It is vital to look at the traffic of a brand’s website, have an idea of that brand’s customers, and make a detailed analysis.
- What are the age and gender distribution of the visitors?
- What is the seasonality within a day, week or over the year?
- What is the device impression share?
In fact, the effectiveness of these features is entirely up to you. Because although these data are specific, how you access them and how you choose to use this data is a decision you have to make. In short, it will be necessary to interpret and classify these data in a certain way. Eventually, you have certain features for your potential customers. These results are the fingerprints of your target users.
If this topic sounds too abstract and difficult to understand, let’s explain it with an example right away. Let’s say we run a B2B business. Suppose we see that the traffic on the website of the brand differs significantly on weekdays and weekends. Actually, this is an issue to be expected, not a surprise for us.
Let’s expand the benchmark and conclusion a little more. Like (Impressions Tuesday + Impressions Wednesday) / (Impressions Saturday + Impressions Sunday). We can call this “job demand”. For our B2B environment, the higher the business demand, the better. That’s why we wish it high.
Search for the brand fingerprint in your non-Brand data
Now let’s get to the exciting part of this whole story. What does your Non-Brand traffic look like? Under normal circumstances, it is different from the original version because non-B2B customers also search keywords. We use the same business demand calculation to find the different account levels we can bid or target. Below you can see two various examples related to this:
- Start by scoring keywords based on business demand. It is a great advantage for us that a small number of impressions is sufficient to score a keyword. By looking at conversions, you need hundreds or thousands of clicks – especially in the long-tail, it’s challenging to get enough samples. If you round the calculated business demand to, e.g. one digit, you get pretty large groups with hopefully a significant gap in performance. We work hard for offers that rely on long-tail keywords and can create great deals. “Feature engineering” is critical in terms of both classifying and developing campaigns that have the effect we want.
- Now how about doing the same for location targeting? It will actually be a very similar process. Under normal circumstances, the sample size is too low even to adjust city-level bidding based on conversion data alone. At this point, how we use the classification becomes essential. By making the classification based on impressions, we can go down to the e-mail level. As we just mentioned, the process is the same: for each postal level, we get a score – we can group conversion data by the calculated score. A straight forward action could be:
Bid Adjustment on Postal = ValuePerClick(Postal group)/ValuePerClick(Total)
At this point, there are some things you should pay attention to and definitely not forget. The fingerprints created in the two examples we just covered were made based on a single feature. You can find many more features depending on your business and preferences. In most of the business accounts, we have seen a completely different performance when the keyword selected according to user demand is mixed with other segments. So don’t be surprised if you see a different performance. This is entirely different depending on what you expect from this procedure.
When you define these micro-features for users, you can offer them with highly centralized and aggressive opportunities. Here, how you categorize the data you have and how you stick to your plan is very important. In this way, creating new campaigns and developing new offer types is a highly beneficial approach for gaining new customers. You can click here to be informed about practical solutions like this.
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