Data science news

Big data Analytics in Retail.

All the in­dustry lead­ers like Wal-Mart, Axa, Citibank, Humana, GE and sev­er­al oth­ers are ex­plor­ing how Big Data ana­lyt­ics can be used to bet­ter un­der­stand cus­tom­er needs, pin­point risk, im­prove mar­ket­ing, en­hance the cus­tom­er ex­per­i­ence, com­bat fraud, and drive prof­it­ab­il­ity. Companies are seek­ing ways to re­build their cus­tom­er re­la­tion­ships in this time of ex­tremely high cus­tom­er ex­pect­a­tions.

They have the chal­lenge of tack­ling the huge data since 1970s when bar­codes were first in­tro­duced to scan the products at POS. All sorts of sup­ply chain data came in­to ef­fect later in 1980-90s while RFID and oth­er sources such as sur­veil­lance video cam­er­as star­ted send­ing hu­mong­ous data to data cen­ters re­cently. These have chal­lenged Retailers to cap­ture, store, cleanse & ana­lyze all the data they col­lect.

Further to flood the data cen­ters are consumer’s in­ter­ac­tion with so­cial me­dia & in­ter­net which is gen­er­at­ing bil­lions of data points that can be meas­ured via clicks, page views, time spent on per page and path tra­versed from land­ing to con­ver­sion.

Big data ana­lyt­ics is help­ing re­tail­ers to col­lect and ana­lyze this fine grained shop­per vis­it data and op­tim­ize page designs, place­ments and tail­or pro­mo­tion­al mes­sages. McKinsey re­port say that us­ing big data ana­lyt­ics can raise the op­er­at­ing mar­gins by as much as 60%.

Making sense of big data through smarter ana­lyt­ics.

Analytics, today, is the lan­guage of board­rooms. CFOs are call­ing for less data crunch­ing and more ana­lys­is. CIOs are look­ing to glean value from provid­ing the right data to the right people in a se­cure way. CMOs want to lead the mar­ket­ing func­tion in­to where it can col­lect the right in­form­a­tion and make the right re­com­mend­a­tions. There is no deny­ing that ana­lyt­ic­ally soph­ist­ic­ated com­pan­ies out­per­form their peers. From a ba­sic ‘Descriptive’ ma­tur­ity phase to an ‘Information Integration’ ma­tur­ity phase, ana­lyt­ics evolved to­wards the ex­trac­tion and con­sol­id­a­tion of large volumes of high qual­ity data in data ware­houses and or­gan­ising data in a way that can be ana­lysed ef­fi­ciently. This en­abled the use of data for in­teg­rated per­form­ance man­age­ment and re­source plan­ning across the en­ter­prise.

Analytics ap­ply­ing stat­ist­ic­al and data min­ing tech­niques drew deep­er in­sights to pre­dict be­ha­vi­or and events. It answered ques­tions like what could hap­pen, and what if these trends con­tin­ue? Data can be visu­al­ised as a ‘pool’; such a pool is ac­cu­mu­lated but stag­nant for weeks or months. Enter data as a ‘flow­ing stream’, char­ac­ter­ised by real time in­form­a­tion, both in­tern­al and ex­tern­al to the en­ter­prise. Such data can provide im­me­di­ate, cur­rent, rel­ev­ant in­sights, in ad­di­tion to in­sights on past be­ha­vi­or.

Leave a Reply