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Anybody in paid advertising will know the importance of making the right decisions. Pay-per-click (PPC) campaigns can fly or falter on the basis of a few key choices. If you make the wrong ones, it can rapidly drain your budget and leave your campaign on the brink of failure. Luckily, a data-driven attribution model can keep you on track so that you keep bad PPC decisions to a minimum.
Let’s explain how you can harness the power of data to gain a better understanding of your PPC campaigns.
For data analysts, the term ‘attribution’ can mean a number of things. It could refer to website traffic sources or it may be the various contributing factors in a conversion.
In 2014, Google acquired the multichannel attribution solution, Adometry. Within that program was an enterprise-level suite called Attribution 360. Since the merger, a simplified version has been released, which is known as Google Attribution. This has several integrations, including:
Research from Nielsen indicates that the average customer will make six visits to a website before they convert. If you aren’t using some form of attribution model, many of the decisions you make about your PPC campaign will be based on incomplete information.
In the past, PPC managers considered the last ad the customer clicked on to be the key factor in securing a conversion. However, with the rise of data, we have greater power to dig deeper and find the truth.
By using a data-driven attribution model, credit is based on the ways people search for your solutions. It considers every touchpoint in the customer journey. Ultimately, this model gives you a much clearer perspective on how people become your customer, as you can learn a lot about your campaigns, ads, and keywords.
So, just how does this type of attribution model help people in paid advertising? Well, let’s take a closer look.
An integral aspect of mastering PPC advertising is understanding your customer’s journey and purchasing behaviors. How do they convert?
With PPC ads, you may attract users that search in different ways before they become a new lead. For example, they could:
In many cases, someone might start by searching for informational words, and then move on to product terms before eventually, they will start searching for specific brands. They may not convert on the first few searches, and typically they won’t convert until they are clicking on brand terms.
Without a data-driven attribution model, you may think that it’s best to invest in brand terms, and decide to reduce your efforts and budget on informational and product terms. Unfortunately, this would be a mistake, as these are key stepping stones on the road to conversion.
A data-driven attribution model can help you discover traffic quality problems in your campaign. By analyzing the data, you can look for user search queries that have:
Any user search query that satisfies both of these conditions can be considered a ‘negative keyword’, as it is attracting irrelevant clicks. These users are invariably people who are not interested in what you have to offer, making them unqualified traffic that is simply wasting your budget.
A positive return on investment (ROI) is a top priority for every PPC advertiser. When you are using a data-driven attribution model, you can quickly see any search terms that have:
As soon as you find search queries that satisfy both of these conditions, mark them as positive-ROI keywords — they are effectively generating revenue.
Quite often, PPC managers will have several ads running at the same time. There is a good chance that one ad will be generating more leads than the others. Unless you have a bottomless pit for a budget, it’s wise to always optimize your ads so you can reduce any wasted spend.
You can consult your attribution model to determine your top-performing ads. You may figure out that some ads are not worth the expense. If Ad #1 is significantly better than Ad #2, just pause #2 and enjoy the gains from #1 alone.
In a similar fashion, as you move from the Ad level to the Campaign level, you can compare your campaigns to gauge performance from one to the next. Imagine your CPA in Campaign #1 is currently below your target. If Campaign #2 is above your target, you can shift your budget to maximize the ROI on #2, and reduce wasted spend on #1 at the same time.
Projections from leading sources in marketing and tech expect the Big Data market to grow beyond $100 billion in the next decade. Data is going nowhere – it is here to stay.
For people in paid advertising, it’s a logical step to embrace the power of data. The customer journey continues to evolve, and their interests and actions aren’t always easy to predict.
However, with a data-driven attribution model at the heart of your PPC campaign analysis, it’s much easier to understand. With the deeper knowledge of your campaigns, ads, and customers that data provides, you get the actionable insights that guarantee your budget will go a lot further in the future.
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