Start by mapping out how measurement and analytics will support your company's recovery.
There's a saying making the rounds at the moment: "Why waste a good recession?" The saying suggests that times like these provide organizations with an opportunity to trim some fat. While it's not necessarily a very pleasant experience, today's economic conditions help organizations focus on being effective and efficient. Data and analytics are helping organizations optimize their processes and improve their returns. My sense, though, is that the focus of analytics over the past year or so has become increasingly tactical and operational. With murmuring in some quarters about green shoots of recovery, it's time to start thinking about how analytics will help business gear up for growth.
Some disciplines that became necessary during these tough economic conditions must be maintained as markets begin to pick up. Organizations have been forced to adopt tight, robust measurement of marketing performance and strict accountability on marketing investments to ride out the storm. As conditions ease, these disciplines must be retained and embedded into business processes for the long term.
In my last column, I talked about the use of maturity models and looked at the WebTrends' model in some detail. The model is a useful contribution to the debate and can potentially provide a framework for comparison and benchmarking. Greater value, however, comes from taking a maturity model approach, customizing it to an organization, and using it to create a road map for developing measurement and analytics capabilities. More simply put, it's about defining where the organization is now, where it wants to be in the future, and how it's going to get there.
Start by looking at an organization's current competencies on a number of different dimensions. WebTrends' Digital Marketing Maturity Model (DM3) provides some suggestions, but there may be others that are more relevant to your business, depending on industry sector or other factor. At a minimum, a model should include such attributes as the breadth of data sources and tools, analytical processes, the level of data integration, and the level of business adoption. A simple model would then capture where the organization is currently on each of those dimensions and where it wants to be in the future, say in 12 months to 18 months. The next stage is to use the model to develop a road map. The road map outlines how the organization will move up the maturity curve and becomes, in effect, the measurement strategy for the next year or so. This provides the framework and context for decisions around data, systems, processes, and people, so it helps to provide answers to questions like:
While it may not look like it, the timing is right for this kind of strategic approach. We won't be in a recession forever (though it may feel like it), and when the upturn comes companies must be poised for growth. New marketing strategies will be created, new tactics will be implemented, and new measurement approaches will be needed to measure the effectiveness of those strategies. Now is a good time for organizations to map out how measurement and analytics will support their recovery and sustain it.
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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.
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