As a marketer, acknowledging that you’re not perfect can be challenging. Often, this admission is relegated to the realm of the apologetic “We’re sorry” email sent after a significant error. Regrettably, the apprehension of making mistakes is holding capable marketers from delivering effective early-stage messaging strategies for clients. Instead, they opt for inaction, demanding almost flawless data sets. Consequently, clients end up with generic communications that are unlikely to align with what they’re trying to achieve.
Why am I talking about this? Recently, I had a client meeting to talk through conversion issues and a lack of overall effectiveness in their early-stage messaging. They wanted to encourage users to move down the funnel and closer to a purchasing decision, but they didn’t have enough data to feel confident about personalisation at that stage of the campaign. My advice: stop trying to be perfect and loosen up the reigns.
In this article, we’re going to explore the potential for compromising on data sets and how marketing businesses can make informed assumptions that will result in early communications effectiveness.
The case for compromising
Let me start with a caveat. Clean data is important, sometimes it is necessary—but that does depend on the purpose of that data. For example, think of an e-learning course offered by a higher education institution. Institutions do sometimes receive general enquiries where the desired course and enrollment year aren’t given, targeting and personalisation would be extremely poor at this point. Marketers cannot get personalisation wrong here because mistakes or assumptions on core information like this damage your brand and the authority of your marketing messaging to the affected consumers.
But what about indirect data to help us target and group? In higher education, your enquirers will be telling you what course they might like and a potential year they may join very early on, but this won’t be set in stone until they have completed the application process. This could be months or years later. By then, we’ve lost the ability to talk to and influence them with the most relevant possible content and language during that time.
Assumptions in action
Because marketing teams don’t even have a rough date to work towards in terms of a countdown, knowing exactly when the student may join the university and their potential marketing cycle ends, the best they can do is provide generic nurture with content for the course and hope to influence them over time. As the length of nurture needed is unknown, stronger content must be spread throughout the journey and will not be focused on the key decision-making times in that individual’s buying cycle.
An example here would be to use the general data given early during the enquiry phase. For example, like a course of interest and enrollment year of interest and use it to personalise the customer journey well before the ‘perfect’ data arrives. From the year of entry, we can assume that most students will be in the September intake (estimated to be 98% of undergraduate and 95% of postgraduate applicants). This gives us a countdown date to work towards that will dramatically improve the journey for almost all users while we nudge them to convert further and give additional information like which intake (September or January) they are interested in. At any time, that new information can be added and can shift our messaging and journey drive through automation.
So, to sum up: background processes that take wide, unfocused data like “Year” and use informed assumptions to drill down to actionable data like “date” can be applied across multiple industries and empower marketing teams to deliver messaging and content in a more effective and efficient way with no risk to brand equity. Indeed, we’re not over promising, and we’re not misunderstanding clear signals. Instead, we’re taking people early in their journey and doing our best to be as relevant as possible to their needs/wants with what they have given us.
This is progress, not perfection.
