Global search man­agers know that the lifeblood of a cam­paign is the data col­lected as the cam­paign is under­way. As I men­tioned in the first post of this series of points I pre­sented at ad:tech in San Fran­cisco, Peter Drucker has it right when he says “busi­ness has only two basic func­tions: mar­ket­ing and inno­va­tion.” Well, inno­va­tion doesn’t blos­som out of empty space in the SEO and search mar­ket­ing world. There must be hard data that sup­ports a new tac­tic or strat­egy. Man­agers must rely on analy­sis from dash­board reports cov­er­ing a broad spec­trum of data points to shape and direct search strategies.

When you look at the data that is cap­tured through sys­tem ana­lyt­ics, the “5 Ws and 1 H” ques­tions that form the basis of jour­nal­ism should be posed.

  1. Why – Why ana­lyze the data?
  2. Who – Who will be respon­si­ble for gath­er­ing data? Who will ana­lyze the data? Who will the data be shared with?
  3. What – What are the risks related to ana­lyz­ing data? What met­rics mat­ter most? What can team leads do to impact change?
  4. Where – Where will you find the met­rics (which tools will you use)? Where are prac­tices out of com­pli­ance with cloud pro­to­cols and/or search algorithms?
  5. When – When will strate­gies and tac­tics change? When (how often) will data be counted?
  6. How – How will gov­er­nance be seg­mented? How will report­ing be shared? How will response be managed?

The inher­ent chal­lenge behind turn­ing Big Data into action­able insight is two-fold: 1) You can’t man­age what you can’t mea­sure, and 2) all that is mea­sur­able should not nec­es­sar­ily be man­aged. So, you should mea­sure every­thing but man­age only the data that dri­ves best prac­tices in SEO and global search marketing.

There’s no short­age of SEO tech­ni­cal data that can be ana­lyzed includ­ing con­tent, site archi­tec­ture, server deliv­ery per­for­mance, and cod­ing, to name a few. What’s impor­tant is to dis­cern between cor­re­la­tion and cau­sa­tion when you ana­lyze data. Data cor­re­la­tion reflects how closely two data sets are related. For exam­ple, your con­tent scores well for rel­e­vance, so the page has to achieve high SERP rank­ing, right? No! Rel­e­vant con­tent is nec­es­sary to com­pete for high vis­i­bil­ity, but search robots include rel­e­vance as part of their 300+ rank­ing fac­tors. Strong page rel­e­vance CAN be cor­re­lated with high SERP rank­ings, but that fac­tor alone doesn’t cause the page to rank higher than other sim­i­lar pages. Cau­sa­tion, on the other hand, implies one con­di­tion has a direct or indi­rect effect on another. Specif­i­cally, you can deter­mine if you do X then Y will result—and you can then roll out that tac­tic repeat­edly. Poorly trans­lated title or meta descrip­tion tags will directly influ­ence lower regional search rank­ings and click through rates (CTR), for instance.

Each stake­holder has key met­rics that affect the holis­tic suc­cess of SEO and search mar­ket­ing prac­tices. Fol­low­ing a cam­paign launch, each SEO team lead takes own­er­ship of data sets within his or her purview. Web strat­egy, for exam­ple, is respon­si­ble for page rank, bounce rate, and for­mu­laic met­rics such as AOV and order rate; IT tracks crawl rate, 404 trends, and redi­rect chains; and global teams look at coun­try Web rank­ings for key terms.

Teams can encounter data bias in their analy­sis process. Whether it’s rely­ing on too small a sam­ple size or sea­son­ally affected data, stake­hold­ers should guard against data bias.

SEO Team

Let’s talk about report­ing. We like to work off an exec­u­tive sum­mary with a sim­ple struc­ture. Report­ing should be focused on data trends. Year-over-year and quarter-to-quarter data show whether the strate­gies, tac­tics, and assets we’ve deployed have suc­cess­fully moved the com­pany for­ward and how we’re per­form­ing against fore­cast. Month-over-month data, on the other hand, reveal oppor­tu­ni­ties for agile man­agers to adjust prac­tices swiftly. In SEO and search mar­ket­ing, KPIs are about vis­i­bil­ity and con­ver­sion. The exec­u­tive sum­mary should report vis­i­bil­ity and con­ver­sion met­rics with enough con­text to allow man­agers to under­stand fac­tors behind changes in data sets.

In order to man­age effec­tively, it’s impor­tant to report some data in mul­ti­ple time frames. For exam­ple, we typ­i­cally look at global rev­enue through MoM, QoQ, and YoY, which dri­ves sharper fore­cast­ing. And don’t for­get this: data sources should be noted in the report. Remem­ber cau­sa­tion? Pro­vid­ing data sources in your report­ing process can help pin­point causal­i­ties on the way to adapt­ing prac­tices to meet mar­ket reactions.

At Adobe, we look at KPIs like vis­its, sub­scrip­tions, rev­enue, trial down­loads, and orders on a monthly basis in order to respond quickly to suc­cesses and fail­ures. Often, we ana­lyze cor­rel­a­tive met­rics such as vis­its to rev­enue ratios (RPV) that, while sim­ply pro­vid­ing a 30,000-foot view, indi­cate whether ana­lyt­ics, e-commerce, Web strat­egy, or other SEO team mem­bers need to take a deeper look at the data.

I’ll leave you with this thought: Big Data isn’t about hav­ing more data to sift, sort, and dis­trib­ute. It’s about reach­ing for the data that mat­ters, iden­ti­fy­ing pat­terns that sup­port or refute whether your prac­tices are suc­cess­ful and anom­alies that may point to poten­tial issues, and then exe­cut­ing the strate­gies and tac­tics that sup­port global search mar­ket­ing success.

In my next post, we’ll wrap up our six-part series with a dis­cus­sion about scal­able planning.

Williams Brown
Williams Brown

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