The six data sea monsters can combine in many awful ways to create harrowing problems for marketing departments and leadership. For a refresher on what these monsters are, check out this post.
As you gather ’round the turkey this Thursday, consider saying a brief thanks that none of these problems are plaguing your marketing department. (Or if they are… let’s talk.)
So when the digital marketing team ran a test on a portion of the website that had already been migrated, the results were predictably awful. This lack of communication between the teams is one of the hallmarks of a non-data-driven culture.
Furthermore, the digital marketing team was using 10 different tools to run their tests, which meant that up to 10 platforms were hitting the website at any time. These tools each had unique ways of measuring and reporting the test results, so any benefit the tool stack might have offered was offset or completely negated by the confusion and work involved in interpreting the tools’ results.
None of the remaining members of the digital marketing team knew about:
Fortunately, we were able to show the rest of the team where to find their keyword-level data and how to use it, but this experience underscored how dangerous it can be to have only one or two people who understand company data.
In general, brand keywords target existing customers while non-brand keywords target new customers. This company’s paid search portfolio revealed that 52%of their total spend went to brand keywords. (We recommend no more than 10%.)
CPL for brand keywords can be a vanity metric, especially when calculated as part of the overall CPL. Brand terms’ costs are lower due to low competition and search volume; non-brand terms are more expensive. The average CPL for this company’s brand keywords was $75, well below their average CPL of $110.
By comparison, the average CPL for non-brand keywords was $175—but even with the higher costs, these keywords were utterly essential for growth. By leaving only 48% of their paid search budget for non-brand keywords, the company was starving the keywords targeting new customers and thus actually sabotaging its growth.
A stronger understanding of paid search data and a culture with regular conversations about interpreting data would have prevented the digital marketing team from sinking so much ad spend into brand keywords.
That said, the company was quite happy with the click-through rate (CTR) of some of their keywords. While their average CTR was only .09%, the CTR for many of their low-impression keywords averaged .60% – almost 7 times the aggregate CTR.
When we examined their keywords to address the company’s issues with impressions, we found that their wide-net approach to keywords had led to deceptive metrics with potentially devastating results.
Many of their low-impression/high-CTR keywords had nothing to do with what the company offered:
Yes, these keywords were delivering clicks and impressions, but they weren’t the impressions and clicks that would turn into customers and revenue. Worse, the company was spending almost 25% of their paid search budget on these non-relevant keywords.
We suspected that so many inexplicable keywords had come as a result of using a keyword planning tool, then adding all keywords in a group without reviewing each keyword individually. A more in-depth understanding of paid search data and tools would have prevented such a significant waste of spend.
We hope this trip to the Davy Jones’s Locker of digital marketing hasn’t been too harrowing. Remember, there are steps you can take to correct these problems and prevent future ones.
You might have noticed that many of these problems are based on a lack of data-driven culture. Next week, we’ll show you how to build just such a culture, from hiring on up.