tag:econsultancy.com,2008:/topics/data-analytics Latest Data & Analytics content from Econsultancy 2017-01-04T14:32:41+00:00 tag:econsultancy.com,2008:BlogPost/68638 2017-01-04T14:32:41+00:00 2017-01-04T14:32:41+00:00 Predictive analytics: What are the challenges and opportunities? Arliss Coates <p><strong>From automation to automaton?</strong></p> <p>Time was identified as a business's most precious resource. Being able to streamline the marketing function through automation and, in particular, the analytics portion, was something executives deemed hugely valuable.</p> <p>But is automation driving out innovation and originality? With so much potentially determined by machines and algorithms, do brands risk losing the essence that made them unique and the innovation that could keep them alive?</p> <p><strong>Understanding where automation delivers real results</strong></p> <p>Nearly 60% of respondents stated that their analytics solutions produced data-based insights without analyst involvement.</p> <p>A further 80% of those stated it saved them significant time as a result. Either the analysts themselves could be redeployed to focus on trickier tasks or the insights generated pointed to opportunities elsewhere.</p> <p>Hotel group IHG's head of CRM, Jim Sprigg, explains his position on automation thusly: "Automation and machine learning will be critical for the sort of thinking that requires many calculations done in a somewhat predictable way."</p> <p>"It is definitely in our roadmap for broad use in predictive modeling which can drive, say, the assignment of offers and content in digital media based on individual customers' attributes, behaviors and transaction histories."</p> <p><strong>Dealing with the routine but complex</strong></p> <p>The idea of automation means different things to different people. For some organizations, particularly those that operate well on defined processes and rules-driven decision making, automation saves a great deal of time.</p> <p>This is either because it can create a trickle down effect of automation allowing other systems to take appropriate action without human intervention, or alert business users when intervention is needed.</p> <p>In complex, real time environments such as programmatic advertising, the automated processing of information into insights, and insights into action is viewed as essential to realizing opportunities before they pass brands by.</p> <p>Danish AI-based media buying agency, Blackwood Seven, is expanding across Europe based on the success of its model that claims clients get a 25-50% improvement in the effect of their media (<a href="http://www.campaignlive.co.uk/article/blackwood-sevens-ai-media-agency-provoking-fear-across-big-networks/1411733">according to <em>Campaign </em>magazine</a>) through using the company's AI technology.</p> <p><iframe src="https://player.vimeo.com/video/170621249?title=0&amp;byline=0&amp;portrait=0" width="640" height="360"></iframe></p> <p>The software analyzes 82 different data inputs (such as sales, YouGov data and weather info) to determine a media plan's likely outcome and optimizes in real time accordingly.</p> <p><strong>Can we automate creativity?</strong></p> <p>The advertising community is already looking at the question of whether or not automation and machine learning can actually create ad executions, not just supply humans with the insight with which to build their own creations.</p> <p>However, the idea that AI would be integral to developing creative is still a pipedream. This begs the question, beyond a degree of grunt work or speedy number crunching to get the the right ads to the right audiences in real time, does automation have anything to contribute to the creative, innovative side of marketing.</p> <p>In a discipline that has always been described as the marriage of art and science, can science begin to replicate (and replace) art?</p> <p><strong>The limits of automation</strong></p> <p>There is a sense, however, that analytics will never fully be automated. The feeling persists that strong marketing is an intelligent marriage of art and science, even in today's data obsessed environment.</p> <p>"Humans still have an advantage over computers," Sprigg insists. "We used to call these the big 'ah-ha' insights. The sort that come from intuition and highly synthesized recognition."</p> <p>Sprigg gives the example of a time he showed the output from an automated learning process that suggested some offers landed differently with customers who came to the company via customer service than for those who used the web.</p> <p>The group to whom Sprigg presented this data made the connection between the streams of information the computers already had access to - that there was a gap in the merchandising. "Humans were synthesizing information along with practical human experience in ways that we would have never known to code into the computer's consideration set," he adds.</p> <p>Sprigg identifies that the biggest problem with automated analytics may yet be human in origin - it is a case of scenario planning.</p> <p>Programmed with the information around any given scenario, a computer could undoubtedly come up with the relevant insight. It's just that humans cannot prepare the machines to anticipate every possible nuance or scenario.</p> <p>"Marketing functions can't build automation for out-of-the-box thinking, but they can recruit for it," Sprigg concludes.</p> <p>Humans, while lacking in the number crunching abilities of their automated colleagues, benefit from years of emphatic "programming" that contributes hugely to strategic success.</p> <p><strong>The dangers of machine-based innovation</strong></p> <p>While marketers may be in danger of forgetting the worth of their human resources in favor of the speed and efficiency of automation, there is also a danger in relying 100% on data outputs to inform future direction.</p> <p>Some executives interviewed for this report warned that an over-reliance on data to substantiate decision-making was hampering innovation.</p> <p>The Hard Rock Cafe's Claudia Infante complains that "the ideas that get shelved are the victims of a hybrid data-driven culture that we're creating around ourselves."</p> <p>"We're no longer as nimble and willing to go looking for the new shiny thing because we have to look at the data. There is no data to back those ideas up and you can't get data unless you activate the idea."</p> <p><img src="https://assets.econsultancy.com/images/0008/2793/magpie.jpg" alt="" width="413" height="316"></p> <p><strong>Paralysis by analysis</strong></p> <p>Automation builds a data-driven culture because it allows for faster reporting, analysis and optimization of existing channels, building on what is known. This use of data as a comfort blanket, however, can slowly suffocate innovative organizations.</p> <p>On that note, Infante adds that "a company may have been innovative and forward thinking but of course it grows on the back of that success. Then, when you're a big player, you have to take care of the day-to-day: make sure the lights are on, guarantee growth. As a result, we end up leaving behind a bit of that ideation process."</p> <p>It's clear from Infante's illustration that companies need to make sure they continue to use automation as a solution to an existing problem rather than a panacea for everything.</p> <p>It may seem faster, and the results from it more tangible, but automation is not going to deliver on every aspect. It's all about finding its place.</p> <p>"Companies have many people analyzing reports trying to identify tactical performace gaps and opportunities so they make predictable adjustments. As a result, companies hire a lot of people who like trying to think like a computer. If that can all be automated, the goal should be to recruit different types of thinkers," Sprigg explains. </p> <p><strong>Automation must be omni-channel</strong></p> <p>The immediate challenge for developers of analytics automation is in creating a solution that moves beyond the point and into an omni-channel environment. IHG's Sprigg explains:</p> <p>"Out-of-the-box solutions tend to be limited in their scope of operations. They are designed to optimize one channel or only one page at a time. This is multichannel optimization. We want to optimize in a way that allows us to maintain a consistent omni-channel experience across all channels and even extends to customer service and in-hotel interactions."</p> <p><strong>Understand the question before anticipating the answer</strong></p> <p>Over and over again however, executives have reinforced the old computing adage of "garbage in, garbage out." Automation in analytics is only ever going to be as good as the premise it is set up to work toward. Marketers must understand its power and its limitations to fully benefit from its potential.</p> <p>For some, it is a simple question of plug and play to speed up number crunching and use the efficiencies to divert resources elsewhere.</p> <p>Other organizations may find that embedding automation in their analytics process requires a wholesale change of departmental and HR organization. In some cases the whole culture of the company could change.</p> <p><em>This post was co-written by Morag Cuddeford-Jones.</em></p> tag:econsultancy.com,2008:BlogPost/68661 2016-12-23T00:01:00+00:00 2016-12-23T00:01:00+00:00 Five trends which will define data-driven marketing in 2017 Jeff Rajeck <p>Speaking to company marketers at a recent Digital Cream Singapore, though, it seems that others have a much different view of marketing data.</p> <p>Many attendees indicated that they are no longer just handing over their data to demonstrate return on investment (ROI), but they are instead using it to change the way their marketing team works.</p> <p>Below are five trends which roundtable participants felt will define data-driven marketing in the coming year.</p> <h3>1) Marketers will increasingly use data for decision making</h3> <p>One trend that most participants agreed on is that that data will be used more often to drive marketing decisions in 2017. Attendees said that data analysis was the best way to find the 'low-hanging fruit' which improves marketing performance.</p> <p><a href="https://econsultancy.com/reports/measurement-and-analytics-report">A 2016 Econsultancy survey</a> of client-side marketers backs up this notion. In the study, marketers were asked to indicate what percentage of their data was useful for decision making and less than one in three (29%) indicated that very little (0-25%) of their data was useful.</p> <p>In the same survey, marketers also agreed overwhelmingly (84%) that analytics drives actionable recommendations which make a difference to their organisation.</p> <p><img src="https://assets.econsultancy.com/images/0008/2614/figure1.png" alt="" width="800" height="367"></p> <p>While optimistic in general, participants also felt that using data to help make marketing decisions also raises new issues.</p> <p>First off, many said that marketers are suffering from data overload. Each channel, every customer touchpoint, and each marketing system has its own data and participants felt that all the data was becoming overwhelming.</p> <p>One delegate mentioned that their customers use chat apps when purchasing and the marketing team found it difficult to use this data for attribution.</p> <p>Another problem with using data for decision making is that additional resources are required to make sense of the data. Companies with small or stretched marketing teams struggle to find the time to analyse the data to an extent where it offers useful insights.</p> <p>Also, while using data can make some decisions easier, data-based decisions can become politicized, too (see point 3 below).</p> <p><img src="https://assets.econsultancy.com/images/0008/2619/2__Custom_.jpg" alt="" width="800" height="533"></p> <h3>2) Agile marketing will become more popular</h3> <p>Interestingly, attendees said that the increased use of data in marketing will allow marketers to work in a more agile manner.</p> <p>Described in a <a href="https://econsultancy.com/blog/68373-what-is-agile-marketing-and-what-do-marketers-think-about-it/">previous article</a>, 'agile marketing' is essentially a working method which encourages individual efforts and frequent collaboration.</p> <p><img src="https://assets.econsultancy.com/images/0007/9892/agile-wall-3.jpg" alt="" width="800" height="600"></p> <p>According to participants agile marketing is enabled by data because group decisions are guided by facts rather than 'the HIPPO' (Highest Paid Person's Opinion). As a result, marketers feel empowered to share details about their work and meetings become more productive. </p> <p>Those aiming to implement agile marketing will still face challenges, though. Companies with a conservative culture may find it hard to accept its unorthodox working methods.</p> <p>Additionally, for agile to work, marketers must be willing to put in extra hours to learn about how to run tests correctly and explain results in detail.</p> <p>Attendees who had already implemented agile marketing said that the results were encouraging. One reported that projects which used to take two to three months, now only took two to three weeks.</p> <p><img src="https://assets.econsultancy.com/images/0008/2620/4__Custom_.jpg" alt="" width="800" height="533"></p> <h3>3) Marketing attribution will still be difficult</h3> <p>While all attendees agreed that they would like to attribute conversions across channels, many feel that they are still some ways away from being able to do so.</p> <p>The first problem attendees highlighted was that the marketing attribution has become political at some organisations. This happens because channel budgets are often set according to how much revenue a channel provides. Channel managers, therefore, are motivated to 'talk up' the value of their channel even if the data does not support it.</p> <p>Another problem was the number of channels. Delegates reported that some of their customers hit 10 or more touchpoints before converting. Piecing together a customer journey of that length and attributing value to each step is a difficult, if not impossible, task.</p> <p>Finally, marketers said that even if the journey could be mapped and an attribution model agreed upon, not all of the data is available. New digital channels are popping up all of the time and many do not integrate with analytics systems (see <a href="https://econsultancy.com/blog/68223-dark-social-it-s-worse-than-we-thought-in-asia-pacific/">Dark social: It's worse than we thought in Asia-Pacific</a>). To add to the problem, offline data is typically even more difficult to obtain than online.</p> <p>So, while marketing attribution will remain a goal of many companies, participants predicted that few will make as much progress toward marketing attribution in 2017 as they would like.</p> <p><strong><img src="https://assets.econsultancy.com/images/0008/2618/1.jpg" alt="" width="800" height="600"></strong></p> <h3>4) Marketers will personalise using data-driven customer insights</h3> <p>Personalisation is already in use at many organisations, but often this meant using segments to deliver content which resonates more with the target audience.</p> <p>In the coming year, attendees felt that personalisation initiatives will be expanded so that consumers will be delivered the 'next best piece of content' to help them make buying decisions.</p> <p>In order to do so, marketers must be able to use the 'data exhaust' of consumer behaviour and use that as a way to determine which content to deliver via email, web, and mobile.</p> <p>Some participants felt that there were issues with this approach to personalisation. Many organisations still suffer from 'data silos' where one department would not allow another to use its data.  This is particularly true between sales and marketing.</p> <p>Others said that their marketing technology stack was not yet up to the task to handle individual personalisation. According to a recent Econsultancy survey, this seems to be the case at many organisations as only 7% strongly agreed that their current data architecture is an 'enabler for personalisation'.</p> <p><img src="https://assets.econsultancy.com/images/0008/2615/figure_20.png" alt="" width="800" height="478"></p> <h3>5) Media agencies will be held accountable for online advertising</h3> <p>Finally, attendees said that in 2017, client-side marketers will require that their agency partners provide more data about their online advertising.</p> <p>In the past, it seems that many marketing teams did not have the analytics capabilities to manage detailed data about ad performance. As a result, many agency reports contained only high-level figures.</p> <p>Now that client-side teams are becoming more data-driven, their expectations for both the quantity and the quality of the data will increase. Issues such as <a href="https://econsultancy.com/blog/67531-fake-likes-clicks-followers-in-asia-what-you-can-do-about-them/">click fraud</a>, <a href="https://econsultancy.com/blog/67366-three-display-advertising-issues-to-watch-in-2016/">viewability</a>, and <a href="https://econsultancy.com/blog/67334-disproving-the-myth-about-display-clicks-conversions/">view-through conversions</a> will become frequent topics of conversations between agencies and data-driven marketing teams.</p> <p>There are still hurdles though. First off, advertising data is complicated and it will take some time for agencies and marketing teams to 'get on the same page', according to one participant.</p> <p>Also, as mentioned above (point 3), even when ad data is understood it still may not help marketers allocate media spend by the effectiveness of the channel.</p> <p>Finally, agencies suffer from the same issue that marketing teams do - they simply do not have all the data. Many online conversions and purchases come through channel partners, such as marketplaces, which do not provide attribution data to their members.</p> <p>So, even with all of the view and click data at hand, marketers who use channel partners will still struggle to know which advertising platforms provide the most value to the business.</p> <h3>A word of thanks</h3> <p>Econsultancy would like to thank all of the marketers who participated at Digital Cream Singapore 2016 and our table moderator for Data-Driven Marketing &amp; Marketing Attribution Management - Frederick Tay, Associate Director, Marketing Operations, INSEAD.</p> <p>We hope to see you all at future Singapore Econsultancy events!</p> <p><img src="https://assets.econsultancy.com/images/0008/2621/end__Custom_.jpg" alt="" width="800" height="533"></p> tag:econsultancy.com,2008:BlogPost/68616 2016-12-22T14:21:00+00:00 2016-12-22T14:21:00+00:00 Learning to trust the machines: AI and company culture Ben Davis <h3>The rise of the machines</h3> <p>Machine learning is used to predict how people will react, which is basically what all marketers want to understand.</p> <p>Applications include:</p> <ul> <li>personalising advertising</li> <li>informing stock levels</li> <li>providing customer service (fairly nascent)</li> <li>conversion optimisation (copy and web design)</li> <li>recommendations</li> <li>lead generation (from unstructured text analysis) </li> <li>image recognition</li> <li>search (natural language processing)</li> <li>fraud detection</li> <li>sentiment analysis </li> </ul> <p>Though in some areas such as search and advertising, machine learning has been working in the background for a while and is an implicit part of their functionality, in other areas the rise of machines presents a cultural issue.</p> <p>In lead generation, for example, it's understandable that those with 30 years experience of an industry are sceptical when told that an algorithm will be better at finding the right accounts to target.</p> <p>I recently spoke with Aman Naimat, SVP Technology at Demandbase, the company that has developed <a href="https://www.demandbase.com/demandgraph/">Demandgraph</a>, an AI solution for account targeting. As impressive as the technology is, Aman confirmed that cultural issues are probably the most pressing challenge when it comes to adoption (over integration, for example).</p> <h3>What do we know about human-machine trust?</h3> <p>The notion of human-machine trust has probably never been as pertinent as it is today, with semi-autonomous cars already on the market and self-driving cars well on the way to realisation.</p> <p>Would you want a self-driving car to sacrifice you, the pilot, in order to save the lives of multiple pedestrians when an accident is inevitable? Most people agree on <a href="https://www.technologyreview.com/s/542626/why-self-driving-cars-must-be-programmed-to-kill/">the ethical answer to this question</a> (self-sacrifice), but wouldn't want to drive such a car.</p> <p>Of course, if people refused to buy such autonomous cars, more traffic deaths would occur; a Catch-22 situation.</p> <p>Away from such gory matters, how do people feel about machines helping them take decisions in nuanced, work-based scenarios? </p> <p>At the Singapore University of Technology and Design, Jessie Yang and Katja Hölttä-Otto designed an experiment. Human participants took part in a memory and recognition task using an automated decision aid.</p> <p>The task involved the memorising of images, which were later to be selected from a pool of similar images. The automated decision aid provided recommendations but, crucially, was designed to do so reliably for some participants and not so reliably for others.</p> <p><a href="http://news.mit.edu/2016/building-better-trust-between-humans-and-machines-0621">As detailed by MIT News</a>, the results revealed that the unreliable automated aids were overtrusted. Conversely, the highly reliable automated aids were undertrusted.</p> <p>On reflection, this seems somewhat like human nature. We may be keen to take advantage of AI but perhaps ultimately we don't fully trust it.</p> <p>Another experiment, this time at MIT by Yang and Julie Shah, Department of Aeronautics and Astronautics Assistant Professor, went one step further, looking at how interface design affects so-called 'trust-reliability calibration'.</p> <p>The pair were interested in alarm displays in high risk industries. Rather than the traditional “threat” or “no threat” alarm, often developed with very low thresholds (for obvious reasons), the introduction of likelihood alarm displays (how likely is the risk event?) could help to mitigate the "cry wolf" effect.</p> <p>Over time, these assessments of likelihood may ensure that trust in the warning system remains higher.</p> <p>Okay, this may seem like a far cry from marketing software, but the principles carry across industries. The more educated the end user, the better the relationship with intelligent technology.</p> <h3>Complexity = vulnerability </h3> <p><a href="http://www.britishscienceassociation.org/news/rise-of-artificial-intelligence-is-a-threat-to-humanity">Research by the British Science Association</a> revealed that 'half of those surveyed would not trust robots in roles including surgical procedures (53 per cent), driving public buses (49 per cent) or flying commercial aircraft (62 per cent).'</p> <p>It's arguably only education that can allay these fears.</p> <p>However, as Kalev Leetaru points out, <a href="http://www.forbes.com/sites/kalevleetaru/2016/01/04/in-machines-we-trust-algorithms-are-getting-too-complex-to-understand/#32c2d1fd2f14">writing for Forbes</a>, even with an increased understanding of how machine learning works, the complexity of web-based services can still scupper trust.</p> <p>Kalev describes systems 'built on top of a layer of trust of other systems such that an error, vulnerability, or mistaken understanding at any level can cascade across the system'. For example:</p> <ul> <li>in 2013 Microsoft’s Azure service faced a worldwide outage due to a simple expired SSL certificate</li> <li>in 2012 a leap day caused an outage when one Microsoft cloud system misunderstood what another was doing</li> <li>in 2013 when a single developer at Amazon was able to impact an entire data center at the height of the Christmas shopping season</li> <li>At the end of 2015, Google experienced an outage when connecting a new network link in Europe manually, which overrode automated safety checks</li> </ul> <p>With education, we can observe that however sophisticated machine learning becomes, it still relies on other infrastructure and data quality. Ultimately, it is still human-limited and we are still refining our trust-reliability calibration.</p> <p>This can be observed anecdotally. Look at the tweet below, something we're all familiar with. Retargeting and recommendations are incredibly powerful when implemented correctly, but the rules don't always stack up.</p> <blockquote class="twitter-tweet"> <p lang="en" dir="ltr">Amazon thinks my recent humidifier purchase was merely the inaugural move in a newfound hobby of humidifier collecting.</p> — Justin Shanes (@justinshanes) <a href="https://twitter.com/justinshanes/status/803453049603690496">November 29, 2016</a> </blockquote> <h3>So, how does this impact culture and strategy?</h3> <p>Enough of my secondary research into human-machine relationships. Aside from educating their employees, what do companies need to bear in mind when considering AI strategy?</p> <p>The main thing is data quality and scope. Supervised AI is only as good as its inputs, and all marketers should be aware of these inputs when relying on machine learning, just as they are when relying on statistical analysis. </p> <p>Richard Sargeant, director of ASI Data Science, a company that helps governments harness AI, <a href="https://www.publictechnology.net/articles/opinion/changing-culture-what-government-must-do-make-most-ai">has recently written</a> about the way that siloed departments can hinder the effectiveness of AI.</p> <p>Here's the important bit about data scope:</p> <blockquote> <p>Government is usually organised by service [(education, health etc)]. ..But this is not sensible in an AI age: if an agency is good at running one kind of digital service, chances are they’ll be good at running a bunch of them.</p> <p>...The most important factor in determining whether [a department] succeeded [in digital] wasn’t their knowledge of their departmental subject matter, but whether they had the organisational leadership and culture to develop and run digital services.</p> <p>And the departmental silos continue to make it very hard for datasets to work together.</p> <p>Why don’t we check benefit records against the death register to avoid paying benefits to people who are dead? Because they are run by separate departments.</p> <p>Why don’t we have one consistent list of companies in the UK? Because HMRC and Companies House maintain their own separate lists.</p> <p>The quality of machine learning and AI is heavily dependent on the quality and volume of data.</p> </blockquote> <p>Education, data quality and volume, transparency within the organisation - all are vitally important.</p> <h3>We still have a ways to go</h3> <p>Many of today's machine-learning powered solutions are 'human in the loop' solutions. That means they rely on humans to validate some of their findings and to provide feedback into the system.</p> <p>Humans in the loop can move AI from 80% accuracy to 90%+. And, of course, algorithms are limited by the humans that set them a-whirring, and the data they are using.</p> <p>That means the role of humans has not been diminished, rather it has increased in importance. We have to understand and govern this stuff.</p> <p><a href="https://www.facebook.com/notes/mark-zuckerberg/building-jarvis/10103347273888091/">In the words of Mark Zuckerberg</a>, 'we know how to show a computer many examples of something so it can recognize it accurately, but we still do not know how to take an idea from one domain and apply it to something completely different.'</p> <p>So, for the next decade at least, marketers and sales people should look upon AI as the incredibly powerful <em><strong>tool</strong></em> that it is.</p> <p><strong><em>Now read:</em></strong></p> <ul> <li><a href="https://econsultancy.com/blog/67745-15-examples-of-artificial-intelligence-in-marketing/">15 examples of AI in marketing</a></li> <li><a href="https://econsultancy.com/blog/68466-could-ai-kill-off-the-conversion-optimisation-consultant/%20">Could AI kill off the conversion optimisation consultant</a></li> <li><a href="https://econsultancy.com/blog/68388-how-klm-uses-bots-and-ai-in-human-social-customer-service/">How KLM uses bots and AI in 'human' social customer service</a></li> <li> <a href="https://econsultancy.com/reports/marketing-in-the-age-of-artificial-intelligence/">Marketing in the Age of Artificial Intelligence</a> </li> </ul> tag:econsultancy.com,2008:BlogPost/68427 2016-12-22T11:00:00+00:00 2016-12-22T11:00:00+00:00 A day in the life of... a location intelligence expert Nikki Gilliland <p>If you're keen to enter into the world of digital marketing or find a new path, make sure you check out our <a href="https://jobs.econsultancy.com/" target="_blank">digital jobs board</a>. </p> <h3>Please describe your job: What do you do? </h3> <p>Being Europe’s general manager for <a href="https://www.near.co/">Near</a> is a big responsibility, but it also gives me a first look at how emerging developments in <a href="https://econsultancy.com/blog/67418-what-is-location-based-advertising-why-is-it-the-next-big-thing/" target="_blank">location-based</a> technology are changing the way we live.</p> <p>Essentially, the buck stops with me for all new business opportunities and operations in the region, which means I cover everything from sales and marketing to account management.</p> <p>It’s my job to boost efficiency, stay ahead of location intelligence trends — and competitors — and most importantly, ensure revenue is always optimised.   </p> <p><img src="https://assets.econsultancy.com/images/0008/0445/Ken_Parnham.jpg" alt="" width="600"></p> <h3>Whereabouts do you sit within the organisation? Who do you report to? </h3> <p>I’m directly involved with multiple everyday functions and higher level strategy, which puts me right in the middle of things.</p> <p>My position is a vital link between our business in Europe and the wider world, so I work closely with the Chief Revenue Officer, who I also report to.</p> <h3>What kind of skills do you need to be effective in your role? </h3> <p>Agility and listening are essential in an industry that evolves as rapidly and as often as technology.</p> <p>There’s a constant flow of new devices, apps and concepts that alter consumer behaviour, so the ability to quickly understand and cut through the complexity is invaluable. </p> <p>Communication is also crucial to maintain momentum. As part of a global organisation, my team needs to be completely aligned with the rest of the business and working towards the same core goals, which means I need to keep them informed and on track.  </p> <h3>Tell us about a typical working day</h3> <p>Digital technologies are by nature ever-changing and two days are rarely the same, but a good day is a frequent occurrence. </p> <p>On good days, my schedule might run something like this: an early start to answer urgent emails, tackle larger strategic issues and liaise with our headquarters in Singapore, then head into the office to catch up with my team and run through a pitch scenario, followed by a meeting with an existing client in the afternoon.  </p> <p>Exploring ways to expand our business and better meet client needs is an integral part of what I do, so refining pitching skills to make sure prospects see what our technology can do for them and checking in with clients to understand what they need are very important.</p> <p><img src="https://assets.econsultancy.com/images/0008/2609/Screen_Shot_2016-12-21_at_16.07.30.png" alt="near.co" width="615" height="231"></p> <h3>What do you love about your job? What sucks? </h3> <p>I feel privileged to be working in an industry at the vanguard of digital innovation.</p> <p><a href="https://econsultancy.com/blog/67000-seven-steps-to-building-a-successful-mobile-data-capture-model/" target="_blank">Mobile data</a> and the insights it generates are creating new possibilities in every sector — location intelligence is already improving targeting efficiency in retail and marketing, alongside healthcare, city planning, and government-level decisions.</p> <p>It’s incredible not just to be part of this revolution, but also to have been there from the beginning. </p> <p>Like any job, there are things that aren’t perfect, but fortunately there aren’t many of them. Sometimes not having as much time, as there are new avenues to explore, can suck. </p> <h3>What kind of goals do you have? What are the most useful metrics and KPIs for measuring success? </h3> <p>There are three key metrics I gauge success by: revenue, market disruption, and workplace culture. </p> <p>Revenue is, of course, a priority for any business but I strive to ensure the majority of it comes from repeat business — not only because this it makes for a sustainable inflow but also because it means we are delivering what our clients want, which is what matters most. </p> <p>Creating disruption and a good working environment go hand in hand. If my team have room to build their skills and are passionate about what they do, our offering will continuously improve, helping us to outpace competitors and influence the global marketplace. </p> <h3>What are your favourite tools to help you to get the job done? </h3> <p>My team are unquestionably the best weapon I have. They are experts in their field who are not content with ‘good enough’ — constantly striving to push boundaries, perfect our services, and find new ways to meet client challenges.</p> <p>Sophisticated technology is a necessity, but having a team that wants to get the best out of it is what inspires me.</p> <p>I believe you should never forget to take time with your recruitment policy; your people will be the foundation of your success.    </p> <h3>How did you get started in the digital industry, and where might you go from here? </h3> <p>I’ve always been fascinated by digital technology and its potential to streamline and enrich our everyday lives.</p> <p>About 18 years ago, I decided to develop my proficiency with the tools of the trade by becoming a computer programmer. I haven’t looked back since.</p> <p>The beauty of this industry is that you never know where it’s going next, but whatever comes next, it’ll be too exciting to miss, so I hope to be there at the centre of it all. </p> <h3>Which brands do you think are doing digital well?</h3> <p>Digital is such a fundamental element of branding now that examples of good usage are everywhere, but if I had to pick I’d say the <a href="https://econsultancy.com/blog/63577-how-virgin-used-big-data-to-inform-its-new-content-strategy/" target="_blank">Virgin Group</a> and <a href="https://econsultancy.com/blog/67541-10-delicious-digital-marketing-campaigns-from-mcdonald-s/" target="_blank">McDonald’s</a> do it especially well.</p> <p>As international, recognisable brands you might not expect them to spend time trying new digital technologies. But there is a reason they are at the top and it’s because they keep pushing the envelope and embracing digital advances.</p> <p>It impresses me every time I meet with them. </p> <h3>Do you have any advice for people who want to work in the digital industry? </h3> <p>Ask questions all the time. The digital industry doesn’t stand still; the number of providers, technologies, sectors and trends it contains is always expanding, and your knowledge base needs to grow with it.</p> <p>It can be hard work, but the rewards make it more than worthwhile. Dive in! </p> tag:econsultancy.com,2008:BlogPost/68650 2016-12-21T11:00:00+00:00 2016-12-21T11:00:00+00:00 The future of programmatic: 2017 and beyond Ben Davis <p>Thanks go to our trio of experts for providing some cogent analysis:</p> <ul> <li>Chris O'Hara, Head of Global Marketing, Krux (Salesforce DMP)</li> <li>Emily Macdonald, Head of Programmatic, International, DigitasLBi</li> <li>Tom Wright, Head of Programmatic, Tomorrow TTH</li> </ul> <h3>The coming democratization of data science</h3> <p style="font-weight: normal;"><strong>Chris O'Hara, Head of Global Marketing, Krux:</strong></p> <p style="font-weight: normal;">If we’ve learned anything over the last several years in programmatic it’s that—in a world of commoditized inventory and <a href="https://econsultancy.com/blog/67674-what-are-first-second-and-third-party-data/">3rd party data</a>—getting a programmatic edge requires diving deep into the data for insights.</p> <p style="font-weight: normal;">That means being better than your competitors at knowing where and how much to bid, which correlates directly with an organization’s skill at data science.</p> <p style="font-weight: normal;">The problem is that most marketers and agencies have little native competence in user scoring and propensity modeling—and even if you had the budget to hire a dozen data scientists, they are incredibly hard to find. </p> <p style="font-weight: normal;">What we are starting to see today are platforms that embed machine learning and artificial intelligence into their user interfaces in such a way that business users can access such capabilities without writing algorithms, using separate data visualization platforms, or having statistical abilities. </p> <p style="font-weight: normal;">This capability was more straightforward when it was just available to display marketers seeking to manage bid pricing thresholds on cookies.</p> <p style="font-weight: normal;">[However,] today, marketers are increasingly using data management technology to map users across their device graph, and expect the ability to score users against their interactions across every addressable channel—not just “display” advertising, but also email, commerce, and website experiences.  </p> <p style="font-weight: normal;">To do this correctly, marketers need to map users to all their devices and be able to store highly granular attribute data going back longer than the life of the typical cookie.</p> <p style="font-weight: normal;">These are “big data” problems that require highly advanced technology. Much of what is happening today is ad hoc reporting in spreadsheets that drives manual optimizations across many different buying platforms. </p> <p style="font-weight: normal;">In 2017, we will start to see the evolution of data science applications as they become more embedded in platforms—<a href="https://econsultancy.com/blog/68496-10-examples-of-ai-powered-marketing-software/">“AI layers” that leverage machine learning</a> within platforms, and make things like user scoring, propensity modeling, lifetime value (LTA) analysis, and next-best action recommendations less manual and more automated. </p> <h3><strong>The march of martech</strong></h3> <p style="font-weight: normal;"><strong>Emily Macdonald</strong><strong>, Head of Programmatic, International, DigitasLBi</strong></p> <p style="font-weight: normal;">No-one could ignore 2016’s massive spending spree by martech companies like Oracle, IBM and Adobe, as they grew their market share via acquisitions, plugged tech stack gaps and invested in areas such as Artificial Intelligence for smart CRM.</p> <p style="font-weight: normal;">Notably, these acquisitions included programmatic adtech companies such as TubeMogul and Krux, making programmatic a key part of the conversation.</p> <p style="font-weight: normal;">[These martech companies offer] brand marketers a fully integrated control centre that coordinates, unifies and simplifies data across all consumer touch points to optimally inform marketing, media activation strategies and spend. </p> <p style="font-weight: normal;">As the martech focuses on quickly integrating acquisitions and retaining experts, they are also pushing change.</p> <p style="font-weight: normal;">We see a desire by some brands and integrated agencies to optimise both operational and performance efficiencies with data, creative, CRM, paid and earned media all under one roof.</p> <h3>Data science is the new measurement </h3> <p><strong>Chris O'Hara:</strong></p> <p>An ongoing challenge in programmatic is measurement.</p> <p>Marketers tend to rely on various industry-accepted currencies to validate their media investments (Comscore for viewability, Nielsen to measure reach into a certain demographic, or Datalogix for purchase data).</p> <p>These are fine yardsticks, but we are starting to see marketers desire a more granular, less panel-based, approach to measurement. </p> <p>Marketers have been quick to embrace enterprise data management over the last several years, and are now starting to build “consumer data platforms” (CDPs) to align their entire organizational data around a single identifier for their customers.</p> <p>As they own more of their own first-party data asset, marketers can now look across the entirety of their data—not just from display advertising, but also from email, IoT, commerce, app, website, social, and search—and begin to get a universal view of cross-channel performance.</p> <p>This granularity of disparate data, available to query in a single platform, and tied to cross-device user identifier, now presents the opportunity for finer-grained measurement and is the first, important step towards changing the attribution game. </p> <p>This means that the ability to query and make sense from a large scale of data using machine learning and algorithmic approaches (essentially called “data science”) is the new basis for measurement moving forward. </p> <p>Will we see marketers moving away from established, traditional measurement currencies in 2017?</p> <p>Probably not, but we will certainly see enterprise marketers leverage their newly acquired data capabilities to challenge the status quo, and supplement the measurement they are currently doing.</p> <h3>Rethinking client-agency relationships</h3> <p style="font-weight: normal;"><strong>Emily Macdonald:</strong></p> <p style="font-weight: normal;">In 2016, the role of the media agency came into question.</p> <p style="font-weight: normal;">As more brands look to take control and invest in or restructure for the omnichannel vision, we can see the dynamic of the client-agency relationship shifting.</p> <p style="font-weight: normal;">Along with the need for greater transparency, expertise and sharing of knowledge, it’s also essential to have a clear partnership focused on putting the personalisation and synchronisation of consumer messaging at the centre of everything.</p> <p style="font-weight: normal;">Brands need to rethink consumer engagement and storytelling with the optimised blend of data, technology, media and creative, rather than operating in brand marketing or creative and media agency silos.</p> <p style="font-weight: normal;">This applies not just to big brand marketers wishing to take programmatic in-house and hiring programmatic expertise, but also to small to medium-sized brands looking to navigate this complex and often confusing new world.</p> <p style="font-weight: normal;">With IBMs Watson, Salesforce's Einstein and Amazon's Alexa, I'm left wondering if the soothing voice of AOL's Connie will return for the programmatic industry. "You've got a new customer", perhaps? </p> <h3>Rise of peer-to-peer data sharing (programmatic 3.0)</h3> <p><strong>Chris O'Hara:</strong></p> <p>The story of programmatic can be summed up as battle for control over the user, and the gateways for audience access.</p> <p>In its first iteration, programmatic meant finding users on exchanges using real-time bidding.</p> <p>It was a difficult and manual process to “whitelist” preferred sites, and impossible to control reach into specific audiences, due to the inherent nature of bidding (you might not win enough bids to get scale). </p> <p>Then, we saw the first green shoots of “programmatic direct” in which premium marketplaces tied to media planning platforms sprung up (iSocket and ShinyAds), where publishers could set their own price for premium inventory and make direct deals with buyers.</p> <p>Those platforms never found scale, mostly due to the lack of dynamic inventory management, and the fact that buyers did not want to embrace another buying technology.</p> <p>The real programmatic 2.0 model started with the introduction of private marketplaces and Deal ID. This was a great way to leverage an efficient <a href="https://econsultancy.com/blog/65677-a-super-accessible-beginner-s-guide-to-programmatic-buying-and-rtb/">RTB</a> buying methodology with some restrictions, and limit access to preferred inventory.</p> <p>We have seen this model further evolve into header bidding technology, which is basically a smarter waterfall approach for publishers. </p> <p>These innovations helped publishers get more for their premium inventory, and marketers can leverage programmatic tech to get more precision reach, with more granular controls over inventory.</p> <p>However, these approaches were built on top of an existing ecosystem that was built to shrink the number of working media dollars and distribute them to technology providers, before money ended up in the publisher’s pocket.</p> <p>Marketers still find a $10 spend reduced to $2 in effective media, after the “ad tech tax” is extracted by trading desks, DSPs, 3rd party data costs, SSPs, and private marketplace fees. Unsustainable, to say the least. </p> <p>What we are seeing now, however, is the rise of a dramatic new approach to data driven marketing that gives the data buyer and owner more control.</p> <p>Marketers have increasingly turned to <a href="https://econsultancy.com/blog/67583-what-does-the-future-hold-for-data-management-platforms/">DMPs</a> to manage their inventory, and publishers are leveraging their DMP’s trust infrastructure to manage exactly which data they can make available to customers—and for very specific use cases.</p> <p>A marketer and publisher on the same DMP infrastructure can choose to “open the pipes” between their instances and share user data for specific campaigns, and start to leverage their audience targeting capabilities on more premium inventory, where people are more engaged.</p> <p>This type of peer-to-peer data sharing is happening today, and we will see not only marketers buying data from premium publishers within DMPs—but also the beginning of peer-to-peer data sharing among marketers.</p> <p>Imagine if a group of CPG marketers pooled their shopping data for non-competitive products. Or, we might see a car rental company start to share business traveler data with its preferred airline. </p> <h3>The programmatic halo effect </h3> <p><strong>Tom Wright, Head of Programmatic, Tomorrow TTH:</strong></p> <p>In an ever increasing programmatic landscape, fluid and responsive media trading has become a necessity not an option.</p> <p>With the acceptance that programmatic media buying is having an incredible impact on multi-channel conversions, now is the time for brands to implement fluid media budget strategies.</p> <p>Programmatic display has grown from being a channel auctioning off unsold display impressions, to being a sophisticated and integral part of the overall marketing mix.</p> <p>As it exists now, buying media programmatically, creates a framework allowing the delivery of data driven, multi media content to audiences via all connected devices, at scale.</p> <p>I believe 2017 will see programmatic media buying cement itself as an infrastructure that paves the way for traditional channels, such as TV, to move into a programmatic format capable of learning, optimising and reacting, considerate of data made available from other programmatic enabled formats.</p> <p>It is this potential for symbiosis which requires a commitment to real fluidity of advertising budget, at scale, in real time between channel, format and device.</p> <p>It will be the responsibility of the 2017 marketer to be brave enough to move away from treating programmatic display in isolation, and start considering the halo effect that the scale and impact of this channel has upon the overall performance of all other paid media channels in the same way TV, Outdoor or print does.</p> <p>This blended approach is something we're championing with our clients and the uplift in performance has been dramatic.</p> <p>Combined with enhancements in attribution technology, it will come down to the orchestration of quantitative evidence and qualitative reasoning to unlock the true power of programmatic media buying, but it will always be about a balance between media trader and the technology at their disposal. </p> tag:econsultancy.com,2008:BlogPost/68611 2016-12-19T14:00:00+00:00 2016-12-19T14:00:00+00:00 What were the biggest ecommerce trends in 2016? Nikki Gilliland <p>For more on this topic, check out these training courses from Econsultancy:</p> <ul> <li><a href="https://econsultancy.com/training/courses/topics/ecommerce/" target="_blank">Ecommerce and Online Retailing Training</a></li> <li><a href="https://econsultancy.com/training/courses/conversion-optimisation/" target="_blank">Conversion Optimisation - How to Deliver Digital Growth Training</a></li> </ul> <h3>Focus on the 'customer experience'</h3> <p><strong>Paul Rouke, founder &amp; CEO, PRWD:</strong></p> <p>This is extremely healthy, although “being customer-centric” is easier said than done. In 2016, there were only a small number of brands who recognised how crucial it is to speak to their customers (and potential customers) one-to-one.  </p> <p>Only a minority of brands know exactly what motivates their customers, how their customers behaviour is changing, and what they can do to differentiate themselves from their competition (and delight visitors at the same time). </p> <h3>Personalisation and data</h3> <p><strong>James Gurd, Owner of Digital Juggler:</strong></p> <p>The increased used of data to tie-up customer touch points has led to better targeting and personalisation, both in marketing campaigns (e.g. personalised product recommendations) and on websites (e.g. surfacing relevant content and products based on user profiles).</p> <p>A smarter focus on data has also resulted in better use of automation technology, for example building lifecycle email campaigns around customer path to purchase journeys.</p> <p>Similarly, techniques like order gap analysis have helped people to understand when different types of customers are most likely to make a purchase or rebuy, and then allow them to schedule campaigns to target people when they’re most likely to be receptive.</p> <p><img src="https://assets.econsultancy.com/images/0008/2169/ASOS_recommendations.JPG" alt="" width="780" height="390"></p> <p><strong>Matt Curry, Head of Ecommerce at LoveHoney:</strong></p> <p>Technologies, and ecommerce's employment of them, have matured.</p> <p>I think we've seen personalisation used more intelligently this year. It's no longer considered good enough to stick some product recommendations on a page and call it personalisation, or have a set number of <a href="https://econsultancy.com/blog/68431-how-to-combine-attribution-and-segmentation-data-to-achieve-marketing-success/">segments</a> you're personalising for. </p> <p>We've had technology to change experiences on the fly based on user behaviour for some time, but now it's finally being used.</p> <h3>Fast and flexible delivery</h3> <p><strong>Depesh Mandalia, CMO of ToucanBox:</strong></p> <p>One of the biggest growing trends has pivoted around delivery - providing customers with both flexibility and speed. </p> <p>This is important because not everyone is crying out for the fastest delivery, but you can bet that most will want flexibility. What use is next day, or even same day delivery, if you're not around to receive it? Choosing a time range and day is the most empowering. </p> <p>Food delivery businesses have mastered this – of course they wouldn’t be able to operate without it - but next year ecommerce can really step ahead in nailing the convenience factor.</p> <p><img src="https://assets.econsultancy.com/images/0008/2168/amazon_delivery.jpg" alt="" width="724" height="483"></p> <h3>Website optimisation</h3> <p><strong>Paul Rouke:</strong></p> <p>This is a clear sign of progression of the slow (but steadily maturing) digital industry and how more brands are now recognising the importance of adopting a test and learn culture to continually enhance their online experience.</p> <p>Steve Webster, Head of Ecommerce at Steinhoff UK, has recently said to me in late 2016, “in 2017 we will be undertaking a full ecommerce platform review, and core to our next platform will be its ability to support continuous, strategic experimentation.”</p> <p><strong>James Gurd:</strong></p> <p>Perhaps the least heralded trend has been greater focus on technical performance optimisation of websites – getting the infrastructural and underlying engineering right to improve the customer experience, speed up sites and minimise outages. </p> <p>I know CX teams who have put much more stringent quality control criteria in place for new developments, so any negative impact on page speed is picked up and resolved before release to live.</p> <p>I think this was demonstrated by the limited number of ‘Shock as Brand X website crashes’ stories over Black Friday weekend – 2014/2015 saw much higher incidents of major downtime.</p> <p><img src="https://assets.econsultancy.com/images/0008/2170/Body_shop_black_friday.JPG" alt="" width="750" height="364"></p> tag:econsultancy.com,2008:BlogPost/68639 2016-12-15T10:02:00+00:00 2016-12-15T10:02:00+00:00 How CRM and a DMP can combine to give a 360-degree view of the customer Chris O'Hara <p>Of course, there is a clearer dividing line between marketing tech and ad tech: personally identifiable information, or PII. Marketers today have two different types of data, from different places, with different rules dictating how it can be used.</p> <p>In some ways, it has been natural for these two marketing disciplines to be separated, and some vendors have made a solid business from the work necessary to bridge PII data with web identifiers so people can be “onboarded” into cookies.</p> <p>After all, marketers are interested in people, from the very top of the funnel when they visit a website as an anonymous visitor, all the way down the bottom of the funnel, after they are registered as a customer and we want to make them a brand advocate.</p> <p>It would be great — magic even — if we could accurately understand our customers all the way through their various journeys (the fabled “360-degree view” of the customer) and give them the right message, at the right place and time. The combination of a strong CRM system and <a href="https://econsultancy.com/blog/67583-what-does-the-future-hold-for-data-management-platforms/">an enterprise data management platform (DMP)</a> brings these two worlds together.</p> <p>Much of this work is happening today, but it’s challenging with lots of ID matching, onboarding, and trying to connect systems that don’t ordinarily talk to one another. However, when <a href="https://econsultancy.com/blog/64545-what-is-crm-and-why-do-you-need-it/">CRM</a> and DMP truly come together, it works.</p> <p>What are some use cases?</p> <h3>Targeting people who haven’t opened an email</h3> <p>You might be one of those people who don’t open or engage with every promotional email in your inbox, or uses a smart filter to capture all of the marketing messages you receive every month.</p> <p>To an email marketer, these people represent a big chunk of their database. Email is without a doubt the one of the most effective digital marketing channels, even though as few as 5% of people who engage are active buyers. It’s also relatively fairly straightforward way to predict return on advertising spend, based on historical open and conversion rates.</p> <p>The connection between CRM and DMP enables the marketer to reach the 95% of their database everywhere else on the web, by connecting that (anonymized) email ID to the larger digital ecosystem: places like Facebook, Google, Twitter, advertising exchanges, and even premium publishers.</p> <p><em>Facebook's custom audiences uses email addresses to target ads</em></p> <p><img src="https://assets.econsultancy.com/images/0008/2423/Facebook_custom_audiences.png" alt="" width="800" height="359"></p> <p>Understanding where the non-engaged email users are spending their time on the web, what they like, their behavior, income and buying habits is all now possible. The marketer has the “known” view of this customer from their CRM, but can also utilise vast sets of data to enrich their profile, and better engage them across the web.</p> <h3>Combining commerce and service data for journeys and sequencing</h3> <p>When we think of the customer journey, it gets complicated quickly. A typical ad campaign may feature thousands of websites, multiple creatives, different channels, a variety of different ad sizes and placements, delivery at different times of day and more.</p> <p>When you map these variables against a few dozen audience segments, the combinatorial values get into numbers with a lot of zeros on the end. In other words, the typical campaign may have hundreds of millions of activities — and tens of millions of different ways a customer goes from an initial brand exposure all the way through to a purchase and the becoming a brand advocate.<br> </p> <h3>How can you automatically discover the top 10 performing journeys?</h3> <p>Understanding which channels go together, and which sequences work best, can add up to tremendous lift for marketers.</p> <p>For example, a media and entertainment company promoting a new show recently discovered that doing display advertising all week and then targeting the same people with a mobile “watch it tonight” message on the night of it aired produced a 20% lift in tune-in compared to display alone. Channel mix and sequencing work.</p> <p>And that’s just the tip of the iceberg — we are only talking about web data.</p> <p>What if you could look at a customer journey and find out that the call-to-action message resonated 20% higher one week after a purchase?</p> <p>A pizza chain that tracks orders in its CRM system can start to understand the cadence of delivery (e.g. Thursday night is “pizza night” for the Johnson family) and map its display efforts to the right delivery frequency, ensuring the Johnsons receive targeted ads during the week, and a mobile coupon offer on Thursday afternoon, when it’s time to order.</p> <p><img src="https://assets.econsultancy.com/images/0008/2425/pizza.jpg" alt="" width="724" height="483"></p> <p>How about a customer that has called and complained about a missed delivery, or a bad product experience? It’s probably a terrible idea to try and deliver a new product message when they have an outstanding customer ticket open. Those people can be suppressed from active campaigns, freeing up funds for attracting net new customers.</p> <p>There are a lot of obvious use cases that come to mind when CRM data and web behavioral data is aligned at the people level. It’s simple stuff, but it works.</p> <p>As marketers, we find ourselves seeking more and more precise targeting but, half the time, knowing when not to send a message is the more effective action.</p> <p>As we start to see more seamless connections between CRM (existing customers) and DMPs (potential new customers), we imagine a world in which <a href="https://econsultancy.com/reports/marketing-in-the-age-of-artificial-intelligence/">artificial intelligence</a> can manage the cadence and sequence of messages based on all of the data — not just a subset of cookies, or email open rate.</p> <p>As the organizational and technological barriers between CRM and DMP break down, we are seeing the next phase of what Gartner <a href="http://www.gartner.com/it-glossary/digital-marketing-hub/">says</a> is the “marketing hub” of interconnected systems or “stacks” where all of the different signals from current and potential customers come together to provide that 360-degree customer view.</p> <p>It’s a great time to be a data-driven marketer!</p> <p>Chris O’Hara is the head of global marketing for Krux, the Salesforce data management platform.</p> <p><em>For more on this topic, see:</em></p> <ul> <li><a href="https://econsultancy.com/reports/the-role-of-crm-in-data-driven-marketing/"><em>The Role of CRM in Data-Driven Marketing</em></a></li> <li><a href="https://econsultancy.com/blog/68408-the-five-fundamentals-of-data-driven-marketing/"><em>The five fundamentals of data-driven marketing</em></a></li> <li><a href="https://econsultancy.com/training/courses/big-data-driven-marketing-how-to-get-it-right/"><em>Econsultancy’s range of Data-Driven Marketing Training Courses</em></a></li> </ul> tag:econsultancy.com,2008:BlogPost/68628 2016-12-14T14:11:58+00:00 2016-12-14T14:11:58+00:00 Amazon could become an ad tech force in 2017 Patricio Robles <p>As detailed last week by the Wall Street Journal, Amazon's first two products launched under the APS umbrella, Transparent Ad Marketplace and Shopping Insights Service, are potential game-changers for the company and signal that Amazon could be ready to make a big ad tech splash.</p> <h3>Shopping Insights Service</h3> <p>Amazon has perhaps the most deep, and therefore valuable, customer database in retail, and its Shopping Insights Service enables publishers to tap into that. Through the service, publishers can gain insight into their audiences based on Amazon's shopping data. </p> <p>Shopping Insights Service has been tested by a number of publishers, including Time Inc., which discovered using Amazon's new service that its Real Simple website is popular with new moms who are interested in purchasing baby products. The media company says that it will be able to use this data to lure advertisers. </p> <p>Amazon's new service shouldn't be a hard sell for publishers. While other companies, like Google and Facebook, offer tools that companies can use to gain insights into their audiences, neither Google nor Facebook has the kind of shopping data Amazon has, so it's likely that publishers will be eager to use Shopping Insights Service.</p> <h3>Transparent Ad Marketplace</h3> <p>The second product under the APS umbrella, Transparent Ad Marketplace, is a cloud-based header bidding solution. Interest in and adoption of <a href="http://digiday.com/publishers/wtf-header-bidding/">header bidding</a> has exploded in the past year thanks to publisher (and <a href="https://econsultancy.com/blog/68460-are-retailers-compromising-site-performance-in-pursuit-of-ad-dollars/">even retailer</a>) motivation to maximize ad revenue.</p> <p>But header bidding, which enables publishers to conduct simultaneous bidding for ad inventory across multiple providers, has also proven to be problematic. The most common header bidding solutions are implemented on the client side, which can impact page performance negatively.</p> <p>Amazon's Transparent Ad Marketplace aims to alleviate that problem. As Amazon VP of Worldwide Advertising Platforms, Tim Craycroft, <a href="http://www.wsj.com/articles/amazon-wants-to-help-publishers-make-more-money-from-ads-1481138120">explained</a> to the Wall Street Journal, Amazon's header bidding technology operates in the company's cloud, not on the client-side.</p> <p>"That should let publishers pull in multiple sources of demand without clogging up their websites with lots of code from different header bidding providers, and slowing down their page loads," he said.</p> <p>In addition to addressing performance concerns that have been a thorn in the side of today's most common header bidding implementations, there is also the potential for Amazon to apply its shopping data to the header bidding process to the benefit of both publishers and advertisers, something that could make Transparent Ad Marketplace particularly attractive.</p> <h3>Amazon's advantages</h3> <p>Amazon's advantages for making a big ad tech splash aren't limited to its data. Observers note that Amazon's ownership of AWS, its cloud computing platform service, offers it access to lots of computing power, something that is necessary for the kind of data crunching that drives the increasingly programmatically-driven digital advertising economy.</p> <p>And Amazon's access to that computing power comes at perhaps a lower cost than any other company.</p> <p>That might explain why Amazon will reportedly not charge publishers for access to Shopping Insights Service or Transparent Ad Marketplace, although the company says it might offer paid features and products in the future.</p> <h3>Success is not guaranteed</h3> <p>Despite its advantages, Amazon's success as a major ad tech player isn't guaranteed. For instance, its cloud-based Transparent Ad Marketplace is attractive on paper, but its viability will likely depend on Amazon's ability to forge relationships and build integrations with header bidders. </p> <p>Google, which is threatened by header bidding, is working on its own header bidding solution. If it can appease publishers with that, Google could disrupt the header bidding market that exists today and make it harder for Amazon to compete with its offering.</p> <p>But Amazon's ad tech success might depend less on tech than the current politics of the ad industry. Publishers and advertisers alike are increasingly concerned about the dominance of Facebook and Google, and while they might be wary of a giant company like Amazon, it's possible the market will decide that a Big 3 is better than a Big 2.</p> tag:econsultancy.com,2008:BlogPost/68626 2016-12-13T14:09:30+00:00 2016-12-13T14:09:30+00:00 Three reasons to appreciate Spotify’s latest data-driven ad campaign Nikki Gilliland <p>Here’s three reasons why it works.</p> <h3>Real-time and relatable elements</h3> <p>Due to roll out in 14 different markets, Spotify’s campaign is designed to draw a line under the strange beast that was 2016, and it does so by showcasing the listening activity of its users on outdoor billboards.</p> <p>Naturally, it also takes the opportunity to draw on the various events that dumbfounded us all throughout the year.</p> <p>For example, one billboard in the UK says: “Dear 3,749 people who streamed ‘It's The End Of The World As We Know It’ the day of the Brexit Vote. Hang in There”.</p> <p><img src="https://assets.econsultancy.com/images/0008/2312/Spotify_1.JPG" alt="" width="750" height="487"></p> <p>As well as using humour to poke fun at its own audience, it’s also a rather wry take on what was an extremely eventful year. </p> <p>By talking about large topics, like global events, as well as the personal and every day, such as the music we listen to, the campaign comes off as both relevant and relatable.</p> <p>The timing is pretty smart, too. Unlike most Christmas campaigns, which tend to use sentimental and syrupy themes, Spotify is going against the grain with its light-hearted and sarcastic tone here.</p> <p>With HotelTonight also creating a similarly funny holiday campaign – there’s obviously a trend for going against tradition this year.</p> <h3>Hyper-localised</h3> <p>As well as talking about global and political events, the campaign is also super personal. </p> <p>It draws on data to pick out the (often questionable) music listening habits of its users, with tongue-in-cheek commentary for added humour.</p> <p>A personal favourite is the Justin Bieber-inspired billboard that says: “Dear person who played ‘Sorry’ 42 times on Valentine’s Day, what did you do?”</p> <p>Other billboards are incredibly localised, mentioning the listening behaviour of local residents, such as: "Dear person in the Theater District who listened to the Hamilton soundtrack 5,376 times this year. Can you get us tickets?"</p> <p>By referencing the surrounding area, it is also effective for targeting and creating a deeper connection with a key demographic.</p> <h3>Creates a memorable moment</h3> <p>The CMO of Spotify, Seth Farban, recently spoke about the debate over big data and how it could potentially be muting creativity in marketing.</p> <p>In contrast to this suggestion, he says: “For us, data inspires and gives an insight into the emotion that people are expressing.”</p> <p>I think this is why the campaign works so well.</p> <p>Spotify is a company that relies on data to give its users a better experience. Let’s say a fashion brand or ecommerce company advertised what customers bought and when – it could come across as creepy or even off-putting.</p> <p>So why is it different for Spotify? Like Farban says, it’s because the brand is wrapped up in the emotion of music.</p> <p>Likewise, it is also expected. Users understand Spotify has access to listener data, using it to dictate the platform’s algorithm and personalisation features. This makes it feel less intrusive. </p> <p>Finally, going back to the relatable element – advertising our ‘guilty pleasures’ or songs we might feel embarrassed listening to makes the intent appear jovial and harmless in nature.</p> <p><img src="https://assets.econsultancy.com/images/0008/2313/Spotify_3.JPG" alt="" width="750" height="498"></p> <h3>In conclusion…</h3> <p>Spotify’s campaign is clever in how it uses its own customer-base as a marketing asset.</p> <p>Building on the platform’s reputation for giving users a curated and personal experience, it uses humour to shine a light on the ‘weird’ but wonderful ways we related to the brand in 2016.</p> <p><strong><em>Related articles:</em></strong></p> <ul> <li><em><a href="https://econsultancy.com/blog/66344-spotify-unveils-new-playlist-based-ad-targeting/" target="_blank">Spotify unveils new playlist-based ad targeting</a></em></li> <li><em><a href="https://econsultancy.com/blog/68522-the-impact-of-technology-and-social-media-on-the-music-industry/" target="_blank">The impact of technology and social media on the music industry</a></em></li> </ul> tag:econsultancy.com,2008:Report/4351 2016-12-08T15:40:00+00:00 2016-12-08T15:40:00+00:00 The New Marketing Reality <p>There can be no doubt that marketers are keen to embrace new platforms and technologies to help them drive growth. Sadly, it would appear that there is still a <strong>gap between those goals and the methods they have at their disposal to achieve them</strong>.</p> <p>The challenge is that while new technologies and the data that underpins them have the potential to create a truly omnichannel customer experience, marketers' methodologies are still forcing everything through the same <strong>outdated, siloed processes</strong>.</p> <p><strong>The traditional funnel no longer works.</strong> It assumes audiences are linear and predictable in their behaviour. At the same time, it doesn't take into account the fact that people will act the way they want to act, rather than sticking to a sequence designed by marketers.</p> <p>To help marketers break free from these processes that are stopping them from capitalising on the opportunities that more agile, disruptive companies are enjoying, this report identifies some <strong>key areas ripe for change</strong>.</p> <p><strong>The New Marketing Reality</strong> report, produced in association with <a title="IBM Watson Marketing" href="https://www.ibm.com/watson/marketing/">IBM Watson Marketing</a>, explores the challenges that marketers face in the three key battlefields of data, customer experience and business outcomes.</p> <p>The research is based on a survey of <strong>more than 1,000 marketing, digital and ecommerce professionals</strong>.</p> <p>Findings include:</p> <ul> <li> <strong>Audience segmentation</strong> is the topmost priority, with 72% of executives stating that they are using their data to support this activity. It is viewed as a standard tactic by even the most laggard of companies. The next most popular data-related activity is <strong>customer journey mapping</strong>, with 67% practising it.</li> <li>The vast majority (80%) of those who rate their ability to understand the customer journey across channels and devices as 'advanced' or 'intermediate' find customer journey mapping or analysis 'highly valuable' and the remaining 20% claim it is 'quite valuable'.</li> <li>Most respondents are still <strong>finding it hard to move out of the channel-focused mindset</strong>, hampered by both technology and organisational structure.</li> <li>Considering that 83% of more advanced companies claim to practise customer journey mapping, we might expect less channel focus but 59% still have <strong>difficulty unifying their data sources</strong> and a further 61% are struggling with the <strong>complexity of their customer touchpoints</strong>.</li> <li>From a business buy-in and organisational perspective, there is still some work to do. The customer journey is still to see the sort of formalised approach that data strategies are only now beginning to enjoy.</li> </ul> <p><strong>Download a copy of the report to learn more.</strong></p>