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Layering in Additional Insights

In my previous post, we discussed the importance of creating a data strategy and modeling your data. Interestingly enough in recent research completed with Econsultancy 46% of ANZ respondents cited integrating data as their key challenge with marketing automation. Once you have your base data model, you may also decide to layer in additional insights based on analysis done either by an in-house team or an agency. This could include data like customized personas based on buying type or what type of customers are detractors.

Following on from the last post, we will continue with our automotive example: The main data object is the buyer and the secondary data object is accessories or factory options. Beyond the base layer, which is the data that you can collect directly from the buyer on a form or from a third-party provider such as Dun and Bradstreet, we can also start tracking their digital body language or external web analytics data to provide insight into how we can better personalize their experience. Digital body language can be tracked through marketing automation platforms that allow us to monitor and view how a client or prospect is engaging across digital channels.

For example, for buyers that purchased in the last quarter were there any common engagement criteria that a buyer has completed that would trigger an intent to buy?

Did they visit the site more frequently?

Did they complete a Sales enquiry form?

Did they schedule an appointment with a Sales Rep?

Can we look for similar activity within our prospect database as provide a more targeted communications strategy to drive conversions based on these additional insights?

Another layer of insight to utilise is mobile usage. Do you know if your customers use mobile versus desktop? Is there app data you can leverage to decide on the frequency or method of communication? If you notice a particular group of buyers searching using their mobile device, perhaps they can choose to receive push notifications versus email. Can you layer any push notifications via SMS or an app especially for time sensitive communications?

From a web analytics standpoint, you can take a similar approach to mobile and analyze patterns in web browsing behavior and see if there are any trends that predominate in a particular buyer group. For example, you may find buyers within a certain age group go online during a particular time of day and search for certain search terms on the site.

In our next post, we’ll discuss how to represent these objects in your marketing automation platform.

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