Customer Look-alike Targeting & Social Re-Targeting with SAS Customer Intelligence 360
Marketers, sales teams, and brands are constantly chasing new audiences to target. This is only natural, as the more you grow your brand's business, the greater your success. It is the main objective of every brand's mission.
But it’s not easy targeting new segments, especially those beyond what your brand normally encounter or engage with. Every company has their “ideal” customer. Often, it’s an individual who has met your micro- or macro- conversion goals, such as a purchase. But sometimes it is difficult to find the right consumers. When your brand finds them, the intent is to hold on to them. Customer lifetime value trends positively through relevant nurturing, relationship maintenance, upselling and loyalty tactics.
Not only do you want to hold onto your valuable customer segments, but you also want to find consumers who think or act like them. This grows your brand's reach so that you have a much larger possible group of prospects in your customer experience funnels.
Look-alike targeting identifies customers and prospects with one or more differentiating attributes regarding their observed behavior. One example of performing look-a-like targeting in SAS Customer Intelligence 360 is to perform a no-code approach leveraging algorithmic decision trees to identify correlated predictors & high likelihood segments which can be used for marketing orchestration. No, low and high code approaches across supervised, unsupervised & deep learning support customizable analysis methods and any of these models can be called via API (SAS, Python or REST) for use in targeting.
Because SAS Customer Intelligence 360 captures first party digital data (web, app, campaign impressions/contacts, conversions, etc.), examples of using targetable data can be applied to:
- Anonymous web and mobile visitors
- Future digital property users
- Retargeting on Google & Social ad platforms
Generally speaking, remarketing is summarized when a user interacts with a brand’s website or digital property, leaves the interaction without meeting a business goal, and the brand targets this individual (or cookie) with paid media advertising in an effort to get them to return. The main objectives of retargeting typically are:
- To acquire customers.
- Build brand awareness.
- To acquire market share from the competition.
A few perspectives we have on retargeting at SAS include:
- Search, display and social advertising represent the majority of organizational paid media spend annually.
- First party data continues to be king!
- Third party data has a place, but the ability to use first party data and context provides a competitive edge.
- Look-alike targeting and social re-targeting provides a mechanism to influence search, display & social.
Figure 1: Customer Look-alike Targeting & Social Re-Targeting with SAS Customer Intelligence 360
How does SAS add unique value to this marketing process?
When considering the customer journey within the perspective of re-targeting, practitioners will likely recognize and admit the data available on advertising platforms for targeting and filtering could be improved with first party data. But what about predictive models, algorithmic segmentation, and customized applications of machine learning? This is where SAS connects the dots and brings more precision to the usage of valuable media budgets.
Within the subject of customer journey activation, let's walk through a presentation and demonstration together in the video below addressing the following questions:
- How does SAS deliver customer look-alike targeting & social re-targeting?
- What data and algorithms does SAS use?
Learn more about how the SAS Platform can be applied for customer journey activation here.
