In my last post I examined how data can be used within social platforms to deliver more targeted advertising without relying on organic reach. Facebook and Twitter lead the way, with proprietary ad interfaces that enable marketers to easily interrogate the abundant data within those platforms and combine online personas with other data sources.
Custom and Tailored; Categories and Audiences – different words, same meaning
Facebook’s Custom Audiences and Twitter’s Tailored Audiences have been around for a few years now and readily incorporate advertiser’s own email data (emails) for retargeting or prospecting using lookalike modelling. Last year Facebook aligned itself to a new ‘people-based’ approach by launching Partner Categories in the UK: off-the-shelf targeting segments provided by data suppliers such as Acxiom and Datalogix. In the next couple of months Twitter will launch Partner Audiences in the UK and which aims to do the same.
By utilising these tools campaigns can now benefit from the direct relevance and accuracy associated with 1st and 3rd party data, for instance:
- An FMCG company may want to connect with households enjoying high equivalised incomes and inhabited by families with children;
- An insurance company might want to target individuals who have a contents policy coming up for renewal;
- An automotive dealer can pinpoint people who have an older model vehicle with a message about an upcoming sale.
However, this is the tip of the iceberg when it comes to using data more effectively. Many agencies and brands are now seeing tangible benefits when first and third party data is combined to create new audiences.
1st party data –the real value of CRM in social
It’s worth taking a moment to define what is meant by 1st party data – to be clear, this is data belonging to you and your brand as opposed to 3rd party data which belongs to somebody else. Specifically, true 1st party data (CRM – customer relationship management) will have been collected throughout the life of a brand relationship across all channels (not just social) so a consumer will have many known attributes with hundreds of combinations for a potential path to purchase. 1st party data is not limited to pixels who have visited a website or emails generated from brochure downloads.
Auto, finance and retail industries, having direct consumer to brand interaction, are rich in CRM 1st party data and will often have significant budgets dedicated to the collection and management of this data. This has led to Custom/Tailored Audiences being used as a cost-efficient targeting tool for those particular verticals where KPI’s are CPA driven. On the flip side, FMCG and Consumer Electronics tend to be data poor due to a non-direct relationship between consumer and brand. This has resulted in a much greater use of third party data in targeting using Partner Categories as a substitute.
The MCA – More than another digital acronym
Within the extensive armoury of Facebook’s product offerings is Managed Custom Audience (MCA). Both Partner Categories and MCA’s involve Facebook’s data partners, but the two are quite different: Partner Categories are pre-constructed segments without customisation per advertiser. MCA is a managed service where a data partner like Acxiom builds a unique Custom Audience on the marketer’s behalf, often using a combination of 1st party and 3rd party data.
Here are three examples:
- An FMCG brand with no 1st party data can create a prospective social audience using research based proxies (TGI, YouGov, CCS) that indicate demographics with an affinity towards a particular product AND be receptive to social media advertising. Small panels (<20,000) can be modelled out into larger, scalable offline data pools (> 47 million) and matched into Facebook or Twitter. Technically, this is bringing together two pieces of 3rd party data to create a strong modelled prospective audience.
- A financial services provider with a database of loyal credit card holders can use that data as an exclusion file or build insight that enables modelling of lookalikes against 3rd party data. Demographic lookalike modelling (income, lifestage, age – offline behaviour) may provide a very different prospective audience to social lookalike modelling (likes, shares, interests – online behaviour).
- An automotive manufacturer with a globally hosted CRM incorporating variety of marketing permissions may not wish to match that data directly with a social platform due to privacy concerns. They may instead enlist a partner to securely manage that data. They may also wish to employ advanced recognition techniques that enables matching on multiple bits of PII (full name, address, previous address, postcode, email, alternate emails….) across a wider variety of publishers with logged in environments.
All of these scenarios require a Managed Custom Audience built by a trusted data partner, independent of the social environment where a campaign is activated.
Avoiding the cost per click trap
Some media buyers have experienced challenges with layering on these additional datasets: the more specific the targeting, the more expensive the audience, particularly on performance-based campaigns which are optimised towards clickers.
However, true ROAS can only take place over a long period, where social is just one of many touchpoints in the consumer journey. This is a hard concept to grasp in a digital ecosystem where measurement is enshrined with the quick wins of impressions, clickthroughs, likes and retweets.
In the next post I will pick up these themes to show how we can now use offline data to truly measure the effectiveness of a social campaign.