潜在客户评分 (Lead Scoring)

人工智能如何帮助营销人员准确预测转化次数 (How AI Helps Marketers Predict Conversions with Accuracy)

You’ve built a crackerjack marketing team, installed best-in-class marketing platforms, and designed super-creative campaigns with compelling messaging and offers. You hope.

您已经组建了一个骗子营销团队，安装了一流的营销平台，并设计了具有吸引力的消息和要约的超创意广告系列。 你希望。

In fact, you’re not sure, and you’re crossing your fingers that all the time and effort you’ve put into content developing and planning will pay off. From past experience, you know demand generation is hit or miss, and anticipating your audience’s reaction to a campaign or message is difficult to predict with accuracy.

实际上，您不确定，您正在交叉手指，投入内容开发和计划的所有时间和精力都将得到回报。 根据过去的经验，您知道需求的产生是成败的，并且很难准确地预测受众对活动或消息的React。

Fortunately, advances in AI are changing all that. According to IDC’s AI/CRM Economic Impact Survey, AI associated with CRM activities will boost global business revenue from the beginning of 2017 to the end of 2021 by $1.1 trillion. Of the AI adopters surveyed, 83% are using or planning to use sales and marketing predictive lead scoring — the practice of attributing a numerical score to leads based on certain pre-defined factors and behaviors.

幸运的是，人工智能的进步正在改变这一切。 根据IDC的AI / CRM经济影响调查 ，与CRM活动相关的AI将从2017年初到2021年底将全球业务收入增加1.1万亿美元。 在接受调查的AI采用者中，有83％的人正在使用或计划使用销售和市场预测的潜在顾客评分-一种基于某些预定义因素和行为将数字评分归因于潜在顾客的做法。

Made possible by AI-powered propensity-to-buy models, predictive lead scoring is helping marketing teams understand which leads are most likely to buy specific products or respond to specific messages.

借助AI驱动的购买倾向模型，可预测的潜在客户评分有助于营销团队了解哪些潜在客户最有可能购买特定产品或响应特定消息。

传统铅计分的演变 (The Evolution of Traditional Lead Scoring)

Marketers have been using lead scoring for a couple of decades. With lead scoring, marketers ascribe a certain number of points to a lead for clicking on two or more pages on your website, or downloading a marketing asset such as an ebook, or attending an event like a webcast. As the points add up for that lead, the lead is considered more qualified and likely to purchase a product or service than other leads. Teams can identify stages, such as Marketing-qualified or Sales-qualified, for instance, and when a lead reaches a certain agreed-upon threshold (e.g. 25 points), it progresses to the next stage in the marketing funnel, triggering additional nurturing activities.

营销人员使用潜在客户评分已有几十年了。 通过潜在顾客评分，营销人员可以为潜在顾客分配一定数量的积分，以便他们点击您网站上的两个或多个页面，或者下载诸如电子书之类的营销资产，或者参加诸如网络广播之类的活动。 随着积分的增加，潜在顾客被认为比其他潜在顾客更有资格，并且更有可能购买产品或服务。 团队可以确定阶段，例如市场营销资格或销售资格，当销售线索达到某个商定的阈值(例如25分)时，它就会进入营销渠道的下一个阶段，从而引发其他培育活动。

Lead scoring helps marketing teams prioritize their time and effort to maximize the ROI of their activities and increase lead conversions by only focusing on leads that are likely to convert. But only in recent years has AI techniques like machine learning enabled predictive lead scoring with a higher level of accuracy.

潜在客户评分可帮助营销团队确定时间和精力的优先级，从而通过仅关注可能会转化的潜在客户来最大化其活动的ROI并增加潜在客户的转化率。 但是直到最近几年，诸如机器学习之类的AI技术才能够以更高的准确性实现预测性线索评分。

Before AI-powered propensity modeling was possible, teams would manually assign a score to each lead based on a subjective evaluation of what a certain action might mean. For example, a marketer would decide that downloading an ebook was more telling than reading a blog post and ascribe a higher value to that action. They might also factor in other known data, such as age, gender, job title and past interactions.

在使用AI技术进行倾向性建模之前，团队将基于对某个动作可能意味着什么的主观评估，为每位潜在顾客手动分配分数。 例如，营销人员会认为下载电子书比阅读博客文章更具说服力，并且将这种行为归功于更高的价值。 他们还可能考虑其他已知数据，例如年龄，性别，职称和过去的互动。

Although traditional lead scoring methods enabled teams to weed out weak leads, it wasn’t great at identifying the best leads. Scoring was subjective and arbitrary, and not rooted in data science. It was also very time-consuming, involving a lot of research and spreadsheets.

尽管传统的线索评分方法使团队能够淘汰弱势线索，但是在识别最佳线索方面并不是很好。 计分是主观的和任意的，并不扎根于数据科学。 这也非常耗时，涉及大量研究和电子表格。

CRM and marketing automation (MA) platforms later helped teams enrich the amount and quality of data about prospects and customers, and they also automated the scoring process. MA platforms such as Marketo and Eloqua made it possible to track people’s online behavior and collect certain types of implicit information, which could then be analyzed in concert with explicit data such as demographics and downloads.

CRM和市场营销自动化(MA)平台后来帮助团队丰富了有关潜在客户和客户的数据的数量和质量，并且还使计分过程自动化。 诸如Marketo和Eloqua之类的MA平台使跟踪人们的在线行为并收集某些类型的隐式信息成为可能，然后可以与诸如人口统计和下载之类的显式数据一起进行分析。

The results were promising. MarketingSherpa wrote about one organization who used rudimentary lead scoring in 2012 and saw a 79% increase in lead conversion. However, without AI, marketers still needed to spend time researching leads before being able to eliminate ones less likely to convert.

结果令人鼓舞。 MarketingSherpa撰写了一篇文章，该组织在2012年使用了基本的铅评分方法，铅转化率提高了79％ 。 但是，如果没有AI，营销人员仍然需要花时间研究潜在客户，然后才能消除转化可能性较小的潜在客户。

AI Super-Charging Lead Scoring (AI Super-Charges Lead Scoring)

Propensity-to-buy modeling enables marketers to predict with higher accuracy what actions prospects are most likely to take in response to marketing outreach.

倾向购买模型使营销人员能够更准确地预测潜在客户最有可能针对营销范围采取的行动。

Propensity-to-buy models leverage machine learning algorithms to spot trends and patterns in datasets that would be impossible to see with the human eye. They analyze data from CRM systems, MA platforms, social media commentary and other sources together, for richer, more complete insight into an individual’s buying behavior. And as the name implies, machine-learning algorithms will learn and adjust results based on new data as it enters the system, which enables predictions to become increasingly accurate over time.

倾向购买模型利用机器学习算法来发现人眼无法看到的数据集中的趋势和模式。 他们一起分析来自CRM系统，MA平台，社交媒体评论和其他来源的数据，以更丰富，更完整地了解个人的购买行为。 顾名思义，机器学习算法将根据进入系统的新数据来学习和调整结果，从而使预测随着时间的推移变得越来越准确。

Because they’re data-driven and statistical, propensity-to-buy models take into account both implicit and explicit information and remove the subjectivity from traditional lead scoring methods, enabling a broader set of predictions. They can identify shared traits among leads who already converted and those that haven’t yet, as well as shared traits among customers who purchased certain products or services, when they decided to purchase, and so on. They can even predict whether a customer will unsubscribe from an email list or stop using a product or service altogether.

由于它们是数据驱动和统计的，因此购买倾向模型同时考虑了隐性和显性信息，并消除了传统线索评分方法的主观性，从而可以进行更广泛的预测。 他们可以识别已经转换的潜在顾客和尚未转换的潜在顾客之间的共同特征，以及购买某些产品或服务，决定购买的顾客之间的共同特征等。 他们甚至可以预测客户是否将从电子邮件列表中退订还是完全停止使用产品或服务。

Armed with knowledge about how likely someone is to take a certain action, marketing teams can focus resources on specific leads, and develop content and messaging that will make engaging with those leads much more meaningful and fruitful.

掌握了某人采取某项行动的可能性的知识后，营销团队可以将资源集中在特定线索上，并开发内容和消息传递方式，从而使与这些线索的互动更加有意义和富有成果。

精确地得分领先 (Score Leads with Precision)

Using an AI-powered lead scoring model can help you ensure your sales team is spending the majority of their time nurturing the prospects that are most likely to become customers, while eliminating time-consuming manual tasks and guesswork involved in developing targeted campaigns and content. But an off-the-shelf lead scoring or predictive analytics solution may fall short of your expectations. That’s because every marketing organization — and every target audience — is unique. A custom propensity-to-buy model can often yield more accurate results.

使用由AI驱动的潜在客户评分模型可以帮助您确保销售团队将大部分时间用于培养最有可能成为客户的潜在客户，同时消除了开发目标明确的广告系列和内容所涉及的费时的手动任务和猜测。 但是，现成的潜在客户评分或预测分析解决方案可能达不到您的期望。 这是因为每个市场营销组织以及每个目标受众都是独一无二的。 定制的购买倾向模型通常可以产生更准确的结果。