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cimm report v9

JANUARY 2018. AND. ROI/Attribution Providers A Comparison of Leading Providers of Media Performance Analyses RESEARCH PROVIDED BY. Participants MARKETING MIX ATTRIBUTION SPECIALISTS. MODELERS WITH WITH CROSS-PLATFORM. ATTRIBUTION PRODUCTS PRODUCTS. Analytic Partners Conversion Logic in4mation insights Convertro, owned by Oath, a subsidiary IRI of Verizon Marketing Evolution C3 Metrics Millward Brown, a Kantar subsidiary Google Attribution 360. (m)Phasize, a Publicis company Merkle, owned by Dentsu Aegis Neustar Visual IQ, a Nielsen company Nielsen TELEVISION OR DIGITAL SINGLE SOURCE PROVIDERS. ATTRIBUTION PROVIDERS. Concentric Data + Math IRI Lift iSpot Nielsen Catalina Placed Oracle Samba TV TiVo SMI. TVSquared WyWy, owned by TVSquared Please note that providers have been grouped according to their core, or original, offering a somewhat subjective grouping. Most providers are constantly refining their offering, so this view of their products may be incomplete.

CIMM/4A’s 2016 study of current practices in attribution and marketing mix modeling identified a range of analytics providers using a variety of data sources and techniques.

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Transcription of cimm report v9

1 JANUARY 2018. AND. ROI/Attribution Providers A Comparison of Leading Providers of Media Performance Analyses RESEARCH PROVIDED BY. Participants MARKETING MIX ATTRIBUTION SPECIALISTS. MODELERS WITH WITH CROSS-PLATFORM. ATTRIBUTION PRODUCTS PRODUCTS. Analytic Partners Conversion Logic in4mation insights Convertro, owned by Oath, a subsidiary IRI of Verizon Marketing Evolution C3 Metrics Millward Brown, a Kantar subsidiary Google Attribution 360. (m)Phasize, a Publicis company Merkle, owned by Dentsu Aegis Neustar Visual IQ, a Nielsen company Nielsen TELEVISION OR DIGITAL SINGLE SOURCE PROVIDERS. ATTRIBUTION PROVIDERS. Concentric Data + Math IRI Lift iSpot Nielsen Catalina Placed Oracle Samba TV TiVo SMI. TVSquared WyWy, owned by TVSquared Please note that providers have been grouped according to their core, or original, offering a somewhat subjective grouping. Most providers are constantly refining their offering, so this view of their products may be incomplete.

2 We encourage readers to reach out to the providers for the most updated information. 1 RESEARCH PROVIDED BY. Introduction CIMM/4A's 2016 study of current practices in attribution and marketing mix modeling identified a range of analytics providers using a variety of data sources and techniques. It's a complicated and frequently overwhelming space for the industry. A comparison of the companies, offerings and approaches will help buyers become more comfortable with the providers and their techniques. Overview This is a comparison of current offerings in digital, cross-platform, multi-touch and television attribution and marketing mix modeling companies available in the US market today. It is descriptive, not evaluative. In the guide, providers are grouped according to their main offerings (Marketing Mix Modelers, Digital/Television Attribution Specialists, Single Source Providers), although it's important to recognize many providers offer a suite of analytic products depending on the needs of the client and availability of data.

3 The list of providers and the variables with which to compare providers were based on recommendations from CIMM and the 4A's Media Measurement committee. Provider Comparison Contents Company Positioning Model Inputs Short overview of company's main reason for being Other marketing variables ( , price/promotion), Primary Offerings external influences ( , weather, etc.) and Rough share of business from attribution and market- competitive behavior modeled at a similar level ing mix projects; can exceed 100% due to multiple Advertising Parameters offerings; in some cases percentages unavailable Diminishing returns, adstock, long-term effects, media Approach interactions and halos, baseline and incrementality Statistics most commonly employed (see glossary) Data Integration Methods Use Cases Process for combining cross-platform datasets in the Applications of the analytics in digital, cross-platform model or full marketing mix assessment Collinearity Work-Arounds KPIs Delivered Statistical approach to teasing out events or Online traffic/conversion, offline retail traffic/sales investments that occur at the same time and brand metrics Model Validation Optimization Areas Method for determining the accuracy of the Digital or cross-media channels, across sales and model findings brand metrics Data Delivery Options Media Covered Dashboards, inflight-optimizers, programmatic media, Full range of media vehicles included in the models data feeds to other applications Source of Television Data Cycle Time Modelers have a range of television viewing data, Typical model update intervals including Nielsen.

4 Smart TV and set top box data Level of Media Granularity An exhaustive glossary of key terms Level of detail at which the modeler works begins on Page 33. 2 RESEARCH PROVIDED BY. Marketing Mix Modelers With Attribution Products Marketing mix modelers (MMM) are the originators of sales contribution and ROIs of each. As a result, they ROI modeling, with the first commercial firms offering provide valuable strategic insights. The negative . these services in 1989. Ironically, both Marketing often associated with these models is the flip side: Management Analytics (MMA) and Hudson River They require 2-3 years of historical data, making them Group, the two veterans of 1989, declined to backward-looking, and are not sufficiently granular to participate in this study. Accenture, the consulting drive tactics. firm with a significant analytics practice, is also not Marketing mix models are also able to estimate both included here for the same reason.

5 The short-term and long-term (quarterly, annual or MMM firms originally built regression models at the multi-year) effects of advertising. However, this is not market level DMAs or other sales territories frequently done since advertisers focus almost with observations by week. Today, they all offer more exclusively on short-term performance. granular analytics with finer geographies and shorter Not all of these modelers are the same. Nielsen and time periods, and have also developed attribution IRI have exclusive access to their store-level data, capabilities within their MMM framework, Unified which provides the perfectly defined view of retail Models. Simple linear regression has given way to promotion tactics so important to CPG marketers. more advanced statistical techniques, frequently Marketing Evolution and Millward Brown both have hierarchical Bayes (see glossary). However, the consumer-level techniques that look below the regression model built on weekly DMA level data is market level, more like attribution modelers.

6 But their still a common denominator. ability to provide a more comprehensive view of the Marketing mix models typically incorporate all of marketing mix gives us reason to group them here. the controllable (trade spending, for instance) and The unique benefit of these approaches is that they uncontrollable factors (weather, for instance) of the can be both strategic and tactical, and offer insights marketing mix, and produce a sound estimate of the into consumer segments. 3. MARKETING MIX MODELERS WITH ATTRIBUTION PRODUCTS. Analytic Partners PRIMARY USE CASE Measure, forecast and optimize the impact of marketing investments, short-term and long-term for multiple KPIs, including revenue, profit, brand equity, acquisition, unique visits, store traffic, etc. PRIMARY OFFERINGS. USE CASES. Marketing Mix 24% TV Attribution .. 30%. Contribution Assessment Digital Attribution .. 8% Unified Models.

7 60%. Digital Campaign (20% location). Cross-Media Campaign Full Marketing Mix APPROACH MEDIA COVERED. KPIs Integrated store/market/geo/ All addressable and non-address- Online Traffic segment-level econometrics and able paid, owned and earned Online Conversion person/user/HH-level discrete media that influence performance, Offline Retail Traffic choice attribution models using such as TV, Radio, Magazines, Out Offline Sales machine learning Of Home, Mobile (Display, Video, Brand Metrics Search, In-app, Social), Digital Display, Online Video, Native Ads, SOURCE OF TV DATA Social, Paid Search, Organic Budget Optimization Rentrak, Kantar, NMR Search, Word Of Mouth, Influencer Across Digital Channels Programs, PR, etc. Across Cross-Media Channels Across Sales & Brand Metrics LEVEL OF GRANULARITY DATA INTEGRATION MODEL INPUTS. Geography varies by media type, CRM data linked by person/ Other Marketing Variables person/user/HH, DMA, Zip, daily, customer; non-addressable External Influences weekly or event-level media type, media aligned on geography Competitors genre, sub-type and property; and time, partner with panel creative at the individual execu- providers, device maps and tion-level.

8 Outcomes: customer onboarding partners ADVERTISING PARAMETERS. segment, market or store-level Diminishing Returns Adstock Long-term Effects COLLINEARITY WORKAROUND MODEL/RESULTS VALIDATION. Media Interactions and Halos Granular data, raw data Normative database and model fit Baseline/Incrementality transformation, experimental statistics; Experimental Design design, statistical techniques Holdout, Forecast Accuracy DATA DELIVERY &. APPLICATIONS. CYCLE & REFRESH TIMING Dashboard Real-time (daily and/or weekly) data updates and weekly, monthly Optimizers or quarterly model refreshes Programmatic Data Feeds to Other Sources 4 RESEARCH PROVIDED BY. MARKETING MIX MODELERS WITH ATTRIBUTION PRODUCTS. in4mation insights PRIMARY USE CASE Marketing mix returns on marketing investments, resources allocation, profit optimization, sales performance and change drivers with highly disaggregated data. PRIMARY OFFERINGS.

9 USE CASES. Marketing Mix TV Attribution .. 0%. Contribution Assessment Digital Attribution ..0% Unified Models .. 0%. Digital Campaign Cross-Media Campaign Full Marketing Mix APPROACH MEDIA COVERED. Econometrics (Hierarchical Digital impressions by DMAs, KPIs Bayesian and network models) device types, Display/Video, and Online Traffic integrating behavioral and campaign. TV and Radio by Online Conversion attitudinal metrics, geo-location dayparts, day of week, positions in Offline Retail Traffic and other metrics break, program genre, source and Offline Sales spot length, Magazines and OOH Brand Metrics SOURCE OF TV DATA. Budget Optimization Client provided Across Digital Channels Across Cross-Media Channels Across Sales & Brand Metrics LEVEL OF GRANULARITY DATA INTEGRATION. MODEL INPUTS. Geo-location ( , store) up to Store and market-level data national, daypart, daily, weekly or are harmonized by time and Other Marketing Variables monthly, media type, genre, source, geography External Influences and spot length; campaign-level; Competitors outcomes at national, market, or store-level ADVERTISING PARAMETERS.

10 Diminishing Returns Adstock COLLINEARITY WORKAROUND MODEL/RESULTS VALIDATION. Long-term Effects Bayesian priors, Bayesian variable Holdout samples and model fit Media Interactions and Halos selection methods and other related statistics Baseline/Incrementality techniques DATA DELIVERY &. CYCLE & REFRESH TIMING APPLICATIONS. Typically quarterly Dashboard Optimizers Programmatic Data Feeds to Other Sources 5 RESEARCH PROVIDED BY. MARKETING MIX MODELERS WITH ATTRIBUTION PRODUCTS. IRI. PRIMARY USE CASE IRI provides a full suite of solutions both marketing mix modeling and attribution solutions as well as matched market or matched store testing. Focused on the CPG industry. PRIMARY OFFERINGS. USE CASES. Marketing Mix TV Attribution ..N/A. Contribution Assessment Digital Attribution .. Unified Models .. Digital Campaign Cross-Media Campaign Full Marketing Mix APPROACH MEDIA COVERED. Marketing mix modeling and TV, Digital, Social, Print, Radio, KPIs attribution studies Mobile and OOH Online Traffic Online Conversion Offline Retail Traffic SOURCE OF TV DATA Offline Sales Rentrak or client Brand Metrics Budget Optimization Across Digital Channels LEVEL OF GRANULARITY DATA INTEGRATION Across Cross-Media Channels Across Sales & Brand Metrics Data at store, zip or DMA level Ingests, normalizes and harmonizes disparate datasets within their data platform.


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