canva画板

Artificial Intelligence (AI) and machine learning (ML) is all the buzz right now, and rightfully so with the significant contributions it has made to redefining many aspects of business. However, many people are still skeptical about the application of AI and ML to enhancing customer experience.

人工智能(AI)和机器学习(ML)如今风靡一时，理所当然地，它在重新定义业务的许多方面做出了巨大贡献。 但是，许多人仍然对AI和ML应用于增强客户体验表示怀疑。

Some would argue that machines cannot possibly take over customer service, something that has a heavy focus on human interaction. Machines lack the empathy and emotional intelligence core to providing a great customer experience. On the other hand, many also see the benefit of applying AI and ML to automate repetitive tasks, allowing humans to dedicate more time to, well, being human.

有人会争辩说机器不可能接管客户服务，而这正是人们互动的重点。 机器缺乏移情和情感智能的核心，无法提供出色的客户体验。 另一方面，许多人也看到了应用AI和ML自动执行重复性任务的好处，从而使人类可以花更多的时间来成为人类。

We reached out to some experts from Oovvuu, Canva and The Minerva Collective to pick their brains about the issue.

我们与Oovvuu，Canva和The Minerva Collective的一些专家取得了联系，就此问题进行了讨论。

客户体验的当前状态如何，您如何看待它如何随着AI和ML技术而发展？ (What is the current state of customer experience, and how do you see it evolve with AI & ML technology?)

Present customer experience is “all over the place, with wildly varying results. Two customers using the same service can have completely different impressions of their experience, and in many cases the service is clunky and poorly structured” says Anthony Tockar, Data Scientist and Co-founder of The Minerva Collective. The unfortunate reality is that 78% of consumers have bailed on a transaction or not made an intended purchase because of poor service experience. In fact, companies only hear from 4% of its dissatisfied customers. With so much choice available to consumers, it’s much easier to find another company with similar offerings than spending time complaining or calling about a problem. Which is why there is a very real need to focus on customer experience, a factor that is becoming increasingly important to retain the modern customer.

当前的客户体验“遍布各地，结果差异很大。 使用同一项服务的两个客户对他们的体验会有完全不同的印象，而且在许多情况下，该服务笨拙且结构不佳。” Minerva Collective的数据科学家兼联合创始人Anthony Tockar说。 不幸的事实是，由于服务经验不佳，有78％的消费者已经保住了交易或没有进行有意购买。 实际上，公司仅从4％的不满意客户那里得到答复 。 有了太多可供消费者选择的选择，找到其他提供类似产品的公司要比花时间抱怨或抱怨问题要容易得多。 这就是为什么真正需要关注客户体验的原因，这一点对于留住现代客户变得越来越重要。

Paul Tune, Machine Learning Engineer at Canva, believes “there are two trends in improving customer experience:

Canva机器学习工程师Paul Tune认为“改善客户体验有两个趋势：

• A trend towards tailoring for the individual, as more data is gathered about each customer at a large scale, and;

  随着大规模收集有关每个客户的更多数据，趋势是为个人量身定制；以及

• A trend towards providing a smooth experience for customers across multiple touchpoints by anticipating their needs. “

  通过预测客户的需求，为跨多个接触点的客户提供流畅体验的趋势。 “

To demonstrate how customer experience has evolved, Paul continues with an example. “Early recommendation systems, such as the recommendation engines developed at Amazon and NetFlix in the early 2000s, provided recommendations at a much coarser level, chiefly for specific groups of customers. The granularity of recommendations in the near future is going to be much finer. For instance, an engineer from NetFlix I spoke to recently, mentioned that a subscriber’s favourite character for a TV series would appear in the menu when the TV series is selected. This means having to learn more about each customer and predicting their habits. We also see this in the form of smart personal assistants, such as Alexa and Siri” he says.

为了演示客户体验的演变，Paul继续举一个例子。 “早期的推荐系统，例如2000年代初在Amazon和NetFlix开发的推荐引擎，提供的推荐范围要大得多，主要针对特定​​的客户群。 在不久的将来，建议的粒度将更加精细。 例如，最近来自NetFlix的一位工程师提到，当选择电视连续剧时，用户喜欢的电视连续剧角色将出现在菜单中。 这意味着必须更多地了解每个客户并预测他们的习惯。 我们还以智能个人助理的形式看到了这一点，例如Alexa和Siri。”他说。

Ricky Sutton, Founder and CEO of Oovvuu, adds on that whilst AI and ML “certainly has an element to play [in customer experience], it also lacks a key element…empathy. So my thought is that it will evolve. The more AI is used, the more it learns and the better it gets, but human-level empathy remains a pipe dream for now.”

Oovvuu的创始人兼首席执行官Ricky Sutton补充说，尽管AI和ML“在[客户体验]中肯定具有一定的作用，但它也缺乏关键的……同情。 所以我的想法是它将发展。 使用AI的次数越多，学习的知识越多，获得的效果也越好。

https://unsplash.com/.https://unsplash.com/ 。

从将智能技术应用于客户体验中学到的最大的教训是什么？ (What is the biggest lesson you have learned from applying smart technology to customer experience?)

For Anthony, the lesson has been the need for people using smart technology to properly understand it — “My experience is that people often don’t trust what they don’t understand. The latest technologies have been great for grabbing headlines, but only the most forward-thinking businesses are serious about applying them to derive value. This isn’t necessarily a bad thing — domain knowledge is essential for good data science, and blindly relying on new approaches has many inherent risks. There is a lot that has been learned about customer experience over time and there is a need to explain smart technology to business people using the right language to allow them to fully realise its value.”

对于Anthony来说，教训是人们需要使用智能技术来正确理解它-“我的经验是，人们通常不相信自己不了解的东西。 最新的技术非常适合吸引头条新闻，但是只有最具前瞻性的企业才认真考虑将其应用以创造价值。 这并不一定是一件坏事-领域知识对于良好的数据科学必不可少，盲目依赖新方法会带来许多固有风险。 随着时间的流逝，已经学到了很多关于客户体验的知识，有必要使用正确的语言向商务人士解释智能技术，以使他们充分实现其价值。”

To Paul, what matters most, is the customer’s end-to-end experience. Meaning that all the touchpoints with the customer should be seamless. For him, “the challenge with integrating smart technology to improve user experience is similar to managing any other complex system: with more moving parts, there is a higher chance of failure in the system. Naively applying machine learning to improve customer experience is misguided. Machine learning works best if it is complementary to the customer experience, serving to enhance the experience of a great product.”

对于Paul而言，最重要的是客户的端到端体验。 这意味着与客户的所有接触点应该是无缝的。 对他来说，“集成智能技术以改善用户体验的挑战类似于管理任何其他复杂的系统：随着移动部件的增多，系统出现故障的可能性更高。 天真地应用机器学习来改善客户体验是错误的。 如果机器学习能够与客户体验相辅相成，那么机器学习的效果最好，这有助于增强优质产品的体验。”

“At Canva, our goal is simple: we want to give the customer the best experience in empowering them to create and design. To that end, there are two aspects that we focus on. Firstly, how do we make the content that they need for their designs easily accessible. Secondly, how do we anticipate what resources might be helpful for them in the future. We achieve these goals by improving our search and recommendation services to enhance customer experience.”

“在Canva，我们的目标很简单：我们希望为客户提供最佳体验，使他们能够创建和设计。 为此，我们关注两个方面。 首先，我们如何使他们设计所需的内容易于访问。 其次，我们如何预计将来会有哪些资源对他们有帮助。 我们通过改善搜索和推荐服务以增强客户体验来实现这些目标。”

The biggest lesson for Ricky is that “AI turns humans into super-humans, but only for certain tasks.” — “When we started Oovvuu, we hired editors to read articles and find relevant videos, and they were able to read one publication each and find 40 relevant videos per day. That same person using the AI tools that we created, can now read 100,000 publishers, and 300,000 stories a day, covering 26 million topics and find relevant videos from more than 40 global broadcasters. AI is mind-blowingly powerful for automating manual human tasks, but humans remain better at all the things that, well, make us human.”

对于Ricky而言，最大的教训是“ AI将人类变成超人类，但仅用于某些任务。” —“当我们开始Oovvuu时，我们聘请了编辑来阅读文章并找到相关的视频，他们能够阅读一份出版物，每天找到40篇相关的视频。 使用我们创建的AI工具的同一个人现在可以阅读100,000个发布者，每天阅读300,000个故事，涵盖2600万个主题，并可以从40多个全球广播公司中找到相关视频。 人工智能在实现手动人工任务方面具有惊人的强大功能，但人类在使我们成为人类的所有事物上仍然表现得更好。”

对于试图将AI＆ML技术和客户体验整合在一起的企业而言，将面临哪些挑战？ (What are some challenges for businesses who try to integrate AI & ML technology and customer experience?)

Anthony, Paul and Ricky all agreed that a huge challenge for businesses is not having a solid data infrastructure, or a deep understanding of what exactly should be measured to achieve business goals and customer satisfaction.

Anthony，Paul和Ricky都同意，对于企业而言，巨大的挑战在于没有坚实的数据基础架构，或者对实现企业目标和客户满意度应采取的措施有深入的了解。

“Many companies approach us seeking to use AI as a ready-made silver bullet for a business problem. Others come to ask to play with AI, so they can find a business opportunity. Neither really works.” Ricky said. “For us, the solution was to know what business problem we were trying to solve: namely, to put a relevant video into every article being published worldwide. We then used AI to solve it, but what we started with was very basic and not up to the job. We have had a team nurturing the teaching for almost 1,000 days to get it where it is.”

“许多公司向我们寻求将AI用作解决业务问题的现成的灵丹妙药。 其他人则要求玩AI，这样他们才能找到商机。 两者都没有真正的作用。” 瑞奇说。 “对于我们来说，解决方案是知道我们要解决的业务问题：即在全球发表的每篇文章中都放置相关的视频。 然后，我们使用AI来解决它，但是我们开始的时候是非常基础的，并不适合工作。 我们已经有一个团队对教学进行了将近1,000天的培训，以使其达到预期的效果。”

Anthony went on to add that “there is no silver bullet — good data scientists are required to translate these algorithms into business value. Having a solid data science strategy is essential, and through good leadership, increased data literacy and an understanding of how to build a high-performance data science team, businesses can harness these technologies to forge a competitive advantage.”

Anthony继续补充说：“没有灵丹妙药，需要优秀的数据科学家将这些算法转化为业务价值。 拥有可靠的数据科学战略至关重要，通过良好的领导，增强的数据素养以及对如何组建高性能数据科学团队的理解，企业可以利用这些技术来形成竞争优势。”

Paul concludes with another common challenge many businesses face when adopting AI & ML into their processes — the volume of data. “Present machine learning techniques rely on a relatively large amount of data to provide good predictions” he says. “While there is fundamental research being carried out presently to (hopefully) reduce the amount of data required to train these machine learning models, the current main technological limitation of requiring a huge amount of data is here to stay for the foreseeable future.” But “fortunately, this effect can be mitigated if the data collected is of sufficiently high quality.”

Paul总结了许多企业在将AI和ML应用于流程时面临的另一个常见挑战-数据量。 他说：“当前的机器学习技术依赖于相对大量的数据来提供良好的预测。” “尽管目前正在开展基础研究以(希望)减少训练这些机器学习模型所需的数据量，但在可预见的将来，当前需要大量数据的主要技术限制仍然存在。” 但是“幸运的是，如果所收集的数据质量足够高，则可以减轻这种影响。”

How are you implementing AI and ML to improve customer experience in your business? Share your story with us in the comments below!

您如何实施AI和ML来改善业务中的客户体验？ 在下面的评论中与我们分享您的故事！

关于贡献者 (About the Contributors)

Anthony Tockar 安东尼·塔卡(Anthony Tockar)

Anthony is a leader in the data science space, and has worked on problems across insurance, loyalty, technology, telecommunications, the social sector and even neuroscience. A formally-trained actuary, Anthony completed an MS in Analytics at the prestigious Northwestern University. After hitting the headlines with his posts on data privacy at Neustar, he returned to Sydney to practice as a data scientist while co-founding the Minerva Collective and the Data Science Breakfast Meetup. He also helps organise several other meetups and programs for data scientists, in line with his mission to extend the reach and impact of data to help people.

安东尼是数据科学领域的领导者，致力于保险，忠诚度，技术，电信，社会部门乃至神经科学领域的问题。 Anthony是一名经过正式培训的精算师，在著名的西北大学获得了分析学硕士学位。 在Neustar从事数据隐私方面的工作成为头条新闻之后，他回到悉尼从事数据科学家的工作，并共同创立了Minerva Collective和Data Science Breakfast Meetup。 他还致力于为数据科学家组织其他几次聚会和计划，以履行其扩大数据覆盖范围和影响力以帮助人们的使命。

Paul Tune 保罗·图恩

Paul is a Machine Learning Engineer at Canva, responsible for developing solutions for tailoring and personalising content for Canva’s customers. He has several publications in prestigious computer science conferences and journals, including the ACM SIGCOMM conference in 2015. His interests include deep learning, statistics and information theory. You can also find him on Medium.

Paul是Canva的机器学习工程师，负责为Canva的客户开发定制和个性化内容的解决方案。 他在著名的计算机科学会议和期刊(包括2015年的ACM SIGCOMM会议)上发表了几篇出版物。他的兴趣包括深度学习，统计和信息论。 您也可以在Medium上找到他。

Ricky Sutton 里奇·萨顿

Ricky is founder and CEO of Oovvuu, an IBM and Amazon-backed start up that uses artificial intelligence to match videos from global broadcasters with publishers worldwide. It’s mission is to use AI to insert a relevant short form and long form video in every article. In doing so, it aims to tell the news in a new and more compelling way, end fake news, and in doing so, repatriate billions from Facebook and Google back to the journalists and broadcasters who make the content.

Ricky是Oovvuu的创始人兼首席执行官，Oovvuu是IBM和亚马逊支持的一家初创公司，利用人工智能将全球广播公司的视频与全球发行商进行匹配。 其任务是使用AI在每篇文章中插入相关的简短形式和长篇视频。 这样做的目的是以一种新颖且更具说服力的方式告诉新闻，结束虚假新闻，并以此将数十亿美元的资金从Facebook和Google汇回给制作新闻内容的记者和广播公司。

Originally posted on Woveon.

最初发布在 Woveon上 。

翻译自: https://towardsdatascience.com/customer-experience-artificial-intelligence-and-machine-learning-748d8c1e1127

canva画板