The Customer Journey, Stage 5: Conflict Resolution

Wel­come to this sixth instal­ment of my series on the cus­tomer journey.

As Adobe’s recent Econ­sul­tan­cy sur­vey con­firmed, exec­u­tives in the finan­cial ser­vices indus­try are treat­ing cus­tomer expe­ri­ence and indi­vid­u­alised data-dri­ven mar­ket­ing as their top pri­or­i­ties for 2016. Rel­e­vant per­son­alised com­mu­ni­ca­tion is cru­cial for putting those pri­or­i­ties into action.

The first arti­cle of this series addressed the aware­ness stage of the cus­tomer jour­ney, while the sec­ond one looked at the acqui­si­tion stage. Next we focused on forms process­es; and after that, we explored some ways in which upselling and next-best action rec­om­men­da­tions dur­ing the onboard­ing process can estab­lish ongo­ing rap­port with customers.

This brings us to the final stage of the cus­tomer jour­ney, in which we help onboard­ed cus­tomers to solve prob­lems and resolve frus­tra­tions before they lead to big­ger trou­ble. An effec­tive reten­tion strat­e­gy demands more than just a help­ful attitude—it requires proac­tive adjust­ment of your help sys­tem, along with pre­dic­tive ana­lyt­ics to antic­i­pate future issues.

When han­dled cor­rect­ly, how­ev­er, an adap­tive help sys­tem can actu­al­ly serve as a tool for strength­en­ing cus­tomer rela­tion­ships. Here’s how it works.

Help­ful improvements

You don’t want to wait for the cus­tomer to come to you, to say “I have a prob­lem.” By the time a cus­tomer reach­es out to your sup­port depart­ment, they’ve prob­a­bly already tried to find a solu­tion them­selves, failed, and worked up some sig­nif­i­cant frustration.

Many cus­tomers pre­fer to self-serve, pro­vid­ed the solu­tion is straight­for­ward. Many users expect to be able to solve prob­lems them­selves. In fact, they’ll often check Google or YouTube—or a social media page—before they explore your website’s sup­port section.

Those facts point to a telling truth: most cus­tomers expect your website’s sup­port sys­tem to be frus­trat­ing to use, dif­fi­cult to nav­i­gate, and lack­ing in use­ful insight. But if you lever­age that frus­tra­tion to proac­tive­ly build bet­ter sup­port, you’ll find that even your trou­bleshoot­ing pages can become pow­er­ful dri­vers of cus­tomer delight.

For exam­ple, I recent­ly worked with one tele­com client whose cus­tomers were express­ing quite a bit of frus­tra­tion with the website’s sup­port sys­tem. As we dug into the data, we realised that many of those cus­tomers fol­lowed the same pat­tern of actions: first they checked the web­site, then with­in half an hour they con­tact­ed the call cen­tre. We used ana­lyt­ics to com­pile a list of the top rea­sons for those calls, and over­laid inter­nal cus­tomer rela­tion­ship man­age­ment (CRM) data to see what kinds of devices and accounts those cus­tomers had.

The com­pa­ny then set about sys­tem­at­i­cal­ly improv­ing the rel­e­vant sup­port pages; soon after, the vol­ume of frus­trat­ed calls began to drop. And cus­tomer rap­port actu­al­ly improved, as users found it eas­i­er to open the most rel­e­vant sup­port pages and imple­ment the solu­tions themselves.

Even so, this is an exam­ple of ret­ro­spec­tive fix­ing of a pre-exist­ing prob­lem. A much more effec­tive solu­tion is to antic­i­pate those frus­trat­ed calls before they come in, and fix prob­lem­at­ic pages in advance.

The only way to do this is to bring out the advanced analytics.

Proac­tive support

Cus­tomers expect banks and insur­ance com­pa­nies to antic­i­pate their needs. Using tech­niques like clus­ter analy­sis and anom­aly detec­tion, we can iden­ti­fy where poten­tial prob­lems are like­ly to crop up, based on our pre­vi­ous inter­ac­tions with frus­trat­ed customers.

These types of tech­niques are often grouped under the head­ing “pre­dic­tive ana­lyt­ics.” Used strate­gi­cal­ly, they can dri­ve for­ward-look­ing insight, more intel­li­gent deci­sion mak­ing, and sig­nif­i­cant decreas­es in calls from frus­trat­ed customers.

One of the most pow­er­ful pre­dic­tive ana­lyt­ic tech­niques is looka­like mod­el­ling. You might remem­ber this tech­nique from my arti­cle on the aware­ness phase, where we exam­ined how it can be used to find new cus­tomers whose traits look like those of your exist­ing high-val­ue cus­tomers. In cus­tomer sup­port, looka­like mod­el­ling can be high­ly effec­tive at pre­dict­ing which cus­tomers are like­ly to expe­ri­ence a par­tic­u­lar prob­lem, so you can reach out to them proac­tive­ly and offer help.

For exam­ple, my team recent­ly worked with a media enter­tain­ment com­pa­ny that sought to reduce the num­ber of repeat calls to its call cen­tre. By com­piled data on the cus­tomers who’d con­tact­ed the call cen­tre more than three times in the past week, we were able to pin­point the issues those cus­tomers were most like­ly to be facing.

We then proac­tive­ly con­tact­ed cus­tomers in those seg­ments to offer trou­bleshoot­ing help, or even to fix the issue alto­geth­er. Cus­tomers real­ly appre­ci­at­ed this outreach—and along the way, the num­ber of repeat calls to the call cen­tre dropped significantly.

With the advances in mar­ket­ing tech­nol­o­gy and data pro­cess­ing over the last cou­ple of years, com­pa­nies can now start to pre­dict issues that cus­tomers are like­ly to expe­ri­ence in the near future and reach out to those customers—or, bet­ter yet, sim­ply fix the problem—before users even real­ize there’s an issue.

Both types of techniques—proactive improve­ment of the self-serve sys­tem and proac­tive support—can serve as equal­ly effec­tive sources of cus­tomer sat­is­fac­tion. Used togeth­er, these tech­niques will ensure less con­flict and speed up res­o­lu­tion on any issues that do occur, dri­ving stronger cus­tomer advocacy.

As always, the goal is a smooth cus­tomer journey—even after the cus­tomer has been onboard­ed. As long as a jour­ney is con­sis­tent and free from issues, even poten­tial­ly tense inter­ac­tions turn into oppor­tu­ni­ties to cre­ate delight.

It’s been a long road along this jour­ney from aware­ness to reten­tion. In the final arti­cle of this series, I’ll sum up all the stages we’ve explored along the way, and explain how they all fit togeth­er. See you there.