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    Kontagent .5 Kontagent .5 Document Transcript

    • Convergence Analytics I have been writing about Marketing Applications for the last 25 years since my earlydays as a direct marketer and have spent the better part of the last two decades morphing thatunderstanding first to web and digital marketing and for the last few years to mobile andconvergence marketing. Much of this content and data comes from a soon-to-be publishedreport from a partnership between Incisive Media ClickZ and Evectyv Marketing where I am apartner.When the Wright Brothers built their flying machine, they took a cluster of existing technologies,combined them in an innovative manner and came up with something long-sought and never-before-achieved. They called it an airplane.So what do we call all of this? Intelligence or Analytics can appear as very similar terms butthey are not. What’s the difference between Business Analytics and Business Intelligence? Thecorrect answer is: everybody has an opinion, but nobody knows, and you shouldn’t care.Do we call this Marketing Analytics, Marketing Intelligence, Customer Intelligence? Or maybeBusiness Analytics, Business Intelligence or Intelligence? And, how has Social and Mobilechanged our challengeHaving worked in the industry over twenty years, I can confidently say that everybody has adifferent notion of what ANY particular term associated with analytics means.For example, when SAP says ―business analytics‖ instead of ―business intelligence‖, it’sintended to indicate that business analytics is an umbrella term including data warehousing,business intelligence, enterprise information management, enterprise performance management,analytic applications, and governance, risk, and compliance.But other vendors (such as SAS) use ―business analytics‖ to indicate some level ofvertical/horizontal domain knowledge tied with statistical or predictive analytics.At the end of the day, there three requirements worth differentiating:The problems in nomenclature typically arise because ― intelligence‖ is commonly used to referboth of these, according to the context, thus confusing everyone.In particular, as the IT infrastructure inevitably changes over time, analysts and vendors(especially new entrants) become uncomfortable with what increasingly strikes them as a ―dated‖term, and want to change it for a newer term that they think will differentiate theircoverage/products (when I joined the industry, it was called ―decision support systems‖ – 1
    • Convergence AnalyticsWhen people introduce a new term, they inevitably dismiss the old one as ―just technologydriven‖ and ―backward looking‖, while the new term is ―business oriented‖ and ―actionable‖.The very first use of what we now mostly call business intelligence was in 1951, with the adventof the first commercial computer ever, dubbed LEO for Lyons Electronic Office, powered byover 6,000 vacuum tubes. And it was already about ―meeting business needs through actionableinformation‖, in this case deciding the number of cakes and sandwiches to make for the next day,based on the previous demand in J. Lyons Co. tea shops in the UK. So, enter Convergence Analytics: thats our name for the confluence of digital marketing,big data, cloud computing, APIs and sophisticated presentation-layer capabilities. The rush is onfor every company that ever measured anything for marketers (and many that never have) toclaim theyve got what it takes to provide the best single-view into all marketing data. Think of convergence analytics as the marketing equivalent of "one ring to rule them all":many application vendors are claiming that within a single application, by connecting data frommultiple sources, the marketer can gain a 360 degree view of the customers. This has long been arequest from the market, and now application vendors are able to combine technologies in anattempt to meet that request. Convergence Analytics still in its infancy as a discipline. But according to our surveyresults, there are a multitude of players in the market already, and many of them are pullingtogether data sources from web usage, call centers, CRM, campaign data, demographics,competitive data, and anything that gets captured off a click, keyword, mobile tap, or anynumber of other customer touch points. Theyre also using advanced data gathering and dataregularization strategies to create a dashboard-like experience for the marketer. For some, this will sound like "business intelligence" (BI) recycled and molded into amore shapely, marketer-friendly package. And to an extent they would be correct. Some entrantsin the market are calling themselves "BI for marketers" and their DNA is in the quant arenawhere power users build cubes and drilldowns in tools like Cognos and Hyperion. Others arecoming from a web analytics background, adding more data streams to their traditionalclickstream. Some have been combining data sets for years under the "predictive analytics" flag.And some are starting in fresh, with new applications and new approaches to solving the data- 2
    • Convergence Analyticscorrelation dilemma for marketers. The goal, as always, is to drive web marketing to deliver on its promise: and that promisehas always been wrapped in the notion that, because it is measureable, it is thereforefundamentally more effective than older, more traditional marketing efforts. Digital marketinghas always thrived on its ability to allow the marketer to learn quickly about how well theirmessaging is working in something close to real time. And the assumption has also been that themarketer, armed with more up-to-date and detailed information about content performance, canthen optimize those marketing effort in a virtuous cycle of improvement leading to a moredemonstrable Return on Marketing Investment. Perhaps the most salient factor in Convergence Analytics today is the speed at whichcompanies from every sector ar converging on it; and the similarity of the problems they seek tosolve. In a phrase, it seems that everybody is measuring everything: and telling the world. How close are we getting to that goal of a single view for all the data? Who are theplayers? Of what should the buyer beware? This report will help provide guidance for marketersand business executives who want to better understand the trends and highlights of this emergingmarket. Our report is based on survey responses from over a hundred different vendors and overfive hundred digital marketing practitioners. A few key findings are as follows: 3
    • Convergence AnalyticsMajor Data Points: Marketers82% of marketers are measuring Multi-channel data ("MC").37.5% say MC means web, social and mobile only35.6% say MC means web, mobile, social, marketing spend, sales, back-office data, off-linechannels (store, TV, radio, print).About half say they require "real time" data, but there is little consensus on what "real time"means.60% say they are either using a Business Intelligence tool now or plan to soon.Over 90% use web analytics tools but only 57% are using it for multichannel optimization; while40% use other tools to perform this work.Company size of Respondents were evenly split between $1-$10m and above $10m.---Major data points: VendorsMany marketing services companies considered themselves vendors, even if they did not appearto have a branded software offering in the market.70% say they collect real time data, but there is little consensus on what real time means.70% say they offer a dashboard; however, 45% say data cannot be queried through theirdashboard; and 30% say they have no direct access to a datamart or database75% say they "join" information from a variety of sources but only 56% use APIs or softwareconnectors55% say they have an analysis layer (software/algorithm)32% say they have predictive algorithms 4
    • Convergence Analytics50% say they have automated "extract, transform, load" capability35% have no self-service component in their offering while fully 84% offer professional servicesto their customers. Foundations (Underlying Technologies): Analytics or Intelligence? Marketing analytics is probably as old as the first time anyone put up a bigger sign thanthe one they had before. Digital analytics is born of what was once universally called ―webanalytics‖, itself the grown-up version of log-file analysis. Log-files are still gathered by everyserver today but they have been surpassed in utility by a combination of web-user trackingtechnologies focused on html-embedded javascript commonly known as ―tagging‖. Until recently, tagging itself was considered the sine qua non of customer analytics (atleast as it related to the web). The intense focus on web marketing produced an explosion ofdigital measurement, much of it based on tagging. The names that were made famous in the webanalytics era include Omniture, Webtrends, Coremetrics and more recently and most famously,Google Analytics. With Google Analytics, digital customer measurement became almost ahousehold word (especially if that household contained a marketer). Siloed in older parts of the organization have long been non-web-based customerdatabases that included CRM (a descendent of the Rolodex), direct mail customer lists, point-of-sale analytics, and survey data. Many of these, until the last few years, required massive on-sitecomputing power and specialists dedicated to making sure iron boxes stayed cool and morespecialists to ask the iron boxes questions the iron boxes could answer. Moores law played no small role in deprecating the in-house server farm. As processorspeed got cheaper, it became possible for server-farm-specialists to rent out virtual server space,and then virtual functioning software, to whomever could come up with a small monthly fee.Software companies retired their CD presses and, relying on the enhanced broadband networksthat now encircle the globe, have made call-and-response via browser interfaces as routine as 5
    • Convergence Analyticscorn flakes for breakfast. The results have been dramatic. Rare is the business today that does notpay someone for some remote processing and/or remote storage. For mid-sized businesses andmany large ones, outsourced software or ―Software as a Service (SAAS)‖ has become the rulewith rare exception. Old, un-wired databases are getting hooked up or getting siphoned dry. Themarket for in-house software has shrunken to a fraction of its former size. At the same time, everyone has taken for granted that understanding data is now a visualexperience. No one considers poring over long lines of numbers. The visor and the sharpenedpencil have given way to the ―user interface‖; the ledger to the dashboard; careful matching ofpuzzle-pieces to the custom report. Finally, social media has run like a fever through marketing departments globally. Whilemuch misunderstood as a marketing tool and while its business impact is difficult to ascertaineven with measurement, social media is so prevalent that it wins a place at the table because ofsheer bulk. Much as you cannot have a zoo without an elephant, you really cant have digitalmarketing these days without social media. History of Marketing Analytics Really, the history of marketing analytics begins with Direct Marketing--a form ofcustomer outreach that dates back several centuries and which, in a mature form, was thetemplate for, and was the basis of metrics for what we know today as either Web Analytics,Digital Analytics and most recently, Mobile and Convergence Analytics. While early references to the discovery of written appeals to the public date back to thedays of cuneiform, we can begin with a couple of instances somewhat closer to our ownhistorical times. In what has been called the first direct advertising booklet, William Penn in the year 1681published in England "Some Account of the Province of Pennsilvania (sp.) in America", whichwas meant to encourage immigration to the colony. He followed it up with several more, andeven included maps of Philadelphia and the surrounding countryside. Pennsylvania is one of themost populous states in the nation, and so clearly Mr. Penn would have been able to claimcampaign success at least over the long term. 6
    • Convergence Analytics In 1872, Montgomery Ward issued its first mail-order catalog; Sears soon published hisown; and what followed was an enormous catalog industry that thrives today both in paper andon line. Where shipping was once almost the sole province of the U.S. Post Office--everythingfrom post cards to threshing machines were sent via post--today delivery can take many differentforms, including, for media at least, a completely digital form (via download). But whatever thedelivery mechanism, the paradigm has remained the same since the days of Montgomery Ward:you can directly order goods from your own home (via a catalog sent directly to you) and havethem delivered without your ever having to "go shopping". In the digital marketplace, our ―sale‖ is not always as easy to define as it would be if wewere publishing mere catalogs of goods. Today the digital marketplace is host to as manybusiness functions as can be imagined: from true catalog sales to brand-awareness efforts toindirect or complex sales processes to self-service offerings; as well as media sites that selladvertising much as print publications still do today. What remains true to the direct marketingparadigm is the notion of a one-to-one relationship between company and customer; and anenduring mark of success is how well the customer feels connected to the brand behind the directmarketing effort whether on line or via an off line effort. Clearly the concept of "direct" marketing or selling has always includeddisintermediation--the removal of barriers between producer and consumer--or the "middle-man"as some might call it. But another key artifact of the catalog-ordering process has also comedown to us today in a digitized form, and that is customer-behavior tracking. Even in the earliestinstances, every catalog order had a record; every buyer had a history. Today, digital marketersseek to obtain insights from the same kinds of data that began to be available in the days ofbuggy-whips and coal-stoves. Many credit the term "direct marketing" to Lester Wunderman, who, according toWikipedia, "identified, named and defined" it in the mid-nineteen-sixties. Wunderman is alsocredited with the creation of the first 1-800 number, and such loyalty programs as the ColumbiaRecord Club and American Express Customer Rewards. For many years until the advent of the web, direct marketing had been associated almostentirely with direct mail. Long darkened in the shadow of Advertising--both print and television- 7
    • Convergence Analytics-direct marketing was considered decidedly unglamorous. Rather than "Whos Behind ThoseFoster Grants" in a major magazine spread, it was a postcard for a questionable hair-removalproduct; or a coupon for a can of deviled ham. The apotheosis of web-based ―advertising‖completely changed this—such that at least the electronic version of direct marketing came tooutstrip and even begin to transform its heretofore more glamorous cousins in the world of printand broadcast. The explosion of digital media would have been quite transformative enough on its own.But what has given it staying power for the enterprise is the same thing that distinguished directmarketing: measurability. And here we find the roots of Convergence Analytics. The web and itselectronic counterparts—email, social and apps—are expected, as a key component of theiroverall value, to deliver detailed customer intelligence. To the extent that this has succeeded or failed tells the story of the digital analyticsmarketplace. Tracking the Digital Customer Arguably the first commonly-used digital behavior tracking tool was a product created byWebTrends called Log Analyzer in the late 1990s. It was able to parse the log (activity) files ofweb servers; and it was able to report such things as ―hits‖ to the server and ―browser type‖ ofthe user and which pages were served and how often. It was enough to whet the marketer’sappetite and made marketers aware of ―web analytics‖ generally. Another early entrant wasKeyLime, which allowed marketers to view, perhaps for the first time, ―real-time‖ activity on itsweb site pages in a dashboard format. What became apparent to data analysts as they reviewed results from log files was thatthere were in fact substantial discrepancies between server activity and user activity. While―hits‖ became almost a byword for ―popularity‖, analysts knew that server activity was far froman accurate indicator of actual browser activity and user behavior. Demand for better accuracyand granularity encouraged the creation of a paradigm now in use almost universally in digitalanalytics. Commonly this is known as ―page tagging‖. Page tagging requires the placement ofsnippets of javascript into the html (usually in the header) of every page to be tracked; and it is 8
    • Convergence Analyticscoupled with a ―web beacon‖ or single pixel graphic file on the same page. When the invisiblegraphic file (or ―beacon‖ loads into the browser, it creates a ―call‖ to execute the javascript, andthe javascript performs the function of tracking the page load and, often, activity within the page.This created a much more accurate way of measurement—because tracking only takes placewhen the page actually loads into a browser. The companies that made this market grow included Omniture (now Adobe),WebTrends, WebSideStory (now Adobe) and CoreMetrics (now IBM), among others. The tagging paradigm remains the standard today. The market still offers products thattrack log files and server behavior; and there are significant products that measure user behaviorvia panels of users and algorithmic extrapolation, including Comscore and the well-knownNielsen company of broadcast television fame. These products have the significant advantage ofallowing customers to benchmark against competitors, but the actual metrics are in general not asdetailed or customized as those available with a tool that measures one’s own site in depth in acustomized, fully-tagged manner. Enter Games (ADD Zynga and the vast amounts of data – how “games drove technology– bandwidth, CPU and Data storage) Enter Mobile AnalyticsMuch like Direct Marketing discussed above, and unlike web analytics, Mobile analyticsrepresents a one to one relationship with ―the customer‖, and the goals, while the same as directand digital marketing which are to ultimately increase conversions, are driven by user actionswith a small screen or multiple screens.(ADD- LTV, Mobile Fist, Small screen, lots of data, and industry growth) The Search for ROI 9
    • Convergence Analytics There would be little reason for tracking user behavior if no action were associated withthe findings. And while too many practitioners today settle for simply knowing ―what’s goingon‖ and have no process for taking direct action based on what the data tells them, the market ingeneral has long been focused on utilizing digital analytics to demonstrate a return oninvestment. In fact, without this goal, the cost of purchasing and implementing digital analyticssolutions could never be justified. In general, digital analytics has failed to deliver sufficient ROI to justify its continuationin an unmodified form. It would be easy enough to point a finger at the application vendors and say they have notprovided the solution. But this would be to ignore the behavior of practitioners as part of theproblem. To be more precise, it would be to ignore the lack of an ROI-focused process on thepart of practitioners in which the applications would play a more productive role. While the purpose of this paper is not to inculcate a new process for digital ROI, it iswithin our purview to suggest what such a process might look like. Even as we move toConvergence Analytics—where many more silos of information get measured and displayedthan in digital analytics—it is safe to say that without an action plan based on discovery, there’snot much reason to engage in measurement at all. Consider the time-honored goal that long-predates digital marketing: sending the rightmessage to the right person at the right time. The output of this process is supposed to be greaterthroughput of goods and services. The only way this paradigm can be approached is withinformation about user-behavior (in old-fashioned direct marketing, this often would have beencharacterized as ―response rate‖). The desire remains the same today even in a far more complex digital marketingenvironment: right message, right person, right time. While the subjects of goal definition and actionability deserve more space than we canallow, we can touch upon the surface of it in order to suggest the reason why analytics continuesto hold promise even as it has often proven disappointing. Suffice to say that if the followingprocesses were taken up by organizations involved in digital measurement, then ROI would bemuch more achievable than currently surmised. 10
    • Convergence Analytics Briefly, there are two phases to a digital marketing action place involving analytics. First, let’s mention goal definition. What kind of content are you measuring? And what isthe goal of publishing that content (whether on the web, via social media, mobile, or in an app)? Type: Brand or Media Goal: Overall Time Spent Interacting with Content Type: eCommerce Goal: Direct Sale Type: Lead Generation Goal: Contact Information; Direct Contact; InboundMarketing Type: Self-Service Goal: Rapid Access to Information The above goals are guidelines to defining when a ―conversion‖ or ―desired action‖ willhave taken place. The frequency with which content drives a desired action equals its success as amarketing tool. Second, let’s describe a five-step virtuous cycle of improvement that can drive betterperformance through the use of marketing data. Step 1: Define Goals (determine content type and expected outcomes) Step 2: Collect Data Step 3: Analyze Findings Step 4: Adjust Content) Step 5: Measure Again and Compare And since this is a cycle of improvement, it should be an embedded process thatcontinually seeks improvement in content performance. Convergence Analytics will require the same approach. How many organizations areready to invest in the above activities in a meaningful way? The ones that do, will find theirdigital marketing performance much enhanced; and as they measure across channels, the samewill apply. Why Convergence Analytics? 11
    • Convergence Analytics It is fair to ask the question: if digital analytics has not succeeded as well as it might havein delivering ROI, why would we now be moving towards an even more complex measurementparadigm? There are at least three ways to approach the answer. First and probably most importantly, it is that marketers (rightfully) still believe in thepower of information to transform their ability to succeed. Some have actually achieved this,with the right tools and the right expertise and the right focus. So while the broader market hasnot achieved all it might have hoped from digital analytics, there are enough actual successstories to keep the market in motion. Second is that technology improvements are driving innovations in marketing analytics.As we mentioned in the Executive Summary, a combination of access to much more data thanbefore; sophisticated algorithms and digital data connectors; inexpensive cloud computing; andmodular application construction tools that supply display layers for end users with relative ease;all these are contributing to a preponderance of offerings, from every corner of the measurementuniverse, towards the same goal. Simply put, Convergence Analytics is happening because it canhappen. Third is that the customer, believing still in the power of data, now also believes in thepower of more data – ―Big Data‖. The cry for seeing data from multiple channels has reached asufficient pitch that application vendors are either launching new offerings, adding features toexisting offerings, or taking their existing offering and pitching it afresh to marketers. Ultimatelythe vendors are meeting the customers on the field of Convergence Analytics: where streamsfrom multiple data sources are overlaid in meaningful ways such that a marketer can see therelationship of trends and pinpoint cause and effect (in other words: Campaign A launched justbefore a spike in Metric B; therefore Campaign A plausibly caused that spike). And while each offering in Convergence Analytics pitches differently, and even as thereare a range of offerings with a range of capabilities, they all offer a similar promise: more datafrom more channels displayed more effectively. The expectation is that with more dimensionaldata streams, more insights can be gained. And if a process is in place to define and take action, then Convergence Analytics holds a 12
    • Convergence Analyticsworld of promise. Predictive Analytics Many applications either include or are in the business of including what are called"Predictive Analytics" features. Its a bit of a misnomer, because there isnt really much"prediction" going on, at least not in the way that the lady at the county fair can see your futurein the bottom of a teacup. What is really happening is that an algorithm is reviewing yourcontent, then for either a single user or a class of users, determining their characteristics based onbehavior, demographics, creditworthiness, purchase history, estimated lifetime value and more;and determining which of your stored content should be shown to that individual at a particularjuncture in their interaction with your digitial properties. The "prediction", if there is one, is thatthis scientific approach will yield a higher conversion rate and a better ROI than a randomsample. The extent to which the predictive layer delivers a higher conversion rate as compared toa random display of data is the measure of its success. Many see this as the keystone in the arch of customer relations. For if the marketer canconstruct a strong enough support mechanism for proper messaging, then the entire structure canbe completed and held together by the power of actionability. At its best, actionable algorithmscan make sure the immense force generated by the data structure is held in place and energized,much the way a keystone holds in place a mighty arch that bears the weight of the entireenterprise. Actionability is certainly not a mainstream capability yet. There are prediction engines inplay today, but no clear winners. As the technology matures, and as more data sources getdeployed against more channels, we may begin to see the keystone being lowered into place. That said, there are large enterprises devoting large sums of money and a great deal oftechnology to build their own predictive response mechanisms. These industry-leadingenterprises will continue to hold an advantage. But the advent of Convergence Analytics willallow mid-sized companies to begin to enjoy perhaps not all of the success of the very bigplayers, but better success at analtyics than they now currently get. 13
    • Convergence Analytics Multichannel Environment Todays multi-channel/device environment makes analysis and visualizationexponentially more complex and difficult. Rather than concentrating on one stream of data, nowdata must be extracted from multiple channels and then deployed against multiple channels.There are so many data intersections that it becomes more of a three-dimensional matrix than alinear progression. The channels the Convergence Analytics applications deploy against include: web, email,CRM, ERP, offline marketing (direct marketing), business intelligence resources, social media,online advertising, mobile apps, games and more. What may be most fascinating about the channel variety is that not only are they all beingaddressed; but that applications that once focused on measuring just one of those channels arenow often making a claim to measure all the other channels too. With the advent of multiple tools converging on convergence, we are beginning to see aparadigm emerge where the goal is a single view of all data in real time; a "one ring to rule themall" paradigm. And the application vendors converging on this paradigm are coming from everychannel. 14
    • Convergence Analytics Product/Service Mix One of the more interesting findings from our survey was to see how many service-oriented companies filled out the part of the survey intended for vendors. These included digitalagencies, marketing companies and analysts who all saw themselves as vendors because theircustomers in some way purchase digital analytics from them. This suggests a much larger issue we have observed, and that is the very gray areabetween what constitutes a software or SAAS offering and what constitutes a professionalservice offering. Much of this lack of clarity is integral to the notion of "software as a service" inand of itself. In the older world of software-in-a-box, the lines were very clear. The vendor sent you adisk and you installed the software. Perhaps there was a VAR involved that helped you set it up;and the software company had a help desk. But with the advent of broadband, cheap storage andrapid processing, it became possible to offer software on line. In a networked world, the modelhas few detractors if any. It empowers both the vendor by letting them control and adjust theoffering much more closely, and the user by allowing them to stop worrying about runningsoftware and "keeping current‖. Today, you can hardly find boxed software on the shelf. Trulythe advantages are many, and rather evenly distributed between vendor and customer. But in a business-to-business environment we are forced to ask what is the nature of thevendor itself, and should the buyer see it as a product or a service or an amalgam of parts thatincludes third party experts? The answer to this question is important because it determines theultimate value and ROI associated with the offering. Simply put, product vendors need to offer services to help users understand thetechnology, goals and new processes. When people, process and technology is new, there is littleoften limited similarity between one customer and the next. This means that each implementationrequires a services component in order to deliver value. There’s no such thing, ―press the button 15
    • Convergence Analyticsand get answers‖. If the vendors do not, or cannot offer services, it is best to find a servicespartner. 16
    • Convergence Analytics Evolving Channels and Roles Historically, analytics vendors have created products known as "point solutions". Aninstructive corollary to this name is the notion of "pain point"; a term that defines a particularproblem that presumably can be solved by a particular tool. For instance, the marketer responsible for "the web site" experiences a pain point at thejuncture where they need to receive data about the sites business success but had no way to getit. Hence, a "web analytics" solution and a ready customer base. The same would have held true for the mobile marketer, email marketer, the social mediamarketer, the SEO specialist, the advertising campaign manager. Entire industries have sprungup around satisfying the needs of these marketers. Typically these marketers would operate atleast semi-independently of one another, and would almost certainly make separate purchases tofix their own "pain point". Typically there is little co-operation between silos. Convergence Analytics races to a single solution for all organizational silos and blursapplication functions, as well as buyer roles and responsibilities. The marketing analytics application market has been defined by a number of categoriessuch as: Web Analytics Mobile Analytics Social Analytics 17
    • Convergence Analytics A/B Testing SEO Email Predictive Analytics Ad Network Analytics Competitive Analytics (Benchmarking) Business Intelligence Content Management Systems But now application vendors from all the above sectors are making efforts to measureresults from several different sources both on line and off line; and so the categories have begunto disintegrate. However, the buyer remains mostly in place as before. This creates a challenge for the vendor. Who is the buyer now? Who is experiencing that"pain point" where all data needs measuring but isnt? By rolling up all of the capabilities into asingle offering, vendors must be careful not to cut themselves off from their customer base. Theywill need to address the needs of all the organizational pain points; but those different parts ofthe organization dont necessarily come together in one area of responsibility--making it harderfor the vendor to gain buy in for their product. In working to serve more and more parts of the organization, the incoming ConvergenceAnalytics tools find some entrenched players already deeply embedded inside the largerenterprises. These solutions include such venerable marques as SAP, IBM, SAS, Axiom, Merkleand Unica. History has shown that large, entrenched organizations can be dislodged by smaller,newer, more agile solutions; and in the current environment its really an oceanic wave ofsolutions all crashing upon the same shore at the same time. The force of it will certainly changethe market, but there is no clarity yet on who amongst the current players will win the day. Much of the battle is to be fought around the success of a given competitors keydifferentiators. In other words, whoever positions themselves best and also has a serviceableoffering, will begin to outpace the others and be seen as the market leader. But the trouble todayis that there is not enough differentiation in what Convergence Analytics vendors are sayingabout themselves or the market. From nearly every part of the product spectrum come similar 18
    • Convergence Analyticsclaims: that the combination of data connectors, common keys and sophisticated display layerscreates a new kind of offering. In this light, many are still searching for their key differentiator:what makes them different or better than several others in the same space? Enter the world of the Data Scientist Historically the difference between success and mediocrity in analysis has been lessabout picking the very best tool than it has been about combining a capable tool with superiorservices and expertise. Many solutions today, with their complex data connections and multi-channel/device capabilities, require perhaps even more attention to experts than ever. Professional services has always been a critical part of success in digital marketing, evenas application vendors tend to emphasize the relative ease and completeness of their tool.Marketers, as customers and practitioners, have always known this to be the case. In theConvergence Analytics market, they should continue to expect a need for professional servicesand perhaps see the trend accelerate. But now Data Scientist (ADD MORE about the role of DS) It may even be the case that the winners will be the companies that either provide thebest customer service themselves; or form strong partnerships with technology implementationpartners; or create a developer environment such that their technology becomes a "standard". Process In addition, the prevalence of services requires the implementation of a set of processesand best practices that remain not established as yet in the marketplace. These processes includethose associated with the phrase "you cant manage what you cant measure"; and theestablishment of such practices like proper KPI definition, agile analysis of results, content 19
    • Convergence Analyticsadjustment and testing. More often than not, these key process elements are in short supply,especially in a complete cycle. Making sure organizations can adequately leverage the tools and put them to work willprobably be amongst the tasks confronting any vendor hoping to come out in front of the pack.This will be especially the case in the market comprising SMB companies--a vast number oflarge companies not amongst the Fortune 500. The very largest companies often enough have thedepth and breadth to be able to supply their own expertise. But smaller companies--by no means"small businesses"--usually cannot. This need will be met by professional services either fromthe vendors, or vendor partners, or third parties such as analytics consulting companies, digitalagencies, or individual experts. But whoever delivers it, its likely to be a main component of anyand every successful data analysis operation. Roles in Marketing While its true that the technology is racing well ahead of our collective ability tosocialize and even understand it, there are stirrings on the buyer side as to role definition. This isespecially true at the most senior levels of marketing, specifically at what has been called theposition of Chief Marketing Officer. Some have said that the role of CMO is "dead". While thisis hyperbolic, it may be true that the role has changed enough to warrant its renaming. Some ofthe nomenclature in play includes terms such as Chief Data Officer, Chief Analytics Officer,Chief Product Officer, Chief Content Officer; Chief Revenue Officer; and even ChiefInformation Officer. This represents a sea change in the role of marketing--where marketing has begun tospread its digitally measurable influence throughout the organization in a way that seems anatural outcome of the basic principle of marketing; and that is to maximize shareholder valuethrough increasing the velocity of customer acceptance for the shareholders products. As marketing has become more measurable, its visibility has grown and so has itsresponsibility. The natural progression of this has resulted in marketing measurementapplications to go through substantial transformations as well. And in doing so, they begin to 20
    • Convergence Analyticsposition themselves for measuring more and more of the organizations data; with marketing atthe hub. Vendors from nearly every sector mentioned above have developed the capability tobuild connectors to several data sources, combine them into a consolidated form, and allow themarketer a more well-rounded view of the factors affecting actual ROI. This has proved to blurthe lines between roles in the organization--as the above discussion of the role of the CMOsuggests. For instance, in an environment where every tool measures everything, whose roleexpands? Does the web site manager take over more of the mobile aspect? Does SEO move intoAd management? Does email move more into social media responsibility? Or, perhaps the Salesdepartment taking more responsibility in the marketing cloud? Or perhaps its the ContentManagers taking control of all of it. The tools can help them--but are they ready to manage it? So the blurring of tool capabilities has created an environment where the clear divisionsbetween organizational responsibilities have begun to erode; thus eroding the surface of thesellable market. And the combination of convergence of messaging, convergence of capability,and convergence of organizational roles will represent significant challenges for participantsfrom every corner of the data analysis community. To Conclude: Recommendations As everything is changing, it is critical to focus on focus on the following – 1) New technology platforms – My strong advice is to start with best-of-breed ―point solutions‖ that address a specific need. For Mobile apps I would look to firms which understand and have some strong experience with both a Mobile Environment AND the use of ―big data‖. 2) Clear definition of evolving roles and responsibilities – As I’ve discussed the roles are clearly changing as new technology platforms help transform the enterprise. It is critical to embrace this dynamic and look for new ―out of the 21
    • Convergence Analytics box‖ ways to define success for individuals. Consider titles and roles of new experts that have titles like Data Scientist to best take advantage of the world of Big Data. 3) And, once you have identified the ROI goals of the business, identified the platform and people, it is also critical to build new agile processes around your platform and people. 22