirt, so it must be informal. I want to take you in a casual fashion into the topic of personalization user data and usage data, and these are complex topics. This Whiteboard Friday will not be able to cover all of the different areas that user and usage data and personalization touch on. But what I do hope to do is expose you to some of these ideas, give you some actionable insights, and then allow you guys to take some of those things away, and we can point to some other references. There are lots of folks who have done a good job in the search world of digging in deep on some of these other topics. Let’s start by talking about some of the direct impacts that personalization usage data have. Of course, by personalization usage data I mean the areas where Google is showing you or other users specific things based on your usage activities, where they are leveraging usage data, broad usage data, for many users to come up with different changes to these types of search results, and where they’re leveraging user personalization on a macro level, taking the aggregate of those things and creating new types of results, re-ranking things and adding snippets. I’ll talk about each of those. In these direct impacts, one of the most important ones to think about is location awareness. This is particularly important obviously if you’re serving a local area, but you should be aware that location biases a lot of searches that may not have intended to be local simply by virtue of their geography. If you’re at a point, if I’m here in downtown Seattle, there is location awareness that affects the results ordering. I can perform searches, for example for Coffee Works, and I will get these Seattle Coffee Works results. Perhaps if I was in Portland, Oregon and they had a Coffee Works in Portland, I would be getting those Coffee Works results. Usage history also gives Google hints about your location, meaning that even if you’re searching on your smartphone or searching on your laptop, and you said, “Don’t share my location,” Google and Bing will still try to figure this out, and they’ll try to figure it out by looking at your search history. They’ll say to themselves, “Hey, it looks like this user has previously done searches for Madison Markets, Seattle Trader Joe’s, used our maps to get directions from Capitol Hill to Queen Anne. I can guess, based on that usage data, that you are in Seattle, and I will try and give you personalized results that essentially are tied to the location where I think you’re at.” A fascinating example of this is I was searching on my desktop computer last night, which I have not made it location aware specifically, but I did a search for a particular arena in Dublin, which is where the DMX Conference, that I’m going to in a couple days and speaking at, is going to be held. Then I started typing in the name of the hotel I was at, and it’s a brand name hotel. What do you know? That location came up, the Dublin location of the brand hotel, even though that hotel has locations all over the world. How do they know? They know because I just performed a search that was related to Dublin, Ireland, and therefore they’re thinking, oh yeah, that’s probably where he’s looking for this hotel information as well. Very, very smart usage history based personalization. Do be aware search suggest is also affected directly by personalization types of results. If you are doing a search that is going to be biased by some element of personalization, either your search history or your location, those kinds of things, auto-suggest will come up with those same biases as the rankings might. Next, I want to talk about the semantics of how you perform queries and what you’re seeking can affect your search as well. Search history is an important bias here, right? Basically, if I’ve been doing searches for jewelry, gemstones, wedding rings, those kinds of things, and I do a search for ruby, Google and Bing are pretty smart. They can realize, based on that history, that I probably mean ruby the stone, not Ruby the programming language. Likewise, if I’ve just done searches for Python, Pearl and Java, they might interpret that to mean, “Aha, this person is most likely, when they’re searching for Ruby, looking for the programming language.” This makes it very hard if you’re a software engineer who’s trying to look for gemstones, by the way. As you know, the ruby gem is not just a gem. It’s also part of the programming protocol. This gets very interesting. Even seemingly unrelated searches and behavior can modify the results, and I think this is Google showing their strength in pattern matching and machine learning. They essentially have interpreted, for example, as disparate things as me performing searches around the SEO world and them interpreting that to mean that I’m a technical person, and therefore as I do searches related to Ruby or Python, they don’t think the snake or the gemstone. They think the programming language Python or the programming language Ruby, which is pretty interesting, connecting up what is essentially a marketing discipline, SEO a technical marketing discipline, and connecting up those programming languages. Very, very interesting. That can modify your results as well. Your social connections. So social connections was a page that existed on Google until last year. In my opinion, it was a very important page and a frustrating page that they’ve now removed. The social connections page would show, based on the account you were inside of, all your contacts and how Google connected you to them and how they might influence your search results. For example, it would say randfish@gmail.com,which is my Gmail account that I don’t actually use, is connected to Danny Sullivan because Rand has emailed Danny Sullivan on that account, and therefore we have these accounts that Danny Sullivan has connected to Google in one way or another. In fact, his Facebook account and several other accounts were connected through his Quora account because Quora OAuths into those, and Google has an agreement or whatever, an auth system with Quora. You could see, wow, Google is exposing things that Danny Sullivan has shared on Facebook to me, not directly through Facebook, but through this protocol that they’ve got with Quora. That’s fascinating. Those social connections can influence the content you’re seeing, can influence the rankings where you see those things. So you may have never seen them before, they may have changed the rankings themselves, and they can also influence the snippets that you’re seeing. For example, when I see something that Danny Sullivan has Plus One’d or shared on Google+, or I see something that Darmesh Shah, for example, has shared on twitter, it will actually say, “Your friend, Darmesh, shared this,” or “Your friend, Danny Sullivan, shared this,” or “Danny Sullivan shared this.” Then you can hover on that person and see some contact information about them. So fascinating ways that social connections are being used. Big take-aways here, if you are a business and you’re thinking about doing marketing and SEO, you have to be aware that these changes are taking place. It’s not productive or valuable to get frustrated that not everyone is seeing the same auto-suggest results, the same results in the same order. You just have to be aware that, hey, if we’re going to be in a location, that location could be biasing for us or against us, especially if you’re not there or if something else is taking your place. If people are performing searches that are related to topics that might have more than one meaning, you have to make sure that you feel like your audience is well tapped into and that they’re performing searches that they are aware of your products getting more content out there that they might be searching for and building a bigger brand. Those things will certainly help. A lot of the offline branding kinds of things actually help considerably with this type of stuff. Of course, social connections and making sure that your audience is sharing so that the audience connected to them, even if they’re not your direct customers, this is why social media strategy is so much about not just reaching people who might buy from you, but all the people who might influence them. Remember that social connections will be influenced in this way. Right now, Google+ is the most powerful way and most direct way to do this, but certainly there are others as well as the now removed social connections page, helped show us. What about some indirect impacts? There are actually a few of these that are worth mentioning as well. One of those indirect impacts that I think is very important is that you can see re-ranking of results, not just based on your usage, but this can happen or may happen, not for certain, but may happen based on patterns that the engines detect. If they’re seeing that a large number of people are suddenly switching away from searching ruby the gemstone to Ruby the language, they might bias this by saying, “You know what, by default, we’re going to show more results or more results higher up about Ruby the programming language.” If they’re seeing, boy a lot of people in a lot of geographies, not just Seattle, when they perform a Coffee Works search, are actually looking for Seattle Coffee Works, because that brand has built itself up so strongly, you know what, we’re going to start showing the Seattle Coffee Works location over the other ones because of the pattern matching that we’re seeing. That pattern matching can be a very powerful thing, which is another great reason to build a great brand, have a lot of users, and get a lot of people around your product, your services, and your company. Social shares, particularly what we’ve heard from the search engines, Bing’s been a little more transparent about this than Google has, but what Bing has basically said is that with social shares, the trustworthiness, the quality, and the quantity of those shares may impact the rankings, too. This is not just on an individual basis. So they’re not just saying, “Oh well, Danny Sullivan shared this thing with Rand, and so now we’re going to show it to Rand.” They’re saying, “Boy, lots of people shared this particular result around this topic. Maybe we should be ranking that higher even though it doesn’t have the classic signals.” Those might be things like keywords, links, and all the other things, anchor text and other things that they’re using the ranking algorithm. They might say, “Hey the social shares are such a powerful element here, and we’re seeing so much of a pattern around this, that we’re going to start re-ranking results based on that.” Another great reason to get involved in social, even if you’re just doing SEO. Auto-suggest can be your friend. It can also be your enemy. But when you do a search today, Elijah and I just tried this, and do a search for Whiteboard space, they will fill in some links for you – paint, online, information. Then I did the same search on my phone, and what do you think? Whiteboard Friday was the second or third result there, meaning, they’ve seen that I’ve done searches around SEOmoz before and around SEO in general. So they’re thinking, “Aha. You, Rand, you’re a person who probably is interested in Whiteboard Friday, even though you haven’t done that search before on this particular phone.” I got a new phone recently. That usage data and personalization is affecting how auto-suggest is suggesting or search suggest is working. Auto-suggest, by the way, is also location aware and location biased. For example, if you were to perform this search, whiteboard space, in Seattle, you probably would have a higher likelihood of getting Friday than in, let’s say, Hong Kong, where Whiteboard Friday is not as popular generally. I know we have Hong Kong fans, and I appreciate you guys, of course. But those types of search suggests are based on the searches that are performed in a local region, and to the degree that Google or Bing can do it, they will bias those based on that, so you should be aware. For example, if lots and lots of people in a particular location, and I have done this at conferences, it’s actually really fun to ask the audience, “Hey, would everyone please perform this particular search,” and then you look the next day, and that’s the suggested search even though it hadn’t been performed previously. They’re looking at, “Oh, this is trending in this particular region.” This was a conference in Portland, Oregon, where I tried this, a blogging conference, and it was really fun to see the next day that those results were popping up in that fashion. Search queries. The search queries that you perform, but not just the ones the you perform, but the search queries as a whole, kind of in an indirect, amalgamated, pattern matching way, may also be used to form those topic models and co-occurrences or brand associations that we’ve discussed before, which can have an impact on how search results work and how SEO works. Meaning that, if lots of people start connecting up the phrase SEOmoz with SEO or SEOmoz with inbound marketing, or those kinds of things, it’s very likely or you might well see that Google is actually ranking pages on that domain, on SEOmoz’s domain, higher for those keywords because they’ve built an association. Search queries, along with content, are one of the big ways that they put those topics together and try to figure out, “Oh yeah, look, it seems like people have a strong association with GE and washer/dryers, or with Leica and cameras or with the Gap and clothing.” Therefore, when people perform those types of searches, we might want to surface those brands more frequently. You can see this in particular when you perform a lot of ecommerce-related searches and particular brands come up. If you do a search for outdoor clothing and things like Columbia Sportswear and REI and those types of brands are popping up as a suggestion, you get a strong sense of the types of connections that Google might build based on these things. All right, everyone. I hope you’ve enjoyed this edition of Whiteboard Friday. I hope you have lots of great comments, and I would love to jump in there with you and suggestions on how you people can dig deeper. We will see you again next week.” Video transcription by Speechpad.com 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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Personalization and SEO – Whiteboard Friday

Mathematical Ideas for Marketers

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