Marketing Data Hubs: When Operations and Analytics Collide

Pharmaceutical CommercePharmaceutical Commerce - November 2021
Volume 16
Issue 4

How to best plan out and implement a marketing data hub as a key tool to harnessing today’s omnichannel transformation in pharma

In recent years, the pharmaceutical industry has undergone significant digital and omnichannel transformations. Amid the global Covid-19 pandemic, the pace of this transformation has accelerated. Fewer face-to-face interactions between pharma reps and healthcare professionals (HCPs) forced many companies to cram five years of digital transformation into one year. For pharma companies, the call for truly mature omnichannel capabilities is growing louder.

In response, many drug firms have shifted explicitly to formal omnichannel strategies over the past few years. They have brought in customer experience (CX) experts, mapped out customer journeys and hired significantly from outside of pharma. However, building omnichannel capabilities into an organization’s DNA is much more challenging than buying tools or hiring. Manufacturers face both regulatory and organizational constraints when undertaking these efforts. For companies looking to expand their omnichannel toolkit, a marketing data hub (MDH) is a great tool to help build a solid data foundation.

What is a marketing data hub?

An MDH is a two-way data exchange that enables both marketing operations and analytics. This data repository should contain records of all marketing interactions with customers and serves as the basis for omnichannel marketing coordination. It is both an operational data store and an analytics data mart.

On the marketing operations side, an MDH helps coordinate real-time marketing activities in conjunction with traditional data sources like customer master lists or preference lists. It acts as a two-way data exchange between channel touchpoints, generating list feeds for a range of outbound marketing and re-marketing activities—be it email campaigns, targeted display ads or even webinar invites. Here, coordinated automation is a priority.

On the analytics side, an MDH should serve up cleaned and harmonized data, supporting an array of analytics activities, from the channel and campaign-level performance to segmentation exercises, marketing mix and machine learning. Literally and figuratively, an MDH is the central data hub around which omnichannel execution and analytics revolve.

Break from tradition

While MDHs, traditional data warehouses (DWs) and customer data platforms (CDPs) serve similar functions, they differ significantly in complexity, time-to-build and more.

DWs are often read-only databases intended to support reporting, business intelligence and analytics. DWs tend to be IT-centric projects with correspondingly long development times. Due to perennial scope creep and constantly changing business needs, the success rate of DW projects over the past 20 years has been shockingly low.

In contrast, CDPs provide digital coordination and customer identity resolution in specific digital spaces. CDPs also add some level of marketing automation and insights. CDPs are digital-first, working well for fast-paced e-commerce or consumer-oriented organizations with relatively straightforward customer activity flow. Pharma, however, must consider a range of privacy concerns, fragmented data inputs, overlapping lines of authority and the added complexity of rep-driven marketing. For these reasons and more, pharma can face challenges in adopting CDPs successfully.

An MDH is typically small and flexible enough to jumpstart within a few business cycles, while creating significant impact at the enterprise level. It is not a read-only exercise like a DW, nor is it digital-centric like a CDP. Unlike most DW projects, minimum viable product approaches to MDH implementations can be developed quickly.

Objectives for a marketing data hub

Here are three broad objectives for any MDH implementation. An MDH should:

  1. Coordinate and automate interactions between marketing systems and channels.
  2. Enable rapid customer and channel performance insights.
  3. Enable granular insight into data availability, data quality (DQ) and analytics readiness.

MDHs in action: A real-world example

A top-10 multinational pharma company with a complex marketing ecosystem faced several enormous data challenges, including inconsistent data availability and poor DQ. The company’s ecosystem included 120+ distinct data vendors, 12+ direct-to-consumer and HCP marketing channels and 100+ monthly inbound files. These challenges resulted in impeded analysis and insight generation and choked decision-making. The company needed faster, analytics-ready data to support more frequent marketing spend recommendations and analytics at scale.

To improve data issues, Axtria employed its MDH concept against nearly $150 million in spending. As a result, the pharma company reaped the following benefits:

  • 400% faster time to data readiness.
  • Clear line-of-sight into data status and quality for all data, with quality key performance indicators (KPIs), tracked across time.
  • Always-on channel performance insights.
  • Near-real time channel coordination capabilities to support marketing operations.
  • 150% faster turnaround times for complex analytics via “analytics-ready” data sets.

What principles should organizations follow in early-stage MDH planning?

  1. Don’t try to solve everything at once. Does the company need to fix a fundamental operational problem around marketing coordination? Perhaps, they must work more quickly to differentiate good versus poor campaign performance or are facing vendor DQ challenges or delivery challenges. Solve a few core problems quickly and ride that momentum into the next set of challenges.
  2. Go all out on DQ. All the hard work and cool technology will be for naught if users don’t trust the data. A basic set of DQ rules that evaluate data as it arrives will catch the most egregious errors. Finding errors before users do builds credibility.
  3. Publish both successes and challenges. Socialize the status of the data, errors uncovered in the DQ process and the impact the MDH has on the organization.
  4. Envision what success feels like for the team. Within most marketing data ecosystems, “cat herding” often occurs, where companies chase vendors to gather data and scramble to gather the latest data to generate the freshest reports. An MDH running on all cylinders ingests and processes data in an organized manner. That creates trust and confidence in data, which, in turn, empowers other teams to do their best work, too.

Bottom line: Omnichannel requires robust and efficient data capabilities

Pharma executives should understand the differences between DWs, MDHs and CDPs. While building an MDH can seem daunting at the outset, teams with moderate technical skills can build the fundamental components in a short period. Importantly, those fundamental components are the same whether a company’s marketing ecosystem contains 10 or 100-plus vendors’ worth of data.

The results include significant near-term impacts to marketing efficiency, improved channel coordination, rapid performance insights and a clear-eyed awareness regarding the status of all the available data.

About the Authors

Philip (Phil) Daniels (left) is a Principal, and Rebecca Lorenzo (right) a Marketing Associate, both with Axtria.

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