Live from OmnichannelX: Updating content types for marketing and omnichannel

Updating content types for marketing and omnichannel, by Michael Priestly

Michael works at IBM and they had a giant analytics failure because of:

  • No common type taxonomy
  • 200+ values in one tool (which wasn’t necessary – they didn’t have so much content that they needed that!) – plus there were other tools…
  • No tool integration

So they set out to understand their goals:

  • Increase quality
  • Reduce waste and confusion

And to do that they would need consistent tagging, coordinated education, and better standards and governance. All of these are hard sells to the organization.

The Process of Driving Quality

First, they needed to divide types of content from purposes. For example, a case study and a client story are both drivers of client success.

Then they needed to determine what a “type” is, based both on the purpose of the content and by the possibility of value.

And then came the winnowing! What values were obsolete, too general, or compound values? What values can be merged? (Under-used values, or synonyms)

The MVP came down to:

  • 2 taxonomies (with ~20 values in each)
  • 3 tools
  • 1 integration

Stage 2: Complication

Any successful work is rewarded with more work. So the expanded scope included:

  • Updating governance plan
  • Moving from 2 goals to 7
  • Moving from 3 goals to 10+
  • Incorporating 10 more taxonomies
  • Shifting across 5 departments (instead of just marketing)
  • Move from simple lists to complex knowledge graphs and AI
  • Incorporate formal, reviewed, and recorded training

Now there’s an aspirational chart of where they’ll get. They want to understand a year in the life of a client. They’re aligning their models on what they know about the content, the products, the client, and their journey. There’s a single ontology for all of this.

One of the biggest challenges is consistency: you need a model for a tagging service, so that everything gets tagged in the same way.

Measuring Success

In order to test their hypotheses, they need data, measurements, and a consistent scale. The vision for the future? Giving customers what they want.

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