The challenge of real time marketing automation

By in Content Marketing & Demand Generation, Older posts

The accepted practice for digital marketing, even for what has been called Marketing Automation, has been a selection process, where a message is chosen or written, a customer segment is chosen (perhaps based on their last response or purchasing power), and then when the message is delivered to the customer segment, an overall conversion rate is captured (and increasingly, individualised conversion rates).

This process works. It certainly works a lot better than when every message is sent to everyone. Or when no analytics, or only overall analytics are captured. But it doesn’t reflect the sophisticated nature of modern content ubiquity, transient customer behavior, and analytical depth.

These three elements of any marketing process (actually they are the same three elements for customer service), have traditionally been seen as dots. Static. This has been a necessary simplification. In reality, they are lines; streams of data. Fully dynamic. Ebbing and flowing every day.

The next generation of marketing and CRM technology will take into account this increased subtlety.

One of the things we have worked hard on at Idio, is to build an architecture and business process that manages content, customer data and analytics as dynamic streams, that can trigger new messaging and interactions, based on predicted responses. The stream of analytics directly impacts the next phase of decision logic, so that the right piece of content, goes to the right customer, at the right time, on the right channel.


Although for specific campaigns, new marketing messages might be authored, a large proportion of marketing, and almost all of customer support, is about delivering the right pre-written message to the right customer at the right time, and in the right way. Content is a strategic asset of any modern organisation, and not just the content that has traditionally been held within the Enterprise Content Management (ECM) system. Social media content, external experts, syndicated news, and customer reviews, can all be considered in the enterprise domain, able to satisfy a particular user at a particular point in time. So content is a constant stream of newly created pieces, as well as a well-structured archive. And an effective and customer-centric marketing process must be able to conduct near real-time filtering of massive amounts of content, to ensure the best bit of content gets to each customer.

Customer data

Likewise, customer data doesn’t just mean a name and basic contact details. It includes an ongoing stream of data about what a customer is saying and doing on the social web, what they are searching for on your site, what they are reading across multiple channels, and what all this activity says about their behaviour and preferences. Effective customer data management must include near real-time segmentation of customers, based on known goals and rules. This ensures that for every business activity or message, only the customers that want and need that message will receive it. Put in a nutshell, what your customer said on Twitter this morning, should influence how you communicate with her this morning.


Finally, analytics; this has been reduced in first generation marketing automation, to a conversion percentage. But, when personalized marketing and customer service is fully adopted, and automated, analytics must be treated as an ongoing stream of data linked to every customer, every piece of content, and every business function. Next generation business intelligence suites present a much more sophisticated and all-encompassing analysis of who did what, with what, when, and where, in order to present learning and decision points to management.

So what?

The upshot of all this, is that more of the decisioning can, and must, be automated. No longer can every message or campaign be authored, targeted and measured by the marketer. Just as e-commerce companies have built detailed life-cycle messaging processes, with automated triggers (such as “user exited shopping basket without purchase” so “email them to remind them”), companies can capitalise on presenting a more personalized experience to their customers (such as “user just shared an article that quotes us favourably on Twitter” so “we should thank them on Twitter and send them a loyalty bonus via email”).

The weight of content analysis, delivery, and measurement is growing, so we must build tools that enable this future. As usual, those that adopt tools, not for the technology, but for the business case, and integrate them into a workflow that is strategic and long-term, will yield considerable benefits.

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