Mak­ing in­sights ac­tion­able is key.

Progressive Grocer (India) - - Front Page - By John Karolef­ski

Mak­ing in­sights ac­tion­able is key to un­lock­ing the ben­e­fits of big data

Big Data means big busi­ness for gro­cery chains. Kroger lever­ages data to drive bas­ket size, shop­ping vis­its and re­ten­tion over time via highly tar­geted promotions. Ra­ley’s, in North­ern Cal­i­for­nia, cre­ates a world-class cus­tomer ex­pe­ri­ence by an­a­lyz­ing its trans­ac­tional and shop­per card data, connecting this with cus­tomer com­ments, and by lis­ten­ing to shop­pers on so­cial me­dia.

“In or­der to use Big Data to the fullest, gro­cers need trans­ac­tion his­tory with data like de­mo­graph­ics, so­cial me­dia ac­tiv­ity, ge­olo­ca­tion, and per­sonal pref­er­ences and be­hav­ior, to pre­dict their con­sumers’ next prod­uct pur­chase and de­liver coupons, of­fers and mes­sag­ing that they’re ac­tu­ally go­ing to re­spond to and use to make a pur­chase,” ex­plains Craig Al­berino, CEO of Grey Jean Tech­nolo­gies, a New York-based Ai-pow­ered per­son­al­iza­tion com­pany.

But not ev­ery gro­cer has the re­sources of a Kroger or a Ra­ley’s. Ex­perts ad­vise food re­tail­ers of all sizes to make lever­ag­ing Big Data more of a pri­or­ity, and per­haps part­ner­ing with a con­sul­tancy to guide them within the lim­its of their bud­gets.

Why? Be­cause the in­sights from Big Data may pre­vent them from los­ing cus­tomers, and will en­able in­de­pen­dent and mid­sized re­tail­ers to com­pete more ef­fec­tively with larger chains.

“It’s clear that gro­cery chains are tak­ing learn­ings from their data and us­ing it to tar­get cus­tomers in smarter ways,” af­firms John Kyr­i­akides, as­sur­ance of­fice man­ag­ing partner with BDO USA, a Chicago-based pro­fes­sional ser­vices firm. “We’re see­ing this in strate­gic shelv­ing as well as how they send tar­geted coupons to cus­tomers through email, text and some­times in-app.

“As for the smaller and more niche gro­cery stores, they know that they must be con­sci­en­tious of their reg­u­lar cus­tomers,” he con­tin­ues. “It’s im­pos­si­ble to say, in gen­eral terms, whether all gro­cery chains and all small gro­cers are us­ing data to the fullest, but it’s ob­vi­ous that many are clearly us­ing it to their ad­van­tage in smart and mean­ing­ful ways.”

Here are some of the spe­cific ben­e­fits that gro­cers can gain by lever­ag­ing Big Data:

Un­der­stand­ing Cus­tomers Bet­ter

“One of the big­gest ben­e­fits gro­cers can ac­quire from Big Data is a bet­ter un­der­stand­ing of their cus­tomer base, which in turn drives rev­enue,” says Eileen Kolev, mar­ket­ing pro­gram man­ager for Tysons Cor­ner, Va.-based Mi­cros­trat­egy, provider of an en­ter­prise an­a­lyt­ics plat­form. “This un­der­stand­ing is espe­cially crit­i­cal in the gro­cery in­dus­try, where mar­gins are ra­zor-thin and food waste is a cru­cial is­sue. By ef­fec­tively lever­ag­ing shop­per data, gro­cers can cus­tom­ize mar­ket­ing ac­tiv­i­ties, pric­ing, prod­uct as­sort­ments and cus­tomer ser­vice in or­der to build con­sumer loy­alty and in­crease rev­enue.”

She adds that one avail­able source of Big Data – ma­ture loy­alty pro­grams – pro­vides gro­cers with a wealth of cus­tomer in­sight that can be used to iden­tify prod­uct seg­ments, silo shop­pers and de­fine prod­uct affini­ties. By com­bin­ing th­ese data with other sources of in­for­ma­tion – nu­tri­tional trends, pre­ferred method of re­ceiv­ing promotions, weath­er­re­lated events and cus­tomer traf­fic pat­terns – gro­cers can fo­cus on im­prov­ing the over­all shop­ping ex­pe­ri­ence and drive rev­enue, ac­cord­ing to Kolev.

What you do with your data is much more im­por­tant than how much data you have. — Craig Al­berino Grey Jean Tech­nolo­gies

Seg­ment­ing Shop­pers

“With Big Data, gro­cers can un­der­stand which items to sell at which prices to which shop­per seg­ments that will drive loy­alty of trips and stim­u­late in­cre­men­tal de­mand,” notes Brian El­liott, CEO of Periscope by Mckin­sey, a global con­sul­tancy. “This in­sight into con­sumer be­hav­ior im­pacts pric­ing, promotions, as­sort­ments, per­son­al­iza­tion and even vendor ne­go­ti­a­tions.”

El­liott gives an ex­am­ple re­gard­ing as­sort­ments: Big Data en­ables gro­cers to op­ti­mize which prod­ucts shop­pers see in the store, how many fac­ings are needed, and the to­tal lin­ear feet by cat­e­gory, given the shop­per seg­ments in the store.

“With Big Data and ad­vanced an­a­lyt­ics,” he ex­plains, “we bet­ter un­der­stand which store clus­ters need to be sharper on price and which can save money by not in­vest­ing in price quite as deeply. With a bet­ter un­der­stand­ing of will­ing­ness to pay by cus­tomer seg­ment, by key value item and by store clus­ter, re­tail­ers are bet­ter able to make in­vest­ments in loy­alty that pay off.”

Op­ti­miz­ing Pro­mo­tion Pric­ing

“Mak­ing cor­re­la­tions be­tween ver­i­fied price-to-con­sumer in­for­ma­tion and a re­tailer’s own POS data al­lows in­di­vid­ual stores to op­ti­mize pric­ing by lo­ca­tion,” points out Guy Amisano, CEO and founder of Salient Man­age­ment Co., a Horse-heads, N.y.-based soft­ware provider. Re­tail­ers can do this in three ways:

• Find­ing the best price point for a spe­cific prod­uct or an en­tire brand

• Ef­fec­tively of­fer­ing pro­mo­tion

• Track­ing prod­uct flows and un­der­stand­ing prof­itabil­ity “Com­bin­ing the mass amounts of data al­ready at a re­tailer’s fin­ger­tips – from in­voice info to scan records to vendor re­bates – al­lows them to gain a clear pic­ture of prof­itabil­ity by day, as well as dig­ging deeper into per­for­mance of each vendor, de­part­ment or in­di­vid­ual store as a whole,” he says.

By ef­fec­tively lever­ag­ing shop­per data, gro­cers can cus­tom­ize mar­ket­ing ac­tiv­i­ties, pric­ing, prod­uct as­sort­ments and cus­tomer ser­vice in or­der to build con­sumer loy­alty and in­crease rev­enue. — Eileen Kolev Mi­cros­trat­egy

Per­son­al­iz­ing Promotions

Ac­cord­ing to El­liott, the con­sul­tant, there are two ways that Big Data en­ables com­pa­nies to mo­ti­vate their cus­tomers. The first is lo­cal­iza­tion, which al­lows com­pa­nies to tai­lor which prod­ucts are avail­able in which stores and which promotions best ap­peal to the lo­cal shop­per mar­ket.

“The sec­ond approach is per­son­al­iza­tion, which is a step be­yond lo­cal­iza­tion,” he ex­plains. “With this, com­pa­nies move from a seg­ment of many to a seg­ment of one. This can be re­flected in sim­ple tar­geted pric­ing promotions as well as ‘aware­ness’ promotions tar­geted to shop­per in­ter­ests with­out re­quir­ing a price pro­mo­tion to get their at­ten­tion.”

He gives the real-time ex­am­ple of a store send­ing a text to alert a shop­per that an item they have pur­chased a lot pre­vi­ously is cur­rently on clear­ance in a nearby store, or to share a recipe near din­ner­time to spur an in­cre­men­tal trip for the in­gre­di­ents.

Sum­ming up the ben­e­fits, Al­berino, of Grey Jean Tech­nolo­gies, stresses that the big­gest piece of knowl­edge gro­cers can take away from the data they’ve ac­cu­mu­lated is that per­ceived “vol­ume” of data doesn’t mat­ter.

“What you do with your data is much more im­por­tant than how much data you have,” he says. “In or­der to pro­vide the big­gest value for your cus­tomers – and, con­se­quently, gro­cers them­selves – gro­cers need to make those data in­sights ac­tion­able. The key to suc­cess­ful Big Data use is the abil­ity to iden­tify ex­actly which cus­tomer data points will help them un­der­stand in­di­vid­ual buy­ers, what mo­ti­vates them and what drives them to pur­chase – and un­der­stand that this data can and will change from pur­chase to pur­chase.”

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