Digital Marketing In A Multi-Touch, Multi-Channel World (Part 2)

In Part 1, we discussed the concept of the marketing or purchase funnel and the functions and interactions played by individual marketing campaigns or channels as Originators, Assists and Converters in guiding customers along at different stages in the purchase journey towards conversion. One particular issue that we highlighted was the tendency of many marketers to attribute the conversion to the last customer touch-point, which can create the impression of a lower cost-per-acquisition (CPA) for that particular campaign or channel.

Issues with Multi-Channel Attribution

Multi-channel attribution is not a new challenge, as marketers have long tried to grapple with the difficulty of quantifying the relative contributions of advertising in different traditional media channels to overall conversion. The ability to accurately attribute the contribution of individual channels to the overall marketing campaign enables a marketer to determine the true value of each channel and allocate promotional resources more efficiently. However, when a marketing campaign involves the use of multiple channels such as TV, radio, print or outdoor displays, either simultaneously or separately over a period of time, there is often no accurate means of determining the contribution of each channel to the improvement in sales or brand awareness throughout the duration of the campaign.

In the absence of accurate, measurable data that links actual conversion to customers’ level of exposure to specific media channels, marketers typically have no choice but to simply measure the overall effectiveness of the entire marketing campaign without attributing the individual contribution of each channel. Thus, there is much to be desired when it comes to the accountability of traditional media advertising.

One advantage held by digital advertising over traditional media advertising is that there is a wealth of click-stream data that can be extracted from web server logs, JavaScript tags and packet sniffers that allows marketers to track and analyze customer behavior, media impressions, acquisitions and conversions across different digital channels. This makes it possible for digital marketers to perform multi-channel attribution to an extent that is not possible with traditional media.

This does not mean, however, that multi-channel attribution in digital marketing is not without its challenges. First of all, not all companies with an online presence are purely online enterprises, and even then, not all of them limit their marketing efforts to the digital sphere. Hence, it is common for many companies to simultaneously run traditional and digital media ad campaigns, and these can complement and act in synergy with each other. While it may be possible to attribute credit for online conversions to individual digital channels, the same cannot be easily done for non-online conversions. Then, there is the original problem of quantifying the contributions of traditional media channels to both online and non-online conversions.

The second challenge faced by digital marketers concerning multi-channel attribution is the proliferation of newer mobile devices such as smart phones and tablets. As of May 2012, it is estimated that mobile devices account for more than 10% of all web page views in the world. However, these mobile devices have smaller screens and oftentimes use different browsers as desktop or laptop computers. Because the click-stream data such as cookies and other tags used to identify unique online visitors are browser-specific via the IP address, it becomes difficult to track the same person’s online activity whenever he switches between devices.

Types of Multi-Channel Attribution

In this excellent blog, Avinash Kaushik identifies three types of multi-channel attribution. The most common type and the most straightforward is multi-channel attribution across different digital channels, which is what most digital marketers refer to when they talk about attribution. Kaushik calls this MCA-ADC, and it involves determining the contributions of individual digital channels to a particular conversion. These digital channels can be in the form of organic search results, pay-per-click (PPC) ads in search engines, e-mail, referrals from an affiliate site, display ads, social media ads or YouTube videos. Through the use of cookies and other tags, it is possible to track the online behavior of individual users and their interactions with every digital channel up until a conversion. This is known as ‘path-to-conversion’ analysis.

The second type is multi-channel attribution from online campaign to store, or MCA-O2S, which involves understanding the impact of digital advertising on the company’s offline performance. Examples of this include retailers whose digital marketing campaigns result in sales increases in their retail stores or an online blood drive that resulted in increased blood donations. Although measuring the contribution of digital campaigns to offline conversion is more challenging than multi-channel attribution across purely digital channels, it is no less important. In fact, the ability to quantify the contribution of digital campaigns to overall performance can greatly influence the size of digital advertising budget that a marketer can wheedle from top management.

The third type of multi-channel attribution is across multiple screens, or MCA-AMS. This involves understanding how customers are being engaged by the company’s marketing efforts across multiple devices and what the results are. These devices are classified into the television, desktop or laptop computers, tablets and smart phones. Because these screens encompass both offline and online behavior, this is the most difficult, if impossible, challenge of all This is because marketers are constrained in their ability to use tracking codes and personal information to track both online and offline consumer behavior due to various laws and regulations relating to data protection and privacy.

The Limitations of Last-Click or Last-Touch Attribution

Despite the availability of web analytic tools that enable marketers to perform path-to-conversion analysis across purely digital channels, it is still common for marketers to credit the entire value of a conversion to the last site at which the customer took an action prior to the conversion, such as clicking or viewing an ad. This methodology is known as last-click or last-touch attribution.

The rationale behind last-click attribution is that the last digital channel that engaged the customer directly influenced the conversion. Therefore, based on this logic, the overall campaign results can be improved by pouring in more marketing dollars into the channel that functioned as the Converter. However, as previously explained in our section on the purchase funnel concept, this approach disregards the equally important contributions of the channels that act as Originators and Assists.

A good example of this is the relative roles played by search ads and display ads on conversion. Search ads are text-based pay-per-click (PPC) advertisements placed on search engines that are targeted to match the keyword search of users. One of the advantages of search ads is that they are tailored towards customers who are already looking for information to satisfy their existing purchase interest. Because of this, search ads usually function well as Converters. In addition to this, it is very easy to measure the contribution of a search ad to conversion since it merely involves counting the actual number of people who clicked on it.

Unlike search ads, display ads are not presented in response to a user’s search query. Instead, they are placed on locations that are likely to be of interest to the target customer. However, studies have shown that many people who are exposed to display ads do not click on them. The click-through rates of display ads are estimated to be as low as 0.06 percent, or roughly 6 out of 10,000 people. This does not mean that the display ad made a favorable impression on only 6 people out of the 10,000 people to whom the ad was shown. In fact, it has been shown in another study that many people who did not click on display ads later performed searches and visited websites, which showed that they had, in fact, been influenced by the display ads. Because of their nature, display ads often function as Originators or Assists in the purchase funnel.

In some cases, however, it is even possible for a customer to directly make a purchase after receiving an impression from a display ad, and this is referred to as view-through or post-impression. However, since there was no click involved in a view-through, the display ad would not be given credit for the conversion in the absence of a specific metric show its influence. This means that the contribution of search ads to the conversion tends to be exaggerated while the contribution of display ads tends to be under-valued. This also illustrates the shortcomings of last-click attribution.

However, for small businesses that are only engaging in one or two digital channels and where cross-channel interaction and synergy may be low, last-click still remains the most logical means of attributing conversion.

Alternative Approaches to Multi-Channel Attribution

For marketers who are harnessing specific or multiple digital channels in their marketing campaigns, there are several alternative methods of attributing conversion other than last-click attribution that may be more appropriate for their needs.

Studies have shown that social media is a great way of encouraging prospective customers to engage with a brand, build brand awareness and drive traffic to a website. However, the nature of social media is that it tends to function at the top of the purchase funnel as an Originator, which means that its contribution to conversion tends to be ignored when last-click attribution is employed. For marketers that focus heavily on social media marketing, the most suitable method to use is first-click attribution since it focuses on the channel that first engaged the customer; thereby ensuring that social media gets 100% of the credit.

For marketers of highly established brands who focus on traditional metrics such as reach and frequency, message recall and purchase intent, determining the individual contribution of each digital channel to conversion may not be as important as ensuring that all possible digital channels are engaged in order to ensure maximum reach and penetration. For these types of marketers, it may be more appropriate to adopt an equal attribution approach that allocates equal credit to all digital channels. This assumes that each channel contributes in its own unique way to the overall campaign, and thus, the 100% credit is divided equally among each channel.

For marketers whose campaign encompasses multiple digital channels and who are interested in determining the conversion performance of each channel in order to come up with the most cost-efficient media mix possible, a more customized approach to multi-channel attribution may be called for. A customized attribution model that allocates fractional credit to each channel requires a thorough analysis of the customer’s interaction with individual digital ad placements. The analysis considers the type of media, the timing of the occurrence of each touch-point prior to conversion, the sequence in which they occurred, and the type of interaction that occurred (whether click, impression, viewing a video, download, etc.).

The main advantage of a customized approach to multi-channel attribution is that it allows a marketer to identify the specific role played by each digital channel in a particular conversion (whether Originator, Assist or Converter). This enables the marketer to accurately understand the true value delivered by each channel during the conversion, and therefore, to accurately allocate fractional credit to each one.

In the next section, we shall take a look at how web analytic tools such as Google Analytics’ Multi-Channel Funnel allows marketers to perform multi-channel attribution across digital channels and how to formulate a digital media mix model based on this.



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