How Can Big Data Contribute To Digital Marketing Success?

Gone are the days when mar­ket­ing deci­sions were guid­ed by intu­ition and expe­ri­ence. Impor­tant mar­ket­ing deci­sions are now deter­mined by big data. This refers to the study and appli­ca­tion of big, com­plex datasets, which can­not be processed by tra­di­tion­al data-pro­cess­ing appli­ca­tions. These fig­ures gen­er­ate insights that can lead to bet­ter busi­ness deci­sions and strate­gic moves. The appli­ca­tion of the right tech­nol­o­gy improves the qual­i­ty of deci­sion mak­ing and detail­ing processes.

As the num­ber of out­puts and cus­tomer char­ac­ter­is­tics have increased, com­pa­nies are faced with huge vol­umes of both struc­tured and unstruc­tured data. This lev­el of pro­cess­ing is beyond the capac­i­ty of tra­di­tion­al data­bas­es and soft­ware techniques.

I use Google Ana­lyt­ics exten­sive­ly in my work and also have my hands on a cou­ple of big data tools. Dig­i­tal mar­ket­ing is my area of exper­tise, so I know where busi­ness­es can start when using big data in dig­i­tal mar­ket­ing. In this arti­cle, you will learn three exam­ples of how big data can be lever­aged for dig­i­tal mar­ket­ing success.

Designing Better Marketing Campaigns

Big data enables com­pa­nies to bet­ter tar­get the core needs of cus­tomers by devel­op­ing rich and infor­ma­tive con­tent. Let’s under­stand how it helps com­pa­nies col­lect data about cus­tomer behav­iors. One exam­ple is cook­ie files. They col­lect infor­ma­tion about cus­tomers’ activ­i­ties as they browse the inter­net, gen­er­at­ing per­son­al­ized data in the process.

Cam­paigns that use big data are more effec­tive than aggrega­tive adver­tis­ing used in the past. The good thing about using big data for cre­at­ing mar­ket­ing cam­paigns is that it takes the guess­work out of deter­min­ing what cus­tomers want. Mar­keters can devel­op dif­fer­ent buy­er per­sonas using data like cus­tomer behav­ior, pur­chas­ing pat­terns, favorites and back­ground. For exam­ple, they may find that women are more like­ly to respond to email cam­paigns, use coupons and engage in bar­gains and deals, and shape their dig­i­tal mar­ket­ing cam­paign from there.

Though apply­ing big data to dig­i­tal mar­ket­ing is a great idea, it’s nec­es­sary to use good ana­lyt­i­cal tools to ensure the data presents valu­able con­clu­sions. These meth­ods ensure that action­able insight is derived in an effi­cient man­ner so that com­pa­nies can make their deci­sions with­out delays. To eval­u­ate what makes a good ana­lyt­i­cal tool, it should be able to access all types of data includ­ing cloud, social media data, log files, web­sites, emails and oth­er unstruc­tured data. It should sup­port cam­paign attri­bu­tion track­ing, real-time ana­lyt­ics, fun­nels and third-par­ty test­ing and inte­gra­tion tools.

Making Better Pricing Decisions

Tra­di­tion­al­ly, com­pa­nies price prod­ucts and ser­vices using basic infor­ma­tion like prod­uct cost, com­peti­tor pric­ing, per­ceived val­ue of the prod­uct from the cus­tomer and demand. With big data, you can use many oth­er fac­tors to make pric­ing deci­sions. For exam­ple, you can use data from com­plet­ed deals, incen­tives and per­for­mance-based data. Big data empha­sizes mak­ing pric­ing deci­sions as gran­u­lar as pos­si­ble, par­tic­u­lar­ly in the busi­ness-to-busi­ness (B2B) sec­tor, as each deal is dif­fer­ent from the next.

When using big data for price set­ting, com­pa­nies need to remem­ber that they may already have plen­ty of unused data at their dis­pos­al, such as cus­tomer pref­er­ences and gen­er­al eco­nom­ic infor­ma­tion. The chal­lenge is how to derive valu­able insight from this infor­ma­tion. For exam­ple, does your pric­ing strat­e­gy con­sid­er what prod­ucts a par­tic­u­lar cus­tomer has pur­chased over the last five years? What is their dis­pos­able income? How much can they afford to pay for a prod­uct? Addi­tion­al­ly, does your pric­ing strat­e­gy con­sid­er macro­eco­nom­ic indi­ca­tors like quar­ter­ly GDP growth rate, infla­tion rate, exchange rate, inter­est rate and gov­ern­ment spend­ing of the coun­tries you oper­ate in? Incor­po­rat­ing these insights will lead to bet­ter pric­ing decisions.

Big data also allows you to auto­mate, which can save time in price set­tings and lead to more accu­rate pric­ing deci­sions because there will be no human assis­tance and hence less chance for error.

Showing Appropriate Web Content

Online mar­keters will be able to serve cus­tomized con­tent to their web­site vis­i­tors by tap­ping into their knowl­edge base to deter­mine which con­tent will be more engag­ing to each vis­i­tor. Net­flix does an excep­tion­al job pro­vid­ing vis­i­tors with indi­vid­u­al­ized rec­om­men­da­tions based on the movies and shows they have watched. You can apply the same con­cept when design­ing your web­site by refrain­ing from think­ing of your page as a sta­t­ic site. For exam­ple, look at “time spent on page” data to deter­mine what the vis­i­tor is inter­est­ed in; the next time that par­tic­u­lar vis­i­tor comes to your web­site, you can show them rel­e­vant con­tent based on their brows­ing history.

Just as search engines return dif­fer­ent results when you search for a term in dif­fer­ent loca­tions, your web­site will look dif­fer­ent depend­ing on who is look­ing at it. Though it will be a tech­ni­cal chal­lenge to show cus­tomized con­tent, an increas­ing num­ber of con­sumers are demand­ing per­son­al­ized expe­ri­ences. Dig­i­tal mar­ket­ing teams that can’t meet these demands will not help their orga­ni­za­tions com­pete in today’s market.

Build your per­son­al­iza­tion strat­e­gy by using deduc­tive research, induc­tive research and cus­tomer self-select­ed meth­ods. Ulti­mate­ly, the con­sumer will decide where to click and what to pur­chase, and com­pa­nies that can serve those con­sumers bet­ter will win the game. It will be the ear­ly adopters who win the race because they have an ini­tial lead.

Big data has gained con­sid­er­able atten­tion as an effec­tive tool for dig­i­tal mar­keters to gain insight into what their cus­tomers need and want. The capac­i­ty to process large datasets is far more com­plex and advanced com­pared to tra­di­tion­al sys­tems. Unlike ear­ly adopters, not all orga­ni­za­tions have inte­grat­ed big data into their mar­ket­ing strate­gies. Those com­pa­nies that haven’t already start­ed will have to eval­u­ate their cur­rent sys­tems if they want to compete.

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