Search advertising has come to dominate performance marketing over the last decade, with advertisers seeing amazing returns from targeting messages at consumers based on their intent.
If a consumer is searching for ‘best golf clubs’ it’s a pretty safe assumption that they’re in purchasing mode and likely to be interested in an advert promoting golf clubs.
But, any search marketer will tell you that one of its weaknesses is that you have to use a degree of guesswork when it comes to audience characteristics.
In the example above, if you knew the consumer was a female then your advertising creative would be far more powerful if it promoted just clubs for ladies. The trouble with search is that unless someone is very specific in their search term, you’re forced to make assumptions.
This weakness has been highlighted as relatively new kids on the advertising block like Facebook have emerged and started offering more detailed information about audiences than has ever been possible before.
So wouldn’t it be great if search marketers could get the best of both worlds: intent combined with audience data? What a powerful picture that would create. Well, now they can.
This year, I expect to see a rise in the use of audience-orientated search marketing, making intelligent use of all kinds of different big data in the process.
Here are four near-term applications of audience-oriented data that have hugely significant implications for performance marketers.
As a performance marketer, you can have a pretty good idea about your customers based on what keywords they type and conversion tendencies. This means when you create search campaigns you can build a picture of intent, perhaps without knowing you’re doing so.
For example, if a travel company creates a ‘budget travel’ campaign and a ‘luxury travel’ campaign, you could make assumptions about the type of user who will choose one option over the other.
Using additional data, such as age, marital status and household income – to ‘flavour’ these insights – will either confirm or challenge your assumptions.
It might be the case that the user you expect to gravitate to a luxury holiday may have a lower household income than you expected because they’re an aspirational buyer. Therefore, you might have to change your advertising strategy.
Knowing more about the type of customers you’re going after could also make you change your media bidding strategy. Granted, the type of customer is important but equally important is the value of the converted sales.
It’s vital to know the likely lifetime value of a client based on identifiable traits. If, for example, you discover that people aged fifty are more valuable than those aged twenty then you should adjust your media budgets.
This means you can translate the concept of ‘buying a demographic’ used in display into your search activity, bidding higher on keywords that are more likely to drive conversions from more valuable people.
With search, retargeting users has only recently become possible via Google Remarketing Lists for Search Ads (RLSA), but it’s already dramatically changed the landscape.
In a basic approach to search retargeting, you can have two different strategies for dealing with just two audiences; people who have been to your site before and people who have not.
You may decide that users who have already visited your site are closer to buying and therefore bid higher when that audience searches for a relevant keyword.
However, the potential targeting options are far greater when you throw first or third-party data into the mix.
Knowing the age and gender of a potential customer may make you adjust your creative or bidding strategy when retargeting that individual. Audience data enables this level of granularity and these same principles can also be applied to display and social advertising.
As you get smarter about understanding existing customers, it becomes much easier to understand which types of users have a higher chance of converting into new customers.
Algorithmic tools have made it possible to not only analyse a category of existing customers, such as ‘budget travelers’, but also to identify similar categories of the same demographic, such as ‘backpacking travelers’ who could convert at a comparable rate.
With this level of intelligence, you can very quickly generate display or social adverts targeted at these similar categories and acquire more like-minded customers.
Who is the audience?
Linking search to customer intent will continue to be incredibly effective at producing fantastic results for digital advertisers. But the ability to couple intent with audience-oriented data is redefining how marketers engage with users.
In short, it’s no longer enough to look at just user intent and remain competitive in today’s digital world. Marketers and advertisers must harness new technology and techniques, and understand how to implement them, to win the battle for online conversions.