Top 10 Practices of Successful Web Analytics Organizations

By , Contributor at Adboe Blog:

What are the key ingre­di­ents for build­ing a suc­cess­ful Web ana­lyt­ics orga­ni­za­tion? You can be a great indi­vid­ual ana­lyst, but for your team to be suc­cess­ful, you need a sup­port struc­ture to turn your rec­om­men­da­tions into actions.

A suc­cess­ful Web ana­lyt­ics team influ­ences the entire orga­ni­za­tion to become data-driven and pro­vides mea­sure­able opti­miza­tion oppor­tu­ni­ties. Fol­low­ing are 10 best prac­tices that help cre­ate a suc­cess­ful Web ana­lyt­ics orga­ni­za­tion to sup­port the team’s success.

Solid Data
It all starts with a good ana­lyt­ics imple­men­ta­tion. You need a solid foun­da­tion to build both results and the trust of your busi­ness part­ners. If you or your part­ners don’t trust your data, then your part­ners can’t trust the analy­sis or your rec­om­men­da­tions. So, how do you get a good implementation?

Con­trol of Tags
How much con­trol do you have over your ana­lyt­ics imple­men­ta­tion? Ana­lyt­ics tag­ging must be updated con­stantly to keep up with site changes, fixes, and new opti­miza­tion oppor­tu­ni­ties. It is impor­tant to have the devel­oper resources that can make changes quickly. Adobe Tag Man­ager is an excel­lent solu­tion that allows you to make quick changes to data col­lec­tion out­side of the nor­mal devel­op­ment cycles.

Tight Inte­gra­tion with Report­ing Sys­tems
Once you have your devel­op­ment process in place, you must be able to val­i­date the data’s accu­racy. If you can eas­ily com­pare sys­tems, you can more eas­ily iden­tify issues quickly and build trust in the data.

Your Web ana­lyt­ics rev­enue data is unlikely to per­fectly match your finance rev­enue num­bers, but the vari­ance should be con­sis­tent. Mon­i­tor­ing that vari­ance often will alert you to any issues with the implementation.

Any vari­ance with your back­end data should be well under­stood and doc­u­mented. It’s com­mon for the Web ana­lyt­ics sys­tem to have a 2–3 per­cent vari­ance with the finance sys­tems, but any more than that might be due to a dif­fer­ence in how you define revenue.

Good Doc­u­men­ta­tion
A good “data dic­tio­nary” will define all the vari­ables and impor­tant caveats. What is the def­i­n­i­tion of rev­enue? Does it include gift cards or dis­counts? This should be clearly defined so that your busi­ness part­ners have con­fi­dence in the data and know how to inter­pret it.

The data dic­tio­nary is often a variable-by-variable guide, explain­ing what each vari­able cap­tures and how that vari­able can be used for analy­sis. No imple­men­ta­tion is per­fect, so you may want to flag vari­ables with incom­plete data or call out any caveats, such as dates when the vari­able was launched or issues occurred.

An “ana­lyt­ics events” cal­en­dar can help ana­lysts under­stand anom­alies in the data due to sale events, code changes, or out­ages. Also, you might want to make an intranet or doc­u­ment repos­i­tory avail­able to your busi­ness part­ners that con­tains links to the data dic­tio­nary and other rel­e­vant documentation.

Venue for Shar­ing Ideas and Suc­cesses
Great analy­sis is use­ful only if it’s used to improve per­for­mance. To this end, suc­cess­ful orga­ni­za­tions hold round­ta­bles of busi­ness users or ana­lysts to share suc­cesses and rec­om­men­da­tions for improved mea­sure­ment. Typ­i­cally, this is done by func­tional area, such as mar­ket­ing or mer­chan­dis­ing. This also helps the ana­lyt­ics man­ager under­stand how the busi­ness users employ the data.

Fur­ther, shar­ing suc­cesses with exec­u­tives helps build sup­port for addi­tional ana­lyt­ics projects and resources. Build­ing out a doc­u­ment repos­i­tory for shared analy­sis is an impor­tant part of this process.

Strong Under­stand­ing of Busi­ness Processes
Learn­ing how your busi­ness part­ners do their jobs gives you bet­ter insight into the types of data and analy­sis that will make them suc­cess­ful. It also helps you iden­tify what types of mea­sure­ment improve­ments will lead to improved reporting.

The goal of a great ana­lyt­ics orga­ni­za­tion is to feed the busi­ness users exactly the right infor­ma­tion at the right time to make informed deci­sions that will lead to higher con­ver­sions. When work­ing with busi­ness part­ners, you must know what actions they will take, not just what data they need.

Early Involve­ment with Projects
Before a new site fea­ture launches, define the mea­sures of suc­cess. Is a site fea­ture going to increase con­ver­sion or vis­its or units per trans­ac­tion? A true data-driven orga­ni­za­tion asks these ques­tions before the project starts. Early involve­ment ensures that the imple­men­ta­tion is updated to cap­ture the key metrics.

The ana­lyt­ics team should also play a key role in devel­op­ing the busi­ness case to cre­ate a new site fea­ture. Addi­tion­ally, you should ask how busi­ness users will opti­mize a new site fea­ture. For exam­ple, for a new search engine, you might want to mon­i­tor search terms and null results and opti­mize the dic­tio­nary; this may require a dash­board for on-going opti­miza­tion. Finally, for each site fea­ture or func­tional group, you may want to doc­u­ment pos­si­ble analy­ses and optimizations.

Play­books are the next step up from a basic data dic­tio­nary. A play­book out­lines how to do basic analy­sis for a spe­cific site fea­ture or func­tional area. It out­lines the basic busi­ness ques­tions and shows exam­ples of how to answer those ques­tions. A play­book pulls together all the details of how mea­sure­ment is imple­mented and how a busi­ness user can use the data to take action and opti­mize the site.

You might cre­ate play­books for cer­tain func­tional groups, like mer­chants or off-site marketing—or you could aggre­gate exam­ples for site fea­tures, like search or refine­ments. Doc­u­ment­ing ana­lyt­ics strate­gies is help­ful for increas­ing your organization’s ana­lyt­ics matu­rity. The doc­u­men­ta­tion is also excel­lent train­ing mate­r­ial for new team mem­bers and busi­ness partners.

A Sys­tem to Log Requests
Man­ag­ing a huge back­log of requests is a com­mon prob­lem for Web ana­lysts. Com­ing up with a sys­tem to log and pri­or­i­tize those requests is crit­i­cal. You must pro­vide back­log vis­i­bil­ity to your man­agers and stake­hold­ers to ensure that you han­dle the most impact­ful requests first.; this can help you jus­tify addi­tional ana­lyt­ics resources.

Your Out­look inbox is not the ideal sys­tem to log and man­age crit­i­cal requests. One solu­tion is an online form, such as Adobe Form­Cen­tral, which is a great way to fun­nel requests into a spread­sheet that can be eas­ily shared, sorted, and updated.

Roadmap for Improve­ments
What are the long-term goals for your ana­lyt­ics imple­men­ta­tion and the entire com­pany? A great ana­lyt­ics orga­ni­za­tion needs an ana­lyt­ics roadmap to iden­tify and pri­or­i­tize top opti­miza­tion oppor­tu­ni­ties. Updates and improve­ments can be made to any imple­men­ta­tion, but such changes must take into account feed­back from key busi­ness users and ana­lysts and management.

Adobe Con­sult­ing has an ana­lyt­ics matu­rity model that out­lines the phases an ana­lyt­ics orga­ni­za­tion can enter and mas­ter from descrip­tive to diag­nos­tic to pre­scrip­tive. It’s impor­tant to under­stand the next steps your orga­ni­za­tion must take to become a highly effec­tive ana­lyt­ics team that dri­ves mea­sur­able results.

Final Thoughts
These tips can help your ana­lyt­ics team become more effec­tive at dri­ving adop­tion and suc­cess through­out your orga­ni­za­tion. It’s impor­tant to build a strong foun­da­tion that will help your ana­lyt­ics team build trust with your busi­ness part­ners and help those part­ners become active par­tic­i­pants in the ana­lyt­ics opti­miza­tion process.



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