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1st Party Data Enhancements to Marketing Attribution in SAS Customer Intelligence 360

Started ‎05-21-2021 by
Modified ‎02-28-2024 by
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It’s amazing to realize that over a 100 years later, the old marketing lament “I know half of my marketing budget is wasted; I just don’t know which half” still rings true today for countless brands. Industry research covering B2C marketing decision-makers continues to harp on the challenge of measuring marketing results. Although many companies address this challenge with measurement and optimization tools, the immense amount of 1st party data awaiting to be harnessed remains under-exploited.

 

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If you’re like me, you’ve grown fatigued and skeptical of the term AI constantly being used within martech industry presentations and vendor solution descriptions without clear explanation of its intent to solve business problems. So, let’s move past the buzz words and provide a definition for performance measurement.

 

  1. It requires gathering data (and from our perspective, 1st party data specifically) about budgetary spend and campaign management tactics.
  2. After recording what your brand does, engagement metrics from consumers who have been targeted is the second piece of the puzzle.
  3. Subsequently, there are many methods to attribute conversions (whether those are sales, donations, subscriptions, etc.) using statistical analytic techniques.

 

The ultimate objective here is to quantify with clarity the financial impact of attributing marketing activities on performance. With that said, let’s take a moment to reflect on our new data-deprived marketing ecosystem. Data deprecation will dramatically shift common, well-adopted approaches to customer acquisition, upsell and retention. Now marketers need to assess and potentially adapt their practices towards transparency and choice, value to the customer, and relationship depth. Three areas for consideration include:

 

Building out zero- and first-party data assets - As inferred data gets harder to acquire, brands should prioritize their owned data assets and direct relationships with consumers. This includes collecting first-party interaction data as well as zero-party data — information that consumers volunteer about their preferences and interests, usually in exchange for a benefit or perk. From our experiences and customer projects here at SAS, it is extremely rare to see any type of data source out-perform 1st party data. In other words, 1st party data has always been and will continue to be king, and brands should double down on extracting customer insight from their owned data instead of trying to infer things like behaviors, context, and intent through sketchy tracking mechanisms.

 

Test alternative targeting approaches - The writing is on the wall for DMPs reliant on third-party cookies. Many brands have opportunities to invest deeper into the adoption of alternative approaches like first-party data-driven look-a-like models, segment-based audiences, or contextual targeting methods to connect relevant ads in higher performing touchpoints to specific audiences.

 

Expanding your marketing analytics strategy - Gone are the days of using cookie-based marketing attribution models to measure ad and touchpoint efficacy. But marketers don’t have to regress back to last-touch or heuristic measurement models. Customer identity data enables and maps out brand interactions & exposure across the journey as a foundation for people-based measurement. Additionally, by using campaign-specific contact and response data, as well as inbound interactions & targeted impressions, advanced measurement models can thrive.

 

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The theme of rethinking customer identity translates to how brands should assess the impact of each data deprecation dimension on their current identity resolution strategy to understand their options for offsetting the loss of critical identity keys. The most likely scenario is that brands will need to adapt identity resolution processes and reconstruct a precise, scaled, and compliant identity graph. The availability and quality of your 1st party data ingredients feeds directly into the potential of AI-based marketing…

 

As insights evolve, and brands get smarter from data and analytics, when we think about touchpoint interactions, brands will always have a limited monetary budget creating competition between different marketing activities and touchpoints. Thus, the more a brand spends on one channel, the less it can spend on another.

 

Visualize a scenario where a customer receives a viewable impression of a display media ad, and then uses a search engine later that day to visit your brand’s website and make a purchase. Did the customer make the decision because they remembered seeing the ad? In another scenario, the customer receives a targeted email, and then receives impressions of an A/B test and targeted content of the website’s home page hero spot. In both cases, there is a question as to how much the first activity influenced the second and how to accurately measure this.

 

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From a measurement perspective, weights of importance can be allocated to help marketers assess the impact of customer journey touchpoints and targeted interactions.  As the questions evolve with increasing sophistication, the focus adjusts back to diversifying the data. Attribution and performance measurement cannot be solved with traffic sources alone.  With more data, we can derive deeper insight from a greater level of granular detail regarding customer journeys.

 

  • What is happening when customers interact with your brand while they are on your website or mobile app? When they are interacting with your outbound communications like email…
  • What are they doing? What signals are they sharing?
  • These could be page views, the videos they are watching, the content they are downloading, the forms they are filling out, the time they are spending within certain categories of your customer experience.

 

By incorporating granular data signals like these into performance measurement, it will change the weighting of importance of journey-based traffic source originations and help increase the accuracy of explaining who is converting at a higher or lesser rate. For readers new to the subject of attribution and journey analysis, here is a recommended white paper

 

Now, let's jump into the presentation and technology demo showcasing new attribution enhancements within SAS Customer Intelligence 360.

 

 

Learn more about how the SAS Platform can be applied for customer analytics, journey personalization and integrated marketing here.

 

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Last update:
‎02-28-2024 01:07 PM
Updated by:
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