An infi­nite loop (also known as an end­less or unpro­duc­tive loop) is a sequence of instruc­tions in a com­puter pro­gram, which loops end­lessly with no begin­ning and no end. The term has taken on other mean­ings in pop­u­lar cul­ture and also refers to a street on the Apple cam­pus. For my pur­poses, an infi­nite loop is use­ful to help us under­stand data paral­y­sis. The idea that we can use data to direct the tone and strate­gies for mar­ket­ing plans is becom­ing com­plex. Moun­tains of data come in; we enter it into our repos­i­to­ries and exec­u­tive dash­boards, and then hope we can pull out use­ful morsels that prove the strat­egy we are using is the right one.

We know we need to man­age risk by align­ing the tac­ti­cal day-to-day to do lists with the strate­gic cor­po­rate goals and objec­tives. These goals and objec­tives are valid. They should stim­u­late appro­pri­ate action to solve real prob­lems. How do we mea­sure the effec­tive­ness of a mar­ket­ing ini­tia­tive? By track­ing and ana­lyz­ing met­rics that have been defined and key per­for­mance indi­ca­tors (KPI’s). The data being col­lected can be sorted through and eval­u­ated via the SMART data analy­sis the­ory I touched on ear­lier. This is your sal­va­tion from being caught in the trap. Keep your eye on your objec­tives; know what qual­i­fies as a suc­cess and what’s white noise.

As I’ve said from the begin­ning, I am a data mechanic. I work with data every day and learn while doing. All the tools in the world will prove use­less if you’re col­lect­ing the wrong data, seg­ment­ing it the wrong way and ana­lyz­ing it against a set of false or vague objec­tives. As a mechanic, the only real way to fix any­thing is to know what’s wrong. Oth­er­wise, you’re just fish­ing in the big pond with­out a rod. Have you ever tried to catch a fish with your hands? Yeah, it’s not easy, and even if you do it, it’s usu­ally pure luck. We can’t afford that in dig­i­tal mar­ket­ing. We need to set smart goals that are aligned with cor­po­rate needs. You need to get your hands dirty.

Dur­ing the plan­ning stage, col­lect­ing and har­vest­ing good data will lay a solid foun­da­tion and greatly reduce the risk of paral­y­sis. Being a data mechanic means learn­ing to tell what is action­able data and what is extra­ne­ous or should be left behind.

In the next sev­eral posts, we will cover each ele­ment of set­ting smarter goals. This is all based on know­ing the cor­po­rate busi­ness goals and objec­tives and align­ing with them from the start.

We’ll use old and new data to make all our mar­ket­ing goals meet five key characteristics:

  • Spe­cific
  • Mea­sur­able
  • Attain­able
  • Rel­e­vant
  • Timely

These char­ac­ter­is­tics must be in a con­trolled infi­nite loop in the feed­back process:

  • Plan refine­ment based on data evaluation
  • Focus on goals
  • Reeval­u­ate data

Then the data eval­u­a­tion stage will pro­duce suc­cess­ful results. We can turn the end­less loop into a pro­duc­tive tool for mar­ket­ing suc­cess instead of being par­a­lyzed by the data we employ to define it.