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关于用户获取和归类:获取和市场营销的新时代(二)

发布时间:2015-04-13 15:30:55 Tags:,,,,

作者:Domitri Williams

既然我们已经了解了一些基础内容,现在便可以深入探讨用户获取以及归类了。为了做到这点,我们需要更进一步着眼于广告。

在我的上一篇文章中,我们讨论了广告归类的受数据驱动模式,以及为何产业正朝着这些模式变化(相对于像最后点击归类等模式)。简单来说,当你使用数据去决定归类的确切资源时,你便能够感受到真正的ROI,并更好地优化你的广告支出。因此你将需要一家归类公司去获得发行商标签,同时也需要一家安装后分析公司以获取用户在游戏中的行为。你应该确保归类公司与分析公司能够有效兼容。

现在模式所存在的问题并非受数据驱动的责任制:换句话说也就是不存在这些元素。作为一名数据科学家,并专注于科学,我认为存在一种更好的方法。当我们基于理论(游戏邦注:如主要的信任应该给予最后点击资源的理论)去划分信任时—-而不是基于可观察模式去创造观点,我们便找不到任何能够基于每个资源去判断支出的方法。

关于今天的归类模式的另一个问题便是它们将在整个过程中不断削减创造性。我知道这听起来可能很奇怪。我们该如何更多地依赖于数据而将创造性带回广告中呢?

让我们举个例子来说。你正在使用一种线性归类模式,它将信任平等地划分到所有的广告资源中。这意味着每个资源都是同等重要的,你需要确保每个资源都能在用户购买前将其带到最后点击中。你的广告变得更加拘谨,完善,甚至变得更加刻板。你在每个平台上都拥有广告,你需要确保每个广告在适当的时候都是吸引人的,并将所有关键信息都添加到每个广告中—-因为你永远都不知道观众会在什么时候进入这里。

如果你是基于同样的方式去对待每个用户,那便不存在足够的空间去调整广告信息。充满竞争性与不和谐的信息经常会掩埋一些优秀的信息以及具有创造性的理念—-或者更糟糕的是,它们将瞄准错误的目标用户。例如第一次接触的信息便与宣传信息截然不同。让我们基于开发者处理教程或新用户体验的方式去看待这一方法。你是否会同时添加所有游戏信息而导致用户难以接受?当然不该这样,一开始你需要添加的是一些具有吸引力且具有情报性的信息。

为了做到这点,我们需要一个受数据驱动的角度。基于受数据驱动的模式,你可以清楚地看到是哪个资源带来了用户,而哪个资源遭到了忽视,以及哪个资源最终推动用户进行购买。基于所有的这些信息,你可以相应地分配资源。横幅广告是无用的,所以不要在此浪费设计资源。我们知道我们的用户对于视频广告的态度不错,所以让我们在这些广告商发挥创造性吧—-真正去抓住观众的注意力。出色的分类模式能够以人们不购买为由而消除相应的资源或广告设置,并将创造性置于信息的试验中。

所以新的归类模式将有益于发挥创造性,而关于发行商呢?这是关于定价并销售广告的网站。他们将根据归类模式去明确广告的价值,但在近几年因为实时竞价的出现,这变得更加复杂了。比起在人们加载网站的时候能够看到一个有效设置的广告,实时竞价将广告空间变成了页面加载时会出现的拍卖场所。

marketing (resortsupportfiji)

marketing (resortsupportfiji)

而发行商则是拍卖专家,将设定价格并提供给用户信息。广告则是竞拍者,将支持那些能够把他们的信息呈现在用户面前的人。这能够有效瞄准目标。在实时竞标时代出现之前,如果你尝试着销售一款体育类电子游戏,你可能会将广告置于与体育有关的网站上:那些你的目标玩家会频繁浏览的网站。而现在,当你的目标用户会出现在与体育完全不相干的网站上时,你也能够将广告呈现在他们面前了。当然了,你可能需要先中标,但这仍然比之前的方式更加有效。

基于这种全新的技术,发行商进入了一个勇敢的新世界,即尝试着明确广告定价的最佳方法并创造出最大的利益。对于广告发行商来说,当前归类模式的不明确性是有帮助的。这并不会使他们扮演一些坏人的角色:他们将尝试着运行一个业务并最大化利益,就像其他人那样。

然而,对于广告商来说事情却变得更加复杂。现在他们需要尝试着通过使用ROI参数去判断自己的支出。虽然这是一种正确的方式,但却并非最精确的,因为他们总是缺少足够的数据。基于更多数据,我们便可以转向更有效的参数:终身价值(LTV)。具有预测性的分析程序便能够计算这一数值。基于他们的支出模式,这些程序能够基于它们所预测的支出而计算用户价值,市场营销者也能够相应地选择程序。

这便为实时竞标添加了另一个层面。如果你只是追逐着最有价值的玩家,你便能够清楚地知道该花多少钱。但找到这些玩家的最佳方法是什么呢?你又怎么会知道该采取怎样的方式去吸引他们?我关于本系列的最后一篇文章将会进一步讨论这一概念。

本文为游戏邦/gamerboom.com编译,拒绝任何不保留版权的转发,如需转载请联系:游戏邦

User Attribution and Acquisition: The New Age of Acquisition and Marketing (Part Two of Three)

by Dmitri Williams

So now that you’ve got the basics down, it’s time to take a deeper dive into user acquisition and attribution as it stands today. To do that, we also need to look more closely at advertising.

In my last article, I discussed data-driven models for advertising attribution, and why the industry is leaning more toward these models (versus models like last-click attribution). To recap, when you use data to determine the exact sources of attribution, you get a better sense of your true ROI, and you can better optimize your ad spend. Thus, you’re going to need both an attribution company to get the publisher tags, as well as a post-install analytics company to connect the users with their in-game behavior. Make sure that your attribution and analytics companies are compatible. They should be, but not all are.

Now, the problem with models that aren’t data-driven lies in accountability: Namely, there is none. As a data scientist, with an emphasis on the science, I say there has to be a better way. When we’re divvying up the credit based on theory (like the theory that the majority of credit should go to the last-clicked source), rather than deriving insights based on observable patterns, there’s no way to justify your spend on each source.

Another problem with attribution models as they stand today is the tendency for these models to sap the creativity out of the process. I know it sounds strange. How would relying more on data put the creative back in advertising?

Let’s look at an example. You’re working with a linear attribution model, which divides credit up equally among all ad sources. This means that each source is equally important, and you need to make sure every source is carrying your audience member down to that eventual last click before they buy. Your advertising becomes stiff, optimized, even formulaic. You have ads on every platform, and you need to make sure that every ad is hitting exactly the right points, cramming every key message into each one — because you never know at what point the viewer is coming into the funnel.

If you’re treating every user the same way, there also isn’t room to fine-tune advertising messaging. Good messages and creative ideas are often lost in the noise of competing and incongruent messages — or worse, they’re targeted to the wrong demographic. First contact messaging, for example, is very different than advocacy messaging. Let’s approach this in the same way developers approach a tutorial or new user experience. Do you cram all the information about the game down users’ throats all at once? Of course not — you take compelling and informative, yet light, first steps into a complex world.

To achieve this, though, we need a data-driven perspective. With data-driven models, you can see exactly what source is bringing consumers in, which source they’re passing over, which source finally drives them to buy. And with all this information, you can allocate your resources accordingly. Banner ads aren’t working, so let’s not waste our design resources with those. We know our audience is responding well to video ads, so let’s get creative with those — really grab our viewers’ attention. A good attribution model eliminates the source or placement of the ad as the reason people aren’t buying, giving creatives the freedom to experiment with messaging.

So a new attribution model would benefit creatives, but what about the publishers? These are the sites that price and sell the ads. They rely on attribution models to figure out how to value their ad, but that has gotten more complicated in recent years with the introduction of real-time bidding. Instead of having an ad in place when people load the site, real-time bidding turns an advertising space into an auction floor as a page loads.

The publisher is the auction master, setting a price and giving information on the user whose page is loading. Advertisers are the bidders, placing their bids for who gets their message in front of that user. This helps with targeting. In the pre-real-time bidding world, if you were trying to sell a sports video game, you might place your ads on sites that had to do with sports: those that your target player would likely frequent. Now, when a target user pops up on a site completely unrelated to sports, you can jump in and place your ad in front of them. Sure, you might have to outbid a few other companies, but it’s still more cost-effective than an ad wash.

With this new technology, publishers are living in a brave new world, trying to figure out the best way to price ads and still make a good amount of money. What’s good for ad publishers is the ambiguity that comes with current attribution models. This isn’t to make them the bad guys: They’re trying to run a business and maximize their profits, just like everyone else.

However, it does make things difficult for advertisers. Right now, they try to justify their spend by using ROI metrics. That’s good, but it’s not the most accurate because of the lack of data. With more data, we can turn to a better metric: total lifetime value (LTV). Predictive analytics programs can calculate this just like in games (see my series on prediction for more information on how that works). Based on their spending patterns, these programs can calculate the value of users based on their predicted spend, and marketers could target their programs accordingly.

This adds another layer to real-time bidding. If you only go after the most valuable players, you know exactly how much to spend. But what’s the best way to find them, and how do you know what way to engage them? My final article of the series will explore this concept more.(source:gamasutra)

 


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