At its core, digital marketing optimization is a combination of philosophy, process, data, and technology. You have to want to optimize and you have to be able to go through the test, learn, and adjust cycles quickly enough to make a difference.
In my last column, I outlined the approach to breaking down the digital marketing process into its main components of acquisition, conversion, and retention and looking at the optimization challenges within each area. This week, I'll take a deeper look at acquisition.
Campaign optimization is probably the most established optimization practice. Acquisition marketing has been the cornerstone of digital marketing for many years, and a number of us can probably remember the focus of the dot-com days: getting as many eyeballs to the site as possible. Often, that was done with little regard to what happened when they got to a site. Things have moved on a bit since then. Thankfully, we now take a more sophisticated approach to measuring and optimizing our acquisition marketing efforts. However, we still a ways to go, as campaign optimization is a multilevel problem. Once you've cracked one level, it's time to move on to the next.
Campaign optimization operates on three levels:
We've been optimizing within a single digital marketing channel (e.g., PPC (define) search, display advertising, etc.), in effect, for years. During that time, we've become more sophisticated. Solid processes have been developed; and analytics and technology have been deployed to help automate optimization. Within PPC search, for example, bid management tools have been around for years and a whole industry discipline has grown up around the optimization of PPC and organic search marketing activities.
Technology has evolved so campaigns can be optimized in almost real time through automated algorithmic approaches like that of SearchRev (for PPC marketing) and Dart Adapt (for display advertising). We're almost at the point of "fire and forget," where once the initial deployment has been made and the system calibrated, little manual intervention is required.
That's all great, as long as you optimize against the right thing and you correctly attribute value to the right behaviors. Take PPC search marketing, for example. PPC optimization is traditionally done at the keyword level, examining the clicks that generated conversions and adjusting bid-management strategies accordingly. However, we know that in a lot of cases it takes multiple touch points to generate a conversion, and so value must be attributed all along the acquisition value chain rather than just at the end.
In-channel optimization is relatively straightforward. It usually involves a single piece of technology and the data to drive the optimization process typically sits in one place. Problem is, marketers rarely use a single digital channel. They use a combination of different channels, often simultaneously to achieve their goals. Display advertising may be used for generating awareness, search marketing may be used for generating response, and so on. Each channel might be optimized in its own right, but what about the impact of one digital channel on another?
A classic example: a client optimized search marketing and display marketing independently from each other. The client concluded that display advertising wasn't working hard enough for the company, whereas its search marketing was. It switched investment from display marketing to more search marketing, then promptly saw search volumes drop. Up until this point, the client hadn't appreciated how much display advertising was driving search volumes because it was optimizing within the channel. In the case of display, it was optimizing against the wrong thing. The client hurriedly reverted to its previous approach and saw search volumes recover. It was none the wiser. It knew there was a relationship between search and display (and its other channels), but it didn't know how to optimize it because its data was all over the place.
The key to cross-channel optimization is data integration. It's necessary to have the different channels' activity and responses in the same database. This might be achieved by using a single campaign management system, deploying a universal tag, or integrating the data in a separate system. Once the data is in the same database, it's possible to look at the effect of different channels on each other and on conversions, then to be able to optimize accordingly.
Increasingly digital marketing doesn't sit in glorious isolation from an organization's other marketing efforts. It's a multichannel world and businesses are looking to integrate their on- and offline marketing strategies and tactics. So the problem becomes broader. It's not just about optimizing your search marketing or managing all your digital channels. It's about how you optimize the total marketing mix, on- and offline. Search, display, TV, radio, print -- the lot. This is the strategic marketing optimization problem companies are beginning to wrestle with. Again, data integration is a main challenge.
On- and offline marketing data generally have different characteristics, which means it's not necessarily easy to integrate each. Online marketing data are usually at the cookie level, tracking an individual user (or more precisely, an individual device). Offline marketing data are usually at the market level, starting at a country or regional level, down to individual stations, publications, and so on. User-level and market-level data can be difficult to marry to each other. With the development of geographical profiling from IP addresses, it's getting easier, but the analysis techniques will be different and are more likely to resemble the inferential modelling techniques of offline marketing rather than the direct cause-and-effect techniques used in the online world.
Campaign optimization is like hiking up a mountain. When you get to the top of one summit, another one comes into view and on you go. If you've just about got your search engine marketing cracked, it's time to move on to the next problem!
Next time I'll be taking a look at conversion optimization. Till then...
Neil Mason is SVP, Customer Engagement at iJento. He is responsible for providing iJento clients with the most valuable customer insights and business benefits from iJento's digital and multichannel customer intelligence solutions.
Neil has been at the forefront of marketing analytics for over 25 years. Prior to joining iJento, Neil was Consultancy Director at Foviance, the UK's leading user experience and analytics consultancy, heading up the user experience design, research, and digital analytics practices. For the last 12 years Neil has worked predominantly in digital channels both as a marketer and as a consultant, combining a strong blend of commercial and technical understanding in the application of consumer insight to help major brands improve digital marketing performance. During this time he also served as a Director of the Web Analytics Association (DAA) for two years and currently serves as a Director Emeritus of the DAA. Neil is also a frequent speaker at conferences and events.
Neil's expertise ranges from advanced analytical techniques such as segmentation, predictive analytics, and modelling through to quantitative and qualitative customer research. Neil has a BA in Engineering from Cambridge University and an MBA and a postgraduate diploma in business and economic forecasting.
May 22, 2013
1:00pm ET / 10:00am PT
June 5, 2013
1:00pm ET / 10:00am PT