How often do you walk into a store you've never heard of, on impulse, and buy the first thing you see? If you answer "very often", you're a digital marketer's dream. You, the uninformed and impulsive buyer, provide the first- and last-click conversion that too many marketers use to measure the success of their efforts.
If the majority of consumers were impulsive, these marketers would likely remove every product in the store that you didn't purchase, and stop promoting the other products that you left on the shelf. Marketing would be the easiest job in the world.
But we know the vast majority of consumers aren't as easily influenced as the one illustrated above. The most likely series of events is that the consumer hears about your store first, comes in to browse your products, compare prices, and finally forms an opinion about your brand before becoming a customer.
Why Use Attribution Modeling?
Let's apply this scenario to the online world.
A consumer's first exposure to your brand may occur through a display ad impression, but he may take no immediate action because he has never visited your site or heard of your brand before. Your display ad, however, has already done a good job of creating awareness. The next time the user sees one of your banners or does a generic keyword search and sees your text ad, he'll likely recognize your brand, and possibly decide to include you in his consideration set.
When this typical and reasonable consumer is closer to making a purchase, he may look for reviews of your products and customer service, searching on keywords like [your brand] + "reviews" and [your brand] + "return policy."
Finally, he may conclude his purchase by visiting the site directly, having bookmarked it after previous visits, or simply remembering the domain name.
If you were to view the analytics for each of these touch points, it would look something like this:
Assume CPC Pricing Model for All Assets
As the table above illustrates, were it not for the initial banner ad impression and the following keyword searches, this consumer may have spent his $1,000 with a competitor when it was time to purchase.
So why do marketers rely so heavily on last-click conversion attribution when making budget and resource decisions? Because digital marketing and attribution modeling are difficult, and digital marketers are under intense pressure to produce conversions at a target ROI in a very, very short amount of time.
Gone are the days where marketers just bought a print ad, hoped for the best, and categorized the investment as a cost of doing business. Today, you're able to evaluate and optimize campaign assets and budgets in real time – with so much data surrounding each touch point and flowing in every millisecond of the day – too many campaign managers try to keep up by optimizing campaigns and allocating budgets as quickly as the data arrives.
Rather than optimizing in a vacuum and giving all the credit to the last click, you should start incorporating some form of attribution modeling into your daily routine. For a variety of reasons, the sooner you do so, the better.
Here are three ways attribution modeling can help you work smarter and more effectively.
1. Showing You the Real Picture
Just as no consumer makes a purchase decision in a vacuum, no digital channel operates in one. To evaluate each channel and campaign based solely on last-click conversions is not only short-sighted, it can limit the results.
For example, display ads typically aren't the strongest driver of last-click conversions, but an attribution model that accurately measures display's contribution can help you determine its real value. As shown in the table below, display is strong in delivering "assisted" conversions, while search is the best driver of last-click conversions.
To review, channels providing "assisted" conversions are those that appear somewhere in the consumer's purchase path but are not always the last exposure the consumer has to your brand prior to purchasing. Some channels are stronger in leading consumers down the purchase funnel (like display) while others are great at closing the sale (like search).
Were you to evaluate display only on last-click conversions, you would potentially cut its budget and miss out on the value it brings by building awareness and helping consumers move further down the purchase funnel. Decreasing display's budget could even result in a decrease in your brand's search demand.
As a 2011 study by Forrester and iProspect illustrated, consumers who see your brand's display ads are more likely to search on your brand and/or category keywords. The interaction of display and search together is more powerful than you may realize if you look at them individually.
2. Making Higher Impact Campaign Optimizations
Attribution modeling can help you avoid fixing things that aren't broken, and focus your attention on optimizations that yield the greatest results. When you see changes in performance, it is important to consider the interactions in your broader digital marketing portfolio before taking action.
For example, assume that the ROI of your search campaign has declined over the past 4 weeks. Let's also assume that you've lowered display budgets in the current period.
Were you to optimize based only on search data, you may fail to revive your campaign's performance because you didn't consider the effect that lowering your display budget could have had on demand for your keywords. As the table below illustrates, the combination of display and search has delivered fewer conversions and you now have more context about your declining ROI problem:
Evaluating combinations of various marketing channels in conversion path reports can help you optimize in ways that achieve a higher impact on your overall performance. In this case, before you spend valuable time optimizing your search campaign, you may want to allocate more money to your display campaign, and vise versa. This more holistic optimization strategy can boost performance and save you time in the process.
3. Achieving A More Unified Digital Strategy
As the two previous examples illustrate, no matter how addicted you are to last-click conversion attribution, digital channels work in combination. Once you incorporate this knowledge into your daily routine as campaign managers, you can start to create campaigns that work even better when unified. An accurate evaluation of first- and last-click conversions, as well as consumer paths to purchases, can give you more insight into the consumer experience.
If the data shows that paths including exposure in both display and search account for a large portion of your conversions, you can modify your messaging in accordance with consumer behavior.
For example, you may want to create banner ads that encourage consumers to "learn" and "compare," rather than complete a purchase on the spot. Putting too much pressure on consumers in a display ad may be the digital equivalent of a store assistant who asks if you want to buy the first product you see the moment you step through the door.
Instead, if you take a less forceful and narrow approach, but rather provide consumers with the information they need at each step of their purchase decision, you will see an increase in your overall conversions.
As digital advertising continues to evolve, campaign managers should take advantage of an evolving set of data. Attribution modeling may sound like a daunting and time-consuming endeavor, but with the advances in reporting features across multiple advertising and analytics platforms, you're no longer limited to last-click conversions.
And since you shouldn't be optimizing campaigns for impulsive shoppers, but for the vast majority of thoughtful consumers, it becomes clear that attribution models with path analysis and channel interactions are necessary for brands to attract new customers and maximize the effectiveness of their online advertising campaigns.
Optimising Digital Marketing Campaigns with Search, Social and Analytics
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