A company’s marketing team was asked to handle an email campaign to promote a video recently. An exclusive list of recipients was shortlisted for a pilot campaign. The email copy was written, the graphics were created, and email content was developed. Just before the emails were blasted, the campaign was stopped by the company’s CMO (chief marketing officer). He demanded to know the result of the campaign before it was executed.
Indeed, a marketer’s decision should be based on science not intuition. With good planning tools and rich information resource at our fingertips, it’s important to develop a hypothesis before spending effort on the execution.
In the example above, the hypothesis was that only a small amount of email recipients would respond to the campaign.
In order to prove the supposition, the CMO developed a scientific procedure to realize the outcome. He profiled the email recipients into three batches based on the relevancy of job functions against the company’s business. Using market email benchmark as the baseline, an expectation metrics was made:
Now, only 10 people would probably be interested to watch the video, which the company has spent money, time, and effort to make. The CMO has to decide whether the effort was justified to achieve a handful of people’s attention.
After considering the pilot list was just a start, the CMO chose to adjust his expectation. He also gave his team a realistic KPI (key performance indicator): to set 20 percent CTOR (click-to-open rate) as the beginning of the conversion funnel for the email campaign.
The whole point of sharing this case is: managing our expectations in marketing is very important aside from knowledge, technique, and practice. It’s always easy to pick a number for KPI. However, if the KPI is destined to fail, then it’s a sad goal for everyone. (On the other hand, to have no KPI is a senseless goal.)
In fact, you can develop any expectation metrics for many marketing scenarios. At this year’s SES conference in Shanghai, I demonstrated an analytics model to narrow down the keyword engagement for website content performance and generated some practical insights.
As you can see from the above metrics, after learning the baseline for the keywords to conversion scientifically, you can either adjust your expectation or lift your goal by enhancing your content relevancy.
To know our limitation and set the right expectation are two elements that should be included in our marketing practice. After all, a scientific and happy marketing result is what we should aim for.
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