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The Unsung Heroes of Marketing Insight White Paper by BECKON


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Consistent, best-practice marketing data management should be the top priority for every omnichannel marketing department. Only if we take the time to perform the unglamorous task of working below the waterline to structure our data in smart, useful ways can we take full advantage of what modern marketing has to offer.

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The Unsung Heroes of Marketing Insight White Paper by BECKON

  3. 3. 3 WWW.BECKON.COM HELLO@BECKON.COM INTRODUCTION Modern marketing intelligence demands that we gather, integrate and analyze far more data streams than ever before, and it demands that we do it very fast—in as near to real-time as possible. Much fuss is made over technology systems required to process ‘big data’ (Hadoop, DMPs, etc.), and on the analytics that matter in this new omnichannel world (mix modeling, test and control, attribution, etc.). But the unsung (and not very sexy) heroes of real- time, omnichannel performance analytics are more fundamental: taxonomy and normalization. These are the keys to turning messy big data into big insight. Without consistent categorization and normalized marketing definitions, critical datasets of spend, marketing activity and business outcomes cannot be merged for analysis. Without a consistently applied taxonomy, any sort of analysis that might be conducted simply can’t to be trusted. We can’t benchmark or drive reliable predictive analytics if we can’t trust our data. And we won’t have confidence in any insight untrustworthy data reveals. The finance discipline figured this out long ago. Using Generally Accepted Accounting Principles (GAAP) to standardize definitions, and a general ledger or chart of accounts as a taxonomy, finance makes sense of its own messy data problem without fuss every day. Marketing intelligence professionals, too, must get ready to work “below the waterline” on taxonomy and normalization— the foundations of data integrity—in order to derive insight from messy performance data. The marketing performance measurement future will be about showing early wins and faster results. —Source, ARF West 2015
  4. 4. 4 WWW.BECKON.COM HELLO@BECKON.COM Until we can be persuasive with good quality data, we’ll continue to have disaffection for what data and models can do for us. —source, ARF West 2015 In this paper, we’ll review best practices for consistent data labeling that applies a marketing-specific taxonomy and normalizes messy marketing data so benchmarking, reliable predictive analytics, and trustworthy insights are possible. We’ll show what is enabled when standardized definitions and categorizations are implemented, and we’ll give examples of how Converse, Microsoft Mobile, UnionBank and others aligned their disparate datasets and unlocked critical insights thanks to the power of taxonomy and normalization.
  5. 5. 5 WWW.BECKON.COM HELLO@BECKON.COM MODERN MARKETING DATA MANAGEMENT—THE NEW NORMAL Marketing insights and research teams know how to structure data. They have been structuring research studies and conducting structured data gathering via surveys and other forms of quantitative research for a long time. And they know the power of standardized aggregations (e.g. males 11-17, females 18 to 34, etc.). What’s shifting is the volume and variety of data intelligence professionals must gather, synthesize and interpret. An immense amount of data has suddenly become available to the modern marketing organization and it’s coming at us from every touch point – email, TV, print, radio, online display, search, social media, mobile and web. These datasets (that we didn’t create) are coming at us in formats we can’t control. But the rate and variety of information streaming into marketing departments today simply cannot be ignored. The available information has never been more rich, nor so fresh. If we can find a way to “read” it, or make sense of it in real-time, we can know right away which content and offers are resonating with which audiences, and what part of our spend is most and least effective across the entire mix. Modern marketing data simply must be wrangled, tamed and synthesized before that can happen. This represents a significant shift for marketing insights professionals. Insight teams can’t just be creators of perfect datasets any longer—they must become master data integrators. Modern marketing success simply demands that we manage and analyze large volumes of disparate datasets with speed and agility. It’s no small task, but no doubt one of the largest imperatives facing omnichannel brands today. Data normalization and categorization is a challenge for any business function (although finance, sales and HR have mastered the discipline far better than marketing has), but it’s especially 1
  6. 6. 6 WWW.BECKON.COM HELLO@BECKON.COM challenging for omnichannel marketers. Our spend and performance data is spread across dozens of specialized execution tools and systems. Our business outcome data (spend and revenue) is somewhere else. Brand health metrics like net promoter scores and customer satisfaction ratings are somewhere else still. Our data lives in spreadsheets, PDFs, word docs, PowerPoint decks and emails. It comes in clicks, likes, gross ratings points, page views, downloads, impressions, foot traffic and retail sales. We simply must create a sustainable data management discipline to get to the level of optimization speed and agility the modern marketplace demands. Normalization and taxonomy are where it begins.
  7. 7. 7 WWW.BECKON.COM HELLO@BECKON.COM TWO BIG DATA SILO-BUSTERS: NORMALIZATION, TAXONOMY If there are two silver bullets that make real-time omnichannel data management and analytics possible, they are: 1. NORMALIZATION Normalization puts the consistency into marketing data. It ensures that we’re performing apples-to-apples comparisons between things. There are two key aspects of our data that must be normalized: metrics and units. Metrics Digital marketing metrics are a relatively new thing and the vernacular we use to describe our world morphs all the time. Which makes for a lot of confusion. For instance, if one dataset calls every video play a “view” metric and another dataset also calls every web page visit a “view” metric, automatically merging the two sets doesn’t illuminate much for a marketer. In fact, it muddies the waters—each is an importantly different customer interaction. The finance profession is lucky in that is has a global set of standards called Generally Accepted Accounting Principles (GAAP) that defines metrics for the entire industry. All finance has to do is adhere to them. The marketing industry has yet to adopt any standard definitions of things, which means we must take the initiative to do it within our own organizations. And, like finance, we must adhere to them. 2
  8. 8. 8 WWW.BECKON.COM HELLO@BECKON.COM Units If one dataset of click metrics includes a week’s worth while another has a month’s worth, or if one set of sales figures is in dollars and the other in yen, then no resulting analytics can or will be trusted. Normalization reconciles disparate data so that apples-to-apples comparisons are possible. Here’s an example of normalizing data that covers two different time periods: Now, we can directly compare the performance of each channel. Of our two silver bullets, normalization is the more straight forward process. Let’s spend a bit more time discussing taxonomy. CHANNEL RAW DATA NORMALIZED DATA Google AdWords 1200 signups/week 1200 signups/week DART 6000 signups/month 1500 signups/week
  9. 9. 9 WWW.BECKON.COM HELLO@BECKON.COM 2. TAXONOMY A taxonomy is a classification system. It’s the process of adding an additional layer of description data to our raw data in the form of tagging, or “metadata”. One familiar example is what we might have learned in high school—the way life is classified by biologists. Here’s an example of the taxonomy that describes man: TAG PROPERTY Kingdom Anaimalia (“animals”) Phyllum Chordata (“chord in the back”) Class Mammal Order Primate Family Hominid (“man shaped”) Genus Homo (“man”) Species Sapien (“wise”)
  10. 10. 10 WWW.BECKON.COM HELLO@BECKON.COM In business, the most familiar taxonomy is the general ledger or financial chart of accounts: ACCOUNT CATEGORY 8200 Administrative Expenses 8210 Office Supplies 8220 Phone & Fax 8230 Postage 8300 Computer Expenses 8310 Software 8320 Consultant Services 8330 Other computer expenses With this system, every one in the company tags financial data on the way in with account codes. When we submit our expenses for reimbursement, for instance, typically we need to report expenses and associate each with the right general ledger category. And because all the messy spend data is categorized on the way in, as data gets entered, the finance team has real-time visibility into the financial status of the whole business. Also, it can instantly drill down into each reporting category to understand spend in any one area. “Tagging” financial information with a consistent account code structure is what allows for instant visibility into detailed financial performance.
  11. 11. 11 WWW.BECKON.COM HELLO@BECKON.COM For marketers, if we want to know which channel most cost effectively drives shoe sales among males aged 11-17, campaign spend and revenue must be tagged, or described in the data by segment, channel and product. In the above example, each ad spend and attributable revenue figure in column 1 would be given the three tags in its corresponding row. For instance, the $150,000 Google AdWords spend would be tagged as follows: PRODUCT LINE = HATS SEGMENT = MALES 11-17 CHANNEL = GOOGLE ADWORDS The $3.5 MM revenue figure in the same row, 2nd column would have the same set of tags. METRICS TAGS AD SPEND ATTRIBUTABLE REVENUE (MM) PRODUCT LINE SEGMENT CHANNEL $150,000 $3.5 hats Males 11-17 Google AdWords $600,000 $8 shoes Males 11-17 Google AdWords $750,000 $16 hats Males 11-17 TV ads $250,000 $14 shoes Males 11-17 TV ads $75,000 $8.5 hats Females 11-17 Google AdWords $450,000 $7 shoes Females 11-17 Google AdWords $500,000 $10 hats Females 11-17 TV ads $750,000 $18 shoes Females 11-17 TV ads
  12. 12. 12 WWW.BECKON.COM HELLO@BECKON.COM Of course in the real world, it gets a bit more complicated—we’re typically active in far more than just two channels. Here’s a handy taxonomy that covers most of the channels available to marketers today and separates them into online and offline categories by adding another tag: channel type. CHANNEL NAME PROPERTY Display Online Email Online Website Online eCommerce Online Mobile Ap Online SEO Online SEM Online Facebook Online Twitter Online Other social Online Video Offline TV Offline Radio Offline Print Offline Out of Home (OOH) Offline Direct mail Offline PR Offline Events Offline In-house Offline Retail Offline Call center Offline
  13. 13. 13 WWW.BECKON.COM HELLO@BECKON.COM But more than just being able to drill down by segment, channel, product line and line of business, we often want frame our marketing data with the activity’s primary intent. Here’s an example tagging system that aggregates marketing performance data into the more holistic and universal frameworks we commonly use—the buyer’s journey, or customer funnel that analyzes our success with driving awareness, engagement and outcomes. It further classifies our awareness metrics by media type. METRIC MEDIA TYPE FUNNEL STAGE Display impressions paid awareness AdWords impressions paid awareness TV impressions paid awareness Facebook likes earned awareness Twitter mentions earned awareness Pinterest Pins earned awareness Website page views owned awareness Community page views owned awareness Sales n/a outcome Sign-ups n/a outcome Downloads n/a outcome
  14. 14. 14 WWW.BECKON.COM HELLO@BECKON.COM As we can quickly see, there are many ways to use tags to classify our data. But this is exactly what has to happen if we are able to drill down into our data at a moment’s notice in order to answer the business questions we get every day: Which is our best channel for laptop sales? Which content resonates best with millennials? How well did women engage with our Mother’s Day Campaign? Here’s a set of further examples of how we might tag incoming metrics. METRIC TAG Click-through-rate on Google Ads offering a 10% discount on back to school laptops targeted to Dads in North America. Channel = [online, SEM], Campaign = [Back to School], Content/offer = [10% off], LOB = [consumer], Audience Segment = [male], product group = [Laptops], and Region = [North America], funnel stage = [engagement] Banner ad impressions offering 2 for 1 pricing on Odwalla products to Millennials served during a Memorial Day campaign in Latin America. Channel = [online, display], Campaign = [Memorial Day], Content/offer = [2 for 1], Audience segment = [millennials], brand = [Odwalla], region = [Latin America], funnel stage = [Impression, paid media]. The count of Lexus RX photos shared by women over Facebook during a Mother’s Day campaign that offered to rebate the first month’s car payment. Channel = [Facebook], Campaign = [Mother’s Day], Content/offer = [First Month Free], LOB = [consumer], Audience Segment = [female], product group = [Lexus RX], and funnel stage = [engagement]
  15. 15. 15 WWW.BECKON.COM HELLO@BECKON.COM STRUCTURED MARKETING DATA REAL-WORLD BENEFITS The process of standardizing definitions and units and organizing marketing data according to a marketing specific taxonomy has very big, real-world benefits. Here are several examples of those benefits and how some of the world’s top brands have realized them: 1. Align marketing activities with business results. The Converse North America marketing team has gone through the exercise of applying standardized definitions, units and taxonomy. They are now able to quickly track marketing spend against target segments like males 11-17, and easily plot that spend against awareness, consideration, purchase intent and sales among males 11-17. They now know which activities aimed at males 11-17 drive the biggest purchase intent and sales jumps among that segment—a huge breakthrough. 2. Faster time-to-insight. By doing the upfront work of categorizing and normalizing data on the way in, Union bank reduced their time to insight dramatically. Now, they’re no longer sorting through piecemeal data looking for insights like needles in a haystack. Union Bank reduced their integrated marketing reporting cycle by 98%—from three months to 24 hours. 3. Big omnichannel ah-ha moments. When Converse opened its new San Francisco store, they were able to see for the first time, that when they ran Out of Home and Print media in that one market, they were getting higher response rates across the board: higher email open rates, more natural search volume, more use of store finder functionality, as well as increased click-through rates of the online display and search ads. Those datasets could not have been aligned and those insights would have gone undiscovered without the process of normalization and the consistent application of taxonomy. 4. Data-driven decision-support. Through normalizing and applying a taxonomy to their marketing data, Microsoft Mobile was able to plot different types of “High Quality Engagements” against phone activations. 3
  16. 16. 16 WWW.BECKON.COM HELLO@BECKON.COM Immediately, they saw that some types of engagements did drive revenue, but others did not. Indeed, only a few particular interactions in the series drove actual sales. Microsoft reallocated spend to focus on those interactions that were data-proven to drive sales.  5. Agency accountability. By tagging all its marketing performance data by the agency owner (another taxonomy best practice), Converse can now easily pull up a report that shows a normalized view of agency performance in an apples-to-apples format: which agency delivers the most per $1 they pay them in fees. This essential data can ensure accountability with whatever partners they work with and helps informs decisions when relationships are up for review. 6. Internal benchmarking. With normalized definitions and consistent taxonomy, GAP, Microsoft, Converse, KB Home, Stubhub and many more, stack-rank all their efforts by any taxonomy category and can easily see which: • country delivers the most per $1 they spend on marketing • ad network delivers the most per $1 they give them • social media platform drives the most engagement per $1 invested • products or teams are getting the best results on facebook • etc.
  17. 17. 17 WWW.BECKON.COM HELLO@BECKON.COM THE DATA STRUCTURE IMPERATIVE: MODERN MARKETING DEMANDS IT Consumer marketing analytics is the hottest space right now—investments in it yield extremely high ROI. —ARF West 2015 Not sexy, but oh so necessary. Taxonomy and normalization are now fundamentally necessary for omnichannel marketing success and there’s no turning back. Consistent categorization, normalized metrics and units are absolutely necessary before we can call our data trustworthy, and all the measurement methodologies we want to engage in—test and control, mix modeling and attribution—just aren’t possible if our data remains a basket of apples, oranges, bananas, persimmons, and kumquats. Consistent, best practice marketing data management should now be every omnichannel marketing departments’ top priority. Only if we take the time to perform the unglamorous task of climbing into the diving bell and working below the waterline to structure our data in smart, useful ways can we take full advantage of what modern marketing has to offer. Marketing performance measurement is a huge opportunity and worth the journey. —ARF West 2015 4
  18. 18. ABOUT BECKON To grow your brand, you need integrated, unbiased data and insights you can trust. You need Beckon, The Source of Truth for Marketing™. Beckon’s rock-solid data management and real-time marketing intelligence power better, faster decisions that let you do more with every marketing dollar. LET’S TALK Want to learn more? Get in touch at—we’d love to connect.