The core of great B2B marketing communications is relevance. If your message is relevant to the audience you are communicating with, it will resonate, if not, no matter how well written it is, it will not resonate. However, the key to relevance is understanding the interests of each prospect so that a marketing message can be delivered accordingly.
Within your universe of prospects, there may be only a small percentage of them at any one time who are the precise buyer role and executive level, at the particular stage of the buying process that your marketing message ideally targets. However, many marketers fall into the temptation to broaden out their messaging to a larger universe in order to get an overall increased effect. Whereas this may seem like a good idea, as it increases the overall campaign results, it can have the unintended effect of alienating a large segment of your audience as we discussed recently in looking at the idea of neutral results in a marketing campaign.
Equally importantly, however, is the fact that a poorly targeted message can lead to highly inaccurate marketing measurements due to the overall effect of a larger population. For example, let’s look at two marketing messages, for comparison. Message one was highly relevant to VPs of Marketing at the Solution Discovery phase of their buying process (2% of your database), and achieved a stellar 30% response rate in that segment. Message two was relevant to Managers of IT at the Awareness and Education phase (10% of your database), but only achieved a 8% response rate in that segment.
For the sake of this example, let’s assume that the general population of your database, outside of the segment to which each message was relevant, responded equally poorly with a 1% response rate.
If this campaign was targeted to the entire database, you can see quickly how the results can show a counter-intuitive message. Message one, would show a 30% response rate in 2% of your database, and a 1% response rate in 98% of your database, for an overall response rate of just 1.58%.
Message two would show an 8% response rate in 10% of your database and a 1% response rate in 90% of your database for an overall response rate of 1.7%. If you look simply at the raw numbers, without diving deeper into the analysis, you can see how the final results will be misleading and will show the reverse of what is true. Clearly, it is the definition of the list, rather than the message success itself, that is causing these results to appear as they do.
Only by first looking at the targeting of your list, including both the fit of the individual, and the stage they are in their buying process, can you successfully show analytics that correctly reflect how effective each message was within that target psychographic or demographic segment. The results might be surprising.
Within your universe of prospects, there may be only a small percentage of them at any one time who are the precise buyer role and executive level, at the particular stage of the buying process that your marketing message ideally targets. However, many marketers fall into the temptation to broaden out their messaging to a larger universe in order to get an overall increased effect. Whereas this may seem like a good idea, as it increases the overall campaign results, it can have the unintended effect of alienating a large segment of your audience as we discussed recently in looking at the idea of neutral results in a marketing campaign.Equally importantly, however, is the fact that a poorly targeted message can lead to highly inaccurate marketing measurements due to the overall effect of a larger population. For example, let’s look at two marketing messages, for comparison. Message one was highly relevant to VPs of Marketing at the Solution Discovery phase of their buying process (2% of your database), and achieved a stellar 30% response rate in that segment. Message two was relevant to Managers of IT at the Awareness and Education phase (10% of your database), but only achieved a 8% response rate in that segment.
For the sake of this example, let’s assume that the general population of your database, outside of the segment to which each message was relevant, responded equally poorly with a 1% response rate.If this campaign was targeted to the entire database, you can see quickly how the results can show a counter-intuitive message. Message one, would show a 30% response rate in 2% of your database, and a 1% response rate in 98% of your database, for an overall response rate of just 1.58%.
Message two would show an 8% response rate in 10% of your database and a 1% response rate in 90% of your database for an overall response rate of 1.7%. If you look simply at the raw numbers, without diving deeper into the analysis, you can see how the final results will be misleading and will show the reverse of what is true. Clearly, it is the definition of the list, rather than the message success itself, that is causing these results to appear as they do.
Only by first looking at the targeting of your list, including both the fit of the individual, and the stage they are in their buying process, can you successfully show analytics that correctly reflect how effective each message was within that target psychographic or demographic segment. The results might be surprising.
1 comments:
It always blows me away to see how many companies market without doing research before hand, and never follow up to see if their marketing was effective. No wonder we have such wayward campaigns.
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