Marketing Tech and Data Science Are Essential Building Blocks of Digital Transformation

Part 3 in a 4-part series

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Legacy marketing organizations need to build an effective data and mar-tech infrastructure to drive their customer experience.
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This four-part series aims to help executives better understand how to succeed in digital transformation. In part one, we identified three critical pillars of success:

  1. The right strategy, culture and partner relationships.
  2. The adoption of sophisticated cloud marketing technologies and data.
  3. A direct-to-consumer brand digital commerce channel.

Here in part three, we’re focusing on the second pillar: marketing technology and data science.

As we discussed in part two of this series, leading brands begin with a strategy that is data-driven. No longer is creative alone enough as marketing budgets are closely monitored. Without empirical data to drive decisions, many legacy brands and antiquated marketing organizations are fading away.

Customer experience driving higher marketing budgets

Meanwhile, CMOs are spending more on tech than CIOs, according to Gartner Research. There has been an uptick in the number of Fortune 1000 clients we serve that have meaningfully accelerated their investment in marketing technology and data solutions over the last 24 months. All of that is largely driven by a growing emphasis on improving the digital customer experience (CX).

Without empirical data to drive decisions, many legacy brands and antiquated marketing organizations are fading away.

The need to both enhance CX and manage an increasing number of touchpoints has spurred the development of sophisticated cloud marketing suites. At the most basic level, a marketing cloud allows a brand team to tie together multiple data streams, discover its most valuable customers and automate relevant, personalized messaging across digital touchpoints. This is accomplished using a central technology hub that can integrate with other point solutions and digital channels.

If the cloud marketing suite is the engine that drives the digital customer experience, data is its fuel. As we have observed working with hundreds of organizations, without a robust data set and analytics tools, brands cannot identify who their customers are, understand their behaviors and needs or effectively target them with personalized, relevant content to move them through the purchase funnel.

It’s not simple to harness the power of data to deliver the ultimate customer experience at scale. Through our work on hundreds of analytics projects across b-to-c and b-to-b brands, we have observed three key phases along this journey.

Phase 1: Capture and unify data across all channels

By tracking interactions across all online and offline channels, brands can access a treasure trove of raw data that includes first-party data as well as third-party data feeds. Brands often start with behavioral data from online activity collected by a digital analytics tool. More mature programs are unifying many different data feeds into a singular source of truth through a customer data platform (CDP), such as Adobe Experience Platform or Salesforce Customer 360. Here, marketers can form a common customer identity and access additional data sources to gain further insights.

Phase 2: Transform data into insight

The data itself doesn’t provide value. Your analytics team will leverage all available data to identify unique audiences or segments through a data management platform such as Adobe Audience Manager or Salesforce Audience Studio. Each customer segment usually has a unique goal (e.g., comparing price, learning about the brand). Brands need to analyze the data to formulate a hypothesis about the experience each segment might want. Then experimentation (A/B testing) will allow users to demonstrate which experience results in higher conversion rates.

Phase 3: Impact experience at scale

Some experiment results will lead to obvious and permanent change that your backend developers can implement. For everything else, a sophisticated and scalable approach is to rely on machine learning through personalization or targeting tools. This allows marketers to test a number of different experiences simultaneously and automatically shift digital traffic to the ones working best in real time. Marketers can capture revenue faster and spend less time analyzing results and deploying content assets.

Significant rewards for putting in the work

These three phases may seem like hard work—and they are. However, the potential rewards are substantial. According to a report by McKinsey & Company, “Companies that make extensive use of customer analytics are more likely to report outperforming their competitors on KPIs, whether profit, sales, growth or ROI.” For example, our men’s skincare client Jack Black (portfolio company of Edgewell Personal Care Co.) increased revenue per visitor 14% after we redesigned their website to align the digital experience to data-driven customer personas.

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