My last column examined the concept of "data-driven marketing" and four key components for its success: philosophy, processes, data, and technology.
At its heart, data-driven marketing underpins the drive towards optimization. But optimization's one of those words that's used a lot but often means different things to different people. It's a bit like segmentation, engagement, and so on. So here and in upcoming columns, I'll offer my take on optimization, what is means, and how it can be achieved.
I first came across "optimization" as part of my college degree course. It was presented as a mathematical problem; how do I maximize a desired outcome given a set of certain constraints? This is the way I've tended to think about optimization ever since, particularly when it comes to marketing optimization. My desired outcome will be things like orders, revenue or profit and my constraints will be things like money, time, and resources. The challenge is to maximize the return on investment in marketing.
The problem with this purely mathematical approach to optimization is that often the problem is too large or complex to solve easily. You must understand and define the relationship between all the variables (such as sales and advertising) and then use the mathematics to determine the optimal allocation across the various inputs. Often, there are too many variables and the relationships are too complex to be solved easily. The approach then is to chunk the problem down and iterate towards a solution. This is where data-driven marketing's components come into play.
In the digital world, the ultimate goal of many marketers is to maximize customer lifetime value and to allocate resources available in such a way to achieve that. Things like customer lifetime value can be difficult to define and to measure and marketers may not have the ability to efficiently manage all the resources appropriately. The intent may be there but the ability to execute may not. As a result, we break the overall process into smaller processes and we need to begin to think about how we can optimize individual separate processes rather than the complete value chain in one go.
We already think about digital marketing as three separate processes, namely:
Sometimes these processes can be too separated with little joined up thinking between the three. Having said that, for the purposes of optimization and given the constraints of data and technology, it probably makes sense still to use them separately as the basis for our optimization strategy. Keep in mind, we're ultimately trying to maximize the allocation of resources and investment across the whole customer lifecycle.
So what problems are we trying to solve? In acquisition, we are trying to optimize our campaigns to increase the propensity of people to visit a Web site and engage. This is an area where there's been a lot of focus over the years and where technology has made a significant impact in either allowing marketers to iterate through the cycles more quickly or where the technology effectively automated the optimization process. However, the goalposts are moving and the problem set is changing. Increasingly, marketers need to be looking to optimize in a different way.
For conversion, what we're trying to do is to increase the likelihood of some desired outcomes, whether that's an order, a registration and download or telephone call. Here there has been a lot more attention given over the past couple of years and where analytical technology has evolved to help marketers understand how to improve site architecture and design. There's more work to be done in this area though and the technologies and the processes to manage them need to be more widely adopted.
With retention marketing what we are looking to do is to increase customer value. Here we are talking about maximizing on investments that have already been made in acquisition and conversion so we don't have to make those investments again. With a few notable exceptions I don't think that many organizations are focused on this area at the moment.
In upcoming columns, I'll look at optimization in more detail, examining acquisition, conversion, and retention and the philosophy, processes required, and data and techniques available to maximize the effectiveness these individual processes. Till then...
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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.
December 12, 2013
1:00pm ET / 10:00am PT