Posts Tagged ‘attribution’

My 2010 Holiday Search Marketing Wishlist

December 14, 2010

The end of the year always brings with it reflection of the good and the bad, the satisfaction of accomplishments and the yearning to do more. I got to thinking about all the great things the search marketing industry has given me, and all the things yet to come…..

Ah, the holiday season. Creative refreshes, keyword expansions, bidding up to capture all those credit card-wielding customers. As yet another action-packed holiday season descends upon us, while we light the menorah again and again, exchange gifts and happily hang delicate decorations on the tree, as we make lists and check them twice, I’m want to reflect on my own wishes for the industry I’ve grown to love over the past decade.

Here is my search marketing wishlist for the holidays this year:

Better SEM Planning Tools – I know, I know. SEM planning is tricky – inventory changes, markets shift, competition escalates, blah blah blah. While there are some crude public tools out there for planning, I wouldn’t want to take any of their data to the bank in Q4. Then again, perhaps the problem lies not in planning tools, but rather in planning cycles. The fact that right now I’m in the middle of the 2010 shopping season and I’m putting together a plan for the 2011 shopping season means that I’ll never get it right. I’ve written in the past (and talk all the time) about how to put processes in place to account for the inherent uncertainty of paid search, but let’s face it, in large companies there is a premium on certainty, a rare commodity in search marketing. Santa, baby, bring me some planning tools!

Actionable Attribution Management – I’ve spent a good deal of time writing about Attribution Management and the related challenges and opportunities. I still believe this is one of the Next Big Things for online marketers and I know that we, as an industry, will figure out how to make it work and integrate it into our marketing workflow. In the meantime, however, I will continue to shout about how we’re still in the dark on attribution management. We don’t even have a hint at the standards and conventions that will allow us to speak a common language when it comes to this up-and-coming marketing discipline, and until we do we’re pretty much just spinning our wheels. Come on, people – take the plunge and let’s get going!

Holiday Bidding Algorithms – Last year was the first holiday season where we used automated bidding algorithms on some of our in-house retail-focused paid search campaigns. Boy, was that ever exciting! We learned a lot about how to (and how not to) build an algorithm that could react to a rapidly-shifting market like Q4 retail, and I think we’re a lot better off this year as a result — but we’ll know much better in a couple of weeks. One of the keys we found, not surprisingly, is that a much shorter time horizon needs to be used in making bidding decisions in a rapidly shifting marketplace. Also, historical (year over year) data can come into play to help predict when marketplace shifts will happen. Above all, bidding automatons, make sure you have methods in place to measure the success of your bidding strategy.

More Bing Traffic – I love the quality of traffic I get in the new Unified Marketplace (Bing + Yahoo!, managed through adCenter). My feeling is that as advertisers get more adept at using adCenter, we’ll get better at optimizing the combined Bing and Yahoo traffic to get the most value out of the Unified Marketplace.

Standards, Standards, Standards – OK, we’ve been doing this search marketing thing for a while now, and we still don’t have any real standards in our industry. Am I the only one who is bothered by that? Big Ups to Google for their adwords certification program, but outside of that it’s still like the wild west out here. On the technical side of the house, the search engines give us API access but there is no formal training, nor is there a blueprint for success on how to use them. Every new large advertiser or tools provider has to reinvent the wheel to figure out how to execute on efficient API management. We could really use an open source or pre-defined set of standards or best practices on how to optimize API calls or data storage for different types of web server or firmware configurations. And what about the marketing side of the house? I’d love to have some consensus around benchmarks for metrics like CTR by position, conversion rates for different kinds of businesses, CTRs and CPCs for brand keywords, etc.

More Innovative Ad Products – I simply adore retargeting and behavioral targeting. But with the rapid ascent of search marketing over the past five or six years, the display advertising industry has taken a back seat, relatively speaking. The fact that inventory supply now generally exceeds advertiser demand hasn’t helped. And now that search marketing has matured so incredibly quickly and competition has reached feeding-frenzy levels, there is a renewed focus on display inventory and how to make it more valuable for advertisers. Ad networks and exchanges have pushed this evolution along by offering CPA and CPC buys, and it helps that more publishers are offering retargeting. So, what’s next? I’ve run into a few companies that are doing some super smart work around automating display optimization at the placement- and creative-level on specific networks. There’s lots of opportunity here, as efficiency makes a big difference. I think the next step will be taking such ideas and optimizing across networks, and soon, hopefully, across search and display.

That should just about cover everything I want this holiday season. Oh, and if somehow the San Diego Chargers can miraculously make the playoffs this year, that’d be just swell. Hey, a guy can wish, can’t he? Happy Holidays!

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Next-level Optimization: Measuring Success

November 17, 2010

Measuring the success of our optimization efforts turned out to be harder than any of us initially thought. It occurred to me that since my situation is anything but unique, it might make sense to write about it. Hopefully others have had similar experiences and we can raise awareness on this rather new topic…

You’ve made the case for advanced optimization, implemented loads of slick technology, and deployed across some or all of your paid search programs. Did you do the right thing? Did you make more money? If so, how much more?

To answer the question ‘did I make more money’ implies a baseline. Trouble is, you don’t really have one. What you’re really asking is ‘did I make more money than I would have otherwise’, and it’s hard to tell with paid search, because the market is inherently dynamic, as are the programs we manage in these markets. This makes benchmarking really tough, and scientific testing nearly impossible.

What you need here is a way to (somewhat) objectively measure success, a methodology not only that people can agree upon, but also one where execution is manageable. Let’s look at a few ideas that might work for you, starting from the simplest and moving toward the more complex:

Trending Success Metrics Over Time.

Look at trends in revenue, profit, ROI, whatever matters to you most from week to week, month to month, etc. Look at overall program performance before you started optimizing, and over time as optimization took over. You just might find out that ROI has increased steadily quarter over quarter, for instance. This approach is relatively easy, and works fine in a steady-state environment, but if you’re subject to seasonality or a shifting market it may not work for you. For example, your optimization efforts may look like a home run in a booming economy or a high season, and conversely you might be inclined to run back to your cube and update your resume if you’re trending results in a low season or a rough economy. What if this method doesn’t give you any conclusive results?

Comparing Paid Search to Your Overall Business

If seasonality or macroeconomic trends are clouding your view of campaign performance, try comparing your results to those of your business as a whole. This may give you the baseline you need, particularly if your paid search campaign is in a somewhat mature state. For example, if your company saw ten percent earnings growth and your SEM profit increased by twenty percent over the same period, you may be able to claim ten percent growth attributed to paid search. That’s not bad for a day’s work, assuming the lift in profit is more than you paid for optimization. But what if your paid search program is on a totally different trajectory than your overall business?

Cohort Analysis

If the first two methods don’t work for whatever reason, here’s another way to look at it. You probably have parts of your portfolio that are managed by automated algorithms, and portions that aren’t. Try looking at these parts separately to understand what happened. Select a cohort of keywords that was managed by each method, and analyze what happened over time. You’ll need to account for seasonality, so ideally look at year-over-year comparisons, or if you don’t have that long a timeline, try to normalize for the seasonality with historical data (try to figure out how much your paid search business as a whole changes with the season). Then, look at the performance of each cohort and compare. For example, if you’re managing through an economic downturn and you see a downward trend in cohort A, but cohort B shows a flat trend line, even though you’re not seeing growth in cohort B you may conclude that it’s doing better by comparison, and you can now quantify the ‘growth’ or ‘lift’.

As I hint at above, once you understand your lift or growth from advanced optimization, you will want to calculate your ROI on the optimization effort as a whole. Particularly if you’re in a big company, you should always be able to hold your projects up against any other effort your company could otherwise use the money to pursue. If you can show that you increased profit by a million dollars at, say, a fifty percent ROI, odds are good that your management will be asking you if you can do more optimization projects, thereby eliminating the need for you to rush back to your cube and update that resume.

Again, I think the key here is to find a method that you and your management can agree upon and a process that is manageable. And once you do, you’ll be well served to have the data and analyses in your hip pocket well before anyone comes asking for them.

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Actionable Attribution Analysis: A Three-Phased Approach

March 11, 2010

“Last-click attribution is dead!”

“Media-mix modeling is the key to marketing success!”

“Without an accurate attribution model you’re throwing away your marketing dollars!!”

With SMX West in town last week the halls were echoing with passionate cries about attribution analysis. It seemed as if all topics (other than the Yahoo-Microsoft search deal) had taken a back seat for a moment, and suddenly the most important thing to consider was attribution analysis, specifically whether or not you are giving too much credit to SEM and not enough to other media.

Believe me, I share people’s enthusiasm on the topic. It’s clearly one of the next big online marketing problems to solve. And despite the fact that moving away from last-click attribution toward a more elegant and accurate attribution model can really only serve to divert budget away from SEM and toward other channels, I do think it’s the right thing to do. But after talking to as many people as I could, gathering my own data and soliciting opinions, I’m convinced that we are a still a long way from being any good at this at all.

Currently there appear to be two basic types of approaches, both of which seem to me to be fatally flawed.

One approach I see in the market, offered by various otherwise credible services, has the advertiser entering percentages into boxes on a screen, assigning portions of the conversion value to different marketing channels – 25% for SEM, 35% for display, 15% for email, and so on. As a large advertiser myself, I can safely say that this approach gives someone like me entirely too much credit as a sophisticated marketer. I don’t know anyone who has a good enough grasp on their business and the implications of attribution analysis to make an intelligent decision in this type of situation. No knock on my fellow advertisers, but seriously, this is way out of our league. Even so, a Google representative stated during a panel I was moderating, that they intend only on providing attribution-related data, placing the burden of analysis on the advertiser.

The other approach I see emerging is a black-box math-based approach. This is more likely to be done in-house by large advertisers, using statistical and predictive modeling to simulate different attribution models, and mapping their outcomes to business metrics like profit, revenue or ROI. While I do think there is significant value in doing the hard math and understanding these problems from a statistical point of view, this methodology tends to be short-sighted. I don’t believe there is a one-size-fits-all approach to attribution analysis where you simply dump your marketing data in, and out magically pops an attribution model that maximizes profit, for example. It’s just not that generic of a problem.

It’s easy for me to sit back and criticize the status quo – so why not offer some solutions, you say? Well, here goes: I envision a three-phased approach that takes some elements of the existing practices, then combines and expands upon them to provide a more complete, appropriate solution for each advertiser.

The first phase involves smart people talking to each other. Revolutionary, no? We need an attribution specialist to lead off this effort by conducting a fairly exhaustive analysis of the advertisers’ business and online marketing programs. Starting with business goals and product adoption cycle, to conversion window analysis, on to a channel-by-channel audit of on- and off-line marketing. The purpose of this consulting and analysis is to provide the proper inputs into phase two.

Phase two is the super-math modeling I describe above. With the proper inputs as they relate to an advertiser’s business and its metrics, statistical modeling is needed to predict all possible outcomes and understand which model will best support the advertiser’s business goals.

Finally, phase three makes all of this actionable. We need a way to pluck the wisdom out of phase two and apply it directly to actual media channels the advertiser is running. Ideally we’ll find a way to automate this or at least automate the recommendations, which can then by easily implemented into the media buys themselves.

But before any of us sprint into the world of attribution analysis and media mix modeling, let’s step back and take a long look in the mirror: I don’t know of a way to realistically pull any of this off if an advertiser doesn’t have a common tracking/analytics system for all marketing channels. So before we start hiring expensive analysts, consultants and statisticians, let’s be sure to clean our own houses and get our own data in order. Standardize your analytics and measurement on a single platform so you can compare ‘apples to apples’. Then you can start to focus on the fun stuff.

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