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DATA & TARGETING 2017: CRAWL, WALK, THEN RUN

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EXECUTIVE SUMMARYDigital’s promise of unified data and one-to-one marketing always seems to be just out of reach for brands. There is always the perceived need for one more site relaunch, one more CRM system, or one more vendor.

In reality, very few brands effectively deploy data on a consistent basis, and brands may never achieve the perfect system for storing and leveraging customer data. Even brands that are considered relative leaders in data and targeting, like Nike, acknowledge the difficulty of consolidating cross-channel data to personalize marketing in new campaigns. But brands cannot let perfect be the enemy of good. Rather than throwing money at bloated data and targeting operations, brands should focus on incrementally improving data collection and deployment mechanisms.

The third annual L2 Intelligence Report: Data & Targeting evaluates the longitudinal performance of 107 consumer brands across seven verticals. Our aim is to provide data, best practices, and case studies to help brands achieve greater return on investment.

Key Questions• What are the most valuable customer data points brands can collect

across digital touchpoints?

• How are brands using data, both collected and implicitly observed, across channels?

• Which digital channels merit brand investment in personalization?

• How have loyalty programs changed recent data collection and personalization efforts?

Key Recommendations• Don’t skip the basics: Customer data explicitly collected through

account signup forms or newsletter opt-ins is low-hanging fruit, but implicit user behavior data gathered as customers navigate a site is a powerful asset. Brands that collect and use implicit data should notify customers that their information is being stored to personalize marketing communications.

• Integration is critical: A given set of customer data has a tendency to live in the channel against which it was collected, but true data-driven marketing adds value when data sets from disparate channels are consolidated.

• Content models must keep pace: The best data ecosystems fall flat if unsupported by relevant, cost effective content.

• Technology is a means, not an end: Third-party analytics and retargeting vendors are often necessary, but over-reliance can lead to data leakage and the erosion of consumer trust. Choose partners judiciously and optimize your customer data sets for the best possible results.

Real-time personalization itself presents a hierarchy of risks. At the high end, decisions about which offers or prices to present to which customers based on segmentation can have legal and ethical repercussions, particularly in regulated industries. Even unregulated industries must abide by antidiscrimination laws, which vary by state in the U.S. and by country.

An AI algorithm can predict a surprising number of personal details — including membership in a legally protected class — from a remarkably limited number of clues. Figure 3 illustrates this point with data from a 2013 study that demonstrated how Facebook Likes could predict a range of sensitive personal attributes, including sexual orientation, race, and religious and political views.2

TARG

ETED

MAR

KETI

NG

DATA CAPTURE & PERSONALIZATION

12% STREAMLINERS

53% LAGGARDS

11% LEADERS

24% DATA MINERS

Data & Targeting: Data Capture, Personalization, and Targeted Marketing Performance

November 2017, n = 107 Brands

Source: L2 Intelligence Report: Data & Targeting, December 2017





https://www.l2inc.com/data-tools/data-targeting-2017


ACTIVEWEARadidasAthletaL.L.Beanlululemon athleticaNew BalanceNikeOakleyPatagoniaREIThe North FaceUnder ArmourVans

FASHIONBurberryCalvin KleinCoachDiane von FurstenbergGucciHugo BossKate SpadeLouis VuittonMarc JacobsMichael KorsPradaRalph LaurenRebecca MinkoffTommy HilfigerTory Burch

BEAUTYbareMineralsBenefit CosmeticsBobbi BrownChanelCharlotte TilburyCliniqueCoverGirlDiorEstée LauderGarnierKiehl’sL’Oréal ParisLancomeMAC CosmeticsMaybellineNARSNeutrogenaNYXOlayProActivSmashboxTarteToo FacedUrban DecayYSL Beauty

SPECIALTY RETAILAbercrombie & FitchAeropostaleAmerican Eagle OutfittersAnn TaylorAnthropologieCrate & BarrelDesigualExpressForever 21GapGuessH&MIkeaJ. CrewPier 1 ImportsPottery BarnSephoraUltaUNIQLOUrban OutfittersVictoria’s SecretWarby ParkerWest ElmWilliams-SonomaZara

BIG BOXBed Bath & BeyondBest BuyCVS

BIG BOX, continuedThe Home DepotLowe’sPetSmartRite AidStaplesTargetWalgreensWalmart

TRAVELFairmont HotelsHilton Hotels & ResortsHyatt HotelsIntercontinental Hotels & ResortsMandarin OrientalMarriott Hotels & ResortsPeninsula HotelsSofitel HotelsStarwood Hotels & Resorts

DEPARTMENT STORESAmazonBarneys New YorkBergdorf GoodmanBloomingdale’sKohl’sMacy’sNeiman MarcusNordstromSaks Fifth AvenueTJ Maxx

BRAND LIST






MethodologyThe third annual L2 Intelligence Report: Data & Targeting evaluates the performance of 107 consumer brands across seven sectors: activewear, beauty, big box, department stores, fashion, specialty retail, and travel. The methodology includes a comprehensive review of brand desktop and mobile sites in terms of data capture, personalization, and loyalty features and capabilities. Targeted communications, such as personalized site content, emails, programmatic retargeting, and text messages, were also analyzed for consistency and effectiveness. Lastly, L2 tracked third-party tags on brand sites to identify adoption of technology vendors.

DATA CAPTURENewsletter:SignupSignup IncentiveDemographic InformationAdditional Data Request

Account:SignupSignup IncentiveBenefits ListedEmail SubscriptionApp IntegrationPhone NumberGenderBirthdayAddress or Zip CodeHousehold Information

Checkout:Check Out as GuestPhone NumberSMS UpdatesGenderBirthdaySave Credit Card DetailsEmail Preferences

Customer Service:Email AddressPhone NumberProblem Categorization

PERSONALIZATIONCommunications:Email FrequencyEmail ContentFederated LoginSMS AlertsFurther CustomizationFavorite/Nearest StorePersistent Cart

Product Detail Pages:Personalization Product LookCross-Selling: Recent ViewsCross-Selling: Most PopularCross-Selling: Also Looked AtCross-Selling: May Also LikeGeolocation for InventoryGeolocation for Shipping

Tools & Services:Guided Selling ToolsSaved ResultsLive Chat

LOYALTY & TARGETINGLoyalty Program RegistrationCoalition Loyalty ProgramEmail SegmentationThird-Party TagsDisplay Spend






December 7, 2017 6

INTELLIGENCE REPORT DATA & TARGETING 2017

EXCERPTINTELLIGENCE

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METHODOLOGY

Data & Targeting LandscapeL2 developed a rubric to score brands on two axes in terms of data and targeting sophistication:

data capture on brand site to achieve personalization and targeted marketing across the web.

Brands were grouped into four archetypes: Leaders, Streamliners, Data Miners, and Laggards.

There were no clear vertical associations by archetype, but differentiating factors tend to include

brand age, digital sophistication, and adoption of digital as core to growth.

Leaders are brands that effectively collect and leverage data both on brand site and in

targeted digital marketing. Leaders adopt advanced site features that add value to the overall

experience like guided selling tools and account customization options. They also maintain

effective partnerships with third-party vendors for programmatic retargeting, email campaign

management, and site performance analytics to process and utilize consumer data.

Streamliners collect less information than the average brand but effectively use what they

do collect to segment and market to consumers. These brands are typified by efficient account

signup processes and adept use of implicit, customer browsing data. However, a choice of

streamlined data collection forgoes the possibility of more intricate segmentation.

Data Miners collect a ton of data in their interactions with customers but fail to take

advantage of it by implementing an effective targeting strategy. This is usually indicative of brands

that made an initial investment in site data harvesting, but failed to build an infrastructure that

allows them to use that data to target customers across the web.

Laggards do not collect or use customer data effectively, missing an opportunity to put

forward engaging and personalized content.

LEADERS STREAMLINERS DATA MINERS

December 7, 2017 6


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METHODOLOGY






















December 7, 2017 6


EXCERPTINTELLIGENCE

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METHODOLOGY






















Data & Targeting LandscapeL2 developed a rubric to score brands on two axes in terms of data and targeting sophistication: data capture on brand site to achieve personalization and targeted marketing across the web. Brands were grouped into four archetypes: Leaders, Streamliners, Data Miners, and Laggards. There were no clear vertical associations by archetype, but differentiating factors tend to include brand age, digital sophistication, and adoption of digital as core to growth.

Leaders are brands that effectively collect and leverage data both on brand site and in targeted digital marketing. Leaders adopt advanced site features that add value to the overall experience like guided selling tools and account customization options. They also maintain effective partnerships with third-party vendors for programmatic retargeting, email campaign management, and site performance analytics to process and utilize consumer data.

Streamliners collect less information than the average brand but effectively use what they do collect to segment and market to consumers. These brands are typified by efficient account signup processes and adept use of implicit, customer browsing data. However, a choice of streamlined data collection forgoes the possibility of more intricate segmentation.

Data Miners collect a ton of data in their interactions with customers but fail to take advantage of it by implementing an effective targeting strategy. This is usually indicative of brands that made an initial investment in site data harvesting, but failed to build an infrastructure that allows them to use that data to target customers across the web.

Laggards do not collect or use customer data effectively, missing an opportunity to put forward engaging and personalized content.

Leaders Streamliners Data Miners






Punch the MonkeyLong proclaimed as a harbinger of a new era of hyperpersonalized, targeted, and effective marketing, digital continues to struggle to realize this potential. In exchange for offering up data, customers deserve relevant and personalized content both in site experiences and ads viewed across the web. However, digital media is increasingly riddled with irrelevant and abrasive promotional content, such that consumers have turned to tools like ad blockers that streamline browsing.

Over 75 million US internet users will use ad blockers in 2017—slightly more than a quarter of the total US internet population.1 To be clear, consumers are not rejecting advertising per se, just irrelevant ads: 83 percent of internet users agree with the statement: “Not all ads are bad, but I want to filter out the really obnoxious ones,” while 77 percent agree with: “I wish there were a way to ad-filter instead of ad-block completely.”2 Spray and pray tactics have become a liability for brands, as consumers tune out irrelevant offers.

Brands’ only option to be heard in this increasingly cluttered landscape is to focus on providing consistent, targeted, and relevant content and messaging. Fifty-two percent of consumers say they are likely to switch brands if a brand does not make an effort to personalize communications, and 65 percent say that personalization influences their loyalty to a brand.3

The rewards for leader brands are self-evident: 63 percent of millennial consumers agree they are willing to share data with companies that send personalized offers and discounts.4 Building personalized experiences for customers fuels a positive feedback loop that benefits product innovation, marketing, and, therefore consumers themselves.

1. “Ad blockers still make up more than one-quarter of internet users,” eMarketer, February 15, 2017.

2. “Why People Block Ads (And What It Means for Marketers and Advertisers),” Mimi An, HubSpot Research, July 13, 2016.

3. “2016 State of the Connected Consumer,” Devon McGinnis, Salesforce, October 24, 2016.

4. Ibid.

December 7, 2017 7


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DATA CAPTURE PERSONALIZATIONKEY TRENDS LOYALTY EMAIL CHECKLIST

Punch The MonkeyLong proclaimed as a harbinger of a new era of hyper-

personalized, targeted, and effective marketing, digital continues

to struggle to realize this potential. In exchange for offering up

data, customers deserve relevant and personalized content both

in site experiences and ads viewed across the web. However,

digital media is increasingly riddled with irrelevant and abrasive

promotional content, such that consumers have turned to tools

like ad blockers that streamline browsing.

Over 75 million US internet users will use ad blockers in

2017—slightly more than a quarter of the total US internet

population.1 To be clear, consumers are not rejecting advertising

per se, just irrelevant ads: 83 percent of internet users agree with

the statement: “Not all ads are bad, but I want to filter out the

really obnoxious ones,” while 77 percent agree with: “I wish there

were a way to ad-filter instead of ad-block completely.”2 Spray and

pray tactics have become a liability for brands, as consumers tune

out irrelevant offers.

Brands’ only option to be heard in this increasingly cluttered

landscape is to focus on providing consistent, targeted, and

relevant content and messaging. Fifty-two percent of consumers

say they are likely to switch brands if a brand does not make an

effort to personalize communications, and 65 percent say that

personalization influences their loyalty to a brand.3

The rewards for leader brands are self-evident: 63 percent

of millennial consumers agree they are willing to share data with

companies that send personalized offers and discounts.4 Building

personalized experiences for customers fuels a positive feedback

loop that benefits product innovation, marketing, and, therefore

consumers themselves.




4. Ibid.

Data & Targeting: Adoption of Ad Blockers by US Internet Users 2014–2018E n Computers n Smartphones

Data & Targeting: Share of Respondants Likely to Switch Brands Under Following Conditions… October 2016, n=7,037 Respondants

US IN

TERN

ET U

SERS

2014 2015 2016 2017 2018E0%

25%

20%

15%

10%

5%

DIFFICULT PURCHASINGOR CHECKOUT PROCESS

NO EFFORT TOPERSONALIZE COMMUNICATIONS

DOES NOT ANTICIPATE NEEDS

POOR MOBILE EXPERIENCE

74%

50%

50%

52%

Source: eMarketer data.

Source: “2016 State of the Connected Consumer,” Salesforce, October 2016.

December 7, 2017 7


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Punch The MonkeyLong proclaimed as a harbinger of a new era of hyper-

personalized, targeted, and effective marketing, digital continues

to struggle to realize this potential. In exchange for offering up

data, customers deserve relevant and personalized content both

in site experiences and ads viewed across the web. However,

digital media is increasingly riddled with irrelevant and abrasive

promotional content, such that consumers have turned to tools

like ad blockers that streamline browsing.

Over 75 million US internet users will use ad blockers in

2017—slightly more than a quarter of the total US internet

population.1 To be clear, consumers are not rejecting advertising

per se, just irrelevant ads: 83 percent of internet users agree with

the statement: “Not all ads are bad, but I want to filter out the

really obnoxious ones,” while 77 percent agree with: “I wish there

were a way to ad-filter instead of ad-block completely.”2 Spray and

pray tactics have become a liability for brands, as consumers tune

out irrelevant offers.

Brands’ only option to be heard in this increasingly cluttered

landscape is to focus on providing consistent, targeted, and

relevant content and messaging. Fifty-two percent of consumers

say they are likely to switch brands if a brand does not make an

effort to personalize communications, and 65 percent say that

personalization influences their loyalty to a brand.3

The rewards for leader brands are self-evident: 63 percent

of millennial consumers agree they are willing to share data with

companies that send personalized offers and discounts.4 Building

personalized experiences for customers fuels a positive feedback

loop that benefits product innovation, marketing, and, therefore

consumers themselves.




4. Ibid.

Data & Targeting: Adoption of Ad Blockers by US Internet Users 2014–2018E n Computers n Smartphones

Data & Targeting: Share of Respondants Likely to Switch Brands Under Following Conditions… October 2016, n=7,037 Respondants

US IN

TERN

ET U

SERS

2014 2015 2016 2017 2018E0%

25%

20%

15%

10%

5%

DIFFICULT PURCHASINGOR CHECKOUT PROCESS

NO EFFORT TOPERSONALIZE COMMUNICATIONS

DOES NOT ANTICIPATE NEEDS

POOR MOBILE EXPERIENCE

74%

50%

50%

52%

Source: eMarketer data.

Source: “2016 State of the Connected Consumer,” Salesforce, October 2016.

Data & Targeting: Adoption of Ad Blockers by US Internet Users

Data & Targeting: Share of Respondants Likely to Switch Brands Under Following Conditions…

Source: eMarketer data

October 2016; n = 7,037 respondentsSource: “2016 State of the Connected Consumer,” Salesforce, October 2016






December 7, 2017 8


EXCERPTINTELLIGENCE

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Risk Versus RewardIn order to market relevant content, brands first have to

capture enough data about the customer to meaningfully

target them. Brands often request data points like

age, gender, and zip code on owned properties during

newsletter and account signup, account customization,

and guest checkout. This data has two main benefits—

at the surface, it helps build customer profiles, but at a

deeper level, it supports downstream marketing efforts.

Account signup marks one of the first touchpoints

where brands can extract multiple data points from a

customer. Analyzed brands collect an average of four data

points during account signup—three mandatory and one

optional (in line with last year’s figures).

But beyond name and email, brands are left with just

two remaining facts to gather about the customer. Brands

generally need to balance the risk of account dropoff from

requiring too much information against the risk of having

limited customer intelligence from requiring too little.

Tommy Hilfiger and Desigual illustrate two different

collection tactics. Tommy Hilfiger asks customers to

complete just two mandatory fields and eight optional

fields during account signup, however, the brand offers

nothing in exchange for the optional data. This gives

the customer no incentive to fill-in the optional fields,

guaranteeing the brand only a name and email to

segment the account holder.

Desigual requires all five of its data points, including

gender, birthday, and age. But the brand offers a 10

percent discount as a clear value proposition to encourage

signup, counteracting drop-off risk. Although 63 percent

of analyzed brands list account benefits at signup, just

11 percent advertise a signup incentive, demonstrating

the limited creativity of brand account signup tactics.

Data & Targeting: Number of Optional vs. Mandatory Account Signup Data Points* November 2017, n=105 Brands With Site Account Number of Brands

OPTI

ONAL

DAT

A PO

INTS

MANDATORY DATA POINTS120 2 31 4 5 6 7 8 9 10 11

0

14

12

10

8

6

4

2

INCENTIVE RISK FORM FATIGUE

DROP-OFF RISKLIMITED CAPTURE

Tommy Hilfiger asks customers to complete 4 mandatory and 8 optional fields at account signup.

With only mandatory signup fields, Desigual relies on a 10 percent discount—a clear value proposition—to incentivize account signup.

*Password Selection Excluded Source: L2 Intelligence Report: Data & Targeting, December 2017.

Risk Versus RewardIn order to market relevant content, brands first have to capture enough data about the customer to meaningfully target them. Brands often request data points like age, gender, and zip code on owned properties during newsletter and account signup, account customization, and guest checkout. This data has two main benefits— at the surface, it helps build customer profiles, but at a deeper level, it supports downstream marketing efforts.

Account signup marks one of the first touchpoints where brands can extract multiple data points from a customer. Analyzed brands collect an average of four data points during account signup—three mandatory and one optional (in line with last year’s figures).

But beyond name and email, brands are left with just two remaining facts to gather about the customer. Brands generally need to balance the risk of account dropoff from requiring too much information against the risk of having limited customer intelligence from requiring too little.

Tommy Hilfiger and Desigual illustrate two different collection tactics. Tommy Hilfiger asks customers to complete just two mandatory fields and eight optional fields during account signup, however, the brand offers nothing in exchange for the optional data. This gives the customer no incentive to fill-in the optional fields, guaranteeing the brand only a name and email to segment the account holder.

Desigual requires all five of its data points, including gender, birthday, and age. But the brand offers a 10 percent discount as a clear value proposition to encourage signup, counteracting drop-off risk. Although 63 percent of analyzed brands list account benefits at signup, just 11 percent advertise a signup incentive, demonstrating the limited creativity of brand account signup tactics.

December 7, 2017 8


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OPTI

ONAL

DAT

A PO

INTS


0

14

12

10

8

6

4

2






December 7, 2017 8


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OPTI

ONAL

DAT

A PO

INTS


0

14

12

10

8

6

4

2






Data & Targeting: Number of Optional vs. Mandatory Account Signup Data Points*

Tommy Hilfiger Desigual

*Password Selection excludedSource: L2 Intelligence Report: Data & Targeting, December 2017


With only mandatory signup fields, Desigual relies on a 10percent discount—a clear value proposition—to incentivizeaccount signup.






Features & GapsBrands have adopted generic but well curated content as a workaround to achieving personalization via substantial data analysis and targeting. This mass personalization approach often takes the form of standard cross-selling—displaying recommended and complementary products on PDPs. However, brands need to strive toward making recommendations based on customer browsing data, such as displaying recently viewed products.

Slightly over a third of analyzed brands display recently viewed products on the homepage or PDPs, such that the majority of brands fail to keep products of interest top-of-mind. Additionally, guided selling features that mimic the in-store experience of consulting with a sales associate, such as live chat or diagnostic tools, lag in adoption. Only 48 percent of brands maintain live chat on their site, while merely 34 percent have a diagnostic tool to help customers find products. Brands that continue to rely on generic product recommendations will soon play second fiddle to brands investing in personalization, as the quality of e-commerce experiences becomes a differentiator.

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Features & GapsBrands have adopted generic but well curated content as a

workaround to achieving personalization via substantial data

analysis and targeting. This mass personalization approach

often takes the form of standard cross-selling—displaying

recommended and complementary products on PDPs.

However, brands need to strive toward making recommendations

based on customer browsing data, such as displaying recently

viewed products.

Slightly over a third of analyzed brands display recently viewed

products on the homepage or PDPs, such that the majority of

brands fail to keep products of interest top-of-mind. Additionally,

guided selling features that mimic the in-store experience of

consulting with a sales associate, such as live chat or diagnostic

tools, lag in adoption. Only 48 percent of brands maintain live

chat on their site, while merely 34 percent have a diagnostic tool

to help customers find products. Brands that continue to rely on

generic product recommendations will soon play second fiddle to

brands investing in personalization, as the quality of e-commerce

experiences becomes a differentiator.

Data & Targeting: Adoption of Personalized Guided Selling Features November 2017, n=107 Brands

Source: L2 Intelligence Report: Data & Targeting, December 2017.

COMPLEMENTARYPRODUCTS

RECOMMENDEDPRODUCTS

49%

60%

DIAGNOSTIC TOOL

34%

LIVE CHAT

48%

RECENTLY VIEWEDPRODUCTS

37%

MASS PERSONALIZATION SESSION-BASED RECOMMENDATIONS

Data & Targeting: Adoption of Personalized Guided Selling Features

November 2017, n = 107 brandsSource: L2 Intelligence Report: Data & Targeting, December 2017






Brands Double Down on LoyaltyLoyalty programs are a mainstay of data-focused brands that seek to collect additional information about shoppers and increase customer engagement. L2 found that 48 percent of brands had a loyalty program as of November 2017, up from 40 percent among the same 90 brands in August 2017. Eight brands added or relaunched a loyalty program, while Diane von Furstenberg shuttered its DVF Insider program.

Abercrombie & Fitch, L’Oréal Paris, Clinique, and Smashbox all relaunched their programs after closing them temporarily. Lowe’s and NYX added programs for professional contractors and makeup artists, respectively. Bed Bath & Beyond launched a beta loyalty program for a $29 per year membership that includes benefits like free shipping and returns on all purchases. Desigual launched a new, tiered program in Europe and is expected to expand it to the US in 2018.

The number of brands launching loyalty programs last year makes it seem easy. Getting users to engage is more difficult. Over the past three years, the number of loyalty memberships per consumer has increased, but the percentage of active memberships has decreased from a high of 72 percent in 2014 to 47 percent in 2017.1 In one success story, Sephora’s Beauty Insider loyalty program has garnered over 10 million members and appears organically on first-page results for 47 percent of Google searches for prestige beauty loyalty programs (e.g. “estee lauder loyalty program”).2

Perhaps the most important factor for user engagement is a loyalty program’s value proposition—what issue will a company’s loyalty program address for both the consumer and the brand? Every loyalty investment should facilitate a mutually beneficial value exchange between the brand and the consumer. When

the value exchange is skewed toward the brand, consumers have little incentive to sign up; when it is skewed toward the consumer, brands bear the burden of maintaining expensive loyalty programs, which cut away at return on investment.

1. “The Loyalty Report,” Bond Brand Loyalty & Visa, 2014-2017.

2. “Sephora Beauty Insider loyalty program,” Loyalty Lion, January 5, 2017.

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Brands Double Down on LoyaltyLoyalty programs are a mainstay of data-focused brands that seek

to collect additional information about shoppers and increase customer

engagement. L2 found that 48 percent of brands had a loyalty program

as of November 2017, up from 40 percent among the same 90 brands

in August 2017. Eight brands added or relaunched a loyalty program,

while Diane von Furstenberg shuttered its DVF Insider program.

Abercrombie & Fitch, L’Oréal Paris, Clinique, and Smashbox all

relaunched their programs after closing them temporarily. Lowe’s and

NYX added programs for professional contractors and makeup artists,

respectively. Bed Bath & Beyond launched a beta loyalty program for

a $29 per year membership that includes benefits like free shipping

and returns on all purchases. Desigual launched a new, tiered program

in Europe and is expected to expand it to the US in 2018.

The number of brands launching loyalty programs last year makes it

seem easy. Getting users to engage is more difficult. Over the past three

years, the number of loyalty memberships per consumer has increased,

but the percentage of active memberships has decreased from a high

of 72 percent in 2014 to 47 percent in 2017.1 In one success story,

Sephora’s Beauty Insider loyalty program has garnered over 10 million

members and appears organically on first-page results for 47 percent

of Google searches for prestige beauty loyalty programs (e.g. “estee

lauder loyalty program”).2

Perhaps the most important factor for user engagement is a loyalty

program’s value proposition—what issue will a company’s loyalty program

address for both the consumer and the brand? Every loyalty investment

should facilitate a mutually beneficial value exchange between the brand

and the consumer. When the value exchange is skewed toward the

brand, consumers have little incentive to sign up; when it is skewed

toward the consumer, brands bear the burden of maintaining expensive

loyalty programs, which cut away at return on investment.

1. “The Loyalty Report,” Bond Brand Loyalty & Visa, 2014-2017.2. “Sephora Beauty Insider loyalty program,” Loyalty Lion, January 5, 2017.

Data & Targeting: Year-Over-Year Loyalty Program Adoption August 2016 vs. November 2017, n=90 Brands


NEW & RELAUNCHED PROGRAMS CLOSED PROGRAMS

LOYALTY ADOPTION

2016 2017

40%ADOPTION

60%NO ADOPTION

52%NO ADOPTION

48%ADOPTION

Data & Targeting: Year-Over-Year Loyalty Program Adoption

August 2016 vs. November 2017, n = 90 brandsSource: L2 Intelligence Report: Data & Targeting, December 2017.






Targeting RulesEmail targeting and personalization, whether based on collected or implicit data, works. Subject and content analysis of a year’s worth of email data from eight big box and department store brands reveals that open rates for targeted emails, with subject lines or content that depicts account information or browsing history, were around 42 percent higher than open rates for generic emails. Targeted emails benefited Nordstrom most, with a 92 percent open rate lift, followed by Saks Fifth Avenue at 82 percent and Bergdorf Goodman at 76 percent.1

Kohl’s is the only analyzed brand that did not see a large increase in open rates due to targeting, but it was a Kohl’s email campaign that experienced the highest open rate during the analyzed time period. The email was a welcome message for new Kohl’s Yes2You loyalty program members notifying them of a 100 point bonus for signing up worth $5 in Kohl’s Cash at the end of the month. The campaign checks many of the boxes of high performers—highly segmented, mobile friendly, re-engages customers, and offers rewards—but is not truly personalized as it does not contain any customer info, such as first name or area of residence. The best-performing targeted campaign came from Saks Fifth Avenue, which provided a tailored discount on select products based on user browsing history.

1. L2 analysis of eDataSource data.

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DATA CAPTURE PERSONALIZATION EMAILKEY TRENDS LOYALTY CHECKLIST

Targeting RulesEmail targeting and personalization, whether based on collected

or implicit data, works. Subject and content analysis of a year’s worth

of email data from eight big box and department store brands reveals

that open rates for targeted emails, with subject lines or content that

depicts account information or browsing history, were around 42

percent higher than open rates for generic emails. Targeted emails

benefited Nordstrom most, with a 92 percent open rate lift, followed

by Saks Fifth Avenue at 82 percent and Bergdorf Goodman at

76 percent.1

Kohl’s is the only analyzed brand that did not see a large increase

in open rates due to targeting, but it was a Kohl’s email campaign

that experienced the highest open rate during the analyzed time

period. The email was a welcome message for new Kohl’s Yes2You

loyalty program members notifying them of a 100 point bonus for

signing up worth $5 in Kohl’s Cash at the end of the month. The

campaign checks many of the boxes of high performers—highly

segmented, mobile friendly, re-engages customers, and offers

rewards—but is not truly personalized as it does not contain any

customer info, such as first name or area of residence. The best-

performing targeted campaign came from Saks Fifth Avenue,

which provided a tailored discount on select products based

on user browsing history.


Data & Targeting: Open Rates of Generic vs. Targeted Department Store and Big Box Emails October 2016–2017, n=15,000 Email Campaigns n Generic n Targeted

A Kohl’s email campaign for new members of the Yes2You loyalty program had the highest open rate out of our sample of over 15,000 department store and big box campaigns.

WINNER


BERGDORFGOODMAN

BLOOMINGDALE’S KOHL’S

17% 17% 19%

27%21%

26%19%

25% 25%

48%

17%

31%

18%25%

17%

30%

17%

28%

AVERAGEMACY’S NEIMANMARCUS

NORDSTROM SAKS FIFTHAVENUE

TARGET

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DATA CAPTURE PERSONALIZATION EMAILKEY TRENDS LOYALTY CHECKLIST

Targeting RulesEmail targeting and personalization, whether based on collected

or implicit data, works. Subject and content analysis of a year’s worth

of email data from eight big box and department store brands reveals

that open rates for targeted emails, with subject lines or content that

depicts account information or browsing history, were around 42

percent higher than open rates for generic emails. Targeted emails

benefited Nordstrom most, with a 92 percent open rate lift, followed

by Saks Fifth Avenue at 82 percent and Bergdorf Goodman at

76 percent.1

Kohl’s is the only analyzed brand that did not see a large increase

in open rates due to targeting, but it was a Kohl’s email campaign

that experienced the highest open rate during the analyzed time

period. The email was a welcome message for new Kohl’s Yes2You

loyalty program members notifying them of a 100 point bonus for

signing up worth $5 in Kohl’s Cash at the end of the month. The

campaign checks many of the boxes of high performers—highly

segmented, mobile friendly, re-engages customers, and offers

rewards—but is not truly personalized as it does not contain any

customer info, such as first name or area of residence. The best-

performing targeted campaign came from Saks Fifth Avenue,

which provided a tailored discount on select products based

on user browsing history.


Data & Targeting: Open Rates of Generic vs. Targeted Department Store and Big Box Emails October 2016–2017, n=15,000 Email Campaigns n Generic n Targeted


WINNER


BERGDORFGOODMAN

BLOOMINGDALE’S KOHL’S

17% 17% 19%

27%21%

26%19%

25% 25%

48%

17%

31%

18%25%

17%

30%

17%

28%

AVERAGEMACY’S NEIMANMARCUS

NORDSTROM SAKS FIFTHAVENUE

TARGET

Data & Targeting: Open Rates of Generic vs. Targeted Department Store and Big Box Emails

Winner: Kohl’s

Source: L2 Intelligence Report: Data & Targeting, December 2017







Data & Targeting ChecklistInevitably, the more complex types of personalization brands attempt, the more data, infrastructure, and by extension, cost is required. The first step is to evaluate appetite for becoming a data-driven sales and marketing brand, which includes endorsement from the CMO/CDO and buy-in from teams via skills acquisition, new hires, and new systems. However, there are versions of personalization for all brands and budgets, from segmenting homepages by target market to one-to-one targeting.

• Editorialize: As a baseline, brands must thoughtfully curate site content and provide customers with the ability to select navigation paths based on gender, preferred styles, or events. Doing so offers shoppers the feeling of a personalized experience on a mass scale driven by customer choice as opposed to customer data.

• Integration is critical: Consumer data has a tendency to live in the channel against which it was collected, but true data-driven marketing adds value when disparate data sets are connected across channels.

• Content models must keep pace: The best data ecosystems fall flat if unsupported by relevant, cost effective content that connects with consumers.

• Technology is a means, not an end: Third-party analytics and retargeting vendors are necessary, but over-reliance can lead to data leakage and the erosion of consumer trust. Choose partners judiciously and optimize your customer data sets for the best results possible.


EXCERPTINTELLIGENCE

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CHECKLIST

December 7, 2017 12

DATA CAPTURE PERSONALIZATION LOYALTY EMAILKEY TRENDS

Data & Targeting ChecklistInevitably, the more complex types of personalization brands

attempt, the more data, infrastructure, and by extension, cost

is required. The first step is to evaluate appetite for becoming

a data-driven sales and marketing brand, which includes

endorsement from the CMO/CDO and buy-in from teams

via skills acquisition, new hires, and new systems. However,

there are versions of personalization for all brands and

budgets, from segmenting homepages by target market

to one-to-one targeting.

1. Editorialize: As a baseline, brands must thoughtfully

curate site content and provide customers with the

ability to select navigation paths based on gender,

preferred styles, or events. Doing so offers shoppers

the feeling of a personalized experience on a mass

scale driven by customer choice as opposed

to customer data.

2. Integration is critical: Consumer data has a tendency

to live in the channel against which it was collected, but

true data-driven marketing adds value when disparate

data sets are connected across channels.

3. Content models must keep pace: the best data

ecosystems fall flat if unsupported by relevant,

cost effective content that connects with consumers.

4. Technology is a means, not an end: Third-party

analytics and retargeting vendors are necessary,

but over-reliance can lead to data leakage and the

erosion of consumer trust. Choose partners judiciously

and optimize your customer data sets for the best

results possible.

COMPLEXITY

TYPE OF PERSONALIZATION

1 TO 11 TO FEW(HUNDREDS)1 TO FEW

(HUNDREDS)1 TO MANY

(THOUSANDS)MASS

LOW HIGH

SEGMENTATION

TESTING

CUSTOMIZATION

TARGETING

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SUPPLEMENTAL CONTENTVideo: Data & Targeting Presentation

December 7, 2017 13


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ADDITIONAL MATERIALS

L2’s Intelligence team presents the findings and case

studies from the Data & Targeting 2017 report to a

live audience at the Crosby Hotel in November 2017.

Data & Targeting: Privacy digs into the data collection

and privacy notification efforts of brands.

Intelligence Modules blend data-driven insights

and performance benchmarks to identify brand

opportunities. They help brands see a greater return

on digital investments.

Dive into the scoring of brands by vertical across

two axes, data and personalization, in this

interactive chart.

Data & Targeting: Technology looks at how brands

are utilizing vendors to help them tackle data and

targeting challenges.

The Data & Targeting Module evaluates a brand’s

investment across data collection and personalization

across site, email, and technology vendors compared

to a selection of competitive brands.

SUPPLEMENTAL CONTENT INSIGHT REPORTS INTELLIGENCE MODULES

Video: Data & Targeting Presentation Data & Targeting: Privacy Intelligence Module

Interactive Chart Data & Targeting: Technology Data & Targeting Module

Release Date: February 2018

Release Date: March 2018

L2’s Intelligence team presents the findings and case studies from the Data & Targeting 2017 report to a live audience at the Crosby Hotel in November 2017.

Interactive Chart

December 7, 2017 13


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interactive chart.













Dive into the scoring of brands by vertical across two axes, data and personalization, in this interactive chart.

INSIGHT REPORTSData & Targeting: Privacy

December 7, 2017 13


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interactive chart.













Data & Targeting: Privacy digs into the data collection and privacy notification efforts of brands.

Data & Targeting: Technology

December 7, 2017 13


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interactive chart.













Data & Targeting: Technology looks at how brands are utilizing vendors to help them tackle data and targeting challenges.

INTELLIGENCE MODULESIntelligence Module

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interactive chart.













Intelligence Modules blend data-driven insights and performance benchmarks to identify brand opportunities. They help brands see a greater return on digital investments.

Data & Targeting Module

December 7, 2017 13


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interactive chart.













The Data & Targeting Module evaluates a brand’s investment across data collection and personalization across site, email, and technology vendors compared to a selection of competitive brands.






https://www.l2inc.com/video/data-and-targeting-2017-briefing

https://www.l2inc.com/video/data-and-targeting-2017-briefing

https://www.l2inc.com/data-tools/data-targeting-2017

data & targeting 2017: crawl, walk, then run€¦ · gartner for marketers [email protected]...

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