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1、FROM DATA TO INSIGHTTHE OUTLOOK FOR MARKETING ANALYTICSAPRIL 2023 Presented byNOTICEThis report contains brief,selected information and analysis pertaining to the advertising,marketing and technology industries and has been prepared by Verista Part-ners Inc.d.b.a.Winterberry Group.It does not purpor

2、t to be all-inclusive or to contain all of the information that a prospective manager,investor or lender may require.Projections and opinions in this report have been prepared based on information provided by third parties.Neither Winterberry Group nor its respective sponsors make any representation

3、s or assurances that this information is complete or completely accurate,as it relies on self-reported data from industry leadersincluding advertisers,marketing service providers,technology developers and agencies.Nor shall any of the forgoing(or their respective officers or controlling persons)have

4、 any liability resulting from the use of the information contained herein or otherwise supplied.All trademarks are the property of their respective owners.Copyright Winterberry Group 2023 All rights ReservedThis report would not have been possible without the significant contributions of the industr

5、y leaders who supported our research and shared their opinions with us.In particular,Winterberry Group is grateful to our project sponsors for their time,efforts and insights:ACKNOWLEDGMENTSASSOCIATION PARTNERPREMIER SPONSORSSUPPORTING SPONSOR3INTRODUCTIONThis whitepapers purpose is to better define

6、 the present and future state of marketing analytics.In the process of developing this paper,Winterberry Group surveyed more than 200 marketers from both the US and Europe and conducted in-depth interviews with more than two dozen analytics agency and vendor executives.The result is an evidence-base

7、d examination of current and emerging marketing analytics use cases,industry challenges and factors for success as demonstrated by companies that are utilizing analytics most effectively.WINTERBERRY GROUP AUTHORSBruce BiegelSenior Managing PartnerMichael HarrisonManaging PartnerCharles PingManaging

8、Director EMEAAlain SanjaumeManaging Consultant EMEAFawzi HalimiDirectorTABLE OF CONTENTS4TABLE OF CONTENTS06 Executive Summary07 Introduction to Marketing Analytics08 Where Are We Today?09 Key Definitions 10 The Spectrum of Analytics Maturity 11 Marketing Analytics Areas and Use Cases 17 Emerging Ty

9、pes of Marketing Analytics18 Industry Pain Points and Obstacles 19 Shortage of Talent 20 Data Quality,Silos 21 Measurement“Black Boxes”21 Third-party Cookie Deprecation22 From Laggard to Leader:How to Effectively Leverage Analytics 22 Data 23 Technology 24 Organizational Culture 26 Processes 27 Part

10、nerships28 Outlook for Marketing Analytics30 Glossary33 MethodologyTABLES AND CHARTS5TABLE OF CONTENTS09 Figure 1 Distribution of respondents across the Maturity Curve11 Figure 2 Distribution of respondents across the Maturity Curve,by Geography13 Graph 1 How would you describe the impact of using a

11、nalytics on your audience targeting and segmentation efforts in terms of performance and efficiency?14 Graph 2 Creative Assets and Content Use Case18 Figure 4 Which of the following challenges do you face when it comes to marketing analytics resourcing?19 Figure 5 Does your company have the necessar

12、y skills to proficiently implement and use marketing analytics?20 Figure 6 Please select the option below that best describes which data structures your company employs for the purpose of marketing analytics 22 Figure 7 Please select the option that best describes your companys data quality practice

13、s23 Figure 8 How well integrated is your organizations marketing technology stack with the necessary data platforms and applications?24 Figure 9 How would you describe your companys culture when it comes to gathering and sharing information25 Figure 10 Which of the following titles does your organiz

14、ation employ for its marketing analytics team?26 Figure 11 On which team within your organization do you rely on for your marketing analytics needs?27 Figure 12 Do you rely on 3rd-party providers to support your analytics efforts?29 Figure 13 Spend on Data Infrastructure and Analytics6EXECUTIVE SUMM

15、ARYAnalytics maturity among marketing organizations is a work in progress,with 47%of organizations reporting themselves as either Emerging or Progressing,and only 10%having moved to leader status.The research identified maturity levels based on the complexity and type of use cases for which the orga

16、nization leverages analytics.The results indicate that most marketers have evolved beyond descriptive analytics and are now leveraging predictive and prescriptive analytics to drive decision-making-with European marketers evolving more rapidly as a result of limits on data availability.The primary a

17、nalytics use cases focus in five areas:audience intelligence,the customer journey and experience,commerce,creative and content,and media measurement and attribution where marketers utilize analytics to better leverage the vast amount of data to understand,predict and optimize their decision-makingMe

18、dia measurement and attribution remains a challenge despite being a priority for marketers.Media measurement and attribution is one of the most prevalent areas for marketing analytics,despite the challenges posed by cookie deprecation,the resulting lack of standard identifiers and the growing number

19、 of data privacy regulations.The future state solution will most likely involve elements of econometrics-based marketing mix modeling,as well as deterministic and model-based attribution solutions from a range of open and closed sources.Marketers sophistication in using analytics to improve creative

20、 and content development is growing rapidly and approaching a key inflexion point.Analytics are helping to drive evidence-based creative decisions that are increasing customer engagement and conversion rates.More than one third of Winterberry Group survey respondents said they were either leveraging

21、 prescriptive analytics to curate the creative assets driving the most performance or leveraging adaptive and autonomous analytics to analyze data and generate/curate content and creative based on predetermined parameters.This will accelerate very rapidly as generative AI matures into embedded solut

22、ions.Analytics Leaders demonstrate comprehensive strategies across the core organizational components:data,technology,organization,processes and partnerships.The sourcing,collection,accuracy,governance and storage of data is a prime differentiator for brands that effectively use marketing analytics.

23、Among Winterberry Group survey respondents,45%of companies defined as Leaders said that standardized processes exist for information gathering and sharing across the organization,versus just 20%of companies characterized as Laggards.The skills gap is still a challenge,but its impact is weakening:As

24、technology adoption spreads,critical challenges inhibiting analytics maturity include the lack of centralization of the analytics function across the organization and the lack of talent availability.Ultimately the difference between the leaders and those who are emerging or lagging is a skills gap,o

25、ne that will take a concerted investment in the benefits of analytics in driving outcomes to resolve.Meanwhile,nearly 75%of all respondents are leveraging third party provider support to bridge the gap.Data aggregation,quality and security lead analytics-related spending priorities.Both US and EU ma

26、rketers have identified data aggregation from multiple sources,data quality and data security as priority investment areas to improve the effectiveness of their analytics efforts.Other spending priorities include investments in data management technology and selecting the right analytics tools.Spend

27、 on marketing analytics and data infrastructure will reach$32BB in 2026:Spending on data and analytics infrastructure is expected to grow at a CAGR of 10%from$22 billion in 2022 to$32 billion in 2026 in the US,UK,and EU.Technological advancements and process improvements have enabled businesses to a

28、chieve more with less reliance on people,empowering business analysts and engineers to leverage readily available technology,while reducing reliance on individuals with advanced degrees and offshore talent.The use cases for analytics are expanding across companies and marketing teams,driven by rapid

29、 advances in technology,a proliferation of customer touchpoints and a heightened need for faster and better data-driven decision-making.There are significant obstacles to growth,however,as the loss of data identifiers,privacy regulation and entrenched organizational cultures force marketers to innov

30、ate in understanding what drives value.These challenges will be overcome as marketing art and science converge,resulting in more widespread use of analytics to innovate how companies identify,understand and engage with prospects and customers.EXECUTIVE SUMMARY7WHERE ARE WE TODAY?THE EARLY DAYS AND T

31、HE OPTIMIZATION OF DIRECT MARKETING Direct marketing in the early days was limited to phone and mail surveys and transactional behavior analysis By the 1990s,direct marketers had greater data capabilities and used RFM and alternative classification approaches to build customer segmentations Call cen

32、ter,TV schedule,and ratings data were combined to understand how channels inter-acted and create“brand response”campaignsTHE EXPLOSION OF DATA-DRIVEN MARKETING The digital era in the early 2000s saw an explosion in data growth with email and display advertising becoming primary channels The appropri

33、ation of cookies as a universal identifier revolutionized the measurability of online activity Marketing analytics evolved quickly with traceability of behaviors across multiple sites,leading to the emergence of performance marketingOMNICHANNEL AND THE EXPLOSION OF DATA As marketing shifted to perfo

34、rmance marketing,an explosion of data occurred,creating a need for innovative methods of storing and processing data.Big data storage and cloud computing allowed organizations to store vast amounts of structured and unstructured data.The expansion of data and compute power enabled analytic methodolo

35、gies to derive deeper insight.Enter Artificial Intelligence and Machine Learning,which can now be used to analyze the vast amounts of data,uncover patterns,and improve marketing effectiveness.A PERIOD OF RAPID CHANGE AND EVOLUTIONMarketing analytics plays a crucial role in helping marketers understa

36、nd their target audience,reach them effectively,and evaluate the success of their marketing efforts.The history of marketing analytics has shown a steady evolution and improvement through iteration,but we are now experiencing a revolution driven by multiple factors,such as seismic shifts in privacy,

37、rapid changes in storage and processing power,and access to data infrastructure technologies that allow structured and unstructured data to be integrated.Additionally,analytical techniques have advanced to include more advanced unsupervised analytical techniques at scale and speed,including machine

38、learning,deep learning,and advanced AI.These developments have enabled marketers to harness the full benefits of more advanced analytics to enhance multiple areas of activity.INTRODUCTION TO MARKETING ANALYTICS8WHERE ARE WE TODAY?WHERE ARE WE TODAY?Analytics is now core to the success of marketers.T

39、echnology continues to develop at speed,bringing with it near-real time access to customer data,insights and activation.Several industry trends are serving to amplify the value of analytics within the marketers toolkit,driving its importance in achieving business outcomes:Consumer choice and data pr

40、otection:The process of increasing the level of choice and control that consumers have around how data about them is collected and used is becoming universal.It is moving at a different pace across regions and countries,and the end solutions adopted by national,or state level governments will not be

41、 entirely harmonious,but there is a clear trajectory.Large brands and leading technological platforms,driven by a fear of external oversight and being mindful of consumer attitudes,have taken privacy steps independently and faster than governmental bodies.From the deprecation of browser-based third-

42、party cookies to the loss of mobile identifiers,the ability to deterministically join and identify marketing audiences has been compromised.As such,marketers will increasingly rely on advanced analytics to surface deeper insights from existing customer data sources.The restrictions on use or loss of

43、 the previously universal identifiers led to a growth of privacy enhancing technologies that aim to preserve individual anonymity whilst enabling meaningful marketing and advertising use cases.These approaches use advanced encryption technologies to deliver marketing solutions.They demonstrate the a

44、dvancement and commercialization of mathematical techniques that have been enabled by the widespread availability of high performance computing power and cost effective storage.Speed of technological change:These rapid advances in computing speed and power are also enabling marketers to perform expe

45、riments,review results and optimize campaigns faster than ever before.Analytics is the key to enabling on-the-fly,real-time adjustments to campaign tactics and strategies.Fragmentation of measurement approaches:The reduction of identifiers has led to a greater investment in first-party data.Those en

46、tities with high volumes of first-party data(usually walled garden platforms),especially those with fast changing data with high levels of variety,are protecting and controlling their assets assiduously.This is one reason why these types business have traditionally controlled the insights and analys

47、is that advertisers can access.Whilst this has led to some cynicism that such businesses are“marking their own homework”they have evolved their own conversion API approaches and delivered“clean room”solutions that allow advertisers to match their own purchase data back to the walled garden user data

48、 in a blinded manner.The best solutions use double blinded analytical environments to ensure that neither party can gain any unfair advantage.We see this approach becoming the accepted standard for sharing first-party data for analytics.Some discussion is made around the evolution of“black box”techn

49、ology,such as neural networks,that are inherently difficult to explain or audit.It should be recognized that in many countries the delivery of an offer to an individual or group that confers certain financial benefits(and therefore excludes some other individuals from the benefits)needs to be audita

50、ble and explainable under data protection or financial regulations.This means that some of these techniques are not applicable in these use cases.What brings brands to us is that theyve been optimizing the performance marketing,but cannot yield more growth.So they move to the upper funnel and want t

51、o optimize those efforts.Chief Data Strategy Officer,Global Marketing Analytics Solutions Provider9WHERE ARE WE TODAY?KEY DEFINITIONSFor the purposes of this whitepaper,Winterberry Group uses the following definitions:Marketing Analytics:A combination of technology,talent and processes to understand

52、 first,second and third-party data to derive insight,predict marketing outcomes and optimize marketing decision-making.Machine Learning(ML):A series of methods,tools,models and algorithms to enable data-driven decision making on vast amounts of data.Artificial Intelligence(AI):The simulation of huma

53、n intelligence processes by machines,especially computer systems.The ultimate objective of AI is to emulate human behavior through processing information(leveraging techniques such as ML,making ML a subset of AI)and acting upon this information.An example subset of AI is“Conversational AI”,referring

54、 to the use of AI technologies to enable machines to communicate with humans in natural language through text or voice-based interface.In addition,weve acknowledge three classifications of analytic approaches currently used for marketing:Descriptive:Analytics that describe what has happened.Predicti

55、ve:Analytics that predict what will happen in the future leveraging data from the past.Prescriptive:Analytics that recommend the next-best action to drive specified objectives.FIGURE 1 DISTRIBUTION OF RESPONDENTS ACROSS THE MATURITY CURVESource:Winterberry Group Survey(2023)2023(%of respondents)Lagg

56、ards leverage descriptive analytics to describe,summarize and analyze historical data across marketing analytics use casesEmerging leverage a combination of descriptive analytics with diagnostic analytics to identify causes and trends across marketing analytics use casesProgressing leverage a combin

57、ation of descriptive analytics with prescriptive analytics across marketing analytics use cases with deficiencies in certain areas and highly adequate strategy in othersEstablished leverage prescriptive analytics to recommend the right or optimal decision across marketing analytics use casesLeaders

58、leverage adaptive and autonomous analytics(including Artificial Intelligence)to monitor,decide and act in an automated or semi-automated manner across marketing analytics use cases5%10%37%38%10%10WHERE ARE WE TODAY?THE SPECTRUM OF ANALYTICS MATURITYLaggards:primarily leverage basic descriptive analy

59、tics across use cases and domains to understand what has happened in the past.Emerging:Rely on descriptive and diagnostic analytics across use cases and domains to understand historical data and identify causes and trends.Progressing:Combine descriptive and predictive analytics across use cases and

60、domains to forecast and predict future outcomes as a guide to decision-making.Established:Combine descriptive,predictive and prescriptive analytics to understand data,forecast/predict future outcomes,and assess optimal course of action.Leaders:Combine analytics with artificial intelligence and machi

61、ne learning for intelligence and to automate the delivery and activation of marketing analytics.Only 10%of companies are truly leveraging marketing analytics to derive insight and automating the optimization of business decisions.Indicating the difficulty brands face to transform to data-driven anal

62、ytical organizations.15%of organizations are early in their transformation with some demonstrating little to no interest in truly being analytically driven.The vast majority of organizations(75%)have made progress in evolving their application of marketing analytics,but have a substantial way to go

63、to garner the true power of marketing analytics.European marketers have been forced to adopt innovative analytics capabilities faster than their US counterparts due to the EUs more restrictive data privacy and identity environmentMarketers in the North America and the EU prioritized Creative and Con

64、tent,Commerce and Measurement and Attribution as their most sophisticated use cases.Customer Journey Management emerged as the use case with the greatest need to evolve in sophistication.To gain greater insight into the current state of the analytics market,Winterberry Group conducted a survey of ov

65、er 200 marketers in the US,UK,France and Germany.The goal was to derive insight into organizational structures,utilization of marketing analytics and the level of complexity in the use cases they address.Accordingly,we categorized respondents organizations into five maturity levels,based on the comp

66、lexity and type of use cases for which they leverage marketing analytics:Europe has experienced a higher level of interest and passion for analytics than the US due to the implementation of GDPR,which forced businesses to explore alternative data options.This led to increased testing and experimenta

67、tion in smaller markets,which created momentum.Furthermore,there are specific data needs in Europe that cannot be fulfilled by the larger and more accessible data solutions found in the US.Although there are advanced markets and laggards,talent challenges in Europe are relatively less compared to th

68、e US.Chief Executive Officer,Global Marketing Analytics Solutions Provider11WHERE ARE WE TODAY?FIGURE 2 DISTRIBUTION OF RESPONDENTS ACROSS THE MATURITY CURVE,BY GEOGRAPHYSource:Winterberry Group Survey(2023)2023(%of respondents)Laggards EmergingProgressingEstablished Leaders7%2%13%6%41%35%39%38%12%8

69、%Europe USAMARKETING ANALYTICS AREAS AND USE CASESAudience and Customer Intelligence:Segmenting,understanding and targeting customers.Customer Journey and Experience:Designing customer journeys and experiences to define how brands engage with consumers.Commerce:Promoting and selling products and ser

70、vices,both in-store as well as online.Creative and Content:Creating,developing,versioning and optimizing assets.Media Measurement and Attribution:Tracking,analyzing and evaluating the impact of various media activities.Descriptive,predictive or prescriptive analytics?These are the questions that mar

71、keters are asking themselves when it comes to what kind of analytics to use to leverage the vast amounts of data at their fingertips and optimize their decision making.The number of use cases for analytics is accelerating quickly.Our research identified five primary marketing areas where analytics a

72、re helping marketers achieve their goals.12WHERE ARE WE TODAY?AUDIENCE AND CUSTOMER INTELLIGENCE With an abundance of data available,audience segmentation techniques have become more granular,enabling marketers to more accurately define groups of customers based on similar interests and behaviors.Mo

73、deling capabilities continue to expand,moving from single acquisition models to holistic modeling systems that optimize segments across next-best experiences to grow customer lifetime value(CLV),better understand likelihood to churn and enhance decision making to increase customer engagement and ret

74、ention.Marketing analytics are allowing brands to utilize unstructured data to segment customers,including conducting sentiment analysis of social media content.Nearly 80%of marketers see performance and efficiency significantly improve when marketing analytics is implemented.Audience and Customer I

75、ntelligenceCustomer Journey and ExperienceCommerceCreative and ContentMedia Measurement and AttributionDescriptiveSegment customers into distinct audiencesUnderstand with which touchpoints a customer segment interactedUnderstand how sales of a specific product are performingUnderstand how the creati

76、ve asset is performing in a campaignUnderstand how campaigns performedPredictivePredict how distinct segments will react to different treatmentsPredict how a customer segment will react when exposed to a sequence of touchpointsPredict how sales of a specific product will perform if a promotion is ac

77、tivatedPredict how a new creative asset will perform in a campaignForecast how campaigns will performPrescriptiveDecide which segments to target with which treatmentsDecide which journeys to enable for a customer segmentDecide whether to activate promotions or not Decide which creative asset to useD

78、ecide in which campaigns to investBrands want to personalize their offerings and marketing to different groups of consumers,which requires a deeper understanding of their market.Marketing analytics is about defining those potential marketing segments.Chief Data Strategy Officer,AdTech Provider13WHER

79、E ARE WE TODAY?CUSTOMER JOURNEY AND EXPERIENCEBrand experience is now a key deciding factor for consumers when it comes to choosing the companies they do business with.Customer expectations for personalized,omnichannel and cohesive experiences have never been higher.Nearly three quarters of customer

80、s expect companies to understand their unique needs and expectations,according to State of the Connected Customer published by Salesforce.Perhaps more importantly,more than half of customers expect brand offers to always be personalized.Use cases for analytics on the customer journey include anticip

81、ating where,when and how a customer wants to interact with a brand.The depth and breadth of data available is allowing marketers to understand each customer at a granular level,enabling them to personalize their experiences and decide on the purchase paths that are most likely to increase engagement

82、,ultimately leading to an increased lifetime value.COMMERCEProduct recommendations are a core part of a personalized customer experience.Using analytics,marketers are becoming more sophisticated in their efforts to deliver relevant offers to targeted audiences.GRAPH 1 HOW WOULD YOU DESCRIBE THE IMPA

83、CT OF USING ANALYTICS ON YOUR AUDIENCE TARGETING AND SEGMENTATION EFFORTS IN TERMS OF PERFORMANCE AND EFFICIENCY?Source:Winterberry Group Survey(2023)20%78%2%Our performance and efficiency have significantly regressed Our performance and efficiency have not noticeably changed Our performance and eff

84、iciency have significantly improved2023(%of respondents)There is no doubt in my mind that A/B testing is something we need to have in the toolbox,as opposed to the old days of someone putting their finger in the air and deciding what is right.Its something we are going to keep looking into building.

85、Chief Product Officer,Commerce AgencyCMOs should look at every tool they have and treat it from the ROI perspective,especially given the ease of creating content with AI the disruption is incredible.Chief Executive Officer,Privacy-Centric Measurement Provider14WHERE ARE WE TODAY?Over 50%of marketers

86、 state that they are leveraging either predictive or prescriptive analytics to predict future outcomes and recommend the best next action.The result is an improved customer experience and increased conversions.Marketers are leveraging analytics to optimize the commerce experience and reduce the risk

87、 of churn.CREATIVE AND CONTENTMarketers sophistication in using analytics to improve creative and content development is on the rise.More than a third of marketers state that they are either leveraging prescriptive analytics to curate the creative assets driving the most performance or leveraging ad

88、aptive and autonomous analytics to analyze data and generate/curate content and creative based on predetermined parameters.74%marketers have seen their creative performance and efficiency significantly improved as a result of using analytics to create,distribute and optimize assets.Such trends refle

89、ct the way brands are using AI-powered analytics to optimize content,particularly for digital audiences across websites,search engines and social media.Heat maps and other data visualization tools analyze time spent on every inch of a webpage or SERP,as well as examine imagery and attributes by cate

90、gory or geography with the goal to improve efficacy.MEDIA MEASUREMENT AND ATTRIBUTIONA wide variety of attribution models are available,including first touch(FTA),last touch(LTA),linear and time decay.Multi touch attribution(MTA)uses statistical modeling to distribute credit for a purchase across mu

91、ltiple touchpoints based on their relative impact.MTA had grown in popularity due to the increasing complexity of the purchase journey across multiple channels.Since MTA models rely on digital interactions they can under represent the impact of non-digital touchpoints,including OOH,direct mail,in-st

92、ore promotions.MTA models can also be subjective,assigning credit based on incomplete data.Brands need a highly accurate identity spine across the digital ecosystem to derive optimal MTA results,and even then they are severely undermined by a lack of the data linkages provided by third-party cookies

93、 The days of MTA being the dominant method of driving media analytics are limited.Media Mix Modeling(MMM),or econometrics as some practitioners prefer,on the other hand,is a“top-down”statistical approach that analyzes the effectiveness of different marketing GRAPH 2 CREATIVE ASSETS AND CONTENT USE C

94、ASE Source:Winterberry Group Survey(2023)21%74%4%Not aware Our performance and efficiency have significantly regressed Our performance and efficiency have not noticeably changed Our performance and efficiency have significantly improved2023(%of respondents)2%15WHERE ARE WE TODAY?tactics(i.e.,adverti

95、sing,pricing,promotions)to achieve business outcomes.The goal is to quantify the impact of marketing activities on sales and other business metrics,and optimize the media mix by allocating resources to the most effective tactics,channels or campaigns.The primary knocks on MMM are that it takes time,

96、it historically had not been granular enough(i.e.,aggregate vs.individual data)and time to insight had been too long to impact digital marketing.However,the universal impact of the advances in computing storage and power have enabled a reduction of these timescales.While data collection is significa

97、nt as more budget shifts to digital the data collection becomes more automated and time to insight shortens.The future state solution will most likely involve elements of econometrics-based MMM,as well as both deterministic and model-based attribution solutions including walled gardens,conversion AP

98、Is(CAPI),neural networks and similar “black box”technologies.In response to the e-commerce boom,a wide variety of retailers have launched Retail Media Networks(RMNs),which combine digital and offline retail media assets to create closed-loop measurement and reporting(see sidebar).Yet RMNs are not a

99、perfect approach,either.In interviews,analytics providers mentioned the opaqueness of RMN black boxes.Given the mistrust of results,many marketers particularly in the consumer packaged goods(CPG)field do not have confidence in which RMNs to invest in and what those investments should be.Retail media

100、 is a fast-evolving market.It is about the measurement,but it lacks maturity.It will change depending on the business model that emerges whether advertiser or retailer built.Client Global Data Lead,Global Advertising and Marketing Holding CompanyRETAIL MEDIA NETWORK MEASUREMENTRetail Media Networks

101、are advertising platforms launched by retailers that can give advertisers access to first-party data and allow them to reach more specialized audiences across all of that retailers property in a privacy-safe manner.RMNs have recently proliferated,particularly as more ad dollars flow to retailers dig

102、ital platforms:retailers are seeking to expand revenue generated from advertising and promise closed-loop reporting.However,at this stage of the markets maturity,there are drawbacks,particularly around the lack of standardized data points and identifiers,which make it difficult to aggregate results.

103、Advertisers in RMNs have complained about the inability to standardize measurement across different RMNs,as each RMN provides its own measurement tool set and is incentivized to enhance the impact of its advertising(i.e.,show a higher impact on sales than advertising actually drove).This has driven

104、brands to rely on independent analytics providers to clarify confusion and develop custom measurements that better fit brand objectives.Google will dictate the next evolution of attribution given the volume of advertising that is done through its platform.It could be a campaign-level attribution of

105、MTA.But,there is this asterisk that goes next to any MTA that is person-based.I think there is something that exists between person-based attribution and MMM.I dont know what it is yet,but I am eagerly waiting on people to make their decisions before I start to attack.Director of Data Science Strate

106、gy&Visualization,Data and Identity Provider16WHERE ARE WE TODAY?Marketers are already developing and adopting proprietary approaches to measurement and working with a variety of third-party analytics partners to support their efforts.These approaches include:First-party data measurement:As many thir

107、d-party data sources and identifiers become more scarce,most notably third-party cookies,brands are becoming better resourced,and more enterprise focused in their use of first-party data.This has required investment in data architecture and management,such as data lakes,CDPs and data clean rooms.How

108、ever,data quality and data volumes remain a concern for many brands who have neither the purchase frequency or breadth of data points that are ideally needed.Walled garden measurement:Data-rich brands and publishers are leveraging a conversion API(CAPI)approach(i.e.Metas CAPI)to track metrics withou

109、t exposing proprietary data.Geolocation measurement:For some specific use cases,brands are measuring ad effectiveness by tracking footfall traffic(i.e.,the number of people entering a store)against campaign-exposed users.This approach may have privacy implications in some geographical areas.KEY TAKE

110、AWAYS:Marketing performance significantly improves after utilizing marketing analytics to enhance audience targeting and segmentation efforts Media measurement and attribution is the most prevalent marketing area for analytics Analytics are driving evidence-based creative decisions that increase cus

111、tomer engagement and conversion rates.Private Retail Media Networks(RMNs)combine digital and offline retail media assets to create closed-loop measurement and reporting Marketers are adopting proprietary measurement approaches including new value-based currencies that emphasize advertising outcomes.

112、Media mix modeling is known as top-down while multi-touch attribution is bottom-up.Weve been talking about the middle-out concept in order to get from top to bottom.Now were able to do cloud computing and the cost is going down,which supports doing this middle-out attribution.Chief Executive Officer

113、,Privacy-Centric Measurement Provider17WHERE ARE WE TODAY?EMERGING TYPES OF MARKETING ANALYTICSAnalytics are rarely,if ever,used in a discrete and compartmentalized manner.Sophis-ticated marketers know how to tie data and results together across different areas to optimize their decision making and

114、leverage technology to do so.The rising influence of data science in marketing organizations will lead to new and innovative types of analytics that will quickly gain traction for marketing use cases.GENERATIVE AI Generative AI is a sophisticated technology that can analyze large amounts of data to

115、generate novel content with context,including text,images,audio and videos,rather than merely process or act on existing data.OpenAIs ChatGPT is the most well-known example of Generative AI but is far from the finished solution.It provides a glimpse of what is possible from the use of Large Language

116、 Models(LLM),a foundational type of ML that uses deep learning algorithms to process and understand natural language.Soon after its release,Microsoft announced plans to integrate ChatGPT into its products,while tech giants Google and Baidu followed suit with Bard and Ernie,respectively.Generative AI

117、 relies on all types of analytics as described here and above,but is also tasked with the creation and generation of new content or action(whereas analytics in and of themselves dont take actions,but just analyze data)Marketers envision generative AI use cases for developing highly targeted versions

118、 of creative and content based on deep insights into customer behavior,preferences and intent.It can also enable new ways to create custom image templates and presentations,automatically sync video footage to music and translate words in designs.VOICE ANALYTICS A critical mass of consumers across th

119、e UK and Europe now uses digital voice assistants and smart speakers.More than 100 million U.S.adults owned smart speakers last year,according to the 2022 Smart Audio Report published by NPR and Edison Research.The survey also found that more than half of smart speaker owners who heard an ad on thei

120、r device said they were likely to respond to those ads.As a result,more brand marketers are exploring the use of voice analytics to understand how customers interact with their brand through voice-enabled devices.This type of“conversation intelligence”uses AI-driven algorithms to analyze voice senti

121、ment,tone and inflection,and can be used by marketers to create more personalized brand experiences for customers.SENTIMENT ANALYTICS Brand marketers are exploring how customers feel about their products and services through the use of emotion detection technology.This includes analyzing facial expr

122、essions,voice tone and even biometric data to gain insights into customer emotions and attitudes.These types of sentiment analytics are already widely used by digital marketers to monetize inbound call channels connected to paid search and social media marketing.AI-and ML-based algorithms are applie

123、d to analyze and“spot”call keywords,phrases and speech patterns for positive or negative signals of conversion intent.What OpenAI and ChatGPT have done is to help drive adoption of AI and ML techniques that existed,but had low penetration because they were too much effort.But right now,the hype is g

124、enuinely helping.Global Chief Executive Officer,Measurement and Analytics Provider18INDUSTRY PAIN POINTS AND OBSTACLES INDUSTRY PAIN POINTS AND OBSTACLES Analytics is not the silver bullet for achieving marketing objectives,but it is a critical element of any marketers toolkit.The analytics industry

125、 still faces a number of challenges to its widespread use and acceptance.Some are new,but many are not including the ability to secure C-level support for analytics investments and find talent with the requisite skills.The top three challenges cited by marketers in both the US and EU are:a lack of r

126、eliance on data-driven decisions;resource/talent limitations;and a lack of centralized data and analytics functions across the orga-nization.What follows is a discussion of some of the primary pain points and obstacles to marketing analytics industry expansion.FIGURE 4 WHICH OF THE FOLLOWING CHALLEN

127、GES DO YOU FACE WHEN IT COMES TO MARKETING ANALYTICS RESOURCING?Source:Winterberry Group Survey(2023)Lack of reliance on data-driven decisionsLack of centralized data and analytics function across the organizationResource/talent limitationsLack of marketing analytics technology stackOverreliance on

128、third-party providers for analytics support22%29%31%27%22%24%21%17%3%4%2023(%of respondents)Europe USATypically,all these organizations operate as silos.One of the hardest things in our industry is all that muscle memory that marketing and sales have always worked separately.Now were trying to conne

129、ct those two and rewire the muscles to make them work together.Its slow going.Chief Product Officer,Commerce AgencyThere is an incredible talent gap that has existed in the industry and has gotten worse.We recognize it from an employee standpoint as we try to scale talent.Customers recognize it as t

130、hey attempt to upgrade their talent and improve the capability of their team members.Chief Executive Officer,Global Marketing Analytics Solutions Provider19INDUSTRY PAIN POINTS AND OBSTACLES Marketers have difficulty finding and hiring the talent to drive successful analytics projects.Marketers have

131、 difficulty finding and hiring the talent to drive successful analytics projects.Smaller firms,including agencies and technology providers,struggle more with these issues,as they compete with larger companies that can offer more lucrative compensation and benefits packages.In addition,analytics requ

132、ires a blend of marketing savvy and data science not an easy combination to hire or develop.SHORTAGE OF TALENTFIGURE 5 DOES YOUR COMPANY HAVE THE NECESSARY SKILLS TO PROFICIENTLY IMPLEMENT AND USE MARKETING ANALYTICS?Source:Winterberry Group Survey(2023)2023(%of respondents)Laggards EmergingProgress

133、ingEstablished Leaders60%31%25%10%35%40%10%1%58%45%50%30%5%10%26%4%We do not have the necessary skills within the company,and we do not have plans to remediate that We have some of the skills required that are distributed across the company,but those skills are not well integrated We have a strong b

134、ase of skills across the company and are working on coordinating their activities better Company-wide,we have all the necessary skills that work together in a coordinated fashionThere arent enough data scientists to handle the amount of data.There are industry dynamics to consider,such as marketing

135、professionals being underpaid compared to professionals in other sectors.There is a lot of competition for skills in other places.Soft skills such as data storytelling and data visualization are lacking.There is a need for more data literacy and commercial understanding.Director of Strategic Plannin

136、g,Identity Provider20INDUSTRY PAIN POINTS AND OBSTACLES Data quality or the lack thereof is a longstanding issue in the marketing realm.For many years,the problem was the lack of customer data.Then,the problem became the overwhelming volume of data being collected.Today,the problem is twofold:dwindl

137、ing sources of third-party identifiers,as well as too much data that resides in isolated systems that dont communicate with each other.Despite the widespread mantra of“data-driven”marketing,many brands still struggle to collect the right customer data and effectively integrate it among the various b

138、usiness units sales,marketing,digital,media,customer support that can work together to acti-vate it.Many of these silos are rooted in organizational structures that are deeply entrenched.DATA QUALITY,SILOS FIGURE 6 PLEASE SELECT THE OPTION BELOW THAT BEST DESCRIBES WHICH DATA STRUCTURES YOUR COMPANY

139、 EMPLOYS FOR THE PURPOSE OF MARKETING ANALYTICS:Source:Winterberry Group Survey(2023)2023(%of respondents)Important data is stored in disparate systemsIndividual business units manage their own data Organizational data infrastructure exists,but are not consistently leveraged within business unitsOrg

140、anization-wide data infrastructure exists and is consistently implemented and used by business units34%25%30%11%One of the biggest pain points in marketing analytics is data quality.Companies need high-quality,accurate data to make informed decisions and drive results.Another challenge is integratin

141、g data from disparate sources.Many companies have data silos,making it difficult to get a complete picture of their marketing performance.Head of Data Science,Decision Sciences Company21INDUSTRY PAIN POINTS AND OBSTACLES Clients are fearful of“black box”technology.This is why we need to avoid black

142、boxes and bring consumer knowledge,interpretation and explanations to what we do.We use data strategists who are more business-oriented to work with data scientists and help explain the why behind our methodologies.Chief Data Strategy Officer,AdTech ProviderMEASUREMENT“BLACK BOXES”Marketplace powerh

143、ouses such as Amazon,Meta and Google have adopted“black-box”approaches to media measurement in an effort to maintain control over their user data and comply with privacy regulations.These walled gardens use conversion CAPIs,neural networks and other technologies to allow marketers to model and segme

144、nt user data within a privacy-safe environment.Given the significant role that Google and Meta play in digital media plans,the approach whilst not ideal is acceptable for many brands.However,a growing number of marketing executives have higher expectations for transparency in analytics technology,in

145、cluding being able to audit walled garden outputs.In the absence of standardization across walled gardens,brands will not be satisfied with the lack of transparency,particularly as marketers will come under increasing scrutiny to justify their investments,and will instead rely on other analytics too

146、ls,solutions and providers to decipher the output received from black box measurement providers and better understand how their campaigns are performing across walled gardens.Everybody wants transparency even if you have machine learning,they want to know how its working and what is behind it.The EU

147、 is also moving in that direction.Chief Executive Officer,AdTech Solutions and Services ProviderTHIRD-PARTY COOKIE DEPRECATIONMarketers continue to lose access to third-party,browser-based cookies.As discussed earlier in their place,a variety of privacy-first data environments have emerged,offering

148、brands the ability to model anonymized audience segments.The impact on marketing analytics could be significant.Whilst many of the alternatives,such as contextual targeting,are thus far not as effective or precise as cookie-based targeting the overall objective of advertising and marketing is one of

149、 gaining a commercial return on investment,and the arguments around lack of precision are easily mitigated by cost efficiency,ease of use,scale of coverage and lower risk of regulatory intervention.Therefore,marketing analytical approaches need to be able to understand the value across a range of ex

150、ecutions and the age of relying on wholly deterministic approaches is over.Data deprecation has caused problems for many brands,challenging established practices given the connectivity of data.Weve seen clients with aggregate models that no longer work because data is becoming so sparse.Chief Execut

151、ive Officer,Global Marketing Analytics Solutions ProviderKEY TAKEAWAYS:Gaps in talent are a significant pain point.Only companies that are Established or Leaders on the analytics maturity scale said they have the necessary skills to proficiently implement and use marketing analytics.Longstanding iss

152、ues with data quality remain.Today,the problem is twofold:dwindling sources of third-party identity,as well as too many data silos.Marketers will not be satisfied with the lack of transparency offered by black box measurement approaches,particularly as they come under increasing scrutiny to justify

153、their investments.Regulation at all levels is evolving,forcing marketers to build and adopt flexible data collection strategies that enable rapid course correction and compliance.There is little consensus across the industry on definitions of new measurement methodologies or analytics models.The int

154、eroperability of data clean rooms and walled gardens remains an issue,with too many proprietary identifiers that are difficult to match to first-party data sets.22FROM LAGGARD TO LEADERFROM LAGGARD TO LEADER:HOW TO EFFECTIVELY LEVERAGE ANALYTICSHow does a company go from Laggard to Progressing,or fr

155、om Established to Leader in its use cases for marketing analytics?Its not easy.In fact,it requires a comprehensive strategy that spans five core parts of the enterprise:data,technology,organization,processes and partnerships.The following section explores each of them in greater depth.The sourcing,c

156、ollection,accuracy,governance and storage of data is a prime differentiator for brands that effectively use marketing analytics.45%of companies defined as Leaders stated that standardized processes exist for information gathering and sharing across the organization,versus just 20%of companies charac

157、terized as Laggards.Demonstrating that organizations further along the maturity curve understand the challenges of centralizing data and creating an infrastructure to be analytically driven,such as a consistent data taxonomy and scalable infrastructure.DATA FIGURE 7 PLEASE SELECT THE OPTION THAT BES

158、T DESCRIBES YOUR COMPANYS DATA QUALITY PRACTICES:Source:Winterberry Group Survey(2023)2023(%of respondents)Laggards EmergingProgressingEstablished Leaders40%70%19%21%60%46%35%25%50%25%29%There are limited processes to ensure data quality There are informal processes in place,but are not widely used

159、Processes are well-defined and implemented across the organization20%10%23FROM LAGGARD TO LEADERDifferent organizations have different incentive schemes.But at the end of the day,they all want to incentivize their team members to create value and support growth.The best way to advocate is if leaders

160、hip can showcase that data will be impactful and successful.Chief Executive Officer,Global Marketing Analytics Solutions ProviderThere is something impressive when you see what happens when you have data quality behind insights and in the data lake or warehouse.The most important thing is data quali

161、ty,more than every other factor.Chief Data and Technology Officer,Global Data CompanyTechnology is changing faster than the market,yet harnessing the power of AI-and ML-driven tools and platforms is an integral pillar of analytics success.Companies that are leveraging marketing analytics effectively

162、 are taking a two-pronged approach.First,they delineate clearly defined working relationships between IT,which is charged with technology implementation and deployment;and marketing,which owns the decision-making.Marketing analysts boast enough technical expertise to be able to communicate with data

163、 scientists.Second,they have invested in a robust technology stack that integrates the data,analytics and activation layer.TECHNOLOGYFIGURE 8 HOW WELL INTEGRATED IS YOUR ORGANIZATIONS MARKETING TECHNOLOGY STACK WITH THE NECESSARY DATA PLATFORMS AND APPLICATIONS?Source:Winterberry Group Survey(2023)2

164、023(%of respondents)Not integrated at allSomewhat integratedFully integrated9%34%37%57%63%Europe USA24FROM LAGGARD TO LEADERORGANIZATIONAL CULTUREAn organizations culture is perhaps the single biggest factor for analytics success.In many interviews,marketing analytics experts spoke about the need fo

165、r a culture that encourages risk-taking and embraces new technology and marketing methodologies.Every facet of the organization from C-level sponsorship to collaborative working environments contributes to a greater return on the analytics investment.To that end,more organizations are employing data

166、 analysts,marketing analysts and business intelligence analysts on their marketing analytics teams,ensuring that business goals and values are an integral part of decision-making.The long-held walls between sales and marketing are also coming down,according to our interviews,with members of those te

167、ams joining more analytics team meetings,along with staff from media and IT departments.There is no“one size fits all”for analytics capabilities within organizations.Leaders are utilizing both internal and external analytics teams,while we found laggards tend to rely more on internal teams.Today the

168、res a clear line between the responsibilities of our team and the IT team,and we work well together.Software engineers work on UI/UX and delivery of product;we are responsible for getting the right data,building the algorithm and getting the predictive insights.The goal of the IT team is to help us

169、get to the goals.Head of Data and Analytics,Commerce AgencyFirms that are successful in leveraging analytics tend to have a culture of data-driven decision making.They prioritize the collection and analysis of data and use it to inform their marketing strategies.Head of Data Science,Decision Science

170、s CompanyFIGURE 9 HOW WOULD YOU DESCRIBE YOUR COMPANYS CULTURE WHEN IT COMES TO GATHERING AND SHARING INFORMATION:Source:Winterberry Group Survey(2023)2023(%of respondents,single select)45%19%26%5%20%30%9%10%42%34%20%5%40%29%11%Information gathering is siloed across business units Information is fre

171、quently shared across teams and business units using ad-hoc processes Standardized process exists for organization-wide information gathering and sharing Information is transparent and widely availableLaggards EmergingProgressingEstablished Leaders70%25FROM LAGGARD TO LEADER58%74%37%32%21%22%13%FIGU

172、RE 10 WHICH OF THE FOLLOWING TITLES DOES YOUR ORGANIZATION EMPLOY FOR ITS MARKETING ANALYTICS TEAM?Source:Winterberry Group Survey(2023)Marketing AnalystData AnalystData ScientistData Analytics ConsultantData Engineer71%69%28%27%19%2023(%of respondents,select all that apply)Europe USAOperations Anal

173、ystQuantative Analyst18%7%Regardless of how much technology you have,especially if you want to go to the transformation application,it still requires domain experts to be deployed and attached to technology,combined with ways to work and enable distributing this broadly in the organization.Global Ch

174、ief Executive Officer,Measurement and Analytics ProviderIn marketing analytics,you need to understand the business problem and the solution for it.The companies that get the most out of marketing analytics are the ones that talk about their pain points and aspirations and the data scien-tists go fro

175、m there.General Manager,Data and Analytics ProviderWe need to avoid black boxes and bring consumer knowledge,interpretation and explanations to what we do.We use data strategists who are more business-oriented to work with data scientists and help explain the why behind our methodologies.Chief Data

176、Strategy Officer,AdTech Provider26FROM LAGGARD TO LEADERFIGURE 11 ON WHICH TEAM WITHIN YOUR ORGANIZATION DO YOU RELY ON FOR YOUR MARKETING ANALYTICS NEEDS?Source:Winterberry Group Survey(2023)2023(%of respondents,single select)Laggards EmergingProgressingEstablished Leaders38%30%17%53%41%63%23%28%9%

177、15%62%26%33%We have a dedicated analytics team that sits outside of the marketing team We have a dedicated analytics team within the marketing team that handles all of our analytics needs We have some analytics capabilities within the marketing team,and we also rely on an outside team25%Established

178、leaders state they have defined processes and consistent objectives between departments such as IT and marketing,and stronger alignment between sales,marketing and analytics teams.70%of Leaders said they have implemented well-defined data quality processes compared to virtually none of the companies

179、 identified as Laggards.Marketers are still in the early stages of implementing standardized processes with only 36%marketers describing their culture as using standardized processes for organizational-wide information gathering and sharing.PROCESSES27FROM LAGGARD TO LEADERWe do not rely on 3rd part

180、y analytics providers,we conduct all of our activities in-houseWe only rely on 3rd party analytics providers when we have clear gapsWe rely on 3rd party analytics providers most or all of the timeFIGURE 12 DO YOU RELY ON 3RD-PARTY PROVIDERS TO SUPPORT YOUR ANALYTICS EFFORTS?Source:Winterberry Group

181、Survey(2023)2023(%of respondents,single select)22%45%48%33%29%Europe USA24%Organizations consistently rely on a wide array of analytics partnerships to improve data quality and storage,audience measurement and attribution,and media planning and reach.Nearly half marketers rely on third-party provide

182、rs when they have clear gaps in executing analytics strategies.PARTNERSHIPSKEY TAKEAWAYS:Marketing analytics is a top-down organizational endeavor,where high-level executives endorse a strong data taxonomy,infrastructure and integration to support success Analytics Leaders are using data lakes,wareh

183、ouses and CDPs to centralize data storage,enhance analytics modeling and create seamless integration across platforms Organizations are employing data analysts,marketing analysts and business intelligence analysts on their marketing analytics teams to work with a robust MarTech stack that integrates

184、 a variety of tools Clearly-defined data processes improve collaboration and consistency between departments such as IT and marketing,and stronger alignment between sales,marketing and analytics teams Third-party partnerships are educating marketers and improving transparency into analytics black bo

185、xes to improve data quality and storage,audience measurement and attribution,and media planning and reach28OUTLOOK FOR MARKETING ANALYTICSOUTLOOK FOR MARKETING ANALYTICSAn organizations culture does make a difference.Data-driven decision making has to be a company culture,and marketing teams that do

186、 it well are encouraged to leverage the data that is available and cut through the media noise to effectively connect data to results.Buy-in from one executive with decision-making responsibilities can propel this cultural shift forward,as can incentivizing stakeholders to reward the effective deplo

187、yment of analytics.Marketers must be flexible and capable enough to link together the disparate metrics offered by publishers and marketplace owners into effective business outcomes.We will discover new use cases for analytics.The rising influence of data science in marketing organizations will lead

188、 to new and innovative types of analytics that will quickly open up new marketing use cases.Sophisticated marketers already know how to tie data and results together across different areas to optimize their decision making and will leverage technology to do so.Creative intelligence and media perform

189、ance optimization are the two primary areas where we will see tangible benefits emerge from marketing analytics.We look forward to seeing what those use cases are.AI-driven analytics will play a larger role.The use of generative AI will expand beyond customer service applications and into content an

190、d creative development that is highly targeted and personalized for granular audience segments.The industry is still at a nascent stage,however,and there are significant unanswered questions especially around remuneration models between publishers,advertisers and everyone in between.Spend on data an

191、d analytics to reach$32B.B.in 2026:In the US,UK,and EU,we forecast that the spending on analytics services and technology(including data technology and analytics services)will increase from$22 billion in 2022 to$32 billion in 2026,at a compound annual growth rate(CAGR)of 10%.The growth in spending w

192、ill be mainly driven by technology and infrastructure,while we anticipate spending on services will grow at a relatively modest pace.This trend can be attributed to several factors,including the advancements in technology and processes that have enabled businesses to achieve more with less,and have

193、empowered business analysts to leverage technology.This technological evolution will reduce the reliance on advanced degrees and enable midmarket businesses,that previously struggled to recruit top-tier talent,to leverage analytics.Moreover,a broader base of talent,with a better understanding of loc

194、al nuances and marketing analytics,will emerge,leading to a declining need to offshore analytics.The winners will beThe companies that have the right company-wide culture and rely on data to drive decision-making and work with the right partners.They will be nimble enough to integrate new data sourc

195、es and more collaborative work environments and combine speed,flexibility and the art and science of leveraging data to drive business outcomes.Winterberry Group predicts strong expansion in marketing analytics investments and use cases across industries.Growth drivers will include rapid advances in

196、 AI and other emerging technologies that improve analytic modeling techniques,and organizational changes that create more collaborative working environments both within the enterprise and with third-party partnerships.Data privacy will remain a serious concern,as regulation in the US and EU curtail

197、access to data sources,as will the proliferation of black box measurement approaches that limit transparency into analytics methods and outputs.29OUTLOOK FOR MARKETING ANALYTICSFIGURE 13 SPEND ON DATA INFRASTRUCTURE AND ANALYTICSNote:Spend on data infrastructure and analytics includes spend on CDPs,

198、CRMs,DMPs and other data technologies,as well as spend on analytics services and measurement.Source:Winterberry Group(2023)(US,UK and EU,2022-2026F,USD BB)20222023F2024F2025F2026F$22.0$24.4$25.4$28.6$32.4 22-26F CAGR:10%30GLOSSARY ARTIFICIAL INTELLIGENCE(AI)The simulation of human intelligence proce

199、sses by machines,especially computer systems.The ultimate objective of AI is to emulate human behavior through processing information (leveraging techniques such as ML,making ML a subset of AI)and acting upon this information.BLACK BOXIn data and analytics,a black box refers to a system or process t

200、hat takes in inputs and produces outputs,but the inner workings of which are not fully understood or transparent to the user.BUSINESS INTELLIGENCE(BI)A set of technologies that analyze and transform company information into actionable insights that inform strategic and tactical business decisions.CO

201、NVERSION API(CAPI)A server-to-server,privacy-centric integration designed to create a direct(and more reliable)connection between marketing data and the walled garden owner,i.e.,Meta and Snap.CONNECTED TV(CTV)Content accessed by apps and streamed over smart TVs,mobile or Over-The-Top(OTT)devices via

202、 an internet connection.CUSTOMER DATA PLATFORM(CDP)Platforms that ingest and integrate customer data from multiple sources,offer customer persona management,support“real time”customer segmentation and make customer data accessible to other systems.DATA CLEAN ROOMPrivacy-safe data environments throug

203、h which platforms,brands and publishers can aggregate first-party user data to expand audiences,gain insights,conduct measurement and determine ad frequency in a security and privacy-compliant manner.DATA INFRASTRUCTURETechnology solutions that enable integrating,storing and processing data for anal

204、ytics and activation.Include DMPs,CDPs,Customer Relationship Management Platforms(CRMs)and others.DATA LAKEA centralized repository that allows marketers to store data in its native format both structured and unstructured at any scale.DATA MANAGEMENT PLATFORM(DMP)An integrated platform used to colle

205、ct,organize and activate structured,unknown audience data from both online and offline sources to build anonymous customer personas that drive targeted advertising and personalization initiatives.DEMAND SIDE PLATFORM(DSP)Software system that has audience data,allowing advertisers to better target vi

206、ewers,resulting in automatic and more efficient TV ad buying.ECONOMETRICSEconometrics uses statistical techniques to help describe economic relationships.In marketing,econometrics may be used to show the relationship between media spending and growth in sales or other metrics.IDENTITY GRAPHProvides

207、a single,unified view of customers and prospects based on their interactions with a product or website across a set of devices and identities.MACHINE LEARNING(ML)A series of methods,tools,models and algorithms to enable data-driven decision making on vast amounts of data.MARKETING MIX MODELING(MMM)A

208、 top-down,statistical approach that analyzes the effectiveness of different marketing tactics(i.e.,advertising,pricing,promotions)to achieve business outcomes.The goal is to quantify the impact of marketing activities on sales and other business metrics and optimize the marketing mix by allocating r

209、esources to the most effective tactics,channels or campaigns.MULTI-TOUCH ATTRIBUTION(MTA)A bottom-up,modeling method that assigns credit for a sale or conversion event to multiple marketing touchpoints with which a customer has interacted before making a purchase.MTA recognizes that a customers buyi

210、ng journey often involves multiple marketing interactions,and seeks to understand the contribution of each touchpoint to the final outcome or conversion.WALLED GARDENA closed ecosystem that enables the marketplace provider to maintain control of user data.Marketers or advertisers are limited to anal

211、yzing,modeling or segmenting data within the confines of the walled gardens“privacy sandbox”to launch new campaigns.GLOSSARY 31ABOUT OUR SPONSORSPREMIER SPONSORSABOUT THE MARS AGENCYThe Mars Agency is an award-winning,independently owned,global commerce marketing practice.With talent around the worl

212、d,they connect people,technology and intelligence to make clients business better today than it was yesterday.Mars industry-leading MarTech platform,Marilyn,helps marketers understand the total business impact of their commerce marketing,enabling them to make better decisions,create connected experi

213、ences and drive stronger results.Learn more at and meetmarilyn.ai.ABOUT ANALYTIC PARTNERSAnalytic Partners is the leader in commercial analytics.Our GPS-Enterprise platform provides adaptive solutions for deeper business understanding,right-time planning and optimization for marketing and beyond.We

214、turn data into expertise so that our customers can create powerful connections with their customers and achieve commercial success.For more information on Analytic Partners,visit .ABOUT ADTHEORENTAdTheorent(Nasdaq:ADTH)uses advanced machine learning technology and privacy-forward solutions to delive

215、r impactful advertising campaigns for marketers.AdTheorents machine learning-powered Platform AT powers its predictive targeting,geo-intelligence,audience extension solutions and in-house creative capability,Studio AT.Leveraging only non-sensitive data and focused on the predictive value of machine

216、learning models,AdTheorents product suite and flexible transaction models allow advertisers to identify the most qualified potential consumers coupled with the optimal creative experience to deliver superior results,measured by each advertisers real-world business goals.AdTheorent is headquartered i

217、n New York,with fourteen offices across the United States and Canada.For more information,visit .ABOUT ACXIOMAcxiom partners with the worlds leading brands to create customer intelligence,enabling data-driven marketing experiences that generate value for people and for brands.The experts in identity

218、,the ethical use of data,cloud-first customer data management,and analytics solutions,Acxiom makes the complex marketing ecosystem work,applying customer intelligence wherever brands and customers meet.For more than 50 years,Acxiom has improved clients customer acquisition,growth,and retention.With

219、locations in the US,Europe,and Asia,Acxiom is a registered trademark of Acxiom LLC and is part of The Interpublic Group of Companies,Inc.(IPG).For more information,visit .32ABOUT OUR SPONSORSSUPPORTING SPONSORSPROMO PARTNERABOUT CACICACIs mission is to help brands put their customers at the heart of

220、 their business,by doing amazing things with data.Our wide range of marketing,technology and data solutions are designed to ensure businesses are driving value at every touchpoint with their customers and prospects.Whether thats through designing innovative customer journeys,creating clean data feed

221、s for their marketing technology,or augmenting customer data with demographics and lifestyle variables to find new insights.With clients such as the RAC,EDF Energy and Legal&General,we have a depth of experience in driving transformation for leading brands.Learn more at caci.co.uk.ABOUT THE ANAThe A

222、NAs(Association of National Advertisers)mission is to drive growth for marketing professionals,brands and businesses,the industry,and humanity.The ANA serves the marketing needs of 20,000 brands by leveraging the 12-point ANA Growth Agenda,which has been endorsed by the Global CMO Growth Council.The

223、 ANAs membership consists of U.S.and international companies,including client-side marketers,nonprofits,fundraisers,and marketing solutions providers(data science and technology companies,ad agencies,publishers,media companies,suppliers,and vendors).The ANA creates Marketing Growth Champions by serv

224、ing,educating,and advocating for more than 50,000 industry members that collectively invest more than$400 billion in marketing and advertising annually.Learn more at .ABOUT AQFERAqfers Marketing Data Platform-as-a-Service is designed to be white-labeled and fully interoperable throughout the MarTech

225、 and AdTech ecosystem.It empowers marketing solution providers to facilitate identity resolution,secure data sharing,universal tag management,and advanced media analytics while also delivering substantial cloud efficiencies and cost savings across massive Big Data sets.Learn more at .33METHODOLOGYME

226、THODOLOGYThe insights in this report were validated by extensive industry research,including a survey conducted in March 2023 of 204 experienced marketers across the United States,France,Germany,and UK.We are indebted to the 25+individuals who provided their opinions in interviews conducted between

227、February and March 2023.SURVEY METHODOLOGYMy team and I support our company in planning our marketing analytics projects,31%My team and I leverage analytics regularly on a day-to-day basis for marketing purposes,69%Please select the option below that best describes your role in your Companys current

228、 and future use of data analytics within an advertising and marketing context:EVP/SVP/VP,50%Which of the following best describes your current role/business title?DIRECTOR,23%C-LEVEL EXECUTIVE,27%DATA AND ANALYTICS,26%INTELLIGENCE/INSIGHT,3%ECOMMERCE,5%MARKETING/ADVERTISING,66%Which of the following

229、 best describes your department or current functional role where you work?Approximately,what is the annual revenue for the company for which you work?$500 MILLION TO$1 BILLION,48%$100 MILLION TO$500 MILLION,33%MORE THAN$2 BILLION,2%$1 BILLION TO$2 BILLION,16%$50 MILLION TO$100 MILLION,1%Country of O

230、riginFRANCE,12%GERMANY,13%US,58%UK,17%Which of the following best describes the industry for which you work?RETAIL(INCLUDING DIRECT-TO-CONSUMER),35%MEDIA AND ENTERTAINMENT,23%OTHER,14%AUTOMOTIVE,9%FINANCIAL SERVICES,7%CONSUMER TECHNOLOGY,5%CONSUMER PACKED GOODS/FAST MOVING CONSUMER GOODS,3%CONSULTIN

231、G/CONSULTANCY,1%TRAVEL AND HOSPITALITY,1%INSURANCE,0%Winterberry Group is a strategic consultancy specializing in the intersecting disciplines of advertising,marketing,data,technology and commerce.We collaborate with stake-holders across the advertising and marketing ecosystemservice providers,techn

232、ology developers,media companies,brands and investor groupsto identify and activate growth opportunities that drive the creation of real and lasting value.We bring decades of experience and deep industry,operational and M&A expertise that bridges strategic development and tactical executiondriving u

233、nprecedented speed-to-action.And through our highly collaborative approach,we enable knowledge transfer and actionability,giving our clients a competitive edge and powering growth in performance,team engagement and shareholder value.Growth StrategyCorporate strategy that drives growth is at the hear

234、t of what we do.We work with clients to identify core competencies,evaluate strategic alternatives and build comprehensive,actionable growth plans.Collaborative ActivationWe guide brands and marketing practices through business process planning efforts aimed at helping them achieve lasting competiti

235、ve advantage.Mergers&AcquisitionsWe leverage our industry knowledge to help financial investors make sound,value-driven investment decisions.Market IntelligenceWe maintain an active research and publishing practice that gives our consultants direct access to insights from senior industry executives

236、and complements our client engagements.34ABOUT WINTERBERRY GROUPABOUT WINTERBERRY GROUPWINTERBERRY GROUP SERVICESCONTACT USBruce BiegelSenior Managing PMichael HarrisonManaging PCharles PingManaging Director,EMEAAlain SanjaumeManaging Consultant,EMEAFawzi HalimiD 61 Broadway,10th Floor,New York,NY 10006