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20 Sep 2024
Healthcare professionals (HCPs) and patients now have an unprecedented number of ways to engage with companies, from traditional channels to an ever-growing array of digital modes. To stand apart and succeed, life sciences marketers must offer compelling personalization experiences. However, effective marketing strategies hinge on understanding customers and engaging with them across multiple touchpoints.
To achieve this, life sciences organizations have turned to Customer Data Platforms (CDPs) as a solution. CDPs act as a central hub for collecting, organizing, and leveraging customer data. However, simply having a CDP in place is not enough to guarantee CDP success. To truly excel in the omnichannel landscape, companies must complement their CDP with an effective omnichannel data strategy. In this blog post, we will explore the concept of omnichannel marketing automation, the role of CDPs, and how an omnichannel data strategy can enhance effectiveness.
Understanding omnichannel marketing
Omnichannel marketing is a holistic approach that focuses on providing a seamless and consistent experience for customers across all channels and devices. Whether an HCP or patient interacts or engages with your brand through a website, mobile app, social media, email, or in-store, they must receive a unified and personalized experience. This degree of consistency and personalization sets omnichannel marketing apart from multichannel or cross-channel approaches.
Creating omnichannel brand awareness
The key to a successful omnichannel marketing lies in the ability to gather and utilize customer data effectively, and this is where CDPs come into play.
Omnichannel data and the role of CDPs
Omnichannel data refer to data generated from every customer interaction with a brand or organization. It provides insights into customer behaviors, preferences, attitudes, and values. Omnichannel data analysis differs from multichannel data analysis in that omnichannel data analysis emphasizes an integrated approach. Rather than simply collecting siloed data and viewing it using a per-channel approach, an omnichannel approach seeks a holistic view. This omnichannel data layer acts as a foundation to a CDP, helping create an effective omnichannel marketing platform.
A CDP is a centralized system that collects, integrates, and manages customer data from various sources, such as website visits, email interactions, social media engagements, and offline engagements. CDPs are designed to create exactly these unified customer profiles, thus providing an invaluable 360-degree customer view. The benefits of using a CDP include:
Customer 360: The core value of a CDP is to create a comprehensive 360-degree profile for each customer.
Automatic segmentation: Marketers can segment their audience more effectively, ensuring that messages are targeted to specific customer groups with relevant content.
Data integration: CDPs can integrate data from multiple sources (online and offline), helping break down silos and providing a comprehensive view of the customer journey.
Real-time orchestration: CDPs enable marketers to orchestrate the customer’s journey in real time across different channels and optimize it.
Real-time insights: Marketers gain access to real-time data and insights such as channel and content preferences, enabling them to make informed decisions and adjustments to their campaigns.
While CDPs offer these advantages, it is important to note that their effectiveness depends on the quality and depth of the data they collect. This is where a well-planned omnichannel data strategy becomes crucial.
Building an omnichannel data strategy that enables effective personalization
An omnichannel data strategy extends beyond the capabilities of a CDP and encompasses the entire data ecosystem of an organization. It involves collecting, processing, and utilizing data from all customer touchpoints, both online and offline. Let us look at different systems and processes that must be in place to both enable an omnichannel data strategy and in turn, enhance the effectiveness of a CDP:
1. Data capture
Achieving 1:1 personalization necessitates that your system has the ability to capture individualized data. User-level data form the core foundation of personalized customer journeys. Consistent channel and content tagging is essential for capturing user-level data across all systems. Data collection methods vary across first-party, second-party, and third-party data sources, yielding diverse engagement metrics across different channels.
For example, website channels can track metrics such as session length, dwell time, and scroll percentage, whereas email channels monitor activities such as opens, clicks, and non-opens. In addition, survey responses provide valuable insights into user preferences. The accuracy of user-level data is pivotal in shaping precise 360-degree customer profiles, ultimately impacting user conversion rates.
| Channels | User-level data | Sample |
|---|---|---|
| Website | ■ Session data ■ Content engagement data ■ CTA clicks | ■ dwell_time = 30 seconds ■ scroll_tracking = 33% ■ pdf_download = Efficacy ■ video_milestone= 75% |
■ Email engagement data ■ Email product name ■ Email product ID | ■ email_activity_event_type = clicked ■ email_product_name = efficacy ■ email_vault_ID = 22222 | |
| CRM | ■ Demographic details ■ 1:1 interaction data ■ Event ID | ■ Address =260, NY ■ meeting_ID: 167554 ■ event_title: Copay card meet |
■ Product name ■ Message status ■ Engagement date | ■ Whatsapp_product_name = Brand-ABC ■ Activity_status = Opened ■ Activity_date = 09.02.2024 | |
| Survey | ■ Survey ID ■ Survey questions ■ Survey response | ■ Survey_ID = Awareness_XY1 ■ Survey_questions = A_XY1 ■ Question_response = BB |
2. Data enrichment:
In addition to user-level data, a comprehensive 360-degree customer profile relies on unique user identifiers and data unification/identity resolution strategies. These crucial methods typically fall into 3 categories:
Deterministic matching: This method employs a binary approach, linking data to solid unique identifiers. It is highly accurate when the relationship between identifying sources is straightforward. Examples of deterministic identifiers include email addresses, mobile numbers, NPI (national provider identifier) IDs, MDM (master data management) IDs, device IDs, and cookie IDs.
Probabilistic matching: The probabilistic approach relies on patterns to determine how the various identifiers are connected. For instance, it may analyze how frequently a device is used at a specific IP address, the shared Wi-Fi usage among a set of devices, or common identifying patterns within devices from the same household. This method is not as accurate and as commonly used in the life sciences industry because of stringent regulations.
Hybrid matching: Hybrid matching combines the strengths of both deterministic and probabilistic methods. It sequentially merges customer profiles from separate deterministic and probabilistic providers, aiming to capture accuracy and cross-channel reach. A well-contextualized hybrid solution can be a powerful tool for achieving this goal.
Examples of some personally identifiable information (PII), which are collected through different sources:
| Channels | User ID |
|---|---|
| Website | Email ID, NPI ID, Cookie ID, and MDM ID |
| Primary & Secondary Email ID | |
| CRM | Clinical Trial Identifier, License Number, Patient Identification Number, Prescription Number, and Electronic Health Record ID |
| Phone Number and Device ID | |
| Survey | MDM ID and Vault ID |
3. Data transformation:
To deliver personalized experiences at scale, a standardized taxonomy data model is essential. This model ensures that customer attributes, like campaign interactions and demographic details, are consistently organized across platforms. By integrating and analyzing this data in real time, life sciences organizations can create tailored content and experiences while maintaining strict privacy compliance.
Taxonomy data model: Achieving extensive personalization at scale demands a high degree of reusability for customer attributes. To accomplish this, a standardized taxonomy data model is required that spans all platforms and assets.
Some notable examples of these attributes include campaign names, behavioral and demographic characteristics, asset tagging (for website, e-detailing, and emails), and data-driven attributes derived from mathematical models. Below is an illustration of standardized user-level attributes:
| Channels | User-level data | User-level attributes |
|---|---|---|
| Website | ■ dwell_time = 30 seconds ■ scroll_tracking = 33% ■ pdf_download= Efficacy ■ video_milestone= 75% | ■ [Campaign-AA] Current Phase ■ [Campaign-AA] Assets Engaged ■ [Campaign-AA] Pdf Download |
■ email_activity_event_type= clicked ■ email_product_name= Efficacy ■ email_vault_ID = 22222 | ■ [Campaign-AA] Campaign Name ■ [Campaign-AA] Email open ■ [Campaign-AA] last active date | |
| CRM | ■ Address =260, NY ■ meeting_ID: 167554 ■ event_title: Copay card meet | ■ [Campaign-AA] Persona ■ [Campaign-AA] Specialty ■ [Campaign-AA] F2F Meetings |
■ Whatsapp_product_name = Brand-ABC ■ Activity_status = Opened ■ Activity_date= 09.01.2024 | ■ [Campaign-AA] Whatsapp Campaign Name ■ [Campaign-AA] Whatsapp clicked ■ [Campaign-AA] last active date | |
| Survey | ■ Survey_ID= Awareness_XY1 ■ Survey_questions= A_XY1 ■ Question_response= BB | ■ [Campaign-AA] Survey Response ■ [Campaign-AA] Survey Submission Date |
Real-time integration and downstream orchestration:
Through seamless real-time or near real-time integration of upstream and downstream channels, life sciences organizations can deliver swift responses to customer interactions, delivering precisely tailored offers and recommendations as customers actively engage with their brand.
In a CDP, specific business rules can be configured based on the type of use case. When a campaign is launched and users begin interacting, the CDP functions as a rule engine that dynamically assigns specific segments to each user, considering their channel preferences and content affinities. This user segmentation serves as the foundation for downstream journey orchestration, enabling the orchestration of personalized content and experiences, as well as improving overall customer engagement and satisfaction.
| Channels | User-level attributes | User-level Audience |
|---|---|---|
| Website | ■ [Campaign-AA] Current Phase ■ [Campaign-AA] Assets Engaged ■ [Campaign-AA] Pdf Download | ■ [Campaign-AA] Pdf downloader ■ [Campaign-AA] Video engager |
■ [Campaign-AA] Campaign Name ■ [Campaign-AA] Email open ■ [Campaign-AA] last active date | ■ [Campaign-AA] Email Non-engager | |
| CRM | ■ [Campaign-AA] Persona ■ [Campaign-AA] Specialty ■ [Campaign-AA] F2F Meetings | ■ [Campaign-AA] Seminar Enthusiast |
■ [Campaign-AA] Whatsapp Campaign Name ■ [Campaign-AA] Whatsapp clicked ■ [Campaign-AA] last active date | ■ [Campaign-AA] WhatsApp Conversationalists | |
| Survey | ■ [Campaign-AA] Survey Response ■ [Campaign-AA] Survey Submission Date | ■ [Campaign-AA] Positive Responder |
Advanced analytics and privacy compliance:
An omnichannel data strategy empowers companies with the capabilities for advanced analytics, encompassing predictive modelling and machine learning. These analytical tools unearth valuable insights and opportunities for highly personalized customer experiences. In addition, a paramount focus is placed on compliance and privacy, reinforcing the company's commitment to safeguarding the interests of both the organization and its customers. Prominent data privacy regulations such as GDPR, CCPA, and HIPAA underscore the critical importance of this commitment, ensuring the responsible handling of data while delivering exceptional personalization.
Unlocking omnichannel success
With omnichannel marketing becoming a standard approach within life sciences marketing teams, CDPs have become essential tools that help these teams manage customer data and deliver personalized experiences. However, to truly unlock omnichannel success, life sciences organizations must go beyond just implementing a CDP. They must adopt an omnichannel data strategy that encompasses all customer touchpoints, both digital and offline.
By doing so, organizations can enhance the effectiveness of their CDP architecture, deliver seamless and personalized experiences, and ultimately, drive customer loyalty and business growth in a highly competitive digital landscape. Talk to us for understanding how you can create an effective CDP-based omnichannel data strategy at your life sciences organization.
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