Baselines: Why you should know your numbers.

Beginning with a historical scorecard to guide your marketing direction.

Baselines. The “you are here” marker of a marketing roadmap.

There are only patterns, patterns on top of patterns, patterns that affect other patterns. Patterns hidden by patterns. Patterns within patterns.
If you watch close, history does nothing but repeat itself.
What we call chaos is just patterns we haven’t recognized. What we call random is just patterns we can’t decipher. What we can’t understand we call nonsense. What we can’t read we call gibberish.
There is no free will. There are no variables.

– Chuck Pahlaniuk, Survivor

So essentially: To know where you are going, you should know where you’ve been and where you are now.

Ok, enough metaphors. Here’s a three-step kickstart to create a Baseline Scorecard to guide your marketing direction.

1) Think in terms of 3 major pillars: Delivery, Engagement, Conversion. All marketing (paid, owned, earned) can ladder up each pillar, an easy mental framework to adhere to.

2) Under each pillar, what are the “averages”? More advanced baselin’ing could involve weighted averages and regression analysis; advisable if you have the resources and knowhow. If not, begin with averages.

Delivery

  • What are average traffic levels? daily (weekdays, weekends)? monthly? Accounting for seasonality.
  • What is the traffic mix? (sources of traffic)

Engagement

  • What does average visit behaviour look like? (Bounce Rate, Time on Site, Pages Visited)
  • Which touchpoints (eg. content & ads) have the highest engagement? lowest engagement?
  • For paid digital , what are channel-specific costs per view, per engagement, per conversion, per second conversion and beyond?

Conversion

  • What content awareness goals are being met? (Look up Goal setting in Google Analytics)
  • What conversion metrics are being achieved? (eg. Signups, Purchases, Installs)
  • What does attribution look like? (Which marketing sources contribute to success?)

3. Using a multi-touch attribution model (eg. linear) can help normalize activity and observe patterns solving for “noisy” ecosystems. More here.

Note: If marketing activity is chaotic and unstructured – think national print campaigns, street teams handing out flyers, and paid Instagram stories from influencers – your attribution is likely full of blind spots.

Dirty data leads to shaky insights. Essentially, guesswork. Quiet some if not all the variables, in order to measure incremental and scalable opportunities in channels.

In summary: Know your numbers = be less prone to bullshit.

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