Target audience vs Relevant audience. The need to move towards the latter.
Definition:
Target: a person, object, or place selected as the aim of an attack.
Audience: the assembled spectators or listeners at a public event such as a play, film, concert, or meeting.
Target audience: a particular group at which a product such as a film or advertisement is aimed.
Relevant: appropriate to the current time, period, or circumstances; of contemporary interest.
So what is the difference between target audience and relevant audience? In simple terms, considering the definition of “relevant” a relevant audience is more likely to not just notice your ad, but also act on it. Reason being you catching him/her at the right time/interest. The target audience can be assumed as people fitting into the profile of people that can be our customers, whereas the relevant audience is within the targeted audience, those who really have the potential / affinity / capacity to be our consumer.
But since relevant audience is usually a subset of the target audience, there seems to be no set rules or guidelines to narrow down on these group. As a result result,ads are targeted expecting them to be relevant. Hence the spillover across most of the platforms – which can go from 5% to 80% in case of improper setup.
So how do we reach this relevant group?
Consider a scenario where the agency is targeting a set of users to promote the launch of their new car. Let us assume that visits to the website is the objective. From whatever I have seen in the past year, KPIs are not set. So typically, the plan may include to bombard users visiting auto related websites, forums, etc. But at the end of the week or sometimes at the end of the campaign, the report reveals that x number of clicks happened translating into y visits at a bounce rate of z%. But all these numbers don’t make sense if they are not defined.
So instead, based on the previous learnings if the KPIs are defined, instead of doing a post mortem of the report, using a 3rd party tool probably in conjunction with the existing analytics tool, a real-time or at least a daily analysis of the ad report vs the incoming traffic behavior is mapped, then one can clearly see who is relevant and what % of the target is currently relevant.
Based on these learnings, the media plan/targeting can then be optimized get more of the relevant ones than just the target.
A funnel needs to be created where learnings are applied to tweak the funnel after each campaign. So right from (a.) age, gender and city targeting, to affinity and interest filters, with (b.) additional in-market segmenting, and in some cases (c.) advanced filters like travelers, premium or luxury shoppers, workplace or proximity to retail stores, etc. needs to be applied. All this must have sophisticated tools used to check on the websites that are showcasing the ads with poor Viewability, Inhuman traffic, Ad Fraud, etc.
All the above will ensure that the campaigns are reaching out to the relevant audiences, on relevant platforms where the ads will get to see some kind of interaction or exposure to those who really matter for the brand, leading to better RoI and effectiveness of the campaign.
To quote some experiences, a cosmetic campaign’s targeted for females for Valentine’s day ended up having more than 80% of its users to be males. If this learning was used the moment it was identified, the campaign targeting could have been changed to leverage this. But unfortunately like most cases, this also turned out to be a post campaign analysis with blame being thrown on both sides of the client and the agency.
Imagine the money/opportunity lost in this example. Unfortunately, a big chunk of digital advertising spends is not being utilized effectively because of this lack of “relevant targeting”.
At What Clicks, we have developed a simple tool that in conjunction with IRA and Google Analytics will help you identify not just relevant audience who are coming to your website from an ad, but also bots and other non human traffic which are contributing to big chunk of ad fraud in recent years.
This is aimed at tackling ad fraud at your site level and help track back the source so that you can take necessary decisions to make sure your ad money is well spent.
Co-author: Toshal Shenai
