The Digital Marketing Podcast

Episode Title: Adobe Target – Automating Hot Tricks of Data Science
Hosts: Kieran Rogers, Daniel Rowles
Guest: Kevin Lindsey, Director of Product Marketing, Adobe Target
Date: September 11, 2016

Episode Overview

In this episode, Kieran Rogers interviews Kevin Lindsey about how data science is transforming digital marketing, specifically through Adobe Target's automation capabilities. The discussion dives into the difference between analytics and data science, the automation of personalized customer journeys, and how marketers can leverage machine learning without dedicated data scientists. The central theme is demystifying data science’s “sexiness” and making its benefits tangible and accessible for all marketers, regardless of their technical expertise.

Key Discussion Points & Insights

The Data Deluge and Data Science’s Role

• Data is Abundant, Actionability is Scarce:
  Kevin notes the central challenge is not the lack of data but making it actionable for marketers.
  “There’s no shortage of data. We have so much data that it’s more of a challenge to figure out what we do with that data and how to make it work for us.” — Kevin Lindsey [00:53]

• Shift in ‘Big Data’ Conversations:
  Kieran comments on the cooling hype around big data, emphasizing the actual size of datasets has only grown.
  “The noise maybe has died down, but [data is] certainly just as big, if not tremendously bigger...” — Kevin Lindsey [01:33]

Defining Data Science’s Value in Marketing

• Data Science Beyond the Buzz:
  Kevin addresses the allure and practical applications of data science, highlighting its critical role in modern customer analytics and decision-making for marketers.
  “Data science is now sort of coming into its own in terms of a key area that the marketing organization needs to pay attention to...” — Kevin Lindsey [01:48]

• Do You Need a Data Scientist?
  Not every organization needs a full-time data scientist, but every business can benefit from data science principles—often by using accessible tools and platforms.
  “The nimble marketing team...still, however, can take advantage of data science in their practice. And I would actually challenge that. They probably need to be taking advantage of data science to some degree.” — Kevin Lindsey [03:43]

Analytics vs. Data Science

• Clear Distinction:
  Kevin draws a line between consuming analytics for insights (generating reports) and using data science for building predictive models and automating decision-making.
  “That distinction comes down to the difference between insights and an action that can be taken on those insights or that data.” — Kevin Lindsey [05:11]

• Automating Actions:
  Marketers can now leverage technologies (like Adobe Target) that bridge the gap between insight and automated action, even without hands-on data scientists.
  “There are technologies today...that expose data science through a marketer-friendly product so that marketer can rely on that decisioning that is happening...” — Kevin Lindsey [07:30]

How Adobe Target Automates Data Science

• Embedding Data Science in Tools:
  Kieran queries if technology can truly replicate a data scientist’s approach algorithmically. Kevin confirms this is now reality, describing lifecycle journeys and decisioning automation that help optimize customer engagement.
  “A data scientist can...build a model to accommodate and optimize against the business outcome that the marketer wants to see...by writing an algorithm...as part of a personalization strategy.” — Kevin Lindsey [08:14]

• Practical Example – Automated A/B Testing:
  Traditional A/B or multivariate testing exposes all variants to traffic until a winner is found. Advanced tools now automatically reallocate traffic as soon as a leading variant emerges, minimizing wasted impressions on subpar experiences.
  “As we start to feel the engine [is] confident that a winner is revealing itself, we’re going to start funneling more traffic over here...so we’re not going to continue to send people toward this dud...” — Kevin Lindsey [11:16]

  • Kieran’s Analogy:
    “The big hairy dog.” — Kieran Rogers, referring humorously to the weakest variant in a test [12:03]

• Surprising Outcomes—What Really Works?
  Kevin shares a story where a big media brand assumed a celebrity-led campaign would drive the most lift, but data-driven testing revealed the opposite.
  “The celebrity appearances were actually not creating lift. It was the one without the celebrity that created lift.” — Kevin Lindsey [13:10]

Machine-Led Optimization and Marketer Trust

• Data Science Yields Superior Results:
  Across Adobe’s client base, machine-driven experiments consistently outperform manual, rules-based targeting.
  “Nine times out of ten, the more data science-driven, machine learning-based approaches are beating the manual...decisions.” — Kevin Lindsey [14:23]

• Transparency and Control:
  The real challenge is helping marketers trust machine learning by providing clear explanations of what’s working and why—bridging the comfort gap in ‘relinquishing’ some control to algorithms.
  “The only way to do it...is by ensuring that the marketer has a lot of insights into why some of this is happening, the variables that...are the most predictive variables, and giving them as much opportunity to interact and really understand...” — Kevin Lindsey [15:06]

Notable Quotes & Memorable Moments

• “Data science and sexy, those don’t seem to be very compatible words...But if you look at the hottest careers right now...data scientists are right up there at the top.” — Kevin Lindsey [03:03]

• “Sometimes it’s really hard to determine which [test variant] is going to be the obvious winner and sometimes we’re surprised. And so letting the data figure this out for you… it’s like a content traffic cop.” — Kevin Lindsey [12:58]

• “Often the big hairy dog is the winner.” — Kieran Rogers [12:46]

Timestamps for Important Segments

• 00:53 – The central challenge: Making data actionable
• 01:48 – Data science’s current role in marketing
• 05:03 – Analytics vs. data science explained
• 07:30 – Marketer-friendly data science tech (Adobe Target)
• 08:14 – Algorithmic personalization and journey modeling
• 11:16 – Automated traffic allocation in A/B testing
• 12:58 – Entertaining “traffic cop” and “big hairy dog” moments
• 13:10 – Celebrity campaign case study
• 14:23 – Machine learning outperforming manual approaches
• 15:06 – The trust and transparency challenge for marketers

Key Takeaways

• Data science isn’t just hype—it’s increasingly central to digital marketing, but using it doesn’t require you to hire data scientists.
• The distinction between analytics and data science is pivotal: analytics produces insights; data science produces automated, scalable actions.
• Tools like Adobe Target automate complex experimentation, optimizing in real time and frequently delivering surprising (and often superior) results compared to human intuition.
• Marketers need transparency and insight into what algorithms are doing to trust and fully leverage machine learning-based optimization.

Further Resources

• Adobe Digital Marketing Solutions: adobe.com
• Adobe Digital Marketing Blog: blogs.adobe.com/digitalmarketing
• Target Internet: targetinternet.com