Data-driven marketing is the answer. Now, what s the question?

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1 Data-driven marketing is the answer. Now, what s the question? The promise of data-driven marketing is undeniably attractive It provides marketers with the insight and information to better understand their customers. With that understanding, they can then deliver a seamless customer experience. What does that mean? It means that no matter where customers are in their buying journey, every engagement is tailored to their specific wants and needs. So marketers can deliver the right message with the right offer at the right time. No spam. No mistaking personas for actual people. No yada, yada, yada. Marketers get to be more relevant. Their customers get the information, offers, and products they want on the terms they want. Everybody wins. In the process, your company strengthens its competitive position by delivering on customer demand. Now, assuming you believe data-driven marketing is the answer, perhaps the first question you should ask yourself is, Given what I m doing now, what do I need to do next? 1

2 Customer-first meets data-obsessed More and more, customer-first is becoming a staple of mission statements and a guiding principle for marketing, sales, and support organisations across every industry. Meanwhile, the product, operations, and finance functions have long been anchored in analytical decision-making processes and frameworks. Yet to thrive in an increasingly digital business environment, front- and back-office functions must work together to ultimately improve and accelerate decisions based upon a data-driven view of the interactions and experiences that matter most to customers. That s why today s CMOs increasingly find their roles converging with their CIO and COO counterparts around a shared desire and mandate to create contextually relevant customer experiences that enhance the brand and bottom line. However, before organisations can deliver, they must establish a solid foundation of customer recognition, marketing attribution, and KPI hierarchy. Customer recognition: a true 360-degree view For years, marketers have pursued a 360-degree customer view meaning they try to link and assimilate all customer behaviour and profile data. Why? To get the full measure of a given customer s behaviour, preferences, satisfaction, and revenue potential. Yet all too often, the 360-degree view is pursued as a data warehousing exercise, where marketing and IT partners try to exhaustively connect data tables and types to one another. Problem is, they often do so without a clear understanding of or uniform approach to classifying data, dependencies, and actions across channels, databases, platforms, and lines of business. As a result, most organisations still struggle to derive meaningful insights from all their data let alone deliver real-time, personalised experiences. Attribution: beyond last touch It s not an overstatement to say that last-click attribution is a baseline for most companies meaning they re trying to correlate a sale to the last click in the customer purchase journey that preceded that sale. Yet to achieve a more complete understanding of attribution, you have to develop customer recognition methodologies with a clear understanding of the entire customer experience. And to overcome the limitations of last-click attribution, you have to understand end-to-end business processes. Only then can companies connect cross-platform tracking and orchestrate experiences across the organisation. In most organisations...the challenge of solving attribution falls firmly in the marketing organisation 70% of the time, according to Forrester. 1 2

3 To guide your move toward multichannel attribution and help marketers create cross-organisational alignment and support, MRM//McCann Analytics has worked with clients to develop a six-layer framework. Solving for Marketing Attribution: Six Capability Layers WHAT WHY IT MATTERS MARKETING LIFE CYCLE Current marketing experiences across paid/owned/earned marketing programmes. This identifies the ecosystem of touch points and associated tagging/tracking implications. TAGGING AND TRACKING METHODS SYSTEM INSTRUMENTATION SEQUENTIAL EVENT STREAMS FRACTIONAL EVENT MATHEMATICS How are various marketing touch points being tagged for performance tracking? How are marketing systems being integrated into touch-point conversions to capture path information? Do the data keys exist in client systems to re-create a customer s marketing path? What is the best way to assign fractional credit to marketing touch points and tactics? Tagging/tracking methods dictate the recognition of channel events and metadata surrounding those events. Business systems and marketing conversion choke points must be instrumented to capture requisite IDs and event data. Data structures must be anticipated for the data assimilation keys needed to connect event data and context to contacts and campaigns. Attribution credit assignment can be done by using a variety of methods, but advanced methods use machine learning. ATTRIBUTION INSIGHTS Which programmes, channels, tactics, and sequences are most effective? Serving attribution insights to a large enterprise includes the need to capture, process, attribute, and visualise impact results. The first two layers of the framework focus attention on current channels used, as well as the tagging and tracking methodologies used to gain visibility as marketers shift their focus from one channel to another. System instrumentation and sequential event streams allow organisations to calibrate their data capture engines to recognise and record the full campaign stack at time of conversion. These two capabilities also help marketers understand the click-event sequences leading up to the conversion event. The final two layers apply weighted attribution credits to marketing tactics commensurate with their impact on a given conversion event. Techno-jargon aside, this approach is the only way to achieve a true understanding of how much influence each marketing interaction is having on a given customer. With that information, marketers can prioritise and make investments accordingly. Predictive modelling: sharpening the focus Predictive modelling is the use of data about past events to gain insight into possible future outcomes. This practice continues to hold tremendous promise for marketers as a means to anticipate demand, create business advantages, and build lifetime loyalty. Increasingly robust attribution methods enable marketers to make connections among relevant data points and clearly identify drivers of customer experience and business performance. By taking a bottom-up approach to understanding the impact of marketing tactics, channels, and customer segmentation, marketers can then drill down into performance and adjust marketing spend and tactics based on algorithmic modelling. Okay, so if a holistic view of your customers, full cross-channel attribution, and bottom-up predictive modelling is the goal, you re probably asking yourself how to assess your current capabilities and put a plan in place to move forward. 3

4 If you don t know where you re going, you ll end up somewhere else. Yogi Berra Data-driven marketing is not a one-size-fitsall solution. And it certainly isn t something you can switch on overnight. Rather, it s a matrix of strategies, processes, and programmes you can tailor to meet your marketing needs. Key to understanding how you can use those elements most effectively is the Adobe Data-Driven Marketing (DDM) Maturity Model. This model shows how data-driven marketing contributes to marketing success and what s required to achieve that success as a company creates programs that are more personalized, dynamic, and scalable. Data-driven marketing needs to be implemented in steps, with a clear road map of where a company wants to go with it. Not all companies will want or even need to reach full maturity. The Adobe DDM Maturity Model is a self-assessment tool that can help you find the stage that s right for you and adopt the marketing methods that meet your needs. The important thing is to determine where your organisation wants to be in the Adobe DDM Maturity Model and then align the functions needed to achieve a higher level of digital maturity. 4

5 Adobe Data-Driven Marketing Maturity Model DESCRIPTIVE DIAGNOSTIC FOUNDATIONAL PREDICTIVE PRESCRIPTIVE COLLECTION MARKETING DATA SOURCES: Owned websites CRM systems Select offline channels MARKETING DATABASES: Fragmented storage MARKETING DATA SOURCES: First-party offline and owned - Digital properties - Apps - Social web MARKETING DATABASES: Single system storage Use basic A/B testing to reveal optimisation opportunities. MERGED SALES AND MARKETING DATA SOURCES: Paid and earned web and outbound systems ( , etc.) Mobile apps and web Offline attributes Offline channels Third-party sources Leverage insights to reveal valuable segments and characteristics (e.g., shopping behaviour). SINGLE SOURCE OF TRUTH: Consolidated collection strategy ALGORITHMS DETERMINE VALUE AND RELEVANCE OF: Owned Offline Third-party data sources Use lookalike modelling to find similar high-value audiences and extend audience size. Second-party (partner) data is merged with other sources for additional audience insights. Adaptive algorithms determine when to activate 360-degree customer data for analysis. Smart systems consume the right data to act on. ANALYSIS Track volume data (e.g. channel activity). TARGET AUDIENCES BASED ON: CTR Impressions Conversion Some engagement metrics Understand audience overlap across sites and media channels. Identify digital property visitor segments. KPI focused. Develop audiences based on stronger, merged data. Understand attributes and behaviours of detailed segments with higher conversion rates. Focus on knowledge-based optimisation. CONFIDENTLY PREDICT OUTCOMES OF PROPOSED CUSTOMER INTERACTIONS: Propensity modelling Lift/gain charts Leverage customer insights for simulation and deep machine learning to drive customer journey optimisation (e.g. next-best action/offer). Analysis in real time for timely insights. DECISION MAKING Opinion-based decisions by individuals and groups with access to volume reports and media platform data. Individuals and groups (informed by requested data) make response-based decisions. Limited A/B testing. Marketers create and activate segments based on available engagement data. Decisions made by individuals per data-based criteria to maximise conversions. Testing hypotheses are guided by data. MARKETERS CAN: Create segments from unified profiles from aggregated data. Use frequency and recency rules to maximise reach. Data is democratised across teams. Some automation is in place for simple, revenue-based decision making (e.g. placement strategy). Higher-performing experiences are automatically promoted. Media buys and campaign decisions automated to maximise ROI. Individuals can act on system recommendations to enable high-value strategic decisions. Advanced marketing optimisation systems prescribe best course of action for marketer. AUTOMATION REGULAR REPORTS AUTOMATICALLY SENT TO KEY STAKEHOLDERS: IT support typically needed Many systems have audience or segment capability. Limited or manual personalisation. AD HOC REPORT DELIVERY: Stakeholders get the data they want when they want it. Data tends to be fragmented due to multiple reporting systems. Limited or manual personalisation. STAKEHOLDERS RECEIVE IN NEAR-REAL TIME: Trend data Basic notifications Alerts Responses are mostly rules-based and are both automated and marketer-driven to offer some personalisation. Goal-based real-time alerts (e.g., intelligent alerting) with probable causal factors (contribution analysis) and recommended actions. DATA DRIVES AUTOMATED RESPONSES: Triggered communications Personalised images, text, offers Triggered campaigns and promotions from decision making fuelled by artificial intelligence. DYNAMIC AND PERSONALISED RESPONSE TRIGGERED: By goals, success metrics, customer actions and anomalies In real time Offering optimised experiences Integrated and efficient reporting. Understand audiences in one system. ATTRIBUTION Single-touch, simple rules-based attribution (e.g. last touch/first touch). Multitouch, rules-based attribution (e.g. even, participation/linear, starter-player-closer). Multitouch, advanced rules-based attribution (e.g. latency/decaying, adjacency, custom). Marketers rely on tests to prove marketing effectiveness. Multitouch, data-driven attribution (e.g. algorithmic attribution). Allow winning experiences to drive new customer experiences. Integrated marketing performance management (e.g. algorithmic attribution fuels media mix modelling and programmatic ad buying). Overlap reports identify high-value audiences. Yes, actually, you can get there from here The Adobe DDM Maturity Model visually represents the range of optimisation possibilities for a company s digital marketing initiatives. As a company moves from left to right along the horizontal axis, those initiatives evolve: From Scheduled Groups Single channel A point in time Measurement Business rules To Dynamic Individual Cross-channel Real time Prediction Algorithms 5

6 To put it another way, as a company moves horizontally within the model, its data-driven marketing becomes more sophisticated, scalable, and personalised. And it becomes increasingly empowered with timely, actionable data about the behaviours, preferences, problems, wants, and needs of individual customers. That s the kind of data that enables marketers to make iterative, results-based decisions. Listed vertically down the Adobe DDM Maturity Model are the functions required to support each maturity level. In contrast to the horizontal axis, movement along the vertical axis does not indicate a quantitative or qualitative progression. All functions are equally important, and they need to align appropriately to support a company s chosen maturity level. To start, you need to understand where you are today (AS IS) and what your desired marketing experience will be (TO BE) to deliver exceptional customer experiences at a business-positive ROI. The MRM//McCann + Adobe Data-Driven Marketing Maturity Curve, below, provides a road map showing the sophistication required to transition a marketing organisation from a descriptive to a prescriptive data-driven organisation. The MRM//McCann + Adobe Data-Driven Marketing Maturity Curve PRESCRIPTIVE PREDICTIVE FOUNDATIONAL Machine Learning DIAGNOSTIC Always On Algorithmic (predictive) ROI optimisation DESCRIPTIVE Closed-Loop Clustering (predictive) Multichannel optimisation Multichannel Master individual profile Channel Dynamic optimisation Smart data consumption A/B testing (present) Dynamic deployment No testing Target segmentation Next-best action Integration of on-off-domain Master marketing profile Ad hoc processes Target clustering Most organisations find themselves further down the curve than they might expect. This is not usually due to resources, capabilities, or lack of desire to change, but rather the difficulty in connecting the data-driven journey across legacy systems, regional considerations, organisational silos, and political fiefdoms. Whether you re taking a measured approach to incremental improvement or moving aggressively towards best-in-class personalisation, a simple road map will help you build consensus across disciplines and departments. So now the question for marketers is: where should my company be in the Adobe DDM Maturity Model? The answer hinges on three things. 6

7 A mature approach to people, processes, and tools People: digital marketing maturity starts with leadership. 2 In a digitally mature organisation, senior leadership actively sets the priorities and data-driven strategies. And marketing leaders partner closely with IT and functional business teams across product development, enterprise technology, and digital strategists to drive business results. 3 Processes: in the brave new world of data-driven marketing, process is power. It s the strategic implementation of marketing activities that enables you to pursue your business goals. These marketing activities leverage technology and automation to support the analytics needed in that pursuit and they are updated to optimise outcomes and become more customer-centric. 4 Tools: as a data-driven marketer, your challenge is to manage three fundamental marketing dimensions: channels, audience, and data. Your toolkit for doing this includes a range of digital technologies and applications that enable you to target audiences, aggregate and analyse data, and optimise digital content. 5 Once you assess your digital maturity, it s important to have a structured approach to implementing an end-to-end framework that allows your organisation to fully optimise targeting strategies, decision flows, processes, and tools to connect customer experiences to business value. With this in mind, perhaps there s another question worth asking now: who is doing this well, and how are they doing it? Two short case studies provide some answers. 7

8 Case studies Global Technology Leader: breaking down silos; building consensus At one of the largest information and communication technology (ICT) companies, numerous stakeholders from different departments were having difficulty agreeing on what data-driven marketing initiatives to implement, as well as on what issues were involved in doing so. It wasn t for lack of trying they had already invested in a number of appropriate technologies. The problem was they hadn t implemented them in a way that enabled the marketing infrastructure to deliver on the promise of data-driven marketing. MRM//McCann began solving the problem by gaining a clear understanding of 1) what the current marketing technology systems were, 2) what data was available and where it resided, 3) what methodology was used for threading information from those systems together, and 4) how marketing assets were tagged and tracked so as to track and understand performance. With that understanding, we outlined a path for them to reach their appropriate level of digital maturity. The MRM//McCann + Adobe Data-Driven Marketing Maturity Curve PRESCRIPTIVE ICT Leader PREDICTIVE FOUNDATIONAL Machine Learning DIAGNOSTIC Always On Algorithmic (predictive) ROI optimisation DESCRIPTIVE Closed-Loop Clustering (predictive) Multichannel optimisation Multichannel Master individual profile Channel Dynamic optimisation Smart data consumption A/B testing (present) Dynamic deployment No testing Target segmentation Next-best action Integration of on-off-domain Master marketing profile Ad hoc processes Target clustering Together, these steps provided a clear path towards realising the end-state promise of real-time marketing optimisation. 8

9 Smart Furniture: making customers comfortable, even on the website Smart Furniture started out as a small company with a two-person marketing team. A rapidly diversifying product portfolio and expansion into different categories soon provided the opportunity to adopt a more customer-centric approach. So Smart Furniture assigned individual marketers to focus on various promotional tactics, including social media, paid search, and more. The goal was simple: to present the right product(s) and message to the right people at the right time. To that end, Smart Furniture turned to the Adobe Marketing Cloud to help it understand the customer base and to test and target digital experiences. With capabilities for a self-learning algorithmic approach, Smart Furniture now conducts multiple tests concurrently. Insight is gleaned on revenue, conversion rates, average order value (AOV) and revenue per visitor (RPV). With baselines established, Smart Furniture uses the results for iterative follow-up, refining offers, messages, and audience segmentation as relevant. The company is also focusing on improving the usability of its site navigation and testing new merchandising tactics. With these steps, the company is taking a more algorithmic approach to its marketing. It now has the customer data to make revenue-based decisions and predict the outcomes of customer interactions. In a few short years, Smart Furniture has achieved, by self-assessment, an advanced state of digital marketing maturity. The MRM//McCann + Adobe Data-Driven Marketing Maturity Curve PRESCRIPTIVE PREDICTIVE FOUNDATIONAL Machine Learning DIAGNOSTIC Always On Algorithmic (predictive) ROI optimisation DESCRIPTIVE Closed-Loop Clustering (predictive) Multichannel optimisation Multichannel Master individual profile Channel Dynamic optimisation Smart data consumption A/B testing (present) Dynamic deployment No testing Target segmentation Next-best action Integration of on-off-domain Master marketing profile Ad hoc processes Target clustering Partial results indicate: Conversion rate is up 300%. AOV is up 5%. Pages per visit are up 2x. Time spent on site is up 3x. There was a dramatic decrease in bounce rates. 9

10 For more information on how Adobe + MRM//McCann can help your company along the path to digital maturity, please visit 1 Forrester. 10/05/2012. Buyers Want Expertise from Attribution Providers Ibid Adobe and MRM//McCann. All rights reserved. 10