Does the value of data for marketers ebb and flow or is it constant, best measured against a specific undertaking?
Should we consider third-party data bloodied and conclusively on the ropes, or perhaps on the threshold of a refashioned second act?
With questions about data framing so much of the future of marketing, I figured it may be helpful to hear from Merkle/CXM, which describes itself as a “leading technology-enabled, data-driven customer experience management (CXM) company.”
I recently asked Merkle/CXM America president Michael Komasinski to share his thoughts on the value of data.
Paul Talbot: Marketers have been gathering and leveraging data for more than a century. What’s taking place right now that you find particularly significant?
Michael Komasinski: There are two big things taking place today that are driving the industry toward a first-party data paradigm. First is the demise of the third-party cookie which is changing the digital advertising ecosystem and the attribution system that underpins the buying, selling and measurement of ads.
The other is privacy and regulation which drive a trend toward higher quality, first-party data that is gathered through more transparent value exchanges around content, experience and commerce.
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Talbot: How should marketers manage their expectations when it comes to the process of engaging AI to provide useful insights?
Komasinski: AI and machine learning have given us powerful tools that can very quickly deliver powerful analytics. That being said, outputs that are produced by AI platforms need to be interpreted and made relevant to the business problem at hand.
The combination of AI and human intervention is critical. Marketers need to ensure they have data scientists that are familiar with the AI platforms and tools and can extract the most value through the combination.
Talbot: To what extent, if any, has data, particularly purchase data, changed the role of research?
Komasinski: Purchase data is critical, as it gives marketers a direct line into what customers want and what they are willing to spend. Fueling research with this data gives marketers the ability to extract preferences and tie them directly to actual behaviors.
Additionally, having purchase data allows testing cycles to be much shorter and across many audiences.
This is why eCommerce is such an effective platform to test, learn and scale. In some cases, this leaves research to tackle the attitudinal and motivational viewpoints of the consumer.
Talbot: Where does the correct interpretation of marketing data most often tend to go sideways?
Komasinski: There are two areas where marketing data can be misinterpreted.
The first is when the wrong question is being asked in the first place. Data can be very powerful in answering a lot of questions, however when trying to solve the wrong problem, marketing can head in the wrong direction.
The second is having a poor test-and-learn strategy.
When test design isn’t thoughtful (low sample sizes or biased samples, for example) the data and subsequent interpretations will be skewed and lead marketers to implement poor decisions.
Talbot: How do you see the value proposition of data for marketers evolving?
Komasinski: First, second and third-party data, obtained in a consented fashion, are becoming increasingly more important as shifts in the marketplace are rendering current approaches unsustainable.
Marketers increasingly see that investments in customer experience only work well when they are fueled by accurate, clean and easily accessible data.
Strategies for personalization and for experience excellence in sales and service quickly expose gaps in data hygiene and availability, so more and more marketers are having to become data and data platform savvy.
Talbot: Any other insights you’d like to share?
Komasinski: Marketers are going to have to focus on enhancing customer experience across all touchpoints of the customer journey. This is only possible through the ability to connect data across the customer journey, as well as having the right team in place to use all of the data to define, create and execute unique experiences for different audience segments.
The three critical components to achieving this are:
- A robust identity management solution.
- A cloud-based data management environment that consolidates data.
- A data science team that can take advantage of the data through AI and human intervention.