Lies, Damned Lies...

October 09, 2017

The Electrification of Marketing

At the tail end of the nineteenth century, electricity was starting to have a profound effect on the world. As dramatized in the excellent novel The Last Days of Night, and shortly in the forthcoming film The Current War, Thomas Edison battled with George Westinghouse (the latter aided by Croatian genius/madman Nikola Tesla) for control over the burgeoning market for electricity generation and supply. The popular symbol of the electrical revolution is of course Edison’s famous light bulb, but perhaps almost more important was the humble electric motor.

The electric motor was so important because it revolutionized manufacturing, enabling factories to create assembly lines and realize huge efficiency dividends. The Ball Brothers Glass Manufacturing Company, for example, replaced 36 workers with a single electric crane for moving heavy loads across the factory where they made their famous Mason jars.

But for all the benefits of electric motors, many factories were slow to embrace the new technology. As this article from the BBC World Service’s “50 Things that Made the Modern Economy” podcast explains, by 1900, almost twenty years after Thomas Edison started selling electricity from his generation plants in Manhattan and London, only 5% of factories had switched from steam to electric power. Powering a factory with a steam engine was costly, complicated, and dangerous. So why the reluctance to move to electricity?

The reason lies in the way those factories were organized to take advantage of steam power generation. A typical nineteenth century factory, for example making textiles, looked like the image above. Mechanical power was generated by a single large steam engine which ran more or less continuously, and was transferred to individual machines (such as looms or lathes) via a series of drive shafts, gears and drive belts. Because the power was being transferred mechanically, the machines were packed closely together. This, combined with the constant spinning of the drive shafts, made these factories very dangerous to work in; in 1900, over half a million people in the US (almost 1% of the population) were maimed in factory accidents.

Simply replacing the central steam engine with an electric motor did not deliver significant benefits – the drive shafts and belts to the machines still broke down, factories were still crowded, inefficient and dangerous, and the central motor (now powered by comparatively expensive electricity) still had to be kept running constantly.

To truly capitalize on electrification, factories had to reinvent themselves, replacing all their individual machines with versions that were powered by their own electric motors, with power transferred to them via unobtrusive wires rather than spinning drive shafts. In turn this meant that machines did not need to be so tightly packed together; factories could be reorganized to be more spacious and facilitate the flow of items, paving the way for the production line and improving factory conditions and safety. Ultimately, it was the qualitative transformation in the way things were made which was electrification’s biggest benefit.

Reorganizing the marketing factory

The story of electrification and how it impacted manufacturing in the first decades of the twentieth century provides an interesting parallel to the impact of data and AI on the marketing industry in the first decades of the twenty-first.

Today, many marketing organizations have adopted data in a similar way to how factories first adopted electricity: by applying it to existing business processes and ways of working. In direct marketing, the core processes of list-generation and campaign delivery have not changed fundamentally in fifty years – marketers build target audience lists, map messages to this list, deliver those messages, and then measure the response. The sophistication and complexity of all these steps has changed dramatically, but the process itself is still the same.

However, as electricity led to the development of new kinds of manufacturing machines, so data is leading the the development of new kinds of marketing machines, powered by AI. These new systems, which I have written about before, promise to transform the way that digital marketing is done. But just as before, getting there won’t be easy, and will require marketing leaders to embrace disruptive change.

The current ‘factory layout’ for many marketing organizations is based around individual teams that have responsibility for different channels, such as web, search, email, mobile and so on. These teams coordinate on key marketing calendar activities, such as holiday campaigns or new product launches, but manage their own book of work as a sequence of discrete activities. At Microsoft we’ve made progress in the last few years on bringing many of these teams together, and supporting them with a common set of customer data and common marketing automation tooling. But individual campaigns are still largely hand-crafted.

AI-driven marketing systems use a wide range of attributes at the customer level, combined with a continuous testing/learning approach, to discover which of a range of creative and messaging should be executed next, for which customers, in which channels. They break down the traditional campaign-centric model of customer communications and replace it with a customer-centric, ‘always on’ program of continuous nurture. For these systems to work well, they need a detailed picture of the customer, including their exposure and response to previous communications, and they need a wide range of actions that they can take, including the ability to choose which channel to communicate in for a given message and audience.

A fairly traditional marketing organization that is looking to evaluate the potential of AI-driven marketing will, prudently, lean towards trying the technology in a relatively limited pilot environment, likely choosing just one campaign or program in a single channel for their test. These choices make sense – few companies can easily try out new technology across multiple channels, for both technical reasons (i.e. wiring the thing up) and organizational reasons (getting multiple teams to work together).

But this approach is a bit like a 1900’s factory owner deciding to replace just a single machine in the factory with an electric version. Dedicated (and expensive) wiring would have to be laid to power the machine; it would still be crammed in with all the others, so its size and design would be limited; and it would likely need a dedicated operator. In this environment, it would be unlikely that the single machine would be so transformatively efficient that the factory owner would rush out to buy twenty more.

And so it is with AI-driven marketing. A test within a single channel, on a single campaign, will likely generate modest results, because the machine’s view of the customer will be limited to their experience with that brand in that channel; its message choices will also be limited, since it can only communicate within the single channel. These problems are exacerbated by the expense of laying dedicated data ‘lines’ to the new system, and of building many creative variants, to give the system enough message choice within a single channel.

What’s needed is for AI-based optimization to be applied as an enabling capability in all marketing campaigns, across multiple channels and products. This requires significant investment in data and channel integration; but even more importantly it requires marketers, and marketing organizations, to operate differently. Digital advertising, CRM and e-commerce teams, and their budgets, need to be brought together; instead of marketers creating many discrete campaigns, marketers need to create more evergreen programs that can be continuously optimized over time. The marketing factory needs to be organized around the customer, not the product or channel.

This kind of model represents very disruptive change for today’s marketing organizations, as it did for yesterday’s factory owners. In the end, much of the rise of electrified factories a hundred years ago was due to the efforts of newcomers to the field such as Henry Ford, who jumped straight to an electrified production line in the production of his Model T. Today’s marketing chiefs would do well to heed this lesson from history, as disruptors like Amazon, Tesla and Stitch Fix use process innovation to create streamlined, customer-centric marketing functions that are poised to exploit the transformative technology of AI.

October 09, 2017 in Big Data, Branding & Advertising, Digital Marketing, Display Advertising, Search, Targeting | Permalink | Comments (0)

September 06, 2017

Is Digital Marketing having its ‘Deep Blue’ moment?

Garry Kasparov will forever be remembered as perhaps the greatest chess player of all time, dominating the game for almost twenty years until his retirement in 2005. But ironically he may be best remembered for the match he failed to win twenty years ago in 1997 against IBM’s Deep Blue chess computer. That watershed moment – marking the point at which computers effectively surpassed humans in chess-playing ability – prompted much speculation and hand-wringing about the coming obsolescence of the human brain, now that a mere computer had been able to beat the best chess grandmaster in the world.

Since then, computers and chess software have only grown more powerful, to the point that a $50 commercial chess program (or even a mobile app) can beat most grandmasters easily. Faced with this, you might expect Kasparov and other top-flight players to have grown disillusioned with the game, or defensive about the encroachment of computers on their intellectual territory; but in fact the reverse is true.

Today’s chess grandmasters make extensive use of computers to practice, try out new strategies, and prepare for tournaments, in the process becoming a little more like the machines that outpaced them in 1997. Kasparov himself was instrumental in pioneering a new type of chess game, Advanced Chess, in which humans are allowed to consult with chess software as they play. In his new book, “Deep Thinking: Where Machine Intelligence Ends and Human Intelligence Begins”, Kasparov writes about an Advanced Chess match he played in 1998 against Veselin Topalov:

“Having a computer partner also meant never having to worry about making a tactical blunder. The computer could project the consequences of each move we considered, pointing out possible outcomes and countermoves we might otherwise have missed. With that taken care of for us, we could concentrate on strategic planning instead of spending so much time on calculations. Human creativity was even more paramount under these conditions.”

What Kasparov and his successors in the competitive chess-playing world have discovered was that, when it comes to chess, the strongest player is not man or machine, but man and machine. In fact, a new kind of chess tournament has sprung up, Freestyle Chess, in which teams of humans and computers compete against one another, each bringing their respective strengths to the game: creativity, strategy and intuition from the humans, and tactical outcome prediction from the computers.

And your point is?

You may be asking what relevance this has to digital marketing. In fact, there are strong similarities between chess and marketing (particularly digital marketing): they are both highly quantifiable pursuits with clear outcomes which have historically relied solely on human intuition and creativity for success.

As in chess, digital marketing relies upon a continuous reassessment of the ‘board’ (customer behaviors and history) in order to decide upon the next ‘move’ (a particular campaign communication aimed at a particular group of customers). Once the move has been made, the board needs to be reassessed before taking the next move.

Today’s digital marketer is much like the chess grandmaster of the early 1990s – they rely on their intuitive understanding of their audience’s makeup and preferences to decide what offers and messages they want to deliver, to which users, and in which channels. Of course, digital marketers understand that measuring campaign outcomes and audience response (using techniques like control groups and attribution analysis) is very important, but most still operate in a world where the humans make the decisions, and the computers merely provide the numbers to support the decision-making.

Luddites 2.0

When Kasparov was asked in 1990 if a computer could beat a grandmaster before the year 2000, he quipped:

“No way - and if any grandmaster has difficulties playing computers, I would be happy to provide my advice.”

Today’s digital marketers can be forgiven for exhibiting some of the same skepticism. Ask them how they came up with a new idea for an ad, or how they know that a particular product will be just right for a particular audience, and they may not be able to answer – they will just know that their intuition is sound. As a result it can seem incredible that a computer can pick the right audience for a campaign, and match the appropriate offer and creative to that audience.

But the computers are coming. As I mentioned in my earlier post on bandit experimentation, companies like Amplero, Kahuna and Cerebri AI are pitching intelligent systems that claim to take a lot of this decision-making about creative choice, audience, channel and other campaign variables out of the hands of humans. But where does that leave the digital marketer?

We welcome our robot colleagues

The clue lies in the insights that Kasparov ultimately drew from his defeat. He realized that the strengths he brought were different and complementary to the strengths of the computer. The same holds true for digital marketing. Coming up with product value propositions, campaign messaging and creative are activities which computers are nowhere close to being good at, especially in the context of broader intangible brand attributes. On the other hand, audience selection and targeting, as well as creative optimization, are highly suited to automation, to the extent that computers can be expected to perform significantly better than their human counterparts, much as chess software outperforms human players.

Clearly humans and machines need to work together to create and execute the best performing campaigns, but exactly how this model will work is still being figured out.

Today, most digital marketers build campaign audiences by hand, identifying specific audience attributes (such as demographics or behavioral history) and applying filters to to those attributes to build segments. The more sophisticated the marketer attempts to be in selecting audience attributes for campaign segments, the more cost they incur in the setup of those campaigns, making the ROI equation harder to balance.

The emerging alternative approach is to provide an ML/AI system with a set of audience (and campaign) attributes, and let it figure out which combinations of audience and offer/creative deliver the best results by experimenting with different combinations of these attributes in outbound communications. But this raises some important questions:

How to choose the attributes in the first place
How to understand which attributes make a difference
How to fit ML/AI-driven campaigns into a broader communications cadence & strategy
How to use learnings from ML/AI-driven campaigns to develop new value propositions and creative executions

In other words, ML/AI-driven marketing systems cannot simply be ‘black boxes’ into which campaign objectives and creative are dumped, and then left to deliver clicks or conversions on the resulting campaign delivery. They need to inform and involve marketers as they do their work, so that the marketers can make their uniquely human contribution to the process of designing effective campaigns. The black box needs some knobs and dials, in other words.

The world of chess offers a further useful parallel here. Chess grandmasters make extensive use of specialized chess software like Fritz 15 or Shredder, which not only provide a comprehensive database of chess moves, but also training and analysis capabilities to help human players improve their chess and plan their games. These programs don’t simply play chess – they explain how they are making their recommendations, to enable their human counterparts to make their own decisions more effectively.

These are the kinds of systems that digital marketers need to transform their marketing with AI. In turn, marketers need to adjust the way they plan and define campaigns in the same way that chess grandmasters have dramatically changed the way they study, plan and play games of chess in the last twenty years, working alongside the computers before, during and after campaigns are run.

In 1997, it was far from clear how chess, and the people who played it , would react to the arrival of computers. Digital Marketing stands on a similar threshold today. Twenty years from now it will seem obvious how marketers’ roles would evolve, and how technology would adapt to support them. We’re in the fortunate position of getting to figure this out as it all unfolds, much as Kasparov did.

September 06, 2017 in Branding & Advertising, Digital Marketing, Display Advertising, Targeting | Permalink | Comments (0)

October 22, 2015

6 steps to building your Marketing Data Strategy

Your company has a Marketing Strategy, right? It’s that set of 102 slides presented by the CMO at the offsite last quarter, immediately after lunch on the second day, the session you may have nodded off in (it’s ok, nobody noticed. Probably). It was the one that talked about customer personas and brand positioning and social buzz, and had that video towards the end that made everybody laugh (and made you wake up with a start).

Your company may also have a Data Strategy. At the offsite, it was relegated to the end of the third day, after the diversity session and that presentation about patent law. Unfortunately several people had to leave early to catch their flights, so quite a few people missed it. The guy talked about using Big Data to drive product innovation through continuous improvement, and he may (at the very end, when your bladder was distracting you) have mentioned using data for marketing. But that was something of an afterthought, and was delivered with almost a sneer of disdain, as if using your company’s precious data for the slightly grubby purpose of marketing somehow cheapened it.

Which is a shame, because Marketing is one of the most noble and enlightened ways to use data, delivering a direct kick to the company’s bottom line that is hard to achieve by other means. So when it comes to data, your marketing shouldn’t just grab whatever table scraps it can and be grateful; it should actually drive the data that you produce in the first place. This is why you don’t just need a Marketing Strategy, or a Data Strategy: You need a Marketing Data Strategy.

A Marketing Data What?

What even is a Marketing Data Strategy, anyway? Is it even a thing? It certainly doesn’t get many hits on Bing, and those hits it does get tend to be about building a data-driven Marketing Strategy (i.e. a marketing strategy that focuses on data-driven activities). But that’s not what a Marketing Data Strategy is, or at least, that’s not my definition, which is:

A Marketing Data Strategy is a strategy for acquiring, managing, enriching and using data for marketing.

The four boldface words are the key here. If you want to make the best use of data for your marketing, you need to be thinking about how you can get hold of the data you need, how you can make it as useful as possible, and how you can use your marketing efforts themselves to generate even more useful data – creating a positive feedback loop and even contributing to the pool of Big Data that your Big Data guy is so excited about turning into an asset for the company.

Building your Marketing Data Strategy

So know that you know why it’s important to have a Marketing Data Strategy, how do you put one together? Everyone loves a list, so here are six steps you can take to build and then start executing on your Marketing Data Strategy.

Step 1: Be clear on your marketing goals and approach

This seems obvious, but it’s a frequently missed step. Having a clear understanding of what you’re trying to achieve with your digital marketing will help you to determine what data you need, and what you need to do with/to it to make it work for you. Ideally, you already have a marketing strategy that captures a lot of this, though the connection between the lofty goals of a marketing strategy (sorry, Marketing MBA people) and the practical data needs to execute the strategy are not always clear.

Here are a few questions you should be asking:

Get new customers, or nurture existing ones? If your primary goal is to attract new customers, you’ll need to think differently about data (for example relying on third-party sources) than if you are looking to deepen your relationship with your existing customers (about whom you presumably have some data already).

What are your goals & success criteria? If you are aiming to drive sales, are you more interested in revenue, or margin? If you’re looking to drive engagement or loyalty, are you interested in active users/customers, or engagement depth (such as frequency of usage)?

Which communications strategies & channels? The environments in which you want to engage your audience make a big difference to your data needs – for example, you may have more data at your disposal to target people using your website compared to social or mobile channels.

Who’s your target audience? What attributes identify the people you’d most like to reach with your marketing? Are they primarily demographic (e.g. gender, age, locale) or behavioral (e.g. frequent users, new users)?

What is your conversion funnel? Can you convert customers entirely online, or do you need to hand over to humans (e.g. in store) at some point? If the latter, you’ll need a way to integrate offline transaction data with your online data.

These questions will not only help you identify the data you’ll need, but also some of the data that you can expect to generate with your marketing.

Step 2: Identify the most important data for your marketing efforts

Once you’re clear on your goals and success criteria, you need to consider what data is going to be needed to help you achieve them, and to measure your success.

The best way to break this down is to consider which events (or activities) you need to capture and then which attributes (or dimensions) you need on those events. But how to pick the events and attributes you need?

Let’s start with the events. If your marketing goals include driving revenue, you will need revenue (sales) events in your data, such as actual purchase amounts. If you are looking to drive adoption, then you might need product activation events. If engagement is your goal, then you will need engagement events – this might be usage of your product, or engagement with your company website or via social channels.

Next up are the attributes. Which data points about your customers do you think would be most useful for targeted marketing? For example, does your product particularly appeal to men, or women, or people within a certain geography or demographic group?

For example, say you’re an online gambling business. You will have identified that geo/location information is very important (because online gambling is banned in some countries, such as the US). Therefore, good quality location information will be an important attribute of your data sources.

At this step in the process, try not to trip yourself up by second-guessing how easy or difficult it will be to capture a particular event or attribute. That’s what the next step (the data audit) is for.

Step 3: Audit your data sources

Now to the exciting part – a data audit! I’m sure the very term sends shivers of anticipation down your spine. But if you skip this step, you’ll be flying blind, or worse, making costly investments in acquiring data that you already have.

The principle of the data audit is relatively simple – for every dataset you have which describes your audience/customers and their interaction with you, write down whether (and at what kind of quality) they contain the data you need, as identified in the previous step:

Events (e.g. purchases, engagement)
Attributes (aka dimensions, e.g. geography, demographics)
IDs (e.g. cookies, email addresses, customer IDs)

The key to keeping this process from consuming a ton of time and energy is to make sure you’re focusing on the events, attributes and IDs which are going to be useful for your marketing efforts. Documenting datasets in a structured way is notoriously challenging (some of the datasets we have here at Microsoft have hundreds or even thousands of attributes), so keep it simple, especially the first time around – you can always go back and add to your audit knowledge base later on.

The one type of data you probably do want to be fairly inclusive with is ID data. Unless you already have a good idea which ID (or IDs) you are going to use to stitch together your data, you should capture details of any ID data in your datasets. This will be important for the next step.

To get you started on this process, I’ve created a very simple data audit template which you can download here. You’re welcome.

Step 4: Decide on a common ID (or IDs)

This is a crucial step. In order for you to build a rich profile of your users/customers that will enable you to target them effectively with marketing, you need to be able to stitch the various sources of data about them together, and for this you need a common ID.

Unless you’re spectacularly lucky, you won’t be issuing (or logging) a single ID consistently across all touchpoints with your users, especially if you have things like retail stores, where IDing your customers reliably is pretty difficult (well, for the time being, at least). So you’ll need to pick an ID and use this as the basis for a strategy to stitch together data.

When deciding which ID or IDs to use, take into consideration the following attributes:

The persistence of the ID. You might have a cookie that you set when people come visit your website, but cookie churn ensures that that ID (if it isn’t linked to a login) will change fairly regularly for many of your users, and once it’s gone, it won’t come back.
The coverage of the ID. You might have a great ID that you capture when people make a purchase, or sign up for online support, but if it only covers a small fraction of your users, it will be of limited use as a foundation for targeted marketing unless you can extend its reach.
Where the ID shows up. If your ID is present in the channels that you want to use for marketing (such as your own website), you’re in good shape. More likely, you’ll have an ID which has good representation in some channels, but you want to find those users in another channel, where the ID is not present.
Privacy implications. User email address can be a good ID, but if you start transmitting large numbers of email addresses around your organization, you could end up in hot water from a privacy perspective. Likewise other sensitive data like Social Security Numbers or credit card numbers – do not use these as IDs.
Uniqueness to your organization. If you issue your own ID (e.g. a customer number) that can have benefits in terms of separating your users from lists or extended audiences coming from other providers; though on the other hand, if you use a common ID (like a Facebook login), that can make joining data externally easier later.

Whichever ID you pick, you will need to figure out how you can extend its reach into the datasets where you don’t currently see it. There are a couple of broad strategies for achieving this:

Look for technical strategies to extend the ID’s reach, such as cookie-matching with a third-party provider like a DMP. This can work well if you’re using multiple digital touchpoints like web and mobile (though mobile is still a challenge across multiple platforms).
Look for strategies to increase the number of signed-in or persistently identified users across your touchpoints. This requires you to have a good reason to get people to sign up (or sign in with a third-party service like Facebook) in the first place, which is more of a business challenge than a technical one.

As you work through this, make sure you focus on the touchpoints/channels where you most want to be able to deliver targeted messaging – for example, you might decide that you really want to be able to send targeted emails and complement this with messaging on your website. In that case, finding a way to join ID data between those two specific environments should be your first priority.

Step 5: Find out what gaps you really need to fill

Your data audit and decisions around IDs will hopefully have given you some fairly good indications of where you’re weak in your data. For example, you may know that you want to target your marketing according to geography, but have very little geographic data for your users. But before you run off to put a bunch of effort into getting hold of this data, you should try to verify whether a particular event or attribute will actually help you deliver more effective marketing.

The best way to do this is to run some test marketing with a subset of your audience who has a particular attribute or behavior, and compare the results with similar messaging to a group who which does not have this attribute (but are as similar in other regards as you can make them). I could write another whole post on this topic of A/B testing, because there is a myriad of ways that you can mess up a test like this and invalidate your results, or I could just recommend you read the work of my illustrious Microsoft colleague, Ronny Kohavi.

If you are able to run a reasonably unbiased bit of test marketing, you will discover whether the datapoint(s) you were interested in actually make a difference to marketing outcomes, and are therefore worth pursuing more of. You can end up in a bit of a chicken-and-egg situation in this regard, because of course you need data in the first place to test its impact, and even if you do have some data, you need to test over a sufficiently large population to be able to draw reliable conclusions. To address this, you could try working with a third-party data provider over a limited portion of your user base, or over a population the provider provides.

Step 6: Fix what you can, patch what you can’t, keep feeding the beast

Once you’ve figured out which data you actually need and the gaps you need to fill, the last part of your Marketing Data Strategy is about tactics to actually get this data. Of course the tactics then represent an ongoing (and never-ending) process to get better and better data about your audience. Here are four approaches you can use to get the data you need:

Measure it. Adding instrumentation to your website, your product, your mobile apps, or other digital touchpoints is (in principal) a straightforward way of getting behavioral events and attributes about your users. In practice, of course, a host of challenges exist, such as actually getting the instrumentation done, getting the signals back to your datacenter, and striking a balance between well-intentioned monitoring of your users and appearing to snoop on them (we know a little bit about the challenges of striking this balance).

Gather it. If you are after explicit user attributes such as age or gender, the best way to get this data is to ask your users for it. But of course, people aren’t just going to give you this information for no reason, and an over-nosy registration or checkout form is a sure-fire way to increase drop-out from your site, which can cost you money (just ask Bryan Eisenberg). So you will need to find clever ways of gathering this data which are linked to concrete benefits for your audience.

Model it. A third way to fill in data gaps is to use data modeling to extrapolate attributes that you have on some of your audience to another part of your audience. You can use predictive or affinity modeling to model an existing attribute (e.g. gender) by using the behavioral attributes of existing users whose gender you know to predict the gender of users you don’t know; or you can use similar techniques to model more abstract attributes, such as affinity for a particular product (based on signals you already have for some of your users who have recently purchased that product). In both cases you need some data to base your models on and a large enough group to make your predictions reasonably accurate. I’ll explore these modeling techniques in another post.

Buy it. If you have money to spend, you can often (not always) buy the data you need. The simplest (and crudest) version of this is old-fashioned list-buying – you buy a standalone list of emails (possibly with some other attributes) and get spamming. The advantage of this method is that you don’t need any data of your own to go down this path; the disadvantages are that it’s a horrible way to do marketing, will deliver very poor response rates, and could even damage your brand if you’re seen as spamming people. The (much) better approach is to look for data brokers that can provide data that you can join to your existing user/customer data (e.g. they have a record for user [email protected] and so do you, so you can join the data together using the email address as a key).

Once you’ve determined which data makes the most difference for your marketing, and have hit upon a strategy (or strategies) to get more of this data, you need to keep feeding the beast. You won’t get all the data you need – whether you’re measuring it, asking for it, or modeling it – right away, so you’ll need to keep going, adjusting your approach as you go and learn about the quality of the data you’re collecting. Hopefully you can reduce your dependency on bought data as you go.

Finally, don’t forget – all this marketing you’re doing (or plan to do) is itself a very valuable source of data about your users. You should make sure you have a means to capture data about the marketing you’re exposing your users to, and how they’re responding to it, because this data is useful not just for refining your marketing as you go along, but can actually be useful other areas of your business such as product development or support. Perhaps you’ll even get your company’s Big Data people to have a bit more begrudging respect for marketing…

October 22, 2015 in Ad industry 101, Big Data, Branding & Advertising, Display Advertising, Targeting | Permalink | Comments (1)

August 26, 2015

Got a DMP coming in? Pick up your underwear

mr-messy-nr-8 If you’re like me, and have succumbed to the unpardonably bourgeois luxury of hiring a cleaner, then you may also have found yourself running around your house before the cleaner comes, picking up stray items of laundry and frantically doing the dishes. Much of this is motivated by “cleaner guilt”, but there is a more practical purpose – if our house is a mess when the cleaner comes, all she spends her time doing is tidying up (often in ways that turn out to be infuriating, as she piles stuff up in unlikely places) rather than actually cleaning (exhibit one: my daughter’s bedroom floor).

This analogy occurred to me as I was thinking about the experience of working with a Data Management Platform (DMP) provider. DMPs spend a lot of time coming in and “cleaning house” for their customers, tying together messy datasets and connecting them to digital marketing platforms. But if your data systems and processes are covered with the metaphorical equivalent of three layers of discarded underwear, the DMP will have to spend a lot of time picking that up (or working around it) before they can add any serious value.

So what can you do ahead of time to get the best value out of bringing in a DMP? That’s what this post is about.

What is a DMP, anyway?

That is a excellent question. DMPs have evolved and matured considerably since they emerged onto the scene a few years ago. It’s also become harder to clearly identify the boundaries of a DMP’s services because many of the leading solutions have been integrated into broader “marketing cloud” offerings (such as those from Adobe, Oracle or Salesforce). But most DMPs worth their salt provide the following three core services:

Data ingestion & integration: The starting place for DMPs, this is about bringing a marketer’s disparate audience data together in a coherent data warehouse that can then be used for analytics and audience segment building. Central to this warehouse is a master user profile – a joined set of ID-linked data which provides the backbone of a customer’s profile, together with attributes drawn from first-party sources (such as product telemetry, historical purchase data or website usage data) and third-party sources (such as aggregated behavioral data the DMP has collected or brokered).

Analytics & segment building: DMPs typically offer their own tools for analyzing audience data and building segments, often as part of a broader campaign management workflow. These capabilities can vary in sophistication, and sometimes include lookalike modeling, where the DMP uses the attributes of an existing segment (for example, existing customers) to identify other prospects in the audience pool who have similar attributes, and conversion attribution - identifying which components of a multi-channel campaign actually influenced the desired outcomes (e.g. a sale).

Delivery system integration: The whole point of hiring a DMP to integrate data and enable segment building is to support targeted digital marketing. So DMPs now provide integration points to marketing delivery systems across email, display (via DSP and Exchange integration), in-app and other channels. This integration is typically patchy and influenced by other components of the DMP provider’s portfolio, but is steadily improving.

Making the best of your DMP relationship

The whole reason that DMPs exist in the first place is because achieving the above three things is hard – unless your organization in a position to build out and manage its own data infrastructure and put some serious investment behind data integration and development, you are unlikely to be able to replicate the services of a DMP (especially when it comes to integration with third-party data and delivery systems). But there are a number of things you can do to make sure you get the best value out of your DMP relationship.

1. Clean up your data

dirty-dishes This is the area where you can make the most difference ahead of time. Bringing signals about your audience/customers together will benefit your business across the board, not just in a marketing context. You should set your sights on integrating (or at least cataloging and understanding) all data that represents customer/prospect interaction with your organization, such as:

Website visits
Purchases
Product usage (if you have a product that you can track the usage of)
Mobile app usage
Social media interaction (e.g. tweets)
Marketing campaign response (e.g. email clicks)
Customer support interactions
Survey/feedback response

You should also integrate any datasets you have that describe what you already know about your customers or users, such as previous purchases or demographic data.

The goal here is, for a given user/customer, to be able to identify all of their interactions with your organization, so that you can cross-reference that data to build interesting and useful segments that you can use to communicate with your audience. So for user XYZ123, for example, you want to know that:

They visited your website 3 times in the past month, focusing mainly on information about your Widget3000 product
They have downloaded your free WidgetFinder app, and run it 7 times
They previously purchased a Widget2000, but haven’t used it for four months
They are male, and live in Sioux Falls, South Dakota
Last week they tweeted:

Unless you’re some kind of data saint (or delusional), reading the two preceding paragraphs probably filled you with exhaustion. Because all of the above kinds of data have different schemas (if they have schemas at all), and more importantly (or depressingly), they all use different (or at least independent) ways of identifying who the user/customer actually is. How are you supposed to join all this data if you don’t have a common key?

DSPs solve these problems in a couple of ways:

They provide a unified ID system (usually via a third-party tag/cookie) for all online interaction points (such as web, display ads, some social)
They will map/aggregate key behavioral signals onto a common schema to create a single user profile (or online user profile, at any rate), typically hosted in the DMP’s cloud

The upside of this approach is that you can achieve some degree of data integration via the (relatively) painless means of inserting another bit of JavaScript into all of your web pages and ad templates, and also that you can access other companies’ audiences who are tagged with the same cookie – so-called audience extension.

However, there are some downsides, also. Key amongst these are:

Yet another ID: If you already have multiple ways of IDing your users, adding another “master ID” to the mix may just increase complexity. And it may be difficult to link key behaviors (such as mobile app purchases) or offline data (such as purchase history) to this ID.

Your data in someone else’s cloud: Most marketing cloud/DMP solutions assume that the master audience profile dataset will be stored in the cloud. That necessarily limits the amount and detail of information you can include in the profile – for example, credit card information.

It doesn’t help your data: Just taking a post-facto approach with a DMP (i.e. fixing all your data issues downstream of the source, in the DMP’s profile store) doesn’t do anything to improve the core quality of the source data.

So what should you do? My recommendation is to catalog, clean up and join your most important datasets before you start working with a DMP, and (if possible) identify an ID that you already own that you can use as a master ID. The more you can achieve here, the less time your DMP will spend picking up your metaphorical underwear, and the more time they’ll spend providing value-added services such as audience extension and building integrations into your online marketing systems.

2. Think about your marketing goals and segments

cpc_01 You should actually think about your marketing goals before you even think about bringing in a DMP or indeed make any other investments in your digital marketing capabilities. But if your DMP is already coming in, make sure you can answer questions about what you want to achieve with your audience (for example, conversions vs engagement) and how you segment them (or would like to segment them).

Once you have an idea of the segments you want to use to target your audience, then you can see whether you have the data already in-house to build these segments. Any work you can do here up-front will save your DMP a lot of digging around to find this data themselves. It will also equip you well for conversations with the DMP about how you can go about acquiring or generating that data, and may save you from accidentally paying the DMP for third-party data that you actually don’t need.

3. Do your own due diligence on delivery systems and DSPs

Your DMP will come with their own set of opinions and partnerships around Demand-side Platforms (DSPs) and delivery systems (e.g. email or display ad platforms). Before you talk with the DMP on this, make sure you understand your own needs well, and ideally, do some due diligence with the solutions in the marketplace (not just the tools you’re already using) as a fit to your needs. Questions to ask here include:

Do you need realtime (or near-realtime) targeting capabilities, and under what conditions? For example, if someone activates your product, do you want to be able to send them an email with hints and tips within a few hours?
What kinds of customer journeys do you want to enable? If you have complex customer journeys (with several stages of consideration, multiple channels, etc) then you will need a more capable ‘journey builder’ function in your marketing workflow tools, and your DMP will need to integrate with this.
Do you have any unusual places you want to serve digital messaging, such as in-product/in-app, via partners, or offline? Places where you can’t serve (or read) a cookie will be harder to reach with your DMP and may require custom integration.

The answers to these questions are important: on the one hand there may be a great third-party system with functionality that you really like, but which will need custom integration with your DMP; on the other hand, the solutions that the DMP can integrate with easily may get you started quickly and painlessly, but may not meet your needs over time.

If you can successfully perform the above housekeeping activities before your DMP arrives and starts gasping at the mountain of dishes piled up in your kitchen sink, you’ll be in pretty good shape.

August 26, 2015 in Big Data, Digital Marketing, Display Advertising, Targeting | Permalink | Comments (3) | TrackBack (0)

May 17, 2015

The rise of the Chief Data Officer

As the final season of Mad Men came to a close this weekend, one of my favorite memories from Season 7 is the appearance of the IBM 360 mainframe in the Sterling Cooper & Partners offices, much to the chagrin of the creative team (whose lounge was removed to make space for the beast), especially poor old Ginsberg, who became convinced the “monolith” was turning him gay (and took radical steps to address the issue).

My affection for the 360 is partly driven by the fact that I started my career at IBM, closer in time to Man Men Series 7 (set in 1969) than the present day (and now I feel tremendously old having just written that sentence). The other reason I feel an affinity for the Big Blue Box is because my day job consists of thinking of ways to use data to make marketing more effective, and of course that is what the computer at SC&P was for. It was brought in at the urging of the nerdish (and universally unloved) Harry Crane, to enable him to crunch the audience numbers coming from Nielsen’s TV audience measurement service to make TV media buying decisions. This was a major milestone in the evolution of data-driven marketing, because it linked advertising spend to actual advertising delivery, something that we now take for granted.

The whole point of Mad Men introducing the IBM computer into the SC&P offices was to make a point about the changing nature of advertising in the early 1970s – in particular that Don Draper and his “three martini lunch” tribe’s days were numbered. Since then, the rise of the Harry Cranes, and the use of data in marketing and advertising, has been relentless. Today, many agencies have a Chief Data Officer, an individual charged with the task of helping the agency and its clients to get the best out of data.

But what does, or should, a Chief Data Officer (or CDO) do? At an advertising & marketing agency, it involves the following areas:

Enabling clients to maximize the value they get from data. Many agency clients have significant data assets locked up inside their organization, such as sales history, product telemetry, or web data, and need help to join this data together and link it to their marketing efforts, in order to deliver more targeted messaging and drive loyalty and ROI. Additionally, the CDO should advise clients on how they can use their existing data to deliver direct value, for example by licensing it.

Advising clients on how to gather more data, safely. A good CDO offers advice to clients on strategies for collecting more useful data (e.g. through additional telemetry), or working with third-party data and data service providers, while respecting the client’s customers’ privacy needs.

Managing in-house data assets & services. Some agencies maintain their own in-house data assets and services, from proprietary datasets to analytics services. The CDO needs to manage and evolve these services to ensure they meet the needs of clients. In particular, the CDO should nurture leading-edge marketing science techniques, such as predictive modeling, to help clients become even more data-driven in their approach.

Managing data partnerships. Since data is such an important part of a modern agency’s value proposition, most agencies maintain ongoing relationships with key third-party data providers, such as BlueKai or Lotame.The CDO needs to manage these relationships so that they complement the in-house capabilities of the agency, and so the agency (and its clients) don’t end up letting valuable data “walk out of the door”.

Driving standards. As agencies increasingly look to data as a differentiating ingredient across multiple channels, using data and measurement consistently becomes ever more important. The CDO needs to drive consistent standards for campaign measurement and attribution across the agency so that as a client works with different teams, their measurement framework stays the same.

Engaging with the industry & championing privacy. Using data for marketing & advertising is not without controversy, so the DCO needs to be a champion for data privacy and actively engaged with the industry on this and other key topics.

As you can see, that’s plenty for the ambitious CDO to do, and in particular plenty that is not covered by other traditional C-level roles in an ad agency. I think we’ll be seeing plenty more CDOs appointed in the months and years to come.

May 17, 2015 in Adserving, Big Data, Branding & Advertising, Digital Marketing, Display Advertising | Permalink | Comments (0) | TrackBack (0)

December 19, 2011

Building the Perfect Display Ad Performance Dashboard, Part II – metrics

Welcome to the second installment in my Building the Perfect Display Ad Performance Dashboard series (Note to self: pick a shorter title for the next series). In the first installment, we looked at an overarching framework for thinking about ad monetization performance, comprised of a set of key measures and dimensions. In this post, we’ll drill into the first of these – the measures that you need to be looking at to understand your business.

How much, for how much?

As we discussed in the previous post, analysis of an online ad business needs to focus on the following:

How much inventory was available to sell (the Supply)
How much inventory was actually sold (the Volume Sold)
How much the inventory was actually sold for (the Rate)

Of these, it’s the last two – the volume sold and the rate at which that volume was sold – where the buck (literally) really stops, since these two combine to deliver that magic substance, Revenue. So in this post we’ll focus on volume sold, rate and revenue as the core building-blocks of your dashboard’s metrics.

Volume, rate and revenue are inextricably linked via a fairly basic mathematical relationship:

Revenue = Rate x Volume

Another way of thinking about this is that these three measures form the vertices of a triangle:

Some business and economics textbooks call Rate and Volume “Price” and “Quantity” (or P and Q), but the terms we’re using here are more common in advertising.

Different parts of an ad business can be driven by different corners of the triangle, depending on the dynamics of how each part is transacted. Here are some examples:

Ads sold on a time-based/”sponsorship” basis are best thought of as driving revenue performance, because deals are done on a revenue basis regardless of volume/rate (though the advertiser will have a volume & rate expectation, which they’ll want to be met).
For premium ads sold on a CPM basis, deals revolve around Rate; the name of the game is to add value to inventory so that, impression-for-impression, it achieves more revenue.
For remnant ads and networks, volume is king (assuming you can maintain a reasonable rate) – you’re looking to maximize the amount of inventory sold, and minimize the amount that has to be given away or sent to “house” advertising.

Because of these different dynamics, measurement of ad monetization can easily fragment into various sub-types of measure; for example, as well as cost-per-thousand (CPM) rate, some ads are purchased on a CPC or CPA basis. So a more complete version of the diagram above looks like this:

However, it’s essential to remember the key relationship and dynamic between rate, volume and revenue, which is manifested in the CPM, Impressions and Delivery Revenue measures in the diagram above. So let’s look at these measures.

Volume

In the online ad business, Volume is measured in Ad Impressions. I have talked about ad impressions before on this blog, in this installment of Online Advertising 101 (you may want to take a moment to read the section entitled “What’s the product?” in that post). From a measurement point of view, whenever your ad server serves an ad (or more accurately, fields a request for an ad), its measurement system should log an ad impression. How much data is logged with this impression will vary depending on the ad server you’re using, but will likely include most of the following:

Date & time of the impression
Advertiser
Campaign and/or creative
Location/placement (i.e. where the ad was served)
Attributes of the individual who requested the ad (e.g. targeting attributes)

We’ll come back to those attributes (and how you can use them to segment your impressions for better analysis) in another post.

Capturing a true view of the ad impressions on your site can be a little more challenging if you are using multiple ad servers or networks to sell your inventory, particularly if you are using a combination of your own first-party ad server (for example, DFP) and redirecting some impressions to a third-party such as an ad network. When you have delivery systems chained together in this way, you may need to combine the impression counts (and other data) from those systems to get a true picture of impression volume, and you will need to be careful to avoid double-counting.

For reasons that will become clearer when we get on to talking about rate, it’s essential that you capture impression counts for your ad sales where you possibly can, even for parts of your site or network where the supply is not sold on an impression basis.

Other volume measures such as Clicks and Conversions become very useful when you’re looking to assess how valuable your inventory is from an advertiser perspective, since both are a proxy for true Advertiser ROI. They’re also useful for deriving effective rate, as we’ll see below.

Rate

At the highest level, rate is a simple function of volume and revenue – simply divide your total revenue by your total volume (and usually multiply by 1,000 to get a more usable number) and you have your overall rate – in fact, you have the most commonly used kind of rate that people talk about, known as “Effective Cost-per-Mille (Thousand)”, or eCPM (don’t as me why the e has to be small – ask e.e. cummings). Just to be clear, eCPM is calculated as:

eCPM = (Revenue) * 1000 / (Volume)

Sometimes eCPM is known as eRPM (Where the R stands for “Revenue”).

The reason we’re talking about eCPM before revenue in this post is because many advertising deals are struck on a CPM basis – i.e. the advertiser agrees to buy a certain amount of impressions at a certain pre-agreed rate. However, even for inventory is not being sold on a CPM basis, it’s essential to be able to convert the rate to eCPM. Here’s why.

The beauty about eCPM is it is the lowest common denominator – regardless of how a particular portion of your impression supply was sold (e.g. on a cost-per-click basis, or on a “share of voice” or time-based basis), if you can convert the rate back into effective CPM you can compare the performance of different subsets of your inventory on a like-for like basis. Consider the following example of delivery info for the parts of a fictional autos site:

Site area	Sold as…	Deal
Home page	Share-of-voice	$10,000 up-front
Car reviews	Reserved CPM	$2.50 CPM
Community	AdSense	$1.20 CPC

With just the information above, it’s impossible to understand whether the Home Page, Reviews or Community site areas are doing better, because they’re all sold on a different basis. But if you add impression counts (and, in the case of the Community area, click counts), it’s possible to derive an overall rate for the site, as well as to see which parts are doing best:

Site area	Sold as…	Deal	Impressions	Clicks	CPC	Revenue	eCPM
Home page	Sponsorship	$10,000 up-front	5,347,592	n/a	n/a	$10,000	$1.87
Car reviews	Reserved CPM	$2.50 CPM	3,472,183	n/a	n/a	$8,680.45	$2.50
Community	AdSense	$1.20 CPC	1,306,368	5,832	$1.20	$6,998.40	$5.36
Total			10,126,144			$25,678.85	$2.53

See? Who knew that the Community area was throwing off so much money per impression compared to the other areas?

eCPM isn’t the only rate currency you can use, though its connection to both volume and revenue puts it at a distinct advantage, and it means most to publishers because it speaks to the one thing that a publisher can exert (some) control over – the volume of impressions that are available to sell.

Revenue

If you sell your inventory on a fairly straightforward CPM or CPC basis, then your site’s revenue will pop neatly out of the equation:

(Revenue) = (eCPM) * (Volume) / 1000

However, if you’re running a larger site and engaging in sponsorship-type deals with advertisers, your revenue picture may look a little more complex. This is because “sponsorships” (a term which covers a multitude of sins) can contain multiple revenue components, some of which can be linked to ad delivery (and which therefore lend themselves to rate calculations), and some of which cannot.

For example, the sponsorship deal on our fictitious autos site referenced above could in fact contain the following components on the invoice sent to the advertiser or agency:

Item	Cost	Impression Target
100% Share-of-voice rotation, 300x250, Home Page (1 day)	$6,000	3,000,000
100% Share-of-voice rotation 120x600, Home Page (1 day)	$4,000	3,000,000
Sponsor branding – Home Page background (1 day)	$8,500	n/a
Sponsored article linked from Home Page (1 day)	$3,500	n/a
Sponsor watermark on Home Page featured video (1 day)	$1,500	n/a

In the above table, only the first two items are expected to be delivered through the ad server; the other three are likely to be “hard-coded” into the site’s CMS and actually deliver with the page impressions (or video stream, in the case of the last one).

There are a couple of different options for dealing with this second kind of revenue (which we’ll call “non-delivery” revenue) which can’t be directly linked to ad impressions. One is to attribute the revenue to the ad delivery anyway, kind of on the assumption that the ads “drag along” the other revenue. So in the above example, with 5,347,592 impressions delivered across the two units, the “overloaded” eCPM for the ad units would be $4.39.

The challenge with this approach is that the extra revenue is not associated with delivery of any particular ad. So in the above example, if you wanted to calculate the eCPM for just the 120x600 unit on the home page (perhaps across an entire month), would you include the non-delivery revenue? If yes, then how much of it? 50%? 40%? The lack of ability to truly associate the revenue with ad delivery makes these kinds of calls incredibly hard, and open to dispute (which is the last thing you want if you are presenting your numbers to the CEO).

The other approach is to treat the “non-delivery” revenue as a separate bucket of revenue that can’t be used in rate calculations. This keeps the data picture simpler and more consistent on the “delivery” side of the house, but you do end up with an awkward block of revenue that people are constantly poking and saying things like “I sure wish we could break that non-delivered revenue out a bit more”.

A complicated relationship

Once you have your arms around these three core measures, you can start to see how they interact, and there lies the magic and intricacy of the dynamics of selling display advertising. The implacable logic of the simple mathematical relationship between the three measures means that if one changes, then at least on of the others must also change. Only by looking at all three can you truly understand what is going on. We’ll dig into these relationships more in subsequent posts, but here’s a simple example of the rate achieved for ads sold on a fictional news site home page:

Someone looking at this chart may well ask “OMG! What happened to our rate in June 2009?” Well, a quick search on Wikipedia will reveal that a certain “King of Pop” died in that month, sending the traffic (and hence the ad impression volume) of most news sites sky-rocketing. In our fictional home-page example, almost all revenue is driven by “share of voice” (time-based) deals, so all that extra volume does is depress the effective rate, because the site earns the same amount per day regardless of traffic levels. So here’s volume and revenue from the same data set, to round out the picture:

We can now see that in fact, June wasn’t a bad month for Revenue; it was the huge spike in traffic that did the rate in.

The above example takes something very important for granted – namely, that we have enough segmentation (or “dimensional”) data associated with our measures to be able to break down site performance into more useful chunks (in this case, just the performance of the home page). In the next blog post, we’ll look at some of the most important of these dimensions. Stay tuned!

December 19, 2011 in Adserving, Building the perfect Display Ad Performance Dashboard, Dashboards, Display Advertising | Permalink | Comments (1) | TrackBack (0)

November 21, 2011

Should Wikipedia accept advertising?

It’s that time of year again. The nights are drawing in, snow is starting to fall in the mountains, our minds turn to thoughts of turkey and Christmas pudding, and familiar faces appear: Santa, Len and Bruno, and of course, Jimmy Wales.

If you are a user of Wikipedia (which, if you’re a user of the Internet, you almost certainly are), you’ll likely be familiar with Jimmy Wales, the founder of Wikipedia and head of the Wikimedia Foundation, the non-profit which runs the site. Each year Jimmy personally fronts a campaign to raise funds to cover the cost of running Wikipedia, which this year will amount to around $29m.

The most visible part of this campaign is the giant banner featuring Jimmy Wales’s face which appears at the top of every Wikipedia article at this time of year. This year the banner has caused some hilarity as the position of the picture of Jimmy just above the article title has provided endless comic potential (as above), but every year it becomes increasingly wearisome to have Jimmy’s mug staring out at you for around three months. Would it not be easier for all concerned if Wikipedia just carried some advertising?

Jimmy has gone on record as saying that he doesn’t believe that Wikipedia should be funded by advertising, and I understand his position. To parse/interpret his concerns, I believe he’s worried about the following:

Accepting advertising would compromise Wikipedia’s editorial independence from commercial interests
Ads would interfere with the user experience of Wikipedia and be intrusive
Wikipedia contributors would not want to contribute for free to Wikipedia if they knew it was accepting advertising

I’m biased, of course, since I work for Microsoft Advertising, but I believe that each of these concerns is manageable. Let’s take them one by one:

Concern 1: Ads would compromise Wikipedia’s independence

There are plenty of historical examples where a publication has been put in a difficult position when deciding what to publish because of relationships with large advertisers. Wikipedia certainly doesn’t want, for example, Nike complaining about the content of its Wikipedia entry. And the idea of Wikipedia starting to employ sales reps to hawk its inventory is a decidedly unedifying one.

But Wikipedia does not have to engage in direct sales, or even non-blind selling, to reach its financial goals with advertising. The site could make its inventory available on a blind ad network (or ideally multiple networks) so that it would be impossible for an advertiser to specifically buy ad space on Wikipedia. If an advertiser didn’t like their ads appearing on Wikipedia, most networks offer a site-specific opt out, but the overall impact of this to Wikipedia would be minimal – Wikipedia carries such a vast range of content that it has the most highly diversified content portfolio in the world – no single advertiser could exert any real leverage over it.

Concern 2: Ads would make Wikipedia suck

As has been noted elsewhere, there are plenty of horrible ads at large in the Internet – intrusive pop-ups, or horrible creative. It would certainly be a valid concern that Wikipedia would suddenly become loaded with distracting commercial messages. But according to the back-of-an-envelope calculations I’ve done, there is no need for Wikipedia to saturate itself with ads in order to pay the bills.

According to the excellent stats.wikimedia.org site, Wikipedia served almost exactly 15bn page views world-wide in October 2011 (around half of which were in English). Assuming no growth in that figure over 12 months, that’s around 180bn PVs per year. So to meet its funding requirements, Wikipedia would need to generate a $0.16 eCPM on those page views (assuming just one ad unit per page). That’s a pretty modest rate, especially on a site with as much rich content as Wikipedia. It would give the site a number of options in terms of ad placement strategy, such as:

Place a very low-impact, small text ad on every page
Place a somewhat larger/more impactful ad on a percentage of pages on a rotation, and leave other pages ad free
Place ads on certain types of pages, leaving others always ad free (such as pages about people or companies, or pages in a particular language/geo)
Deploy a mix of units across different types of page, or in rotation

This also assumes that Wikimedia needs to raise all its funds every year from advertising, which it may not need to – though once the site accepted advertising, it would definitely become more difficult (though perhaps not impossible) to raise donations.

To preserve the user experience, I would definitely recommend just running text ads, which could be placed relatively unobtrusively. Sites running text-based contextual ads (such as those from Google AdSense or Microsoft adCenter) can usually expect to get at least around $0.30 eCPM, so there would be some headroom.

I would also recommend that Wikipedia not run targeted ads – or at least, only work with networks that do not sell user data to third parties. It could cause significant backlash if it became felt that Wikipedia was effectively selling data about its users’ browsing habits to advertisers for a fast buck.

Concern 3: Ads would make contributors flee

I can speak to this concern less authoritatively, since I am not that familiar with the world of Wikipedia contribution, but so long as Wikimedia made it clear that it was remaining a non-profit organization, and continued to operate in a thrifty fashion to cover its costs, the initial outrage of Wikipedia contributors could be managed. After all, plenty of other open-source projects that rely on unpaid contributors do provide the foundations for commercial activities, Linux being the best example.

In any case, in its deliberations about balancing the needs of its contributors with its need to pay the bills, Wikimedia will need to face some hard questions: Will it always be able to cover its costs through donations? Does the current level of investment in infrastructure represent an acceptable level of risk for a site that serves so many users? Is it acceptable to rely on unpaid contributors indefinitely? If Wikipedia ran out of cash or went down altogether, the righteous indignation of its contributors may not count for very much.

Apart from advertising and donations, the only other way that Wikipedia could pay the bills would be by creating paid-for services – for example, a research service. But would the unpaid Wikipedia contributors really be happier with this outcome than with advertising? It would effectively amount to selling the content that they’d authored for free. At least with advertising, it’s the user that is the product, not the content. So long as Wikipedia can maintain editorial independence and retain a good user experience, advertising feels like the better option to me.

November 21, 2011 in Branding & Advertising, Display Advertising, Industry | Permalink | Comments (2) | TrackBack (0)

November 09, 2011

Building the Perfect Display Ad Performance Dashboard, Part I – creating a measurement framework

There is no shortage of pontification available about how to measure your online marketing campaigns: how to integrate social media measurement, landing page optimization, ensuring your site has the right feng shui to deliver optimal conversions, etc. But there is very little writing about the other side of the coin: if you’re the one selling the advertising, on your site, or blog, or whatever, how do you understand and then maximize the revenue that your site earns?

As I’ve covered previously in my Online Advertising 101 series, publishers have a number of tools and techniques available to manage the price that their online ad inventory is sold for. But the use of those tools is guided by data and metrics. And it’s the generation and analysis of this data that is the focus of this series of posts.

In this series, I’ll unpack the key data components that you will need to pull together to create a dashboard that will give you meaningful, actionable information about how your site is generating money – or monetizing, to use the jargon.

We’ll start by taking a high-level look at a framework for analyzing a site’s (or network’s) monetization performance. In subsequent posts, we’ll drill into the topics that we touch on briefly here.

Getting the measure of the business

Ultimately, for any business, revenue (or strictly speaking, income or profit) is king. If you’re not generating revenue, you can’t pay the bills (despite what trendy start-ups will tell you). But anyone running a business needs a bit more detail to make decisions that will drive increased revenue.

In the ad-supported publishing business, these decisions fall into a couple of broad buckets:

How to create more (or more appealing) supply of sellable advertising inventory
How to monetize the supply more effectively – either by selling more of it, or selling it for a better price, or both

Another way of thinking about these decisions is in a supply/demand framework that is common to almost all businesses: If your product is selling like hot cakes and you can’t mint enough to meet demand, you have a supply problem, and you need to focus on creating more supply. If, on the other hand, you have a lot of unsold stock sitting around in warehouses (real or virtual), you have a demand problem, and you need to think about how to make your products more compelling, or your sales force more effective, or both.

Online publishers usually suffer from both problems at the same time: Part of their inventory supply will be in high demand, and the business will be supply-constrained (it is not easy to mint new ad impressions the way a widget manufacturer can stamp out new widgets). Other parts of the inventory, on the other hand, will be hard to shift, and the business will be demand-constrained – and unlike widgets, unsold ad inventory goes poof! when the clock strikes midnight.

So analysis of an online ad business needs to be based on the following key measures:

How much inventory was available to sell (the Supply)
How much inventory was actually sold (the Volume Sold)
How much the inventory was actually sold for (the Rate)

It’s ultimately these measures (and a few others that can be derived from them) that will tell you whether you’re succeeding or failing in your efforts to monetize your site. But like any reasonably complex business (and online advertising is, at the very least, unreasonably complex), it’s really how you segment the analysis that counts in terms of making decisions.

What did we sell, and how did we sell it?

Most businesses would be doing a pretty poor job of analysis if they couldn’t look at business performance broken out by the products they sell. A grocery chain that didn’t know if it was selling more grapes or grape-nuts would not last very long. Online advertising is no exception – in fact, quite the opposite. Because online ad inventory can be packaged so flexibly, it’s essential to answer the question “What did we sell?” in a variety of ways, such as:

What site areas (or sub-areas) were sold
What audience/targeting segments were sold
What day-parts were sold
What ad unit sizes were sold
What rich media types were sold

The online ad sales business also has the unusual property that the same supply can (and is) sold through multiple channels at different price points. So it is very important to segment the business based on how the supply was sold, such as:

Direct vs indirect (e.g. via a network or exchange)
Reserved vs remnant/discretionary

Depending on the kind of site or network you’re analyzing, different aspects of these what and how dimensions will be more important. For example, if you’re running a site with lots of high-quality editorial content, analyzing sales by content area/topic will be very important; on the other hand, if the site is a community site with lots of undifferentiated content but a loyal user base, audience segments will be more relevant.

Bringing it together – the framework

I don’t know about you, but since I am a visual person to start with, and have spent most of the last ten years looking at spreadsheets or data tables of one sort or another, when I think of combining the components that I’ve described above, I think of a table that looks a bit like the following:

This table is really just a visual way of remembering the differences between the measures that we’re interested in (volume, rate etc) and the dimensions that we want to break things out by (the “what” and “how” detail). If you don’t spend as much of your time talking to people about data cubes as I do, these terms may be a little unfamiliar to you, which is why I’m formally introducing them here. (As an aside, I have found that if you authoritatively bandy about terms like “dimensionality” when talking about data, you come across as very wise-sounding.)

In the next posts in this series, I shall dig into these measures and dimensions (and others) in more detail, to allow us to populate the framework above with real numbers. We’ll also be looking at how you can tune the scope of your analysis to ensure that

For now, here’s an example of the kinds of questions that you would be able to answer if you looked at premium vs non-premium ad units as the “what” dimension, and direct vs indirect as the “how” dimension:

As this series progresses, I’d love to know what you think of it, as well as topics that you would like me to focus on. So please make use of the comments box below.

November 09, 2011 in Building the perfect Display Ad Performance Dashboard, Business Intelligence, Digital Marketing, Display Advertising | Permalink | Comments (6) | TrackBack (0)