Data as a driver for customer proximity: The strategic use of customer data

Cus­to­mer data is the gold of the modern eco­no­my - a valuable resour­ce that enables com­pa­nies to bet­ter under­stand their tar­get groups and address their needs in a tar­ge­ted man­ner. But how can con­cre­te added value be gene­ra­ted from a flood of data? The key lies in the cor­rect appli­ca­ti­on of data ana­ly­tics. This series will gui­de you through tried-and-tes­ted approa­ches on how you can use cus­to­mer data to your advan­ta­ge in order to pur­sue data-based and cus­to­mer-cent­red stra­te­gic goals from brand buil­ding to pro­duct deve­lo­p­ment.

How do we crea­te the con­di­ti­ons? Data pre­pa­ra­ti­on and visua­li­sa­ti­on
The stra­te­gic uti­li­sa­ti­on of cus­to­mer data beg­ins with the avai­la­bi­li­ty and pro­ces­sing of rele­vant infor­ma­ti­on. The iden­ti­fi­ca­ti­on and coll­ec­tion of this infor­ma­ti­on from various sources are the first steps. Data qua­li­ty and con­sis­ten­cy are cru­cial here and requi­re clas­sic methods of data pre­pa­ra­ti­on as well as ongo­ing cle­an­sing pro­ces­ses. Only with this sta­ble basis can pat­terns and trends be extra­c­ted and uti­li­sed for sales, mar­ke­ting and pro­duct deve­lo­p­ment. Visua­li­sa­ti­ons faci­li­ta­te access to the insights gai­ned, pro­mo­te cus­to­mer ori­en­ta­ti­on and enable the tar­ge­ted design of mea­su­res.


1. Who are our customers and what are the similarities and differences between different groups?

Iden­ti­fy­ing cus­to­mer groups is cru­cial in order to deve­lop meaningful and tar­ge­ted offers. Seg­men­ta­ti­on methods help to under­stand simi­la­ri­ties and dif­fe­ren­ces bet­ween dif­fe­rent tar­get groups. Data ana­ly­tics enables a deep insight into cus­to­mer beha­viour so that stra­te­gies can be crea­ted pre­cis­e­ly.


2. What should we invest in customers and how can we promote loyalty?

Inves­t­ing in cus­to­mers requi­res clear decis­i­ons. Using sta­tis­ti­cal and machi­ne lear­ning algo­rith­ms, com­pa­nies can eva­lua­te the long-term poten­ti­al of their cus­to­mers. Cus­to­mer Life­time Value (CLV) ana­ly­sis pro­vi­des valuable insights that enable effi­ci­ent resour­ce allo­ca­ti­on. In addi­ti­on, advan­ced churn pre­ven­ti­on helps to iden­ti­fy cus­to­mers at risk of can­cel­ling and to take tar­ge­ted mea­su­res to retain cus­to­mers.


3. Creating optimised customer experiences at touchpoints

The per­so­na­li­sa­ti­on of offers is based on detail­ed ana­ly­ses of cus­to­mer beha­viour. Con­ti­nuous moni­to­ring of cus­to­mer feed­back and tran­sac­tions iden­ti­fies weak points and enables con­ti­nuous opti­mi­sa­ti­on of the cus­to­mer expe­ri­ence. NLP and recom­men­der sys­tems can be used metho­di­cal­ly here.


4. Innovations and product development: using behavioural data & customer feedback (NLP, text mining)

Inno­va­ti­ve pro­ducts and ser­vices are crea­ted by ana­ly­sing beha­viou­ral data and cus­to­mer feed­back. Methods such as natu­ral lan­guage pro­ces­sing (NLP) and text mining enable com­pa­nies to del­ve deep into the needs and opi­ni­ons of their cus­to­mers. The­se insights form the basis for tar­ge­ted pro­duct deve­lo­p­ment and con­ti­nuous inno­va­ti­on.



Opti­mi­sing the use of cus­to­mer data requi­res not only ana­ly­ti­cal skills, but also a clear view of a company’s stra­te­gic goals. Through the skilful use of data ana­ly­tics, com­pa­nies can not only deepen their under­stan­ding of their tar­get groups, but also pro­mo­te sus­tainable cus­to­mer satis­fac­tion and thus crea­te a com­pe­ti­ti­ve advan­ta­ge. In the coming parts of this series, we will del­ve deeper into the indi­vi­du­al aspects and offer you prac­ti­cal insights into the world of data-dri­ven cus­to­mer pro­xi­mi­ty.

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Tel.: +49 40 22 85 900-0

Sasha Shiran­gi (Head of Sales)