tag:econsultancy.com,2008:/topics/big-data Latest Big data content from Econsultancy 2016-07-04T10:16:58+01:00 tag:econsultancy.com,2008:BlogPost/68029 2016-07-04T10:16:58+01:00 2016-07-04T10:16:58+01:00 Why transparency in data is key to building trust for The Guardian Maeve Hosea <p>When Julia joined Guardian News and Media in 2012 the brand was beset with difficulties.</p> <p>Revenue streams from its print circulation were in decline and it faced problems in monetising the digital audience, which was compounded by Facebook and Google’s dominance over the UK’s digital advertising market.</p> <p>In order to survive the newspaper needed to make its existing audience more profitable and engage with them in a personalised and relevant way – data was a key component in achieving this.</p> <p>“Advertising isn’t going to be the only revenue stream and we need to find other ways of improving our income and that has to be transacting with our readers and finding things to sell that they’ll find of interest,” says Porter.</p> <p><img src="https://assets.econsultancy.com/images/0007/6724/guardian_ipad.jpg" alt="" width="700" height="466"></p> <p>“You need to understand who those people are and how to build valuable relationships with them on a one-to-one basis.”</p> <p>Capturing more data is easier said than done.</p> <p>“At the time, the Edward Snowden story was big and there was a real danger that you ended up conflating that focus on ‘bad people doing bad things with data’ with our legitimate activities, in terms of asking people to share data and being transparent about why,” she says.</p> <p>The brand aimed to explain why it needed customer data in a relaxed and open way, knowing that trust is incredibly important to the relationship with the customer.</p> <p>In 2014, it launched its customer charter ‘Why Your Data Matters’ with a video demonstrating its transparent attitude to data, explaining what that information can do for the paper.</p> <p>Information including how data allows the newspaper to be able to charge premium advertising revenues and how that is central to funding the paper’s journalism going forward were relayed.</p> <p>In addition, how that data deepens The Guardian’s understanding of who its readers are, therefore allowing tailored messages with content that can be valued by those readers.</p> <p><iframe src="https://player.vimeo.com/video/107730078" width="640" height="360"></iframe></p> <p>“One of the reasons this was an award winning piece of work was [because] we decided to go down the road [of] being very open and transparent but also moving away from ambiguity and complexity of legal language,” says Porter.</p> <p>“It felt to me that we did something quite innovative with that video and it took a lot of effort to get to that point because it got us talking about the importance of being transparent with our customers.”</p> <p>The video was just the first part of the journey that The Guardian and its agency MRM Meteorite has been on to become customer driven.</p> <p>After obtaining all of its valuable data currency, the media owner then needed to extract value from it.</p> <p>The depth of data enabled a very powerful model to be built that drives what to say to who, when, where and how.</p> <h3>Segmenting the audience</h3> <p>The brand created a detailed segmentation to understand its customers. Understanding the different audience profiles and preferences means it was able to see what different products and services it would target at different cohorts.</p> <p>“The Nirvana - and we haven’t fully succeeded in doing this - is being able to understand how you can create a sweet spot between what people do and why they do it,” says Porter.</p> <p>“If you are able to marry up transactions and behavioural data with attitudes and demographic data, then you will be able to understand what life stage people are at, what they are interested in buying and how they will respond to messages.”</p> <p>Personas that gave texture to the audience analysis included ‘John the hipster’, who buys the brand’s masterclass courses and is a potential customer for the digital subscription pack.</p> <p>Also, ‘Zoe the professional’ who buys books and might well buy holidays.</p> <p>Having ascertained who its different cohorts were, The Guardian was able to do a mapping of what topics readers would be interested in.</p> <p>This allowed it move on and start developing its products and services, its subscription business, membership business and to relaunch the bookshop ecommerce site.</p> <p><em>This article was originally published on <a href="http://www.marketingweek.com/2016/06/15/why-transparency-in-data-is-key-to-building-trust/">Marketing Week</a>.</em></p> <p><em><strong>The <a href="http://conferences.marketingweek.com/ds/home">Data Storytelling Awards</a> is now open for entries. The deadline is Tuesday July 19, 2016.</strong></em></p> tag:econsultancy.com,2008:BlogPost/67968 2016-06-24T01:00:00+01:00 2016-06-24T01:00:00+01:00 Marketing in China: Where mobile and content marketing are driving change Steven Chang <p>However, the market in China is evolving incredibly rapidly, taking on its own dimensions and creating its own rules.</p> <p>This evolution is taking place as more people come online and the potential of mobile, social and data come together.</p> <p>This confluence of trends is called “Internet Plus” in China, and refers to how traditional industries are being affected by new business models, new ways of using data and new ways of interacting with each other. </p> <h3>The impact of mobile on content consumption </h3> <p>Part of this evolution is down to how people access the internet in China.</p> <p>According to last year’s CNNIC research, China had around 668m internet users compared to around 279m in the US; yet this represents a penetration rate of around 49% of the whole population, compared to 87% of the population of the US.</p> <p>However, the biggest difference is around smartphone usage: around 90% of Chinese netizens are on smartphones compared to around 57% in the US. </p> <p>Audience behaviour here is different too when it comes to marketing via mobile.</p> <p>US internet users would respond to mobile adverts around 56% of the time, and only 59% of those users were more likely to shop on mobile.</p> <p>In comparison, around 79% of Chinese internet users will respond to mobile ads, while 78% would shop via their phones.</p> <p>At the same time, it’s also important to understand how interaction with phones is different in China too.</p> <p>For example, iPhones have a ‘shake’ function that can be used to undo the last action.</p> <p>However, shake is much more commonly used in China as part of applications – one of the first uses in social was <a href="https://econsultancy.com/blog/67490-10-things-you-didn-t-know-about-wechat/">WeChat</a>, which has a tool to find other users that are local and shaking their phones at the same time.</p> <p>This has progressed into other apps and social services.</p> <p><em>Burberry's WeChat presence</em></p> <p>             <img src="https://assets.econsultancy.com/images/resized/0005/1201/burberry_wechat-blog-third.png" alt="" width="200" height="356">     <img src="https://assets.econsultancy.com/images/resized/0005/1202/burberry_wechat1-blog-third.png" alt="" width="200" height="356"></p> <p>Why is this important for marketers to know? Well, research shows that shake response is much higher than services that use “click to respond” buttons for interaction.</p> <p>Estee Lauder ran an advert campaign on social with “shake to respond” in place alongside target-driven incentives.</p> <p>The response rate for this campaign was 13% with shake – 85 times higher than the “click to respond” version.</p> <p>This was also around 50 times higher than the industry standard rate for banner ad click-throughs, which stands at 0.23%. This understanding of local social channel design can help international marketers tailor their campaigns.</p> <p>Within China, mobile is becoming the dominant platform for internet use by younger generations – internet users born after 1990 spend an average of 234 minutes a day on the internet via mobile.</p> <p>This compares to the overall population where 110 minutes is spent online per day via PCs. Alongside this, 47% of Chinese internet users will spend more than two hours per day using social apps to run their social lives.</p> <p>This increase in Chinese people living their lives through their mobile devices has gone alongside an increase in demand for content to fill that window.</p> <p>In turn this has provided more opportunities to gather data on user interests. This data can be used for <a href="https://econsultancy.com/admin/blog_posts/67968-marketing-in-china-where-mobile-and-content-marketing-are-driving-change/edit/n">greater personalisation</a> and targeting.</p> <h3>How marketing on mobile links up social, data and creativity</h3> <p>This availability of data can help both brands and agencies work on campaigns to make the most of their budget.</p> <p>The first step here is to consolidate all potential data sources to build a picture of the total audience, as well as looking at those members of the audience that have more than one relationship with the brand.</p> <p>For Mead Johnson, a manufacturing company involved in baby food and other nutritional products, this involved looking at its social media presences, its online fans and those who had purchased products online through <a href="https://econsultancy.com/blog/67771-a-beginners-guide-to-alibaba-s-tmall/">Tmall</a>. </p> <p>Based on these sets of data, the firm found 7.84m individual contacts in total; using data mapping and matching, users with more than one account on each of these platforms made up 54% of the total.</p> <p>The second step here is to create a consumer portrait based on the data acquired via mobile, social and ecommerce.</p> <p><em>Mead Johnson's Tmall store</em></p> <p><img src="https://assets.econsultancy.com/images/0007/6256/Mead_Johnson.png" alt="" width="800" height="579"></p> <p>This can include quantitative data like demographics, location and preferences for access to channels; however, this can also be more qualitative too.</p> <p>Rather than looking at potential audiences in theory, it’s possible to build up a better picture of actual preferences around key opinion leaders, content preferences and celebrities followed. </p> <p>In total, around 20 data dimensions were created and used for planning campaigns.</p> <p>This data was then used to design a new campaign based on co-creation of content alongside key opinion leaders in China that could be delivered via online video and social channels. </p> <p>From these steps, Mead Johnson saw its potential consumer audience grow by a factor of four.</p> <p>On the campaigns designed with data, the company saw brand preference increase by 115.2% and purchase intention go up by 84.2%.</p> <p>This integrated approach is important for success in China today.</p> <p>The increasing competition for attention around mobile means that targeting specific audience groups is becoming much more important for campaigns to be successful.</p> <p><em>For more on this topic, see:</em></p> <ul> <li><a href="https://econsultancy.com/reports/the-china-digital-report-q1-2016/"><em>The China Digital Report, Q1 2016</em></a></li> <li><a href="https://econsultancy.com/blog/67928-building-the-business-case-for-customer-experience-cx-in-china/"><em>Building the business case for customer experience (CX) in China</em></a></li> </ul> tag:econsultancy.com,2008:BlogPost/67991 2016-06-23T17:05:49+01:00 2016-06-23T17:05:49+01:00 What is the role of marketing agencies in data management? Stefan Tornquist <h4>Q. It seems like the industry press is continually heralding the decline of media agencies, but they seem to be very much alive. What’s your take on the current landscape? </h4> <p>For a very long time, agencies have been dependent upon using low-cost labor for media planning and other low-value operational tasks.</p> <p>While there are many highly-skilled digital media practitioners - strategists and the like - agencies still work against “cost-plus” models that don’t necessarily map to the new realities in omnichannel marketing.</p> <p>Over the last several years as marketers have come to license technology - data management platforms (DMP) in particular - agencies have lost some ground to the managed services arms of ad tech companies, systems integrators, and management consultancies. </p> <h4>Q. How do agencies compete?</h4> <p>Agencies aren’t giving up the fight to win more technical and strategic work.</p> <p>Over the last several years, we have seen many smaller, data-led agencies pop up to support challenging work - and we have also seen holding companies up-level staff and build practice groups to accommodate marketers that are licensing DMP technology and starting to take <a href="https://econsultancy.com/blog/65677-a-super-accessible-beginner-s-guide-to-programmatic-buying-and-rtb/">programmatic buying</a> “in-house.”</p> <p>It’s a trend that is only accelerating as more and more marketer clients are hiring Chief Data Officers and fusing the media, analytics, and IT departments into “centers of excellence” and the like.</p> <p><img src="https://assets.econsultancy.com/images/0007/6426/analytics.jpg" alt="" width="750" height="442"></p> <p>Not only are agencies starting to build consultative practices, but it looks like traditional consultancies are starting to build out agency-like services as well.</p> <p>Not long ago you wouldn’t think of names like Accenture, McKinsey, Infinitive, and Boston Consulting Group when you think of digital media, but they are working closely with a lot of Fortune 500 marketers to do things like DMP and DSP (demand-side platform) evaluations, programmatic strategy, and even creative work.</p> <p>We are also seeing CRM-type agencies like Merkle and Epsilon acquire technologies and partner with big cloud companies as they start to work with more of a marketer’s first-party data.</p> <p>As services businesses, they would love to take share away from traditional agencies. </p> <h4>Q. Who is winning?</h4> <p>I think it’s early days in the battle for supremacy in data-driven marketing, but I think agencies that are nimble and willing to take some risk upfront are well positioned to be successful.</p> <p>They are the closest to the media budgets of marketers, and those with transparent business models are really strongly trusted partners when it comes to bringing new products to market.</p> <p>Also, as creative starts to touch data more, this gives them a huge advantage.</p> <p>You can be as efficient as possible in terms of reaching audiences through technology, but at the end of the day, creative is what drives brand building and ultimately sales. </p> <h4>Q. Why should agencies embrace DMPs? What is in it for them?</h4> <h4>It seems like yet another platform to operate, and agencies are already managing DSPs, search, direct buys, and things like creative optimization platforms.</h4> <p>Ultimately, agencies must align with the marketer’s strategy, and DMPs are starting to become the single source of “people data” that touch all sorts of execution channels, from email to social.</p> <p>That being said, DMP implementations can be really tough if an agency isn’t scoped (or paid) to do the additional work that the DMP requires.</p> <p>Think about it: A marketer licenses a DMP and plops a pretty complicated piece of software on an agency team’s desk and says, “get started!”</p> <p>That can be a recipe for disaster. Agencies need to be involved in scoping the personnel and work they will be required to do to support new technologies, and marketers are better off involving agencies early on in the process. </p> <h4>Q. So, what do agencies do with DMP technology? How can they succeed?</h4> <p>As you’ll read in the new guide, there are a variety of amazing use cases that come out of the box that agencies can use to immediately make an impact.</p> <p>Because the DMP can control for the delivery of messages against specific people across all channels, a really low-hanging fruit is frequency management.</p> <p>Doing it well can eliminate anywhere from, 10-40% of wasteful spending on media that reaches consumers too many times.</p> <p>Doing analytics around customer journeys is another use case - and one that attribution companies get paid handsomely for.</p> <p>With this newly discovered data at their fingertips, agencies can start proving value quickly, and build entire practice groups around media efficiency, analytics, data science - even leverage DMP tech to build specialized trading desks. There’s a lot to take advantage of. </p> <h4>Q. You interviewed a lot of senior people in the agency and marketer space. Are they optimistic about the future? </h4> <p>Definitely. It’s sort of a biased sample, since I interviewed a lot of practitioners that do data management on a daily basis.</p> <p>But I think ultimately everyone sees the need to get a lot better at digital marketing and views technology as the way out of what I consider to be the early and dark ages of addressable marketing.</p> <p>The pace of change is very rapid, and I think we are seeing that people who really lean into the big problems of the moment like cross-device identity, location-based attribution, and advanced analytics are future-proofing themselves. </p> <p><em>Go <a href="http://hello.econsultancy.com/the-role-of-the-agency-in-data-management/">here to download the full report</a>.</em></p> tag:econsultancy.com,2008:BlogPost/67922 2016-06-07T01:00:00+01:00 2016-06-07T01:00:00+01:00 Really big data: Managing customer insights in China Jeff Rajeck <p>To find out, Econsultancy invited dozens of client-side marketers in Shanghai to discuss this and other topics at roundtables in April of this year.</p> <p>The roundtables were moderated by volunteer client-side marketers and subject matter experts from Econsultancy and our event sponsor Epsilon.</p> <p>Below is a summary of the main talking points during the day about the topic: <strong>Data-Driven Marketing - Making Big Data Actionable.</strong></p> <h3>Managing customer data </h3> <p>Delegates at the table decided that there are three aspects to managing customer data:</p> <ul> <li>Understanding marketing data</li> <li>Realising insights from the data</li> <li>Making decisions using the data</li> </ul> <p>Below are summaries of the discussion around each of these aspects.</p> <p><img src="https://assets.econsultancy.com/images/0007/5693/1__Custom_.jpg" alt="" width="800" height="533"></p> <h3>Understanding data</h3> <p>Participants said that in order to manage customer data, marketers first need to agree on the data needed to achieve marketing goals.  </p> <p>Then, once there is agreement, everyone must understand the data sources and their limitations.</p> <h4>Challenges</h4> <p>The first challenge, noted one attendee, was that <strong>there are no standardized measures or metrics for different touchpoints</strong>.  </p> <p>Marketers can get data from a variety of, say, display touchpoints and each will have its own interpretation of what a 'view' is or even a 'click'.</p> <p>Another problem marketers face when understanding data is that <strong>each department has different benchmarks and so it is difficult to establish key performance indicators (KPIs)</strong>. This is especially true with companies which have many brands and a single marketing department.</p> <p>Finally, one participant noted, that his team <strong>struggled to convince the company of the commercial value of data-driven marketing</strong>. The systems required to capture, store, and manage data were expensive when compared with the overall cost of marketing.</p> <h4>Looking ahead</h4> <p>Delegates noted that quantitative data, such as views, clicks, and conversions, is the most important data to marketers for now, but looking ahead they agreed that qualitative data will become more important.</p> <p>Data sources such as surveys, focus groups, and Net Promoter Score are starting to emerge as a way for brands to record data about how people are reacting to content and marketing strategies.</p> <p><img src="https://assets.econsultancy.com/images/0007/5694/2__Custom_.jpg" alt="" width="800" height="533"></p> <h3>Realising insights</h3> <p>Once marketers have the data, participants agreed that the next part of making big data actionable is to organize and interpret it.</p> <p>Discussions on the day focused on three key data sets used by participants:</p> <ul> <li> <strong>Social metrics:</strong> Followers, fans, likes, shares, and comments</li> <li> <strong>Web metrics:</strong> Page visitors, unique visitors, average page depth</li> <li> <strong>Ecommerce metrics:</strong> Customer purchase behaviour</li> </ul> <p>Organizing customer data into these categories helped marketers from even the largest brands start the process of making their big data actionable.</p> <p>Social data showed how their messaging was performing against other brands.  </p> <p>Web metrics help with determining whether they were delivering a high-quality customer experience. And ecommerce metrics were necessary to report on performance to management.</p> <h4>Challenges</h4> <p>Attendees indicated that marketers in China faced some unique challenges when trying to realise insights from their data. Here are a few that were mentioned:</p> <ul> <li> <strong>Data Management Platforms (DMPs) are unreliable.</strong>  Other countries are able to use external data from DMPs to enhance their internal data, but most participants felt that DMP data in China was not reliable.</li> <li> <strong>Cleaning data is expensive.</strong> When third-party data was available, attendees felt that it was time consuming and resource intensive to clean and verify it.</li> <li> <strong>WeChat data is sparse</strong>. Many brands use WeChat in China for delivering content, but the channel does not provide insightful data about the people who view the content.</li> <li> <strong>Third party sites own ecommerce.</strong> In China, a lot of ecommerce traffic involves one of the main ecommerce sites (TMall, JD). Because of this, it is hard to get full access to customer data.</li> </ul> <h4>Looking ahead</h4> <p>Participants decided that when trying to tackle 'big data', it was best to start with small projects which deliver quicker results.</p> <p>Some examples provided were:</p> <ul> <li>Use website data to map out and better understand the customer journey.</li> <li>Use social data to understand how individual campaigns affect fan and follower numbers.</li> <li>Track customer loyalty with transaction history and other CRM data.</li> </ul> <h3><img src="https://assets.econsultancy.com/images/0007/5696/4__Custom_.jpg" alt="" width="800" height="533"></h3> <h3>Making decisions</h3> <p>Finally, attendees discussed how they make decisions from the data that they have collected and organized.</p> <p>Some examples provided were</p> <ul> <li>Collating customer profiles using website data, presenting customized homepages for segments, and delivering personalized content.</li> <li>Ensuring customers get a marketing message at the right frequency. Not so infrequent that they don't see it, but not too much that they become annoyed with the brand.</li> <li>Monitoring real-time feedback, such as social media and <a href="https://econsultancy.com/blog/9366-ecommerce-consumer-reviews-why-you-need-them-and-how-to-use-them/">customer reviews</a>, to make sure customers understand the brand message.</li> </ul> <h4>Challenges</h4> <p>In order to use customer data to make marketing decisions, <strong>marketers must become very familiar with compliance and policies in China</strong>.</p> <p>It is difficult to re-use customer data on most digital platforms in China. They are not designed for things like <a href="https://econsultancy.com/blog/64980-put-your-email-list-to-work-facebook-custom-audiences/">Facebook custom audiences</a>.</p> <p><strong>B2B marketers still rely on industry data and third party analytics</strong> because their customers are often middlemen such as resellers and distributors.</p> <h4>Looking ahead</h4> <p>Participants indicated that they would like to make better decisions based on the data they have about consumer behavior.  </p> <p>Few, however, felt that the data required to do so was easily available. And, once they did have the data, a lot of effort had to go into cleaning and verifying the data before using it to make decisions.</p> <p>But there is hope. Participants agreed that <strong>qualitative data, such as emotional impact and engagement with campaigns, will help marketers decide which campaigns are truly meaningful to customers</strong>.</p> <h3>A word of thanks</h3> <p>Econsultancy would like to thank all of the client-side marketers who participated on the day and our sponsor for the event, Epsilon.</p> <p>We would like to extend a special thanks to the table moderator for this topic, <strong>Louise Au, Co-founder &amp; Partner at Axis Business Consulting.</strong></p> <p>We appreciate all of the helpful discussion points participants provided on the day and we hope to see you all at our upcoming Econsultancy events!</p> <p><img src="https://assets.econsultancy.com/images/0007/5521/5__Custom_.jpg" alt="" width="800" height="533"></p> tag:econsultancy.com,2008:BlogPost/67835 2016-05-19T11:21:14+01:00 2016-05-19T11:21:14+01:00 Bringing data into creativity in a programmatic world Glen Calvert <p>Data isn’t sexy, consequently, it isn’t loved by brand advertisers. In their minds, data is the preserve of the far less noble direct marketing realm.</p> <p>The idea of putting data at the core of campaigns, which the direct marketer does, is an anathema to the brand advertiser.</p> <p>A neat illustration of this thinking is through <a href="https://econsultancy.com/training/courses/personalisation-enhancing-the-customer-experience/">personalised advertising</a>. Brand marketers can’t deny that they’d like to connect with us all individually.</p> <p>The “Share a Coke” campaign in which cans and bottles were personalised was a huge brand success.</p> <p>Around 1,000 name variations were available on shelves and over 500,000 available through the online store.</p> <p>So, why do brand advertisers seem reticent to deploy personalisation techniques online – a media tailor-made for such activity due to data?</p> <p>Why do we so rarely see good examples of this type of campaign in the digital environment?</p> <h3><strong>Falling in love with data?</strong></h3> <p>The answer to the previous question is branding’s lack of love for data. However, this mind-set could be changing due to a couple of factors.</p> <p>Brands love TV because it’s a wonderful platform to tell stories at scale.</p> <p>In comparison, online platforms for telling good brand stories at scale using data and creative have been more constrained.</p> <p>With smaller screen sizes and more limited ad ‘real estate’, brand banner advertising is more of a challenge.</p> <p>However, the skills and appetite for meeting this challenge and using data efficiently are increasing.</p> <p><img src="https://assets.econsultancy.com/images/0007/4847/share-a-coke.jpg" alt="Share a Coke Bottles" width="460" height="330"></p> <p>This improvement in the banner format is combining with a growth in other branding-type formats in display advertising, such as video and <a href="https://econsultancy.com/blog/63722-what-is-native-advertising-and-do-you-need-it/">native advertising</a>.</p> <p>The IAB’s latest <a href="https://econsultancy.com/blog/67746-10-action-packed-digital-marketing-stats-from-this-week" target="_blank">digital ad spend figures</a> showed both video and native spend grew around 50% last year to account for nearly half of display ad spend.</p> <p>These two parallel developments in display prove its increasing allure as a branding medium - FMCG advertisers, historically considered the least relevant in regards to online ads, are now the dominant spender on display, accounting for nearly £1 in every £5.</p> <p>We’re seeing this increasing willingness to embrace data manifested by clients taking control of their data destiny.</p> <p>A number of high profile brands are taking on long-term software contracts with data management platforms (DMPs), showing the appetite clients have to both control and exploit the data opportunity.</p> <h3><strong>Programmatic plumbing</strong></h3> <p>Alongside the rise in online branding formats, the other factor changing mind-sets among brand advertisers, rather surprisingly, could be <a href="https://econsultancy.com/blog/65677-a-super-accessible-beginner-s-guide-to-programmatic-buying-and-rtb/">programmatic</a>.</p> <p>Something originally seen as even less sexy than data.</p> <p>The “plumbing”, or logistics, side of programmatic is becoming less of an obstacle to using data and creative to tell a good brand story.</p> <p>The amount of heavy-lifting required is reducing in terms of time, resources and money among agencies and vendors to connect the data, the creative and the inventory.</p> <p>Consequently, there’s a growing sense of enthusiasm about take-up among brands.</p> <p>So, as programmatic matures, many of these growing pains are less pronounced.</p> <p>As the plumbing between creative, data and buying becomes more automated, it means the industry can move more towards programmatic as a creative solution.</p> <h3><strong>Programmatic as creative</strong></h3> <p>In turn, <a href="https://econsultancy.com/blog/67554-2016-the-year-of-programmatic-creative/" target="_blank">programmatic creative</a> has become more advanced and more flexible, without compromising scale and automation, to meet the specific creative requirements and nuances that advertisers have for being able to tell their brand story.</p> <p>Programmatic creative is now flexible and advanced enough to insert dynamic and personalised elements into online ads to enable the idea of “mass personalisation”, which was essentially what the big idea “Share of Coke” brand campaign was shooting for.</p> <p>These developments hopefully thaw the relationship between brand marketers and data, particularly as they open up exciting and innovative brand campaign ideas that can be brought to life in this brave new world.</p> <p>Take, for example, Netflix’s campaign to promote the addition of all ten seasons of Friends to its library.</p> <p>Conceived by Ogilvy Paris, it’s a pre-roll video campaign that responds dynamically to videos watched on YouTube by inserting a clip from Friends that relates to the video topic searched for.</p> <p><iframe src="https://www.youtube.com/embed/K_3uKmLFHRI?wmode=transparent" width="560" height="315"></iframe></p> <p>Essentially, it uses data to relate Friends to almost anything you search for on YouTube.</p> <p>What will be your big brand idea this year that comes alive through data?</p> <p><em>To learn more on this topic, book yourself onto Econsultancy's <a href="https://econsultancy.com/training/courses/programmatic/">Programmatic Training Course</a>.</em></p> tag:econsultancy.com,2008:BlogPost/67850 2016-05-16T14:29:08+01:00 2016-05-16T14:29:08+01:00 Quantitative marketing: You have enough data to improve performance. I promise. Evan Dunn <p>The thing is: <strong>there is always enough data to improve marketing performance</strong>. </p> <p>Allow me to explain. There are many ways to improve marketing's ability to accomplish its objectives.</p> <p>Each of these analytical methods is at its best with first-party (internal) data, like the juicy stuff found in campaign measurement, but each can also benefit heavily from the application of third-party (external) data.</p> <p>Even without consistent campaign data, however, there is always a wealth of insight to be garnered from analyzing first-party data, both digital and otherwise.</p> <p><a href="https://econsultancy.com/blog/66415-three-key-trends-from-our-data-driven-marketing-briefing-digital-cream-2015/">Data-driven decision-making</a> was the most critical method for driving growth for 83% of enterprise marketing executives in 2015, according to an <a href="https://www.ana.net/content/show/id/37128">Association of National Advertisers study</a>.</p> <p>Each of the following types of data are almost certainly somewhere in your organization.</p> <p>The trick is, of course, extracting them at a speed that enables marketing rather than hinders it. But that's less difficult that one might think these days.</p> <p>Once extracted, the data must be "harmonized" (taught how to play nicely with other data) in order to enable cross-channel, cross-regional and cross-departmental insights.</p> <p>We call this "quantitative marketing" - the idea that marketing can be as much science as it is art. </p> <h3>Sales (or Conversions or Transactions or Orders)</h3> <p>If you don't have this data, you're not in business. Okay, okay - you, marketing executive, may not have this data, especially not in any way that is valuable to your marketing decision-making.</p> <p>But some silo somewhere in your organization holds this treasured data set. And it can inform marketing a great deal.</p> <p><em>Obligatory stock photo of some silos</em></p> <p><em><img src="https://assets.econsultancy.com/images/0007/4946/silo.jpg" alt="" width="700"></em></p> <p>The more "digital" your business category, the more likely it is that this data is easy to access.</p> <p>For example, ecommerce tends to lend itself quite readily to the spread of sales data, since it is inherently collected in digital databases. But brick-and-mortar categories are a few degrees more complex.</p> <p>Even more complex are CPG and some B2B2C businesses, whose sales data is consumed outside of their organizations and (hopefully) reported back accurately and with ready utility. </p> <p>So, <strong>how to use sales and conversion data to improve marketing:</strong></p> <ul> <li>Analyze the data itself. Do certain products sell more than others? Have you run any campaigns for those products, or are the products you're marketing not selling well?</li> <li>Overlay the sales or conversion data onto your trendlines for spend and KPI breakdowns for different segments (territories, channels, tools, agencies, etc.). "Correlation does not imply causation" is relevant here - but that doesn't mean correlations aren't sometimes meaningful.</li> <li>Contract (or better yet, hire) <a href="https://econsultancy.com/blog/67203-data-analysts-vs-data-scientists-what-s-the-difference/">a data scientist</a> to run cluster analyses (and other fancy names of fancy mathematical techniques) on the data. Performance by geography, product category, product color and other attributes inform a variety of business operations. Marketing can use the insights to grease the engines of campaigns.</li> </ul> <h3>Spend</h3> <p>It feels counterintuitive to reference marketing expenses as an opportunity for improving marketing performance, but hear me out.</p> <p>There are three primary types of marketing spend data:</p> <ol> <li>Media buys.</li> <li>Agency vendors.</li> <li>Marketing technology vendors.</li> </ol> <p>Typically, agency vendors includes some media buy and some marketing technology, but it's difficult to truly parse the percentage of each with many vendors.</p> <p>Some vendors combine two or three of these spends into one bundled cost, like conversion shops you pay a cost-per-lead fee, or aggregators, or programmatic media. </p> <p>It seems a lot of the marketing world gets caught up in the parsing of these costs, when what really matters is that all costs must be accounted for.</p> <p>Media-mix modeling/optimization is one way of analyzing the spend (media buy) vs. the return (sales) using statistics.</p> <p>But the same theoretical models can be applied to martech and agency vendors - using data science to predict which vendors will deliver what results.</p> <p>Even if you don't go that deep, however, a lot can be gained from the quantification of spend data, and the visualization of spend alongside sales, with all available breakdowns.</p> <h3>Third-party data</h3> <p>Ingesting and analyzing third-party data gives quick wins to large marketing organizations. Here are some examples:</p> <p><strong>1.</strong> Ever wondered if your offline campaigns are connecting with the right audience? ComScore just acquired Rentrak - and the two together provide fascinating levels of detail into offline audiences.</p> <p><strong>2.</strong> Ever thought about what's working and what's not working on your website? <a href="https://econsultancy.com/blog/66230-a-beginner-s-dictionary-of-google-analytics/">Google Analytics</a> is free. Use it - and hire someone who knows how to dig up truly meaningful insights.</p> <p><img src="https://assets.econsultancy.com/images/0007/4947/Google_Analytics.png" alt="" width="800" height="505"></p> <p><strong>3.</strong> Ever wondered if your campaigns are successful driving awareness? If you're consumer-focused, use <a href="https://econsultancy.com/blog/67137-social-monitoring-listening-what-is-it-and-do-you-need-it/">social listening technology</a> to analyze what people say about your category vs. your competitors vs. your brand/product.</p> <p><strong>4.</strong> Looking to see how well you know your target audience, online opportunity and customers? </p> <p>Dig into Facebook Audience Insights (for free in its advertising portal), run your emails through TowerData, analyze deep mobile demographics/psychographics with PushSpring or Analyze Corp's Clientell tool.</p> <p><strong>5.</strong> Considering launching <a href="https://econsultancy.com/reports/paid-search-marketing-ppc-best-practice-guide/">paid search campaigns</a>? Use tools like SEMRush to approximate competitor investment in paid search.</p> <p><strong>6.</strong> Considering launching a new social presence or new social accounts? Use <a href="https://econsultancy.com/blog/65560-what-s-the-difference-between-paid-owned-and-earned-media/">owned social analytics tools</a>, coupled with social listening technology, to derive what your competitors are doing, and which of their activity seems successful.</p> <p><strong>7.</strong> Considering launching display advertising campaigns? Use Moat.com to analyze how your competitors are using online display.</p> <h4>The list keeps going...</h4> <p>...and this is just the tip of the iceberg of the applications of data to marketing activity - but you get the two big ideas:</p> <ul> <li>Useful data is out there.</li> <li>Useful data is in your organization.</li> </ul> <p>These types of quantitative analyses are foundational to marketing success. As Mike Schmoker said...</p> <blockquote> <p>What gets measured (and clearly defined) gets done.</p> </blockquote> tag:econsultancy.com,2008:BlogPost/67828 2016-05-10T14:58:27+01:00 2016-05-10T14:58:27+01:00 Palantir's woes bring Big Data challenges into focus Patricio Robles <p>That's <a href="https://www.buzzfeed.com/williamalden/inside-palantir-silicon-valleys-most-secretive-company">according to</a> BuzzFeed's William Alden, who obtained internal documents detailing how Palantir has struggled with some of its blue chip clients, some of which pay more than $1m per month for the company's services.</p> <p>According to Alden three of those clients, Coca-Cola, American Express, and Nasdaq, "have walked away" in the past 13 months, and Palantir's effort to create a data sharing consortium for CPG companies "has stumbled."</p> <p>The documents also reveal that not all of Palantir's current clients are convinced that their collaborations are paying off yet.</p> <p>For example, Alden points to Michele Buck, the North American president for The Hershey Company, who indicated that the company "did not see value from Palantir in 2015."</p> <p>A Hershey Company spokesperson told BuzzFeed that it considers Palantir "a valued partner" and stated "we have now identified areas for commercial and operational value and are targeting our efforts there," but Alden's story highlights a number of challenges that companies are facing as they seek to take advantage of Big Data.</p> <h3>Big data requires people</h3> <p>Companies have more data than ever, but data on its own only becomes truly valuable when it's translated into actionable insight.</p> <p>One of Palantir's selling points is that it has the brilliant people required to do just that. But even with more than $2bn in funding and access to the Silicon Valley labor pool, the company has apparently struggled to retain employees.</p> <p>According to Alden:</p> <blockquote> <p>A chart from Palantir’s internal wiki said the departures through mid-April amounted to 5.8% of all staff, or an annualized rate of 20%. That compares to a departure rate of 13.6% in 2015, 12.2% in 2014, and 9.2% in 2013. </p> </blockquote> <h3>Domain expertise is often important</h3> <p>But delivering actionable insight isn't just about having butts in seats. It's about having the right butts in seats.<br></p> <p>Coca-Cola conducted a pilot with Palantir in 2014 that was designed, in part, "to help revive sales of Diet Coke in North America through analysis of customer data."</p> <p>But the beverage behemonth ultimately decided not to sign a five-year agreement with the big data analytics firm.</p> <p>An internal email from a Palantir executive revealed that Coca-Cola "wanted deeper industry expertise in a partner" and that the brand's staff often found it hard to work with Palantir's team which, like many companies in Silicon Valley, skews young.</p> <h3>Data is cheap but Big Data analytics is expensive</h3> <p>Then there's the issue of cost. While generating and storing data is increasingly cheap, hiring a company like Palantir to make sense of it isn't.</p> <p>Coca-Cola "balked" at the contract Palantir presented, which called for $18m in fees in the fifth year of the deal.</p> <p>And Kimberly-Clark, when presented with an agreement that also called for $18m per year in fees, also got cold feet. According to an email from a Palantir executive, the CPG giant "wanted to see if they could do it cheaper themselves."</p> <p>That makes sense. After all, if Big Data analytics can really move the needle in a big way, wouldn't companies like Coca-Cola and Kimberly-Clark want it to become a core competency?</p> <p>Paying a third-party, one which may not have industry expertise and also faces staff turnover risk, might be easy, but it seems like a short-sighted strategy.</p> <h3>The Palantir response</h3> <p>Palantir has taken steps to address staff turnover issues and suggests that its turnover is expected given that its "really strong culture" isn't for everyone.</p> <p>It can also point to seemingly successful relationships like those it has with oil company BP, bank Credit Suisse, credit card processor First Data and insurer Axa.</p> <p>Palantir's 10-year deal with BP could be worth more than $1.2bn.</p> <p>One Palantir business development rep felt that executives at American Express were "low-vision," a reminder that shared vision and values, not just technology and smarts, can make or break relationships for firms like Palantir and their clients.</p> <p>Finally, <a href="https://www.quora.com/What-does-Joe-Lonsdale-think-of-BuzzFeeds-Inside-Palantir-article">according to</a> Palantir co-founder Joe Lonsdale, who is no longer involved in the company's day-to-day operations:</p> <blockquote> <p>[Palantir] had been expansive in who it worked with and then scaled with the areas that made sense and were aligned with its ethos and goals. Of course a few of its client relationships might not have worked out - if that wasn't the case it would have meant they weren't exploring new industries properly.</p> </blockquote> <p>That is perhaps the key take-away for brands exploring their Big Data opportunities.</p> <p>As the number of third parties offering products and services that promise to turn Big Data into big bucks grows, brands should remember that many of these firms are themselves trying to figure out their own markets and will experiment accordingly.</p> <p>The obvious risks this creates doesn't necessarily mean that brands should bring their Big Data efforts completely in-house.</p> <p>But the realistic, forward-thinking ones probably won't put all their eggs in one basket either.</p> tag:econsultancy.com,2008:BlogPost/67759 2016-05-09T15:15:00+01:00 2016-05-09T15:15:00+01:00 Expanding your marketing playbook with predictive analytics John Kelly <p>For some people, their feelings about those horses and what they say about the brand transcends more logical price or taste comparisons.</p> <p>While categories with basic emotional appeals may always exist, today’s customer is more sophisticated and the marketing necessarily more complex.</p> <p><iframe src="https://www.youtube.com/embed/dlNO2trC-mk?wmode=transparent" width="560" height="315"></iframe></p> <p>Simultaneously, there is great pressure on chief marketing officers to deliver organic growth for their companies. The playbook must have more pages!</p> <p>One of those new pages is <a href="https://econsultancy.com/blog/64743-predictive-analytics-machine-learning-and-the-future-of-personalization/">predictive analytics</a>, which empowers marketers by providing insights into customer behavior and how certain strategic decisions can increase sales.</p> <p>Those who can best predict the customer and act on those insights will ultimately take market share from their less attuned competition.</p> <p>In fact, <a href="https://econsultancy.com/blog/67315-eight-tips-for-getting-corporate-buy-in-for-data-analytics/" target="_blank">83% of marketers</a> say they now use predictive metrics to build competitive experiences and make smart product decisions.</p> <h3>Predictive Analytics X's &amp; O’s</h3> <p>Predictive analysis of data allows you to play out different “what if” scenarios so you can develop campaigns that achieve optimal growth.</p> <p>Getting more specific, here are four predictive analytics plays CMOs might consider, depending on their business:</p> <h4>1. Sentiment analysis</h4> <p>A sentiment analysis identifies and categorizes the opinions expressed in a section of text to determine whether the writer’s attitude is positive, negative, or neutral.</p> <p>If customers post product reviews on their blogs or discuss your services on social media, a sentiment analysis will dissect their text for clues about their satisfaction levels.</p> <p>Scoring the relative sentiment expressed in mediums from social media commentary to call center transcripts, and comparing peaks and valleys and their drivers over time is powerful.</p> <p>It essentially transforms the idea of “<a href="https://econsultancy.com/reports/voice-of-the-customer-listen-measure-act/">voice of the customer</a>” from a concept to a real tool to sharpen campaigns, products, and customer service operations. </p> <h4>2. Hedonic analysis</h4> <p>Hedonism means the pursuit of self-indulgence, so it follows that a hedonic analysis studies consumers’ preferred features.</p> <p>Instead of guessing what people want, you identify which options are most attractive to them. If you’re an automotive manufacturer, your next model could include dozens of feature combinations.</p> <p>Understanding which combinations will achieve the highest value in the marketplace is useful for optimizing product offerings to best match customer desires and, ultimately, demand. </p> <h4>3. Credit analysis</h4> <p>Credit rating agencies originated in the early 1900s, making credit the oldest form of predictive analysis and a basic tenet of modern businesses.</p> <p>Whenever you offer financing, you’re asking, “Does the customer have both the good faith and the ability to pay?”</p> <p>If you can tweak your approval formulas to qualify more candidates without triggering higher default rates, you’ve mastered organic growth creation. Someone figured that out already, right?</p> <p>The difference today is the availability of abundant supplemental behavioral data that, it turns out, have a lot to do with understanding whether the customer will be a faithful creditor.</p> <p>Social media patterns, cellphone ownership, and usage are just a few ways credit can be predicted more reliably, which is very useful in markets that don't have traditional credit-prediction methods like FICO scores.</p> <h4>4. Churn propensity analysis</h4> <p>Another behavior prediction measurement, churn propensity analysis, anticipates how likely customers are to cancel their annuity contracts.</p> <p>This metric is perfect for <a href="https://econsultancy.com/blog/66034-the-pros-and-cons-of-subscription-ecommerce-models/" target="_blank">subscription-based businesses</a> as well as credit card providers, media brands, cellphone carriers, and direct response companies.</p> <p>If you know which customers will leave and when, then you can court them to remain with you by employing preventive marketing measures.</p> <p>Altered service options and tailored discounts help you avoid losing their business long-term.</p> <p>One rule of marketing that hasn’t changed is that it’s less expensive to <a href="https://econsultancy.com/blog/11051-21-ways-online-retailers-can-improve-customer-retention-rates/">retain a customer</a> than to acquire a new one.</p> <h3>Use with caution</h3> <p>Be careful with the possibilities enabled with the new playbook.</p> <p>Predictive analytics enable you to engineer “wins” such as mastering <a href="https://econsultancy.com/blog/65327-why-dynamic-pricing-is-a-must-for-ecommerce-retailers/">dynamic pricing</a>, increasing profits, and retaining more customers, but those victories aren’t achieved by numbers alone.</p> <p>The most successful CMOs marry in-depth metrics with original thinking to deliver standout campaigns. </p> <p>“Data analytics — big data — is not a substitute for innovation. It’s not a substitute for creative thinking and leadership,” said Sandeep Sacheti, executive vice president for customer information management and operational excellence at Wolters Kluwer.</p> <p>CMOs who can tap into both the logical and creative will achieve greater professional success today and in the future.</p> <p>Anthony Scriffignano, senior vice president and chief data scientists at Dun &amp; Bradstreet, echoed this idea: “The environment is busy and chaotic. Marketers are under a lot of stress and pressure to deliver the growth,” he said.</p> <blockquote> <p>This new data era moves away from the creative and the gray to the very specific, the black and white, the binary. They’re not going to deliver that growth with data-driven analytics unless they take the careful time and process to do it right.</p> </blockquote> <h3>Know your data strategy</h3> <p>With some success, perhaps in the form of a pilot project, you’ll want to get much deeper.</p> <p>Consider your overall scheme if you and your company are going to be real data players.</p> <h4><strong>1. Get intimate with your data</strong></h4> <p>Verse yourself in your analytics and look for gaps in the numbers.</p> <p>It’s worth identifying your current analytics state and your ideal. What would the perfect setup look like, and which metrics do you need to get there?</p> <p>But be discerning about which numbers you emphasize. You may not need every metric available to you, and your vision for how to use your data should evolve as your company grows.</p> <p>Blind pursuit of an ideal state that doesn’t match your organization is wasteful, so evaluate which information moves you toward your goals.</p> <p>As you determine the most valuable threads, you’ll have to make spending decisions about how to gather, validate, integrate, and analyze the right data.</p> <h4><strong>2. Think scientifically</strong></h4> <p>You don’t need to be <a href="https://econsultancy.com/blog/67203-data-analysts-vs-data-scientists-what-s-the-difference/">a data scientist</a> yourself to use analytics effectively, but you do need to hire some.</p> <p>Point-and-click technology is not an honest replacement for the hard work data scientists perform to root out causal relationships, and it creates a false sense of control over the numbers.</p> <p>Your competitors are already catching on to the importance of data scientists and going after top talent via direct hires or consultancies.</p> <p>You can’t afford to fall behind in this area, and data science is not a DIY game. Recruit the best data team you can find by offering candidates big, bold projects and a culture that values their work. </p> <h4><strong>3. Know what matters to you</strong></h4> <p>Do you know your five most impactful marketing data metrics? I don’t mean in concept; I’m talking about hard numbers. Have you tested your data, assessed its weaknesses, and identified statistically proven causal relationships?</p> <p>For instance, you might find that if you drop service wait times by 10%, you can increase prices by 5%.</p> <p>The data might indicate that changing a single term of your offering like the subscription cancellation policy and its accessibility could reduce customer churn by 10%.</p> <p>Make a plan to discover the undeniable facts of your business performance. As a leader or marketer, you must deal in proven, impactful metrics, or what I call the lowest-hanging analytical fruit. </p> <p>Predictive data and <a href="https://econsultancy.com/events/future-of-digital-marketing-london/">the future of marketing</a> are intertwined. “Data analytics is going to be standard, expected practice in every business function and business model,” Sacheti said.</p> <blockquote> <p>Just like HR is a function that exists everywhere in every company and finance exists everywhere in a company, big data scientist is going to be a profession that’ll be expected in every company.</p> </blockquote> tag:econsultancy.com,2008:BlogPost/67758 2016-04-29T14:01:05+01:00 2016-04-29T14:01:05+01:00 Big data tools & techniques successful CMOs need to know John Kelly <p>Take the entertainment and ticketing business.</p> <p>Andrew Rentmeester, senior vice president of revenue planning and operations at The Madison Square Garden Company, sees it this way:</p> <blockquote> <p>What the CMO uses now (and will always need) are simple scrapes of the ticket inventory system and what’s sold today. If you don’t have that, you really don’t know where you are in the business.</p> </blockquote> <p>Rentmeester adds that, even though it’s just an inventory system for tickets, an old-school Excel sheet works. While it isn’t ideal, it’s needed, nonetheless.</p> <p>Consider the marketing of tires. Tire manufacturers struggle to understand the market value of their brand and products.</p> <p>Typically, they web-scrape prices listed by local retailers and make rough estimations of the value of their brand versus benchmarks.</p> <p>Even the application of this crude method of <a href="https://econsultancy.com/blog/67699-how-online-retailers-can-improve-price-optimization-strategies/">price optimization</a> improves margins in a very competitive market sector. </p> <p>Shawn O’Neal, vice president of global marketing data and analytics at Unilever, suggests that tool exploration begins before its analysis:</p> <blockquote> <p>You have to know what you want before you build the database with data tools. You have to understand what attributes you’re going to scale in the hierarchy and segmentation before you ever build the database.</p> <p>If you don’t have the database built for the data, you don’t capture it.</p> </blockquote> <h3>Big data tools on the CMO’s wish list<br> </h3> <p>Rentmeester says he would like to start his morning in this way:</p> <blockquote> <p>Marketing leadership and I need a dashboard concept that we look at and know what the overall state of our key marketing levers are so that we can use that to drive the business forward.</p> </blockquote> <p>He likes the idea of a type of dashboard mechanism that allows for quick insight into key sales drivers, year-to-date numbers, and prior-year numbers (and one that also provides a way to view the revenue funnels in parallel).</p> <p>For example, if you have a website and different digital marketing strategies for that website, you want to know which method is working best, how they are stacking up against each other and, most importantly, how they are relating back to sales. </p> <p>However, in a world where large, monolithic-type reporting engines still exist, Rentmeester finds that, by the time a report is generated, it’s already out of date because the questions have changed.</p> <p>If he wants to see one specific metric — such as the average ticket price in Section 340 for Knicks games — how does he get that metric quickly?</p> <p>He argues that the reporting structure isn’t usually oriented to answer that question at that point in time, which could prove to be a challenge for a CMO.</p> <p>He’s looking for a tool that allows him to get granular and to get as much data as he needs in order to make use of it as quickly as possible.</p> <h3>Tools or Techniques?<br> </h3> <p>Other executives claim there is more value to knowing a few standard analytical techniques above any one tool that should be leveraged.</p> <p>Sandeep Sacheti, executive VP at Wolters Kluwer, suggests the following “big five”.</p> <h4>1. A/B Testing</h4> <p><a href="https://econsultancy.com/blog/67249-a-beginner-s-guide-to-a-b-testing/">A/B testing</a>, as the name implies, involves a comparison or test. It is the simplest testing method possible, measuring the effectiveness of one path versus another.</p> <p>Some ideas for areas to A/B test include webpage design or timing of messages in an email campaign. One can test creative and response rates to specific offers as well.</p> <p>Although most marketers are well-informed of this basic technique, in the rush to get the job done and get to market, fewer employ it than one would expect.</p> <h4>2. Net Promoter Score (NPS)</h4> <p>NPS is an inherently simple concept to measure customer loyalty: It’s a tally of whether customers would recommend your business to others.</p> <p>While it might seem crazy that entire consultancies have been testing and reporting something as simple as the NPS concept, that number leads to the need for real strategic changes if not at its ideal level.</p> <p>Again, start simple: Are you asking your customers for it? And have you tracked how your score moves over time?</p> <h4>3. Customer Lifetime Value (CLTV)</h4> <p>Here’s another simple metric, but this one accomplishes a complex transformation — getting an organization to shift its priorities from quarterly profits to the health of customer relationships.</p> <p><a href="https://econsultancy.com/blog/65435-what-is-customer-lifetime-value-clv-and-why-do-you-need-to-measure-it/">CLTV</a> is most applicable to businesses that can successfully achieve an annuity from their clients (think financial services, for example).</p> <p>Beyond that, it answers this question: “What is this customer worth to me?” to which there are three more consequential questions:</p> <ul> <li>Should I encourage this customer’s business, or let it go? </li> <li>If I want to encourage his or her business, how likely is he or she to continue?</li> <li>And what do I need to invest to keep that annuity going?</li> </ul> <h4>4. Recency, Frequency, Monetary (RFM) Analysis</h4> <p>This is the most basic method of measuring CLTV.</p> <p>Scoring of all three — combined with a weighting of each to reflect the specific importance of each to your business — is the essence of this simple tabular calculation.</p> <h4>5. Customer Wallet Estimation</h4> <p>Maintaining a base level of analytics ensures you know what your customer spends with you in a given period.</p> <p>However, in a competitive marketplace, do you know how much money that customer is spending in that same time period across your industry?</p> <p>This measure involves more advanced statistical analysis and some outside market audit data, including a small sample of customer spend with competitors.</p> <p>A reliable marketwide number can be derived from this small sample by employing rules of statistics.</p> <p>Provided in context, knowledge of this number is good for relative comparison combined with other data.</p> <p>For example, are some of your marketing dollars achieving as much customer money as your competitor’s marketing dollars are? </p> <h3>Making the most of the CMO’s big data toolkit<br> </h3> <p>What needs to happen to make these tools most effective for CMOs today and in the future?</p> <p>O’Neal insists that setting up big data infrastructures with big groups of people and big budgets is no longer the way to go.</p> <p>Analytics should be built to empower people to do work in a demand-driven way — and not in the way IT systems were built in the 1990s.</p> <p>He goes on to agree with Rentmeester above:</p> <blockquote> <p>We build minimum requirements that are highly alterable, not capacity models that hope demand will grow and become what you envision. Because what you envision today is changing so rapidly that, tomorrow, it’s out of date.</p> </blockquote> <p>Rentmeester, however, believes that more people are the answer to actually marshal the data and make it quickly usable.</p> <p>A large staff, with enough data sense and business acumen to drive business by the numbers, can achieve the right balance of analytical agility, innovation, and most importantly, actionable results.</p> tag:econsultancy.com,2008:BlogPost/67668 2016-04-04T14:25:51+01:00 2016-04-04T14:25:51+01:00 Data can be toxic, here's how companies should handle it Patricio Robles <p>Schneier <a href="https://www.schneier.com/blog/archives/2016/03/data_is_a_toxic.html">blames</a> the "hype cycle of big data" on the risks that have been created...</p> <blockquote> <p>Companies and governments are still punch-drunk on data, and have believed the wildest of promises on how valuable that data is.</p> <p>The research showing that more data isn't necessarily better, and that there are serious diminishing returns when adding additional data to processes like personalized advertising, is just starting to come out.</p> </blockquote> <p>He also points out that many companies underestimate the risks and impacts of data breaches and overestimate their ability to mitigate against them.</p> <p>And in some cases, Schneier believes, companies choose to take unreasonable risks with data because they're encouraged to.</p> <p>"The culture of venture-capital-funded startup companies is one of extreme risk taking," he argues.</p> <blockquote> <p>[These companies] are so far from profitability that their only hope for surviving is to get even more money, which means they need to demonstrate rapid growth or increasing value.</p> <p>This motivates those companies to take risks that larger, more established, companies would never take. They might take extreme chances with our data, even flout regulations, because they literally have nothing to lose.</p> </blockquote> <h3>Realistic versus unrealistic solutions</h3> <p>Not surprisingly, as a security expert and privacy advocate, Schneier wants greater regulation of data "collection, storage, use, resale and disposal" and even suggests that certain business practices that involve "surveilling people" be made illegal.</p> <p>Ostensibly, this includes much of the activities associated with digital advertising.</p> <p>While greater regulation around data is indeed likely given the growing number of costly breaches, it's highly unlikely that large swaths of the big data economy will be rendered illegal.</p> <p>Even so, companies shouldn't ignore Schneier's arguments.</p> <p>Data is digital black gold and it's similar to the black gold that comes out of the ground. That black gold, when controlled, fuels the industrial economy, but when spilled, is the source of environmental disaster.</p> <p>Likewise, digital black gold <a href="https://econsultancy.com/blog/67674-what-are-first-second-and-third-party-data/">fuels the internet economy</a>, but can also be the source of disaster when it leaks.</p> <h3>What companies should do</h3> <p>So what should companies do to avoid disaster? Here are several suggestions.</p> <h4>1. Develop a data strategy</h4> <p>In most cases, companies aren't collecting more and more data because storing it is so cheap. Many are storing all the data they can get their hands on because they don't have <a href="https://econsultancy.com/blog/67296-how-to-create-a-clear-data-strategy-for-your-business/">a data strategy</a>.</p> <p>Without a strategy, decision makers will favor storing any and all data in the hope that they might develop a use for it later on.</p> <p>In reality, "we don't know if we'll need it therefore we'll keep it" is typically a poor excuse for data collection and retention, the result of laziness and not true lack of knowledge.</p> <h4>2. Develop data acquisition and retention policies</h4> <p>With a data strategy in place, companies can create sensible data acquisition and retention policies.</p> <p>Such policies can ensure that they have the data they need to meet business goals while reducing the risk that they're storing data that they don't need, or storing data in ways that are unnecessarily risky.</p> <h4>3. Treat data differently</h4> <p>Sensible data and retention policies will inherently reflect the fact that data differs in nature.</p> <p>For example, data that contains personally identifiable information (PII) isn't the same as data that doesn't contain PII, and should be handled and stored differently as a result. </p> <h4>4. Embrace compliance and risk management</h4> <p>Certain types of data are already subject to regulation.</p> <p>For instance, in the US, some health information is protected by <a href="https://econsultancy.com/blog/67498-digital-media-vs-hipaa-violations-risking-your-reputation-in-healthcare/">Health Insurance Portability and Accountability Act (HIPAA) rules</a>.</p> <p>Companies subject to these rules should see compliance as an opportunity to ensure that they're taking all the steps they can to secure their data.</p> <p>Even companies that aren't subject to government regulation have the opportunity to embrace data security through risk management.</p> <p>It's now possible to acquire data breach insurance, and companies that opt to do so can use the process as a means to implement data security best practices.</p>