Optimizing Marketing Operations – eMetrics Lite

This session started out with Gary Katz of Marketing Operations Partners running through A LOT of content somewhat quickly.

I didnt write down much of what he said, but I did get his “Top 10” style list

You know your marketing resources are at risk…..

  1. Your metrics for success are ill-defined
  2. People are slammed
  3. Institutional memory is leaking
  4. Creativity is suffering from constipation
  5. Team & supplier relationships are dysfunctional
  6. Decision making ….(ok so I didnt get them all)
  7. Marketing portfolio is lacking

Next up was Gary Angel of Semphonic and Nancy Abila of O’Reilly.

They talked about the process and used O’Reilly as the example.

Much of the presentation was them talking over slides (go figure, I know) and explaining the ups and downs and the process they went through to move O’Reilly more towards an Analytical Company.  They went over the process of getting buy-in from stakeholders, C-Levels, and everyone else. As this is the Lite version, I have included some of what I wrote down below:

Some of the Challenges they highlighted include:

  • Data integrity
  • Resources (busy teams)
  • Evolving online strategy
  • Changing tools and platforms

What does the Future hold?

  • Continue building the foundation
  • Longer term – continue to educate, process of road map project, and create culture of actionable analytics

Deep Data Diving – eMetrics Lite

I am at eMetrics all week and will post some of my quick session notes as the week goes on. If you want to follow what everyone is seeing, I advise you check out twemes.com.

The first session was: Deep Data Diving – Bringing Online and Offline Inline w/ Rufus Evison.

13 million pieces of mail every quarter using 9 million variables

Data (is meaningless on its own) -> Information (has context) -> Insight (taking actions based on information)

EMetrics

Justification -> Usability -> Information Strategy

Great Example: Someone had said that they did a test of Gap, Macys and (some other store) with a set number of users. Those users were given $1000 to spend and one hour to spend it in. The worst store had only 5% of its users complete the task of making a purchase. The best one only converted 60%. Is this due to usability?

Talks about the McNamara Fallacy… lots of details

Gives some interesting numbers about loyalty cards, shopping habits and the UK.

Recap

– Loyalty cards help us change data -> information

– There is a difference between data and information

There was some Q&A but since this isnt Live Blogging – I didnt take notes for you :)

UPDATE: So that Susan doesnt get mad at me… follow @BruceClayInc for more coverage.