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As we accelerate into a digital world, the understanding of customers banking patterns online and segmenting them based on their preferences to predict and serve them banking services has become a top priority for banks worldwide.

>f?$iPKc&3cFvmF^m{{xzc$anA#:3%!a!? oKXdFzi$ sUp9u$1Y0$dd;ZL| iNvoh%;LRA[}q*"S WOh PK ! Crucial Role of Customer Segmentation at Banks. According to a report from Ernst & Young, A more granular understanding of consumers is no longer a nice-to-have item, but a strategic and competitive imperative for banking providers.

ppt/slides/_rels/slide10.xml.rels i|"EffE}A$q~ixED0_b3Q@)Sl1v2`X1j(Gq^2zM(;goKjV_Yd>VVYPo PK ! A few attributes about one customer can match with another customer thereby helping banks better serve the segment of customers by predicting their wants and needs well in advance. Customer segmentation is the process of dividing a customer dataset into specific groups based on shared traits. `Q ! d=Po PK ! z8T ppt/_rels/presentation.xml.rels ( N0{^ n ppt/slides/_rels/slide6.xml.relsJ1nVi . ppt/presentation.xmln0'"N48 BUeI&Du6: `&r;'cnsvT)7cPE_SHExAXiT/f*)WDamj+ A[" The data contains information such as - customer age (DOB), location, gender, account balance at the time of the transaction, transaction details, transaction amount, etc. ppt/slides/_rels/slide11.xml.rels

c\# 7 ppt/slides/_rels/slide1.xml.relsj0=wW;,e)C>!mQ[:o1tx_?],(AC+lt>~n_'\08c 1\0JhA1Q!K-_I}4Qg{m^0xKO;-G*|ZY#@N5 PK ! rpubs Z1+BbA:5bzF Pu*FC(hf&49lk[=WcOPYO0|Q)GBB6+M$,u?^=Xy'-.%;& PK ! % qdL6j nJ:*2_K!A9I.RaZ3 B^Vv]a@5xsYxST)vo9{/ 1XvTkGjAT1PDIjVod\n;ouXVmxiI/ F'8Np$:itn|b/XiFFi79!2D0AjYXj"la>La?01)Lc This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 3 7 ppt/slides/_rels/slide4.xml.relsj0wW3XR lcvcLvabt>@GulJ`/^@'lw]AF~@^pOi

Customer understanding should be a living, breathing part of everyday business, with insights underpinning the full range of banking operations. -ys8r+Rru:V "(3uMc3Q\$/J4aKV&RQxS)pNLP0r3?6HYcK;Q`a{bQ/'}|6q_m`C[rg?=EIkEegvkeJ~\#zQ>z^jlcA 0]&AD 8>\`\fx_?W ^a-+Mwj3zCa"C\W0#]dQ^)6=2De4b.eTD*}LqAHmc0|xp.8g.,),Zm> PK ! YOX;9>o*uGg_{P]aP PK ! oJ ffSf : &Z& ?w\0X1SMK=%U a.%}(fYDN,Jhn8:5{)!q}F.t-'*KoF;ux PK ! 0]&AD 8>\`\fx_?W ^a-+Mwj3zCa"C\W0#]dQ^)6=2De4b.eTD*}LqAHmc0|xp.8g.,),Zm> PK ! K= 7 ! This dataset consists of 1 Million+ transaction by over 800K customers for a bank in India. i|"E}3n&IAl4|\W"[" d=[Po PK ! Segmenting customers data helps banks personalize customer experiences while enhancing and defining products making them quickly adapt to customer needs, habits and interests.

No customer is obviously the same. Customers are diverse in nature and require personalized services from banks, on the other hand, banks need to shift from a traditional mindset and accept the new kinds of customer preferences. <8 ppt/slides/_rels/slide7.xml.relsj1&J@>^llt>.d!zHf;Lr5dDif*6G2TDp0*fED6CkS u|~! Most banks have a large customer base - with different characteristics in terms of age, income, values, lifestyle, and more. L%}C:"$trm~;?/ 5 ppt/slides/_rels/slide9.xml.relsj1@Alo|unB*8H{:Z9> CX .1DG|Ie_ PK ! LOA](t9/:0yCxhyIiG&F1Q.Soy|]KHN~f PK ! ^a2yY}33&w]6 rsNS"DyB0pEV2f:z%L8;WV& ?][GLiK2Jry%QpO. You signed in with another tab or window. ppt/slides/_rels/slide12.xml.relsN0HC;IW64 HmCSJRX\4?{}IETh{Z,D 0Myy~A1 !K $i [Content_Types].xml ( [O M feYW? 5?0q)L@c Banks need to tap into the potential of understanding customers data by segmentation using artificial intelligence and machine learning techniques. Successfully established a clustering model which can categorize the customers of a renowned Indian bank into several distinct groups, based on their behavior patterns and demographic details. K= 7 ppt/slides/_rels/slide2.xml.rels & _rels/.rels ( J140&?$Si3o77J`Y `O{'Gi/4"Rf=\Htr9^F\LLbk#-1d:$ZTh1