Inside Franchise Business - - Contents - SAMEER BABBAR CEO, SVB Group

15 rea­sons why a retail fran­chise should ac­quire, store, man­age and track cus­tomer loy­alty data.

Here are 15 rea­sons why a retail fran­chisee should ac­quire, store, man­age

and track cus­tomer loy­alty data.

Aloy­alty pro­gram is a pop­u­lar mar­ket­ing tool for re­tail­ers. So if you’re in­vest­ing in a retail fran­chise it’s im­por­tant to un­der­stand the value of loy­alty card data. Here’s why.


Ac­cord­ing to Bain and Co., a 5 per cent in­crease in cus­tomer retention can in­crease a com­pany’s prof­itabil­ity by 75 per cent.

A loy­alty pro­gram quite sim­ply helps cus­tomers con­tinue to pur­chase from your store and the re­ward points ac­cu­mu­lated can help the cus­tomer ben­e­fit from higher lev­els of ser­vice.

In ex­change, the in­for­ma­tion re­ceived about cus­tomers helps re­tail­ers to meet their needs ef­fi­ciently, ef­fec­tively and en­gag­ingly.

The sym­bi­otic process helps cus­tomers stay cus­tomers. The en­ergy starts build­ing once the cus­tomers start re­deem­ing points.

The data gen­er­ated by loy­alty pro­grams helps to seg­ment cus­tomers for sales, mar­ket­ing and cus­tomer ser­vice. Cus­tomer needs and de­sires vary based on time, lo­ca­tion, oc­ca­sion, des­ti­na­tion and in­ten­tion. All this can be un­der­stood eas­ily through loy­alty data.


Once cus­tomers start re­deem­ing re­wards, word of mouth cre­ates a flow-on ef­fect that brings more in­ter­ested prospects through the door. You need to make sure you have the mech­a­nisms and rea­sons for ex­ist­ing, happy cus­tomers to broad­cast.

Do you need to ex­tend your reach? In ad­di­tion to nur­tur­ing ex­ist­ing cus­tomers, look at up­selling, dis­count­ing or cross sell­ing op­por­tu­ni­ties. An easy way is to iden­tify the de­mo­graphic and psy­cho­graphic look-alike seg­ments of ex­ist­ing best cus­tomers that fit the cri­te­ria. These ex­ist­ing best cus­tomers should be pro­gres­sively tar­geted as new prospects.


Think video game re­wards – as cus­tomers move up the loy­alty tiers they un­lock a new set of re­wards. Their abil­ity to ac­cess greater ben­e­fits in turn helps cre­ate a more de­fined pro­file for the busi­ness.

This is of­ten linked to in­creased spend­ing or spend­ing on par­tic­u­lar goods or ser­vices within a time­frame. This could also be lo­ca­tion spe­cific while you are build­ing traf­fic to a new lo­ca­tion that has just opened up.


These are the cus­tomers you don’t want or need. These cus­tomers are un­prof­itable, waste your time, com­plain with­out rea­son and cre­ate a bad brand im­age. You need to re­tire them im­me­di­ately (do it now). Los­ing them will cre­ate room within your busi­ness to go out and ac­quire new cus­tomers. Some call it cherry pick­ing; we call it well seg­mented. You may very well send them to your com­pe­ti­tion.

Your loy­alty pro­gram should re­ward good cus­tomers and not bad ones. You can use a scor­ing cri­te­rion to mea­sure the de­gree of bad ex­pe­ri­ences with the cus­tomers in con­junc­tion with po­ten­tial life­time value.

Your loy­alty data should eas­ily in­di­cate if the life­time value and sat­is­fac­tion lev­els are ris­ing for that par­tic­u­lar cus­tomer. Should both be go­ing down for rea­sons beyond your busi­ness, then con­sider re­tir­ing them (this is very sim­plis­tic; how­ever, real anal­y­sis may need to dig deeper).

Philip Kotler’s adap­ta­tion of the Pareto Prin­ci­ple sug­gests that the top 20 per cent of cus­tomers gen­er­ate 80 per cent of the prof­its, while the bot­tom 30 per cent of cus­tomers eat up 50 per cent of the prof­its that the oth­ers pro­duce. This is a good rea­son why you should aim to elim­i­nate, sack or fire dud cus­tomers.


It is eas­ier to con­tact pre­vi­ous cus­tomers who have not used your busi­ness for a while than it is to at­tract un­known prospects. Once you have cus­tomer in­for­ma­tion in your data­base it’s easy to get in touch, either us­ing ex­ist­ing com­mu­ni­ca­tions chan­nels or find­ing new plat­forms to en­gage with them – op­tions could be email mes­sages with vouch­ers if postal no­ti­fi­ca­tions haven’t worked at get­ting the cus­tomer back to your store.


As Seth Godin1 would put it. These are cham­pi­ons who pro­mote your of­fer­ings, ex­hibit­ing the high­est form of loy­alty. They in­fect oth­ers with their pas­sion for your prod­ucts or ser­vices. A mea­sure net pro­moter score2 is some­thing you should look for if you want to mea­sure the will­ing­ness of your ex­ist­ing cus­tomers in rec­om­mend­ing prod­ucts and ser­vices to new prospects.

These sneezers would in­fect oth­ers; in other words they are so pas­sion­ate about the prod­ucts or ser­vices they re­ceived from you, they will tell oth­ers about your prod­uct. A clas­sic ex­am­ple is Amway, which uses mul­ti­level mar­ket­ing and this mech­a­nism to gar­ner new busi­ness.


Poor site se­lec­tion can be a sig­nif­i­cant drain on in­fra­struc­ture, re­sources and the mojo of the com­pany.

Se­lect­ing a new store lo­ca­tion can be done eas­ily by loy­alty card data. It en­ables you to find the pro­file and de­mo­graph­ics of ex­ist­ing best cus­tomers, and then find the de­mo­graphic and psy­cho­graphic looka­like for new lo­ca­tions.

Ad­di­tion­ally, if the ad­dresses of ex­ist­ing cus­tomers are known they can be plot­ted ge­o­graph­i­cally and a new lo­ca­tion can be iden­ti­fied where there are large num­bers not served by ex­ist­ing stores.


If a rea­son­able num­ber of your best cus­tomers are will­ing to buy a prod­uct at a price then re­duc­ing the price fur­ther sim­ply sug­gests you are at­tract­ing oc­ca­sional cherry pick­ers. These may not be en­trenched enough to give you an on­go­ing rev­enue stream. With loy­alty card data it is easy to find es­tab­lished cus­tomers and their will­ing­ness to pay3 based on past pur­chases or dis­counts. This in­for­ma­tion can be drilled down to cus­tomer seg­ments, and the most prof­itable pric­ing for a prod­uct or ser­vice can be set.


Us­ing the loy­alty data it is easy to link pur­chases to cus­tomers and you can then iden­tify the cus­tomers that are likely to move to new com­pe­ti­tion. They can be lured back by pro­vid­ing cus­tomer-spe­cific spe­cial of­fers or by di­rect out­reach.

Us­ing loy­alty data, it is easy to dif­fer­en­ti­ate be­tween reg­u­lar shop­pers and oth­ers and in­cen­tivise reg­u­lar shop­pers via mail or elec­tron­i­cally when a new com­pe­ti­tion opens up and starts op­er­at­ing in the area.


In the sim­plest terms the profit from each cus­tomer should be more than the cost of ac­quir­ing them. It is the cal­cu­la­tion of net profit from a cus­tomer dur­ing the en­tirety of re­la­tion­ship.

In math­e­mat­i­cal terms it is the net present value of pro­jected fu­ture cash flows from busi­ness from a given cus­tomer. Re­tain­ing cus­tomers and get­ting them to keep com­ing back is part of the game.

As an ex­am­ple, if a busi­ness loses 30 per cent of its cus­tomers each month and does not ac­quire any new cus­tomers, in five months they will have no cus­tomers.

An en­hanced cus­tomer life­time value (or CLV) will in­crease the value of busi­ness.


This con­cept sim­ply sug­gests iden­ti­fy­ing the best cus­tomers and then spend­ing en­ergy, time, money and re­sources on them to max­imise re­turn on in­vest­ment. You can then look at mov­ing the cus­tomers who are not best but not far from the best cus­tomer cri­te­ria into the best cus­tomer pool or stop serv­ing them. This might sound like a ruth­less ap­proach, how­ever it pays to serve the best cus­tomers and make room to in­vite other best cus­tomers in.


Keep the stock in line with what the best cus­tomers buy fre­quently and ex­pand on those lines. When you align the stock to the most prof­itable cus­tomers, the en­tire store be­comes more ap­peal­ing to your best cus­tomers and prospects who are sim­i­lar to your best cus­tomers. It is a slow process but is a com­bi­na­tion of invit­ing cus­tomer “looka­like”. Self-se­lec­tion and ex­clu­sion of cus­tomers who don’t fit your best cus­tomer cat­e­gory oc­curs by us­ing this process.

In terms of stock­ing, you can grad­u­ally re­move the lines that your best cus­tomers don’t pre­fer and add the lines they do, and grad­u­ally your busi­ness will be shaped in line with the mar­ket you wish to serve.

Take an ex­am­ple of a Louis Vuit­ton shop, with se­cu­rity at the en­trance and a queue that only al­lows a cer­tain num­ber of prospects in the shop. They make you feel elated and priv­i­leged to en­ter the busi­ness premises. This works as a fil­ter to keep out those who are un­likely to buy, but the sense of priv­i­lege boosts the will­ing­ness to pay for those who may be sit­ting on the fence.


Most suc­cess­ful busi­nesses re­duce the fric­tion be­tween the cus­tomer and trans­ac­tion, ir­re­spec­tive of type of busi­ness (on­line or off­line). The re­duc­tion in fric­tion sim­pli­fies the cus­tomer jour­ney, feed­ing the en­ergy back into re­la­tion­ship build­ing and in­creased bot­tom line prof­its. If the cus­tomer tries to move to an­other provider, the in­creased fric­tion of trans­ac­tion else­where will bring them back to you.

You would have come across the say­ing “It’s who you know” – the pur­pose here is to re­duce the trans­ac­tional fric­tion by deal­ing with some­one you may al­ready know.

That said, you need to choose who you want to build re­la­tion­ships with. At­tempt­ing to part­ner with all cus­tomers, re­gard­less of their char­ac­ter­is­tics, might not al­ways be the best way for­ward as you may end up pick­ing dead weight.


When you run a bas­ket anal­y­sis for a set of cus­tomers you can iden­tify which lines or prod­ucts are bought at the same time, and in par­tic­u­lar which are bought by your best cus­tomers.

Planograms (place­ment of prod­ucts in 3D) can then be planned ac­cord­ingly to en­cour­age cross-pur­chas­ing. Bas­ket anal­y­sis with­out any loy­alty pro­gram is suf­fi­cient for this pur­pose.

How­ever, once you in­clude de­tailed knowl­edge of who the cus­tomer is, their spend, where they live, work or travel, then you can de­cide whether it is worth putting a dis­play of items that are bought to­gether by a spe­cific seg­ment of the mar­ket on a spe­cific day of the week.


Mov­ing to tar­geted ad­ver­tis­ing. In­stead of send­ing out thou­sands of fly­ers of which a sig­nif­i­cant chunk is thrown away, or news­pa­per added that peo­ple skip as ir­rel­e­vant, tar­geted ad­ver­tis­ing reaches out to in­di­vid­ual cus­tomers pro­vid­ing a tai­lor­made of­fer.

This can be done via email based on past sales. The more so­phis­ti­cated type of loy­alty pro­gram can tar­get ad­ver­tis­ing ma­te­rial al­most in­di­vid­u­ally to its many mil­lions of mem­bers and can ac­cu­rately mea­sure the re­sponse rates to those ad­ver­tise­ments. UK su­per­mar­ket Tesco does this. Its Clubcard mag­a­zine, packed with tar­geted money-off coupons, is mailed to ap­prox­i­mately 13 mil­lion cus­tomers four times a year. Not only does this form of ad­ver­tis­ing save Tesco money, it earns it money.

Should your cus­tomers be also on­line you can use tools from com­pa­nies like Dig­i­tal En­voy4 to tai­lor your en­tire web­site based on cus­tomer’s pref­er­ences and past pur­chase; fur­ther you can use their IP ad­dress to iden­tify their lo­ca­tion or shop­ping be­hav­iours.


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