tag:econsultancy.com,2008:/topics/data-analytics Latest Data & Analytics content from Econsultancy 2017-07-20T14:00:43+01:00 tag:econsultancy.com,2008:BlogPost/69270 2017-07-20T14:00:43+01:00 2017-07-20T14:00:43+01:00 Analytics play a key role in helping to drive digital transformation [New research] Linus Gregoriadis <p>The key findings of the <a href="https://econsultancy.com/reports/measurement-and-analytics-report/">2017 Measurement and Analytics Report</a> — now in its tenth edition — were unveiled last night at a Web Analytics Wednesday event in London, hosted by Lynchpin and featuring panellists from Addison Lee, Saga and The Book People.  </p> <p>This year’s research, based on a survey of more than 900 companies, has found that 81% of client-side respondents now believe that digital analytics are important to digital transformation programmes within their organisations, an increase of four percentage points from 77% in 2016. </p> <p><em><strong>How important are digital analytics to your organisation’s digital transformation programme?</strong></em></p> <p><img src="https://assets.econsultancy.com/images/0008/7658/chart_1.png" alt="" width="900" height="588"></p> <p><em>Respondents 2017: 352; Respondents 2016: 332 </em></p> <p>Mark Carlock, head of analytics at The Book People, described how analytics were playing an important role at the bookseller as the company evolved from being a catalogue business into an ecommerce company. </p> <p>According to Graeme McDermott, chief data officer at Addison Lee, the data and analytics function he runs was established as a result of new company ownership — the London taxi company was acquired by private equity firm the Carlyle Group in 2013 — and largely in response to the disruptive threat of Uber in the marketplace. </p> <p>The research shows that executive sponsorship is widely seen as a prerequisite of digital analytics success, describing how “a top-down approach to digital analytics and data-driven decision making is crucial for ensuring that a culture of analytics is embedded within the organisation”.  </p> <p>The overwhelming majority of this year’s survey respondents (94%) agree that executive sponsorship is important for promoting digital measurement and analytics internally, but only 50% say they have this support within their businesses. </p> <p>According to McDermott: “Ultimately the CEO has to believe data and analytics is an intrinsic part of decision making, or else the other executives will just pay lip service.”</p> <p>While the panellists generally agreed that a top-down approach to data and analytics was crucial, Dave Rhee, head of analytics at Saga PLC, stressed the importance of instilling a ‘bottom-up’ approach to ensure that the right culture permeated the business over the long term, rather than being reliant on a particular sponsor. </p> <h3>Strategies and frameworks still missing </h3> <p>Reflecting the lack of senior buy-in for analytics at many organisations, the research has also found that related strategies and frameworks are missing with many companies.  </p> <p>Almost two-thirds (64%) of responding companies are lacking a documented data analytics strategy, up from 62% last year. Meanwhile, the percentage of companies that have a measurement framework in place remains at 59%.</p> <p>Lynchpin’s head of consultancy Gary Douglas, who was also on the panel, stressed the importance of having the right over-arching metrics in place: “You need to have metrics in place that are linked to commercial outcomes for the business, that make a difference and help to define long-term success.” </p> <h3>GDPR – ‘You don’t have to outrun the bear’ </h3> <p>Panelists also discussed how prepared companies were for May 2018 when the European Union’s new General Data Protection Regulation (GDPR) comes into effect.</p> <p>The imminence of this legislation is resulting in a compliance headache for companies simultaneously seeking to mine client data more extensively and intelligently, while at the same time trying to protect it. The extensive EU text seeks to harmonise and tighten privacy laws across Europe, with additional obligations around areas such as consent, data portability, consumers’ ‘right to be forgotten’ and breach notification. </p> <p>The chart below shows how prepared analytics teams are for GDPR. Only 10% of client-side respondents said their teams were ‘already compliant’, while a further 38% said they were ‘in the process of making the necessary changes to be compliant’. </p> <p><em><strong>Is your analytics team (or are your clients’ analytics teams) prepared for when the EU General Data Protection Regulation (GDPR) will come into effect in 2018?</strong></em></p> <p><img src="https://assets.econsultancy.com/images/0008/7660/chart_2.png" alt="" width="900" height="588"></p> <p><em>Company respondents: 317; Agency respondents: 221</em></p> <p>Asked whether there were similarities with the Millennium bug (when there seemed to be minimal impact compared to the Y2K hype that had preceded the new millennium), McDermott said that GDPR could be much more significant for businesses, adding that ‘your systems won’t fall over, but you will get fined a lot more money’. </p> <p>Saga’s Rhee stressed that it was important to read what the new laws say, and then take a view as to what they actually mean, rather than relying on what other people are telling you. </p> <p>Confirming that Saga would be ready for the deadline, he said that some companies would get away with not being fully compliant, as long as they were more prepared than others: ‘You don’t have to outrun the bear, you just have to outrun your companion,’ he said. </p> <p><strong><em>The <a href="https://econsultancy.com/reports/measurement-and-analytics-report/">2017 Measurement and Analytics Report</a>, published by Econsultancy in partnership with Lynchpin, is now available to Econsultancy subscribers for download. </em></strong></p> tag:econsultancy.com,2008:BlogPost/69263 2017-07-20T11:00:00+01:00 2017-07-20T11:00:00+01:00 Google Analytics will soon be able to answer questions in plain English Patricio Robles <p>Google <a href="http://analytics.googleblog.com/2017/07/ask-question-get-answer-in-google.html">says</a> the technology, some of which comes from Android and Search, will help Google Analytics users "better understand and act on [their] analytics data."</p> <p>According to Google, analysts it spoke to indicated that they spend "half their time answering basic analytics questions for other people in their organization." By making it possible for analysts and business users to use plain English to obtain analytics data, they will in theory be able to get the information they need more quickly, giving both groups the ability to focus their energies on the higher-value aspects of their roles.</p> <p>Ultimately, Google's goal is to make "getting answers about your key business metrics...as easy as asking a question in plain English."</p> <p><img src="https://assets.econsultancy.com/images/0008/7624/NY557_-_Gif_-_360p__1_.gif" alt="" width="528" height="297"></p> <p>Today, the new functionality, which is dubbed Analytics Intelligence, can answer a number of types of questions, such as those related to basic metrics like traffic and referrals, performance, and trends. Users can add qualifiers like date ranges and ask answers to include percentages.</p> <p><img src="https://assets.econsultancy.com/images/0008/7623/analyticsintelligence.png" alt="" width="619" height="413"></p> <p>In addition, Analytics Intelligence is capable of answering questions about user groups. For instance, a retailer could ask questions about conversion rates in specific countries, and an advertiser could ask questions about which paid search keywords convert best.</p> <h4>Of course, while impressive, Analytics Intelligence has a huge limitation: it can't tell users what questions to ask.</h4> <p>Analytics Intelligence will no doubt help many Google Analytics users who don't have the in-depth knowledge of the service to answer questions on their own. Google Analytics is a powerful service but getting the most out of it often requires a level of knowledge and experience many companies don't have. Thus, many fail to maximize the value of the free service.</p> <p>But Analytics Intelligence won't necessarily fully address that issue because Analytics Intelligence can't tell users what questions they <em>should</em> be asking.</p> <p>This is one of the biggest challenges facing companies today. There is more data than ever, and sophisticated tools for analyzing it, but that data and those tools are of minimal utility if the people using them don't <a href="https://econsultancy.com/blog/63373-which-metrics-should-publishers-be-using-in-google-analytics/">use the right metrics</a> and ask the right questions. For instance, Analytics Intelligence can answer questions like "Which channel had the best conversions in July?" but the answer might or might not be of real value to the business. Analytics Intelligence doesn't know. And it can't suggest better, more relevant questions because it doesn't know what those questions are.</p> <p>What's more, because Analytics Intelligence can't answer strategic questions like "Which campaign should I invest in?", it's important for users to make sure that the ease of the new functionality doesn't encourage over-production of reports and charts that don't actually help answer key business questions.</p> <p>While Google is trying to address this with <a href="https://analytics.googleblog.com/2016/09/explore-important-insights-from-your.html">Automated Insights</a>, which was released last year, there's still no substitute for starting an analytics query with a business question.</p> <p><strong><em>To learn more on this topic, check out Econsultancy’s range of <a href="https://econsultancy.com/training/courses/topics/data-analytics/">analytics training courses</a>.</em></strong></p> tag:econsultancy.com,2008:Report/4535 2017-07-20T09:35:00+01:00 2017-07-20T09:35:00+01:00 2017 Measurement and Analytics Report <p>Never have marketers, analysts and ecommerce professionals had more data to work with as part of their ongoing efforts to improve business and organisational performance.</p> <p>At the same time, the growing challenge for individuals and organisations alike has been to avoid being overwhelmed by proliferating sources of data and metrics across a burgeoning number of marketing channels and technology platforms.</p> <p>The <strong>2017 </strong><strong>Measurement and Analytics Report</strong>, produced by Econsultancy in partnership with analytics consultancy <strong><a href="http://www.lynchpin.com/">Lynchpin</a></strong> for the tenth year running, looks at how organisations are using data strategically and tactically to generate insights and to improve business performance.</p> <p>The report aims to cut through the noise to understand how companies are using measurement and analytics to boost revenue and profit growth, while also looking at the types of technology and data which are used to meet these ends.</p> <p>The research, based on a survey of almost 1,000 digital professionals, focuses on the important role for data and analytics in supporting their attempts to build a competitive advantage by becoming more customer-centric. The report also explores how the worlds of data science and digital analytics are converging as companies strive to extract valuable insights from a wealth of information relating to digital activity in the context of the wider business.</p> <h2>What you'll learn from this research</h2> <ul> <li>Understand how analytics can help to meet financial goals and what the most common growth and profit-related requirements are.</li> <li>Discover how organisations are using data and analytics to build a competitive advantage by becoming more customer-centric.</li> <li>Benchmark the make-up of your analytics or data team and investment plans against those of your peers.</li> <li>Find out where the biggest analytics skills gaps are and what the most common challenges related to deploying tools and technologies organisations face.</li> </ul> <h2>Key findings from the report</h2> <ul> <li>The majority of companies (64%) do not have a documented data analytics strategy.</li> <li>Only 50% of organisations report executive sponsorship of analytics.</li> <li>Half of organisations surveyed regard digital analytics as ‘very important’ to their digital transformation programme (a jump from 43% in 2016).</li> </ul> <h2>Contributors</h2> <p>Econsultancy would like to thank the following people for their contributions to this report:</p> <ul> <li>Amiy Chatley, Digital Analytics Manager, TUI</li> <li>Matteo Fava, Global Head of Analytics, Delivery Hero</li> <li>Graeme McDermott, Chief Data Officer, Addison Lee</li> <li>Andrew Morris, ‎Head of Digital Insight Delivery, RS Components</li> <li>Alejandro Pereda, Head of Insight, Euromoney Institutional Investor plc</li> </ul> <p><strong>Download a copy of the report to learn more.</strong></p> tag:econsultancy.com,2008:Report/4507 2017-07-20T08:45:00+01:00 2017-07-20T08:45:00+01:00 2017 Digital Trends in IT <p>The <strong>2017 Digital Trends in IT </strong>report, based on the seventh annual trends survey conducted by Econsultancy and <a title="Adobe" href="http://www.adobe.com/marketing-cloud.html">Adobe</a>, explores the digitally-driven opportunities and challenges facing organisations from the perspective of IT professionals.</p> <p>IT is now seen as an increasingly strategic function within the business, and pivotal to organisational attempts to embrace digital transformation and customer experience initiatives. It is no longer sufficient for the IT department to act merely in a support role when it comes to delivering against the company’s overarching business objectives. IT leaders need to take ownership and drive change within the modern, digitally-enabled organisation.</p> <p>The research is based on data from more than 500 IT leaders (manager level or above) who were among more than 14,000 digital professionals taking part in the seventh annual Digital Trends survey, carried out in November and December 2016.</p> <h3>The following sections are featured in the report:</h3> <ul style="font-weight: normal;"> <li>What keeps IT leaders up at night?</li> <li>2017 priorities for success</li> <li>Challenges of digital transformation</li> <li>Actionable tips to help future-proof your IT function</li> </ul> <h3>Findings include:</h3> <ul> <li>There is heightened pressure on IT practitioners to stay abreast of customer trends, and to deliver infrastructures that enable the real-time and personalised services users increasingly expect in the digital age. <strong>Keeping up with changing customer expectations and behaviour</strong> was cited as a key challenge by 40% of respondents, a greater proportion than those worried about keeping IT systems up and running.</li> <li> <strong>The threat of security breaches and cyber-risk threats</strong> is cited as a key concern by a higher proportion of respondents (41%) than any other area, and security of business and customer data is the most commonly cited IT leader priority for 2017.</li> <li>Larger organisations are less confident than their smaller counterparts when it comes to the <strong>adequacy of digital skills and talent</strong> within their business. With the rise of digital transformation, data scientists are at a premium, and few organisations have all the resources they need to make use of new analytics tools and capabilities.</li> <li>The impact of digital technology on workflows within organisations has been vast, affecting every business function from HR to finance, and marketing to procurement. Nearly half (49%) of IT executives indicate they have prioritised <strong>enhancement of digital workflows</strong>, for example via cloud-based tools, for 2017.</li> <li> <strong>Keeping ahead of major technology connected to innovation</strong> is another key challenge for IT leaders. Executives at large companies are notably more inclined to feel pressure regarding tracking technology and innovation trends than smaller company peers (46% versus 36%).</li> </ul> <p style="font-weight: normal;"><strong>Econsultancy's Digital Intelligence Briefings, sponsored by <a title="Adobe" href="http://www.adobe.com/marketing-cloud.html">Adobe</a>, look at some of the most important trends affecting the marketing landscape. </strong><strong>You can access the other reports in this series <a title="Econsultancy / Adobe Quarterly Digital Intelligence Briefings" href="http://econsultancy.com/reports/quarterly-digital-intelligence-briefing">here</a>.</strong></p> tag:econsultancy.com,2008:Report/4538 2017-07-17T01:00:00+01:00 2017-07-17T01:00:00+01:00 State of Marketing Automation in Australia and New Zealand <p>The 'holy grail' of marketing automation envisaged by marketers sees the complete elimination of internal data silos to build a 360-degree view of the customer, and the utilisation of this intelligence to enable deeper, personalised engagement with prospects and clients.</p> <p>But how close are today’s marketers to realising this?</p> <p>This is Econsultancy’s first <strong>State of Marketing Automation in Australia and New Zealand</strong> report, published in association with <a title="Oracle Marketing Cloud" href="https://www.oracle.com/marketingcloud/about/australia-new-zealand.html">Oracle Marketing Cloud</a>.</p> <p>The research is based on a survey of over 350 marketing professionals based in Australia and New Zealand, and evaluates current adoption levels, tools and processes employed as well as barriers to effective use of marketing automation.</p> <p>Key insights from the research include:</p> <ul> <li> <strong>The majority of companies are choosing to manage their marketing automation in-house.</strong> Three in five (59%) organisations have an in-house team managing marketing automation activities, with only a fifth outsourcing them to an agency. Large organisations (with annual revenues of more than $50 million) are more likely to outsource their marketing automation.</li> <li> <strong>Budgets and internal buy-in are there, but a capability gap is hampering the potential of marketing automation.</strong> Encouragingly, a lack of budget and organisational buy-in prevents only a minority of organisations (20% and 12% respectively) from implementing their automation strategy. The most common barriers are related to data integration and inadequate resources.</li> <li> <strong>There’s a pressing need for data unification.</strong> Only a quarter of companies are working towards the creation of a unified database. Furthermore, nearly half of companies say that integrating data is the most significant barrier to effectively implementing a marketing automation strategy.</li> <li> <strong>Cloud-based SaaS platforms lead the way at an enterprise level.</strong> Large organisations (with annual revenues of at least $50 million) are more likely to use cloud-based SaaS platforms that include automation (38% vs. 28% of smaller organisations).</li> </ul> <p><strong>Download a copy of the report to learn more.</strong></p> <p>A <strong>free sample</strong> is available for those who want more detail about what is in the report.</p> tag:econsultancy.com,2008:BlogPost/69237 2017-07-14T14:46:00+01:00 2017-07-14T14:46:00+01:00 A beginner's guide to building a data science team Ben Davis <h3><img src="https://assets.econsultancy.com/images/0008/7434/stock_graph.jpg" alt="stock graph and people" width="500"></h3> <p>In this article we will cover: </p> <ul> <li>Definitions of data science</li> <li>The purpose of data science</li> <li>How data science teams should integrate into the organisation</li> <li>Recruiting for data science</li> <li>Team roles</li> </ul> <p>Though Econsultancy is marketing focused, there's plenty in here to appeal more broadly. </p> <h3>First, an attempt at a definition</h3> <p>It seems trite to say that data science's applications are broad, but they are. And data science teams come in different forms, within different organisational structures and under different names.</p> <p>There's a pretty good Venn diagram developed by Drew Conway which gets to the heart of the ambiguous phrase 'data science'.</p> <p><img src="https://assets.econsultancy.com/images/0008/7442/ds_venn.jpg" alt="data science venn" width="528" height="504"></p> <p><em>Data science Venn diagram <a href="http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram">by Drew Conway</a></em></p> <p>In Conway's words, "The difficulty in defining these skills is that the split between substance and methodology is ambiguous, and as such it is unclear how to distinguish among hackers, statisticians, subject matter experts, their overlaps and where data science fits."</p> <p>I recommend heading over to Conway's article to read more of his thoughts. But the basic takeaway for a layman like me is – there's a hell of a lot to learn and many different skillsets that can be brought to bear on data.</p> <p>Whilst data science has many grey edges, it's probably worth including some fairly dry definitions of two common teams – 'Big data analytics' teams and 'data product' teams. The former looks for predictive patterns in data without necessarily having a preconceived notion of what they are looking for, and the latter works to implement automated systems that are data-driven.</p> <p><strong>Data products -</strong> Ben Chamberlain, senior data scientist at ASOS, describes a data product as "an automated system that generates derived information about our customers such as predicting their lifetime value. This information is then used to automatically take actions like sending marketing messages or it gets sent to another team who use it for insight."</p> <p>If you don't have any statistical knowledge and you fancy a challenge, you can read <a href="https://arxiv.org/pdf/1703.02596.pdf">one of Chamberlain's papers</a> about this very ASOS CLV data product.</p> <p><strong>Big data analytics -</strong> IBM gives us <a href="https://www.ibm.com/analytics/us/en/technology/hadoop/big-data-analytics/">a serviceable definition</a> of big data analytics: "..a term applied to data sets whose size or type is beyond the ability of traditional relational databases to capture, manage, and process the data with low-latency.</p> <p>"..it has one or more of the following characteristics – high volume, high velocity, or high variety. Big data comes from sensors, devices, video/audio, networks, log files, transactional applications, web, and social media – much of it generated in real time and in a very large scale."</p> <h3>Remember, data science must tackle a problem (duh!)</h3> <p>As I read in a Harvard Business Review article, economist and Harvard professor Theodore Levitt once said that "People don't want to buy a quarter-inch drill, they want a quarter-inch hole."</p> <p>The same applies to data science – the business needs to see a solution. It's another obvious thing to say, but I'm writing it because new(ish) and complicated disciplines such as cognitive computing can temporarily blind marketers to the fact that normal rules of business apply – what is the problem that needs solving? What data can be brought to bear, and how can the data be used to create most value?</p> <p>This is something summed up very nicely with another trusty Venn diagram on a <a href="http://www.juiceanalytics.com/writing/building-better-data-products-getting-started">Juice Analytics article</a>. (The intersection of the three circles is where successful data products live.)</p> <p><img src="https://assets.econsultancy.com/images/0008/7377/venn_data_science.png" alt="venn diagram solving a data problem" width="615" height="338"></p> <p>Parry Malm, co-founder of <a href="https://phrasee.co/">Phrasee</a> (email marketing language generation software), takes a pragmatic tone and warns about employing a data science team before you know exactly what you want to achieve.</p> <p>"The first step," he says, "is to really, really, really clearly define what problem you're trying to solve... only then consider whether or not an analytics team or whatever is the right approach. What you DON'T want to do is to hire 10 'data scientists' or something, and then have a huge working capital hit for an undefined outcome, when the money could potentially be spent better somewhere else."</p> <h3>How data science should interact with the wider org</h3> <p>Before we move on to all the roles in a data science team and the challenges involved in setting one up, it's worthwhile considering how the team will interact with the rest of the organisation.</p> <p>Simply parachuting data scientists into a company ignores the differences in culture and skills between marketing and finance teams, and these statisticians and programmers.</p> <p>To get full value out of your data science team you need to consider what peripheral roles and processes are needed.</p> <p><strong>1) Transparency and a customer service culture</strong></p> <p>The danger is that data products or big data analytics will either be implemented and deliver no business benefit or will be underutilised / underprioritised by a business which fails to recognise their value.</p> <p><a href="https://hbr.org/2013/07/five-roles-you-need-on-your-bi">Writing</a> in Harvard Business Review, various members of McKinsey's analytics teams say there is a need for data teams to operate in a customer service culture.</p> <blockquote> <p>..[think] of the business owners as customers. As any good retailer will tell you, you need to understand your customers to be successful. Have regular meetings with them to understand their needs and get feedback on the performance of the team’s models. Always ask yourself, “Who in the business will be helped by my analytics?” and “Do they agree you helped them succeed?</p> </blockquote> <p>Again, this all feels pretty obvious but will be integral to success. Is the business ready to accept suggestions from a data-led team? If not, what education is needed in the first instance or how can stakeholders be more involved in the effort?</p> <p><strong>2) Data-science communication</strong></p> <p>Science communication generally is a noble cause. In an article in The Guardian in 2016, Richard Holliman <a href="https://www.theguardian.com/science/political-science/2016/may/10/what-has-science-communication-ever-done-for-us">reports</a> that it is an undervalued vocation. He writes that "For too long, research has shown that science communication is seen as a second-class option for academics."</p> <p>Holliman continues, adding that though science communication has improved, "There is still work to be done to ensure that excellence rather than acceptability becomes the hallmark of these activities. The introduction of new ways to discuss and publish the outputs from research, and alternative mechanisms for reward and recognition suggest that a shift in this direction is underway."</p> <p>I'm going off topic here, but there's a corollary with how data science teamwork is translated within businesses and to the end consumer. There needs to be a surrounding network of skillful communicators.</p> <p>These communicators can include: </p> <ul> <li>Data visualisation specialists - To make outcomes more readable and accessible.</li> <li>Data strategists - In a recent <a href="https://econsultancy.com/blog/69187-channel-4-on-the-future-of-tv-personalisation-gdpr/">interview</a> with Econsultancy, Channel 4's director of consumer insights Sarah Rose described this role as "the bridging point between the data science team, who work on the models that we put into our products, and the rest of the business." Their knowledge may include some data science and some industry expertise.</li> <li>Campaign experts - With knowledge of tech and marketing (could be a developer).</li> <li>T-shaped leaders - The leader of the data science team must absolutely be all about data science; it's integral they be an expert in the field. But if you can also find one with business skills, then all the better.</li> </ul> <p>Idrees Kahloon, data journalist at The Economist <a href="https://www.quora.com/session/Idrees-Kahloon/1">says</a> that "Often, the best way to present data is the simplest: people readily understand means, medians and sums. Fancier statistical models appeal to wonks, but are harder to explain to a general audience."</p> <p>Of course, Kahloon is talking about data journalism and getting a concept across to general readers, but there's still plenty of wisdom to be applied to business communication. How do you present data science findings in a way the business can understand?</p> <p>(If you're a bit bored at this, the halfway point in the article, why not watch David McCandless on the beauty of data visualisation, below.)</p> <p><iframe src="https://www.youtube.com/embed/5Zg-C8AAIGg?ecver=2&amp;wmode=transparent" width="640" height="360"></iframe></p> <h3>Recruiting for data science teams</h3> <p>On to the finer detail of how to actually get hold of some of these data science people. It's worth starting with a reality check from <a href="https://econsultancy.com/blog/68933-a-day-in-the-life-of-a-data-scientist-in-an-ai-company">Neil Yager</a>, Chief Scientist at Phrasee. He says, "In general, this is a challenging task and people should manage their expectations up front. This is a relatively new field and demand is high. Therefore, the pool of available talent is rather limited."</p> <p>It's for this reason that the number of vendors offering embedded cognitive computing functionality has skyrocketed over the last couple of years. There are now hundreds that offer some machine learning capability, with martech a particular growth area.</p> <p>The shortage of expertise means that if you are going ahead with your own in-house team, your first hire and the team leader is particularly important. Yager explains: "..due to high demand and short supply, salaries tend to be at the high end. My recommendation is that the first hire be someone relatively senior and experienced. Don't be tempted to build a larger team of less experienced people -- this will be counter productive in the long run."</p> <p>Neil goes on to recommend companies "attend or host local meetup events for big data, data science, AI or machine learning. These are active communities, and the people who attend these events tend to be very engaged."</p> <p>However, even if you dive into your local Hadoop meetup, you may not find the person you need straight away. Data science teams often employ people from a variety of analytical or scientific backgrounds, precisely because it's hard to find somebody with all the skills you need.</p> <p>Maloy Manna <a href="http://www.datasciencecentral.com/profiles/blogs/what-roles-do-you-need-in-your-data-science-team">writing</a> on the Data Science Central blog says:</p> <blockquote> <p>There are actually probably just a handful of the “unicorn” data scientists on the planet, who have superpowers in maths/stats, AI/machine learning, a variety of programming languages, an even wider variety of tools and techniques, and of course are great in understanding business problems and articulating complex models and maths in business-speak.</p> </blockquote> <p>Of course, maintaining links with academia is also important (these will probably cross over with meetup groups). Most companies using data science (including the previously mentioned ASOS and Channel 4) will work with PhD students and a university, as well as employing graduates into their first jobs.</p> <p>Finally, if you want to read how a tech unicorn goes through the recruitment process, Riley Newman, head of analytics at Airbnb, has discussed how they interview their data science candidates <a href="https://www.quora.com/How-does-Airbnb-hire-data-scientists">over on Quora</a>.</p> <h3>Data science team roles</h3> <p>Here are a selection of roles that may be needed in your data science team. Ultimately, some of these roles may overlap, and you may not need one of each - it depends on what your team wants to achieve.</p> <p><strong>Team leader</strong></p> <p>The team leader must have chops when it comes to data science. Leadership and business skills alone are not enough. Christopher Doyle, who works in pricing and analysis at Aspen Dental, <a href="https://www.linkedin.com/pulse/5-steps-build-data-science-team-christopher-doyle%20">sums this up</a> well:</p> <p>"A new analytics team absolutely needs a leader who possesses strong mathematical modeling skills. The reason is simple: Mathematical modeling skills are hard to learn and require years of experience working under experts. While data mining and business savvy skills are certainly valuable, these should ultimately be secondary considerations, since they are skills that can be easily learned."</p> <h4>Data strategist</h4> <p>We have discussed this role already as the bridging point between the data science team and the business. This role may work with campaign experts from the marketing team.</p> <p>Data strategists may be similar to <strong>product managers</strong>, and may need to work with front-end developers and UX professionals as part of a wider data product team.</p> <p>There may also be <strong>data analysts</strong> involved, much like on a more descriptive analytics team, who do data processing and may also visualise data.</p> <h4>Data scientist</h4> <p>As the Venn diagram of data science suggests, a data scientist should have expertise in both statistics and software development. They will likely be able to use Hadoop or Spark to analyse large datasets and they will be familiar with R or Python. The team leader will be a data scientist.</p> <p><strong>Data engineers and architects</strong></p> <p>These roles are about understanding how data is structured in the organisation. That means databases, cloud computing, distributed frameworks like Hadoop and some programming languages expertise.</p> <p>Data architects capture, organise and centralise data. Engineers then test, maintain and get the data ready for analysis.</p> <p>Elizabeth Mazenko has done some great research for BetterBuys on what capabilities various members of the data science team typically have, and the following chart is a useful rough guide.</p> <p>You'll see many more job titles mentioned in other articles – data hygienists or business solutions architects, for example – but most should correlate with the three or four roles outlined here.</p> <p><img src="https://assets.econsultancy.com/images/0008/7443/data_science_roles.jpg" alt="data science roles and capability" width="615" height="759"></p> <p><em><a href="https://www.betterbuys.com/bi/comparing-data-science-roles/">Via Better Buys</a></em></p> <h3>A final thought for marketers </h3> <p>Christopher Doyle, director of market analysis at Aspen Dental, writes:</p> <p>"Even though the marketing department is our top customer, I prefer keeping them at arm’s length. Everything in the marketing department needs to happen immediately, so keeping some distance between them and the analytics team allows the analysts to manage the workflow more efficiently."</p> <p>Though marketers and Agile digital teams may have just got a taste for iterating and innovating, data science can take time. From data cleansing (which could take months) to developing models and implementing products, marketers need to understand the scale of investment (both time and money) required in data science teams.</p> <p>However, once these teams start to bear fruit, advantage over the competition can be significant.</p> <h3>A final, final thought</h3> <p>Parry Malm, co-founder of Phrasee:</p> <p>"Here's another option: in 1997, I sat next to a guy in a university computer science class who was called Neil, who's now known as <em>Dr</em> Neil Yager, our chief scientist. So another option is to build a time machine, go back 20 years, and make sure you're sat next to the right person."</p> <p><em>To learn more on this topic, book yourself a place on Econsultancy’s <a href="https://www.econsultancy.com/training/courses/gdpr-data-driven-marketing">GDPR &amp; Data-Driven Marketing Training Course</a>.</em></p> tag:econsultancy.com,2008:ConferenceEvent/840 2017-07-13T06:02:28+01:00 2017-07-13T06:02:28+01:00 Digital Cream Sydney <p style="border: 0px; vertical-align: baseline;">Exclusive to 80 senior client side marketers, <strong style="border: 0px; font-style: inherit; font-variant: inherit; vertical-align: baseline;">Econsultancy's Digital Cream</strong> is one of the industry's landmark events for marketers to:</p> <ul style="border: 0px; vertical-align: baseline;"> <li style="border: 0px; font-weight: inherit; font-style: inherit; vertical-align: baseline; font-variant: inherit;">convene and network with like-minded peers from different industries</li> <li style="border: 0px; font-weight: inherit; font-style: inherit; vertical-align: baseline; font-variant: inherit;">exchange experiences</li> <li style="border: 0px; font-weight: inherit; font-style: inherit; vertical-align: baseline; font-variant: inherit;">compare benchmark efforts</li> <li style="border: 0px; font-weight: inherit; font-style: inherit; vertical-align: baseline; font-variant: inherit;">explore the latest best practice</li> <li style="border: 0px; font-weight: inherit; font-style: inherit; vertical-align: baseline; font-variant: inherit;">discuss strategies</li> <li style="border: 0px; font-weight: inherit; font-style: inherit; vertical-align: baseline; font-variant: inherit;">learn from others who face the same challenges with suppliers, technologies and techniques. </li> </ul> <p style="border: 0px; vertical-align: baseline;">In a personal and confidential setting (It's Chatham House Rules so what's said at Digital Cream, stays at Digital Cream), the roundtable format is a quick and sure-fire way to find out what's worked and what hasn't, an invaluable opportunity to take time out and come back to the office full of ideas.</p> <h3 style="border: 0px; vertical-align: baseline; color: #004e70;">Roundtable Format</h3> <p style="border: 0px; vertical-align: baseline;">There are 8 roundtable topics and each delegate chooses 3 table topics most relevant to you, each session lasting about an hour and fifteen minutes. Each roundtable is independently moderated and focuses on a particular topic discussing challenges or areas of interest nominated by the table's attendees in the time available. This level of input ensures you get the maximum from your day.</p> <p style="border: 0px; vertical-align: baseline;">Digital Cream has been devised by the analysts and editors at Econsultancy in consultation with the most senior digital buyers in the world and runs in London, New York, Melbourne, Sydney, Shanghai, Singapore and Hong Kong.</p> <p style="border: 0px; vertical-align: baseline;"><strong style="border: 0px; font-style: inherit; font-variant: inherit; vertical-align: baseline;">Attendees pick three tables choices from the following full list of topics offered (extra topics will be removed at a later stage. If there is a topic you'd like to discuss which is not listed here, you can suggest it while registering):</strong> </p> <p style="border: 0px; font-weight: inherit; font-style: inherit; vertical-align: baseline; font-variant: inherit;">1. Agile Marketing - Develop a more responsive &amp; customer-centric approach</p> <p style="border: 0px; font-weight: inherit; font-style: inherit; vertical-align: baseline; font-variant: inherit;">2. Content Marketing Strategy</p> <p style="border: 0px; font-weight: inherit; font-style: inherit; vertical-align: baseline; font-variant: inherit;">3. Customer Experience Management</p> <p style="border: 0px; font-weight: inherit; font-style: inherit; vertical-align: baseline; font-variant: inherit;">4. Data-Driven Marketing &amp; Marketing Attribution Management</p> <p style="border: 0px; font-weight: inherit; font-style: inherit; vertical-align: baseline; font-variant: inherit;">5. Digital Transformation - People, Process &amp; Technology</p> <p style="border: 0px; font-weight: inherit; font-style: inherit; vertical-align: baseline; font-variant: inherit;">6. Ecommerce</p> <p style="border: 0px; font-weight: inherit; font-style: inherit; vertical-align: baseline; font-variant: inherit;">7. Email Marketing - Trends, Challenges &amp; Best Practices</p> <p style="border: 0px; font-weight: inherit; font-style: inherit; vertical-align: baseline; font-variant: inherit;">8. Integrated Search (PPC/SEO) - Trends, Challenges &amp; Best Practices</p> <p style="border: 0px; font-weight: inherit; font-style: inherit; vertical-align: baseline; font-variant: inherit;">9. Joining Up Online &amp; Offline Channels Data</p> <p style="border: 0px; font-weight: inherit; font-style: inherit; vertical-align: baseline; font-variant: inherit;">10. Marketing Automation - Best Practices &amp; Implementation</p> <p style="border: 0px; font-weight: inherit; font-style: inherit; vertical-align: baseline; font-variant: inherit;">11. Mobile Marketing</p> <p style="border: 0px; font-weight: inherit; font-style: inherit; vertical-align: baseline; font-variant: inherit;">12. Online Advertising - Retargeting, Exchanges &amp; Social Advertising</p> <p style="border: 0px; font-weight: inherit; font-style: inherit; vertical-align: baseline; font-variant: inherit;">13. Real-Time Brand Marketing - Using Data &amp; Technology To Drive Brand Impact</p> <p style="border: 0px; font-weight: inherit; font-style: inherit; vertical-align: baseline; font-variant: inherit;">14. Social Media Measurement &amp; Optimisation</p> <p style="border: 0px; font-weight: inherit; font-style: inherit; vertical-align: baseline; font-variant: inherit;"><strong style="border: 0px; font-style: inherit; font-variant: inherit; vertical-align: baseline;"><strong style="border: 0px; font-style: inherit; font-variant: inherit; vertical-align: baseline;">&gt;&gt;</strong> <strong style="border: 0px; font-style: inherit; font-variant: inherit; vertical-align: baseline;">View past Digital Cream event photos (source: facebook page)</strong><br></strong></p> <p style="border: 0px; font-weight: inherit; font-style: inherit; vertical-align: baseline; font-variant: inherit;"><a href="https://www.facebook.com/pg/Econsultancy/photos/?tab=album&amp;album_id=10153875617599327" target="_blank">Digital Cream Sydney 2016</a>, <a style="border: 0px; font-weight: inherit; font-style: inherit; vertical-align: baseline; color: #004dcc; font-variant: inherit;" href="https://www.facebook.com/media/set/?set=a.10153214103704327.1073741876.90732954326&amp;type=3" target="_blank">Digital Cream Singapore 2015</a>, <a style="border: 0px; font-weight: inherit; font-style: inherit; vertical-align: baseline; color: #004dcc; font-variant: inherit;" href="https://www.facebook.com/media/set/?set=a.10153124439974327.1073741873.90732954326&amp;type=3" target="_blank">Digital Cream Sydney 2015</a>, <a style="border: 0px; font-weight: inherit; font-style: inherit; vertical-align: baseline; color: #004dcc; font-variant: inherit;" href="https://www.facebook.com/media/set/?set=a.10152276242849327.1073741856.90732954326&amp;type=3" target="_blank">Digital Cream Melbourne 2014</a> and <a style="border: 0px; font-weight: inherit; font-style: inherit; vertical-align: baseline; color: #004dcc; font-variant: inherit;" href="https://www.facebook.com/media/set/?set=a.10152209218799327.1073741854.90732954326&amp;type=3" target="_blank">Digital Cream Hong Kong 2014</a></p> tag:econsultancy.com,2008:BlogPost/69228 2017-07-06T15:06:39+01:00 2017-07-06T15:06:39+01:00 Predictive analytics: Four prerequisites of an effective strategy Ben Davis <p>The stages are shown below.</p> <p>In summary, the earliest stage of analytics competency is characterised by disparate data sources, some descriptive analytics and no dedicated team resource.</p> <p>Next come dedicated staff and a change in culture to prioritise actionable insight. Lastly, predictive analytics capability is built on a single data lake, supporting data-driven decisions throughout the business.</p> <p><img src="https://assets.econsultancy.com/images/0008/7302/maturity_predictive_analytics.png" alt="maturity model predictive analytics" width="615" height="462"></p> <p>A model is all well and good, but how are marketers moving between these stages of maturity. What challenges do marketers face in establishing predictive analytics capability?</p> <p>In a new report (<a href="https://www.econsultancy.com/reports/achieving-predictive-maturity">Achieving Predictive Maturity</a>) Econsultancy and RedEye round up some feedback from brand marketers and analytics specialists.</p> <p>As more companies begin to understand the possibilities of automation and machine learning, there's a need to rationalise the time and resource needed to prioritise predictive modelling, and weigh this against potential benefits.</p> <p>The report identifies four crucial aspects of effective predictive modelling. They are:</p> <h3>1. Appropriate sources of data </h3> <p>One of the most fundamental points to consider is whether data is indeed capable of providing an answer to every question that the organisation has. There may be data gaps. Matthew Curry, head of ecommerce at Lovehoney, picks up the baton:</p> <blockquote> <p>When you're building a new product, whether it's some software or a physical item, are you going to be able to find all the data you need? I'm not entirely certain you are. We have sales data and buying patterns. We know our customers like rechargeables and different colours. So we build on that. That's as complex as it gets.</p> </blockquote> <p>There are many factors that determine the appropriateness of a data source, from regulation and privacy to volume and velocity.</p> <p>Interviewees urged caution when branching out beyond first-party data. Nathan Ansell, director of customer loyalty at Marks &amp; Spencer implies that third-party data is inherently less valuable than first-party data because it is not exclusive to your platform.</p> <p>He says "A lot of the data we have exists within our own big data platform – transactional or weather data for example. Other sources of data around customer intent such as social media activity also exist in other organisations."</p> <h3>2. Data cleanliness and usefulness</h3> <p>After deciding on the appropriate data sources to use, the cleanliness of that data should be assessed and, ultimately, its usefulness.</p> <p>Lara Izlan, director of commercial platforms and operations at Auto Trader, picked up the theme of consolidation, warning that incorporating too many data sources (however clean) may ultimately be self-defeating:</p> <blockquote> <p>Any new data sources that feed into the model can add value but you probably hit the law of diminishing returns after a while. Auto Trader already serves quite a large proportion of the UK car buying audience so we have already moved quite far along that data value curve.</p> <p>Prioritising the data sources along the car buying journey helps optimise predictive models towards conversion. If you were starting with a wider consumer base and a platform where the media was aimed more at prospecting new users you might need more data inputs to get a purchase.</p> </blockquote> <p>Of course, there is value to be found in third-party data sources, particularly using anonymous third-party data at the top of the funnel to target paid media. But marketers are focused on getting their own data in order first and can be surer of its provenance.</p> <h3>3. Automation and machine learning</h3> <p>Machine learning may be something that more and more martech vendors are trumpeting as a part of their software, but making use of these algorithms more broadly in your company's analytics teams is not a plug-and-play scenario.</p> <p>Auto Trader's Lara Izlan describes the scale of the task:</p> <blockquote> <p>Most people are looking at machine learning and trying to figure out if they can get the resource for it. Data scientists, technologies, not to mention the amount of cloud storage that you need. I've learnt over the last year that it's not just about chucking data into a warehouse and accessing it.</p> <p>Machine learning is a massive undertaking to structure, having all the data sources talking to each other and then making it all actionable in real time.</p> </blockquote> <p>The new General Data Protection Regulation (GDPR), which comes into force in May 2018, is also affecting companies' plans to skill up in this area. Consumers will be entitled to know how their data is being processed and can contest <a href="https://www.marketingweek.com/2017/06/27/artificial-intelligence-force-good-evil/">'black box' style analysis</a> that is entirely automated.</p> <p>“We had great plans but now GDPR is coming in and it makes us question whether there is still a business case for machine learning," says Katrina King, head of customer marketing at Direct Line Group.</p> <p>King continues, identifying the potential difficulty when working with partner data: "Not knowing what is going to be okay under GDPR makes it difficult to move forward. The key thing will be explicit consent and we don't have a sense today of how many partner companies will have that consent. Eighteen months from now when there is much more clarity, the companies and agencies that can prove they have their consent already in order will be at a distinct advantage.” </p> <h3>4. Meeting business objectives</h3> <p>The final step of putting effective predictive analytics in place is determining whether your activity is meeting business objectives.</p> <p>Predicting whether a customer will click or buy in the short term is all very fantastic, but marketers must still consider the broader picture. Colin Lewis, CMO at OpenJaw Technologies, says “If you're using predictive modelling for the right reasons, you're doing it to build the best customer experience. The problem is when personalisation is designed around the idea that you only want to sell more stuff. Then you'd be just as well giving the customer an offer.”</p> <p>What Lewis is getting at is that predictive modelling requires a lot of management for something that is only going to be used to move the needle slightly. Instead, it should really be used to enhance a service, for example, to <a href="https://econsultancy.com/blog/69187-channel-4-on-the-future-of-tv-personalisation-gdpr/">personalise curated content in the case of Channel 4's All 4 platform</a>.</p> <p>Richard Clark, marketing director of N Brown Group gives us the final word on the agility of leadership required to make best use of predictive analytics:</p> <blockquote> <p>As leaders you have to know and believe in your strategy. If the model shows up something of interest, you have to make quick decisions about whether or not to pursue it. I'm all for change when it's appropriate but not about just jumping on things because they look interesting.</p> </blockquote> <p><em><strong>Subscribers can download the <a href="https://www.econsultancy.com/reports/achieving-predictive-maturity">Achieving Predictive Maturity</a> report now.</strong></em></p> tag:econsultancy.com,2008:BlogPost/69232 2017-07-06T11:41:00+01:00 2017-07-06T11:41:00+01:00 Marketers can rest easy, AI is not about to make them redundant Nikki Gilliland <p>Sounds pretty simple when you put it like that, right? </p> <p>Of course, actually getting to this point isn’t <em>quite</em> so easy. Neither is convincing businesses that artificial intelligence is actually worth investing in, especially considering it is nearing the dreaded “trough of disillusionment” on the infamous Gartner Hype Cycle.</p> <p><img src="https://assets.econsultancy.com/images/0008/7299/Gartner_hype_cycle.JPG" alt="" width="650" height="476"></p> <p>Reflecting <a href="https://www.econsultancy.com/blog/68046-five-pioneering-examples-of-how-brands-are-using-chatbots">the various examples of brand chatbots</a> we’ve seen throughout the past year or so, the conversation at Supercharged ranged from the inspiring to the silly. Here’s a summary of the day’s biggest talking points, along with insight into how brands of all kinds are implementing artificial intelligence.</p> <h3>Rapid rate of change</h3> <p>While many people can get carried away with what artificial intelligence might look like far into the future, John Straw kicked off Supercharged with an inspiring talk about how the technology will evolve in the next couple of years.</p> <p>Right now, of course, it has its limitations, with most marketers creating augmented decision trees and calling it a chatbot. Then again, John reminded us of the prediction that bots will be in everyday use by 2020, also suggesting that the rapid rate at which the technology is evolving means the bots will look (and sound) far different to how they do now. In fact, he said that by mid-2018, the technology will have advanced so much that users won’t even realising they’re talking to a bot. </p> <p>As someone who has <a href="https://econsultancy.com/blog/68636-pizza-express-channel-4-and-tfl-three-examples-of-brand-chatbots/" target="_blank">reviewed quite a few (mediocre) examples</a> in the past year or so (not counting <a href="https://econsultancy.com/blog/69146-five-things-we-learned-from-launching-a-facebook-messenger-chatbot/" target="_blank">our own</a>, of course), I feel that John's prediction sounds rather optimistic. </p> <p>Then again, as John explained, just because we’re not seeing the technology in practice right now, does not mean it is not in existence. Take the healthcare sector, for instance, where new companies such as HealthTap and Babylon Health are looking to revolutionise the early stages of patient diagnosis. </p> <p>Instead of endlessly waiting on hold to speak to a human or Googling their aches and pains, patients can liaise with AI-powered doctors to speed up and streamline the process.</p> <p>As John said, the net benefit of this kind of technology is greater satisfaction, not just in the context of a doctor-patient scenario but in relation to all kinds of customer service. Instead of being passed from pillar to post and ending up “talking to a 19-year-old in a call centre”, people will be able to talk to a single entity to get the answer they want much faster. </p> <blockquote class="twitter-tweet"> <p lang="en" dir="ltr">Proud to be nestled among some of A.I.'s best. <a href="https://t.co/EtnEzSPCXR">https://t.co/EtnEzSPCXR</a> <a href="https://t.co/sNIOJIIVKv">pic.twitter.com/sNIOJIIVKv</a></p> — babylon (@babylonhealth) <a href="https://twitter.com/babylonhealth/status/880715572379611136">June 30, 2017</a> </blockquote> <h3>The benefits of NLP</h3> <p>A lot of brand chatbots involve scripts and decision trees to force users down a specific path. And while some can be frustratingly limited, others can work surprisingly well.</p> <p>Alex Miller from <a href="http://www.bytelondon.com/">Byte London</a> cited Adidas as a prime example, with the sports brand using a scripted chatbot to enable Facebook Messenger users to book a free session in an East London fitness studio. Users could interact with the bot to book times, get reminders, and find out location details. The results showed a 76% retention rate after 23 weeks, 1.1m interactions, and 46,000 fitness sessions organised in all. </p> <p>So, scripted bots can work well for events, but what about scenarios where users are more inclined to ask questions?</p> <p>JustEat is one brand that has successfully combined scripted technology with NLP (natural language processing), going on to create a chatbot that is both functional and entertaining.</p> <p>To do so, it put together a large collection of possible user queries, alongside a list of how the bot would answer in response.</p> <p><img src="https://assets.econsultancy.com/images/0008/7300/JustEat_chatbot.JPG" alt="" width="760" height="419"></p> <p>Of course, this still has its limitations. There’s only a certain amount of language it is programmed to recognise, however it's still a good example of a bot that goes beyond basic commands to inject personality and humour into the mix.  </p> <p>For JustEat, it meant that 40% of people who interacted with the bot went on to actually place an order online, as well as the brand seeing an average dwell time of 2mins 14secs.</p> <h3>Programming personality into AI</h3> <p>Speaking of personality... according to Nick Asbury, writer for Creative Review and one-half of agency <a href="http://asburyandasbury.com/about/">Asbury &amp; Asbury</a>, character remains a largely untapped area of artificial intelligence. </p> <p>This seems strange, he suggests, especially considering humans are instinctively drawn to any kind of inanimate object that appears to have a personality. Meanwhile, with most humans naturally inclined to choose text or email – even in the context of social relationships – why would we want to spend time having a conversation with Amazon's Alexa when we could skim-read textual information? </p> <blockquote class="twitter-tweet"> <p lang="en" dir="ltr">This alarm clock is so confused <a href="https://t.co/j6bbHp98nh">pic.twitter.com/j6bbHp98nh</a></p> — Faces in Things (@FacesPics) <a href="https://twitter.com/FacesPics/status/878651935435485184">June 24, 2017</a> </blockquote> <p>Putting these negatives aside, the positive is that most people are also open to the idea of artificial intelligence taking on more human characteristics. As Nick explained, we’ve traditionally seen this in popular culture, with robots taking on all kinds of human traits in films ranging from Knight Rider to 2001: A Space Odyssey.</p> <p>Ultimately, this means that there is a huge amount of unexplored territory in terms of chatbot tone and personality. If ‘neutral’ or an Alexa-type chatbot is the middle of the spectrum, a large percentage of all brand communication does not tend to stray very far from this. </p> <blockquote class="twitter-tweet"> <p lang="en" dir="ltr">.<a href="https://twitter.com/asburyandasbury">@asburyandasbury</a> on giving AI personality: "Most chatbots are neutral, polite or helpful. Lots of unexplored traits" <a href="https://twitter.com/hashtag/supercharged17?src=hash">#supercharged17</a> <a href="https://t.co/2e86Pt8aG6">pic.twitter.com/2e86Pt8aG6</a></p> — Econsultancy (@Econsultancy) <a href="https://twitter.com/Econsultancy/status/882184030388670469">July 4, 2017</a> </blockquote> <p>So, instead of concentrating on just one aspect (either functionality or personality) Nick suggests that brands should explore different areas of the tonal map – even embrace sounding like a robot. </p> <p>Nick specifically mentioned Zhuck – an app that Asbury &amp; Asbury worked on in partnership with a Russian bank. Described as an ‘endearingly grumpy smart ass’, it was deliberately designed to be more interesting and engaging to use, with a character that set out to entertain as much as serve a functional purpose. </p> <p><iframe src="https://player.vimeo.com/video/128130687" width="640" height="360"></iframe></p> <h3>Fusing AI with human roles</h3> <p>Unsurprisingly, a lot of discussion at Supercharged revolved around the automation of jobs, and the natural backlash that has occurred because of it.</p> <p>So, from a marketer’s perspective, will we see AI disrupt specific areas such as content creation? And what about from a wider branding perspective – could we even see artificial intelligence informing brand straplines or mission statements?</p> <p>While companies such as <a href="https://phrasee.co/">Phrasee</a> (which uses software to generate email subject lines) shows that artificial intelligence can beat humans in terms of scale and immediacy, it still feels like we’re a long way from bots replacing human creativity.</p> <p>Jukedeck is a company that uses artificial intelligence to compose music that’s suited to individual needs and contexts. Patrick Stobbs, the company’s co-founder, gave some interesting insight into this idea. When asked whether or not this kind of technology creates <a href="https://en.wikipedia.org/wiki/Filter_bubble">a filter bubble</a>, he argued that – in contrast – it actually gives creative people the tools to improve and enhance their craft.</p> <p><iframe src="https://www.facebook.com/plugins/post.php?href=https%3A%2F%2Fwww.facebook.com%2Fjukedeck%2Fposts%2F784081638422118&amp;width=500" width="500" height="476"></iframe></p> <p>Other brands at Supercharged spoke about how they are using artificial intelligence to streamline services, as well as to upskill and aid traditional roles rather than automate them out. </p> <p>Nicola Millard from BT suggested that most jobs are made up of an intricate series of tasks, regardless of seniority level or industry. As a result, instead of the ‘automation will take our jobs’ scenario coming true – the reality might be more like 60% of jobs having about 30% of their roles automated in the next 10 years.</p> <p>In relation to companies like BT that currently rely on people for customer service, Nicola emphasised that it will not be a battle between bots and agents, but rather a partnership that combines the (very different strengths) of the two. </p> <p>IntelligentX Brewing Company is another brand that cited this belief, insisting that its own product – a beer brewed by AI – requires human involvement throughout the entire manufacturing process. Instead of automating out the human elements, people work in conjunction with the AI (in terms of testing, assessing and providing feedback on AI-produced recipes) to create the very best result.</p> <blockquote class="twitter-tweet"> <p lang="en" dir="ltr">Found my way to the <a href="https://twitter.com/IntelligentX_ai">@IntelligentX_ai</a> beer tasting at <a href="https://twitter.com/hashtag/smlates?src=hash">#smlates</a>! Beer that evolves with consumer feedback. <a href="https://t.co/NkIHPVbvih">pic.twitter.com/NkIHPVbvih</a></p> — Michelle Reeve (@michelleareeve) <a href="https://twitter.com/michelleareeve/status/771058383566860288">August 31, 2016</a> </blockquote> <h3>Dealing with data issues</h3> <p>The final panel talk of the day centred around how data and artificial intelligence can fuel personalisation and brand loyalty. But when does AI cross the line from cool to creepy? Moreover, with the <a href="https://econsultancy.com/blog/69119-gdpr-needn-t-be-a-bombshell-for-customer-focused-marketers" target="_blank">GDPR deadline rapidly approaching</a>, will greater regulation impact automated processes such as customer profiling and segmentation?</p> <p>While this is not as relevant in cases whereby automation doesn’t have a significant or legal impact, it still reflects the dangers of using customer data to such an extent that it feels like a violation of privacy.  </p> <p>For brands like ASOS, artificial intelligence certainly underpins targeting strategies, with AI processes impacting what products to show which customers and when. However, even ASOS realises that data should be used with caution, agreeing that <a href="http://www.businessinsider.com/the-incredible-story-of-how-target-exposed-a-teen-girls-pregnancy-2012-2?IR=T">Target’s recent fail</a> proves some lines should not be crossed. The retail brand sent coupons for baby items to a teenager (and her unsuspecting father), having determined from data tracking that she was pregnant.</p> <p>While other brands like ShopDirect show that using artificial intelligence in this way can generate results – i.e. to identify and retarget a customer who might have run out of lipstick – it’s clear that there’s a long way to go before basic human judgement becomes redundant. </p> <p><em><strong>Related reading:</strong></em></p> <ul> <li><em><a href="https://econsultancy.com/blog/68770-an-introduction-to-ai-and-customer-service/" target="_blank">An introduction to AI and customer service</a></em></li> <li><em><a href="https://econsultancy.com/blog/69112-what-s-the-difference-between-ai-powered-personalisation-and-more-basic-segmentation/" target="_blank">What's the difference between AI-powered personalisation and more basic segmentation?</a></em></li> <li><em><a href="https://econsultancy.com/blog/67745-15-examples-of-artificial-intelligence-in-marketing" target="_blank">15 examples of artificial intelligence in marketing</a></em></li> </ul> tag:econsultancy.com,2008:Report/4533 2017-07-04T11:00:00+01:00 2017-07-04T11:00:00+01:00 Achieving Predictive Maturity <p>The <strong>Achieving Predictive Maturity</strong> report, published in association with <a title="RedEye" href="https://www.redeye.com/">RedEye</a>, follows on from a survey of 400 marketers carried out for the <a title="Predictive Analytics Report" href="https://econsultancy.com/reports/predictive-analytics-report/">2016 Predictive Analytics Report</a> and aims to examine how companies move from a limited state where they are 'starting out' through to a 'strategic' state where predictive capabilities lie at the heart of the business.</p> <p>It is based on a series of interviews with senior practitioners along with insights from other primary Econsultancy research.</p> <h3>Key themes</h3> <p>The following themes are featured in the report:</p> <ul> <li>Having the right data remains the critical first step</li> <li>Data quality is an ongoing hygiene factor</li> <li>Automation and machine learning pave the path to the highest levels of predictive maturity</li> <li>Business goals need to remain front of mind</li> </ul> <h3>Contributors</h3> <p>Econsultancy would like to thank the following people for their contributions to this report:</p> <ul> <li>Nathan Ansell, Global Director of Loyalty, Customer Insight and Analytics, Marks &amp; Spencer</li> <li>James Backhouse, Marketing Director, Evans Cycles</li> <li>Richard Clark, Marketing Director, N Brown Group plc</li> <li>Matthew Curry, Head of Ecommerce, Lovehoney</li> <li>Lara Izlan, Director of Programmatic Trading, Auto Trader</li> <li>Simon Kaffel, Head of Data Transformation – EMEA, HSBC Retail</li> <li>Katrina King, Head of Customer Marketing, Direct Line Group</li> <li>Colin Lewis, CMO, OpenJaw Technologies (former marketing director, BMI)</li> <li>Peter Markey, Marketing Director, TSB</li> <li>Clement Mazen, Senior Growth Manager, HomeAway</li> </ul>