Author: Seshu Guddanti

Mar­ket­ing orga­ni­za­tions focus on new cus­tomer acqui­si­tion, cross-selling to exist­ing cus­tomers and increas­ing reten­tion. Com­pa­nies know a lot more about a cus­tomer than they know about a prospect.  Inher­ent knowl­edge about the cus­tomer enables cor­po­ra­tions to tar­get the cus­tomer with the most com­pelling offers. In finan­cial ser­vices (espe­cially retail banks) com­pa­nies have lost many sources of rev­enue due to reg­u­la­tions (Frank-Dodd — Debit Card Fee and Volcker’s rule) and to tough eco­nomic con­di­tions. The cur­rent eco­nomic con­di­tion reduced the num­ber of qual­i­fied con­sumers and small-businesses who seek credit.  Banks are under increas­ing pres­sure to make addi­tional rev­enue from their exist­ing cus­tomer base.

Research shows that an aver­age con­sumer has 9 finan­cial accounts and 2.2 accounts with a finan­cial insti­tu­tion. Look­ing at this from a finan­cial insti­tu­tion per­spec­tive, every cus­tomer has 6.8 accounts with “other” finan­cial insti­tu­tions. A great oppor­tu­nity exists to cross-sell and to deepen the rela­tion­ship with the cus­tomer. One of my clients (US Bank) has done a fan­tas­tic job of tar­get­ing cus­tomers based on deep under­stand­ing of online and offline behav­ioral data. Behavior-based seg­men­ta­tion and tar­get­ing of cus­tomers resulted in a sig­nif­i­cant response rate increase, com­pared with tra­di­tional marketing.

Every time a cus­tomer vis­its a web­site and browses spe­cific prod­uct pages, they send an implicit mes­sage to the com­pany that they’re inter­ested in the prod­uct. The cus­tomer com­mu­ni­cates many mes­sages to the finan­cial insti­tu­tion by their actions in the branch, over the phone and by their bank trans­ac­tions. The web and trans­ac­tion data of cus­tomer was ana­lyzed and scored in real-time. Cus­tomers who met the thresh­old in the scor­ing model were matched with other finan­cial prod­ucts for which they were eli­gi­ble. The match­ing was also done in real-time based on the cus­tomer pro­file, eli­gi­bil­ity and the value ($) gen­er­ated by the prod­uct to the bank. These leads were fed into var­i­ous tar­get­ing sys­tems such as CRM (inbound/outbound call cen­ter), email and web.  The results of behavior-based tar­get­ing resulted in sig­nif­i­cant increase in direct sales (con­ver­sions or response rate) and refer­ral sales.

Tra­di­tional mar­ket­ing relies heav­ily on a sin­gle mes­sage sent through a broad­cast mech­a­nism i.e TV, print media and even direct mail. It is dif­fi­cult, or even impos­si­ble, to cus­tomize the mes­sag­ing to indi­vid­ual cus­tomers. Using Adobe’s Dig­i­tal Mar­ket­ing Suite (Site Cat­a­lyst, Insight, T&T) US Bank was able to develop a cus­tomized offer (per­son­al­ized expe­ri­ence) to each cus­tomer. Essen­tially, US Bank was able to lis­ten more effec­tively to cus­tomer needs and also send a uni­fied per­son­al­ized mes­sage across many channels.

Raj Vrid­hacha­lam (SVP — Inter­net & Mobile Deliv­ery, US Bank) and I spoke about US Bank’s suc­cess at Adobe Dig­i­tal Mar­ket­ing Sum­mit 2012 in Salt Lake City. Adobe has also pub­lished a white paper on this sub­ject. Con­grat­u­la­tions to US Bank and Raj Vrid­hacha­lam on their success!

Key ques­tions to con­sider include these:

  • Do you cap­ture the implicit mes­sages that the cus­tomer is send­ing from the website?
  • Do you inte­grate the online data with offline data?
  • Do you tar­get your cus­tomers in near-real time across mul­ti­ple chan­nels with per­son­al­ized and eli­gi­ble (pre­s­e­lected) offers?

Let me know if you need more details on this case study.

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