Even the most sophisticated of technologies can be used in the dumbest ways. Get smart in a multi-screen world and learn to recognise customers across devices and platforms.
It's funny how this happens... as I sat down to write this column with this topic in mind, I flicked to ClickZ to see what was taxing the minds of other columnists. I read with an increasingly silly grin on my face my friend Jim Sterne's recent post bemoaning Amazon's stalking of him around the web, trying to sell him a product that he had already bought, from them.
Things like this remind you that the most sophisticated of technologies can be used in the dumbest of ways. Mind you, I have some sympathies for Amazon, as it's not always easy to recognise a customer when you see one.
Let's think about it, we have more stats than you can shake a stick at, telling us we live in a multi-device, multi-channel world:
• Just under a quarter of visits to e-commerce sites in the US are from tablets and smart phones.
• A third of all page views in the UK are from mobile devices.
• Over 90% of young Danish people access web content on a mobile device every day.
With smartphone penetration at well over 50% in the US and tablet penetration on the rise, the majority of digital consumers can connect on multiple devices and platforms to browse and purchase your goods and services. The goal for organisations has increasingly become a need to understand and optimise the customer experience across devices and channels. Yet if organisations struggle to recognise customers in a single channel, what are the chances they're doing it right across multiple channels and devices?
Unless an organisation has a strategy for joining the dots and can successfully execute, the chances they can capitalize on the multi-screen customer opportunity are slim. Building good customer intelligence can't be left to chance; it needs an overall strategy and framework, as well as the technology to handle it. So, what's the approach needed here?
Define Your Customer
First of all, although this sounds obvious, there needs to be the notion of a ‘customer' in your data and preferably that notion is consistent across your different data sources. It won't always be as easy as that, as you may have different types of customers; for example, registered users vs. subscribers in media. Customers may be at the individual level or the household level (or both), or companies and users in a B2B environment. It's important to define the ‘customer' or understand your different customer types.
Do the Planning
The second stage is to develop the framework for identifying those customers across different channels and devices. What are the data points or mechanisms that allow you to identify a customer in different places at different times? This is something that Forrester has recently termed "Touchpoint Interaction Keys" (TPIKs).
For example, a cookie is an identifier for a device (or specifically a browser within a device), but it's not good for identifying customers until it can be linked with something like an email address, which can then be linked with say a loyalty card account. It's important to note that this is not a perfect science and there are many ‘data issues' that need to be managed.
Consistency of data collection across different systems is a major consideration that is likely to need some level of strategic oversight. As identified in a recent Econsultancy report:
"The largest challenge with stitching together a full customer journey and the insight associated with this journey is the ‘broken' nature of the processes and the specifics of the customer-company interaction.
A consistent use of unique IDs or primary keys across website processes and handoffs helps later matching efforts. Far too few data capture processes on sites fail to capture data basic information such as name and address in a common format. This makes it difficult to tie together these two sessions."
In any multi-channel business, there will be a significant amount of analysis and planning required to develop the framework and creating the conditions necessary for identifying customers across devices and channels, before data consolidation can begin.
Start Small and Build Out
Finally, there's the actual process of data consolidation and integration. A suggestion here is not to go for the ‘big bang' approach and try to integrate everything with everything all in one go, but to build out the capability in a phased manner.
For example, consolidating your digital data across digital properties, products and devices might be a good place to start, in order to get a coherent view of your digital customer first. This will help you understand how customers are using different devices to complete different tasks and goals and how to optimise for that experience.
Building out from that, you could add customer profiling data from a CRM system to understand the context of those behaviours in more detail. For example, which type of people tend to use which devices to access what types of content at what stage in their journey?
Probably a more challenging phase will be to add in data from other channels, such as the physical store or the contact centre. Here, years of siloed structures and information need to be brought together; this is one area where the levels of data consistency and integrity across customer records will be a major consideration and additional data management will be required.
As you can see, it's not always easy to recognise a customer when you see one. But as Jim Sterne's article proved, recognition is increasingly expected by customers to be a normal part of their brand experience. Time to join those dots.
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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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