Organizations that strive for an integrated approach to data and insight must overcome three challenges: organizational, cultural, and technical. Let's consider each.
In many organizations, there are distinct boundaries between the various channels. For data and insight, it's common to see separate teams working in online channels and offline channels. There are arguments for and against this approach.
Online research and analytics teams typically serve internal clients that are themselves focused on the online channel. These teams have a distinct set of analytical tools in their toolbox such as Web analytics data, voice of the customer systems and user testing approaches. As a result, online data is collected, managed, and analysed in a different way than traditional "offline" data. However, as different organizations work more closely together, so too must data and insight teams. At the very least, online and offline data and insight teams must have a greater understanding of what each other is doing or is capable of.
Should businesses combine online and offline analytical and insight teams? There is a case for doing this, but the integrated team must be carefully managed. For example, there are times when Web analysts are made part of the main insight team. But, people running the insight team don't understand what's happening online and aren't able to help develop the online analytics and insights functions. As a result, Web analysts feel isolated from their colleagues working on offline analytics and also from the internal client they serve in the online channel. So, the integration of insights teams should follow the integration of the marketing or business teams rather than lead it. There are enough challenges at the moment within a single channel.
In the online channel, there are cultural challenges around joining up data and insight. Typically, these challenges involve differences between quantitative and qualitative data. When it comes to understanding online business performance, the quantitative data tells you what happened and when it happened. Qualitative data tells you who did it and importantly why they did it, or didn't do it. Quantitative data like Web analytics data generally comes in large volumes and requires complex systems to collect and manage. Analysis is generally a question of finding patterns in data and interpreting facts. Qualitative data is generally sparser and analysis focuses on finding the threads in that sparse data. Interpretation is perhaps more intuitive and instinctive. These often require different skill sets that are found in different teams, even within the online channel.
Customer experience measurement requires the integration of both quantitative measures and qualitative measures. But Web analytics teams are often separate from user experience teams, presenting cultural challenges to bringing these data sources together. During a recent project, a client wanted to work with us because we had the skills across the quantitative and qualitative spectrum. However, it felt like we were working with two different clients: one in charge of Web data and another looking after the user experience requirements. I don't claim this is easy to do.
On another recent project I was leading, we were pulling together Web analytics data, online survey data, and results from user experience testing to make recommendations around a particular site. I was working with analysts and researchers from each discipline who each approached the problem from their particular perspective. Pulling those perspectives into a single view wasn't easy, but when it does come together the result is incredibly powerful. Organizations should do more to bring their quantitative and qualitative skill sets together, either through formal structures or by encouraging joint working or by creating multi-disciplinary project teams.
The final challenge to data and insight integration is the technical one. Online and offline data can look very different from each other. When it comes to quantitative data in the online world we generally have a relatively small amount of data on a lot of people (everyone who visits the website). In the offline world, we might have a lot of data on a relatively small amount of people (our actual customers). Bringing these two things together is not often easy but again that may be as much an organizational problem as a technical one. Web analytics data is generally in the preserve of the Web analytics team and the customer data is often managed by a business intelligence or customer relationship management (CRM) function. If these teams don't talk to each other, it's hardly surprising that the data cannot be integrated. Each needs something different to complete their view of the world and so the data should probably flow both ways. Some customer data into the Web analytics system will help with segmentation and understanding different customer journeys. Some Web analytics data into the customer database will help to complete the 360-degree view that the business needs.
Integrating data and insight across sources and across channels is not easy to do. It requires businesses to look at how they organize and manage both their human and technical assets across the organization. Organizations that start facing up to these challenges will be best placed for success.
Neil is off today. This column was originally published on November 23, 2010 on ClickZ.
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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