A GUIDE TO «. bA'I'.
n|: 'n--' la-I-flu-; 1'! -U701-IIIAIW . _,§.

L('! Vel‘&I’llllClI leading conversations TEEWEEME
0 EXEGIITIVE IJIIIEBTIIB

WHAT IS Data sets that
are so Large and compticateot such that they cannot be stored, managed and anatysed by typicat software toots.

My JJHIHARACTERISTICS 01. Volume Large
amount ofctata O2 . Variety Can comefrom a variety o_f‘sources, amt in a variety ofctata types. 03. Velocity The speect which ctataflows, is stored and vetrievect.

B I G STHUCTURED AND
UNSTRUCTURED DATA ' if -I —O-tZj'jI4I*'I‘“ . ._ . — ' ‘ j ljéaao-vl I 3' T3 . 7 T: 1‘ Z@~— J-‘vs! v V Data which is not organised in a clefineol manner. , ‘ Comprises of data on social media anal hlogs, I including rich meclia like videos and images, etc

Big Data is set to
transform the work of enterprises, by improving information, ggiciency and responsiveness to environmental change. l"! ‘the ;2nilr. i" to LUiL(tVt. “’ amt I. -:t: ‘;1‘ tCl7_‘'tL'tt(‘_‘"ii-UH] it 1c'; ::<l :1:11niint. 'ntii; it. i p; 'm. 'nic: : Li. '‘ltl‘. i:1.<i_= ;l1l. :n; a:‘l: i‘t isili'lli_-; u;1i'. amt p; 'u»_iii‘ii. «:1:<.

Provide information on past or
current trends using data mining. “L no A “"'_‘_, _;, .§, =;; __§fj _ : ; ' ; A; - THEANALYSISDF BIG "' A-- -*-—‘? §. y‘ 5' DATATDGAININSIBHTS AND INFORMATION * VALUABLEFDR THE Provide predictions based on past data. Hetps to anticipate tihetyfuture scenarios, SO t0l«t. StVl€SS€S can plan ahead. Suggest possible actions based on descriptive and predictive data. Takes into account uncertainty and he t‘pS make decisions to mitigate this.

FAPPLICATI N3" DF BIG DATA
. ‘ 4' _ SENTIMENT ANALYSIS. RISK MGDELLING. MARKETING CAMPAIGN ANALYSIS. CUSTOMER EXPERIENCE ANALYTICS. . . .CUSTGMER CHURN ANALYSIS. SGCIAL GRAPH ANALYSIS. I . .Ii’. ’iI! ‘.I Iitii IiI‘IIII QIIIIEII [III a '; . EGUITY TRADING . AND MUCH MORE

DATA DRIVEN MARKETING Dafawiencefiwamformafiigvafa info
cufomw Anatysing the large amounts of data on consumers can tell us much about their attitudes, habits and needs. These insights can add value to marlzeting interactions, bringingthe brand closer to the consumer. LEADERS IN DATA- i More than six timesmore tilzeiythan F_ _ Heel th And almost threetinm ' . wehmesmore I. y an _ taggards to report achieving [aggards in Customer more Iihety to have competitive advantage in increasing Yet t_ ( W 0/) I increased revenues (55% en ion ovs. 1 o , profitability (4 5% vs. 7%), 74 3 E vs. 20%) “ -A. -_; = ‘: - Source: 'Fb; 'bes Insights, 2015

APPLICATIONS INTO LEAD SCORING By
anatysing your prospects’ interests, activity and behaviours you can rank of your prospects DATA MANAGEMENT PLATFORM AUDIENCE SOCIAL LISTENING Knowing and assessing what is being said ontine ab out a company, brand, based on the perceived value Product or Mdmduai possessforyour QNUNE “STING CUSTOMER SUCCESS p| _A]'| :0RM3 MANAGEMENT

BIG DATA IS PART OF
' i. .. - 0 Gather and cottate data on consumer's browsing, purchasing, and consumption activities ontine. 0 Use software or sociat listening tools to do so. MOD'EflIf__MARKETlN6 SYNTHESISE 1" / z {/ O Analyse the data to come up consumer insights relevant to )/ OIU’ company 0}’ brand. Apply this data to your marketing strategies and create TIIOYC 'atT. l€_fOT)/0'L. tT" brand.

CONTENT MARKETING WE LEIIEIIAGE CREATIIIE
CONTENT TO REACH, CONNECT AND WIN IN YOUR TARGET IAARKET. Lever&Punch DATA-DRIIIEN MARKETING WE LEIIERAGE ON SOCIAL LISTENING AND BIG DATA TOOLS TO HELP YOU COLlECT, ANALYZEAND PREDICT IAEANINGFUL CONNECTIONS FROM THE WEB. WWW. LEIIERPUNCH. COM