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Getting personalized CX right with content analytics​

Executive Summary

Customers across the healthcare value chain (patients, healthcare professionals, and more) demand personalized experiences from the organizations they interact with. They expect information that is relevant and personalized to their evolving needs, interests, and expectations. However, there is a significant misalignment between how organizations push information and how patients and HCPs want to receive them, indicating poor experiences and low levels of engagement. The need of the hour is for organizations to build advanced content analytics capabilities.
In this article, we highlight how modern technologies like artificial intelligence and predictive analytics can extract hidden insights into the content consumption habits of customers, giving commercial teams golden insights into what their HCPs, patients, and other customers like, don’t like, and what resonates with them the most. This enables organizations to hyper-personalize content based on what their customers truly want - bolstering their CX strategy at scale.

Patients, Healthcare Professionals (HCPs), and other customers in the healthcare value chain don’t just prefer personalized experiences today, they demand them - pushing organizations across the healthcare ecosystem to upgrade their commercial models rapidly. Customers want to be heard and understood by the organizations they interact with. They expect to be on a frictionless and uninterrupted journey that caters to their every need, interest, and expectation.

At the heart of such personalization lies a powerful customer-first content strategy. One that allows a customer‘s behavior and preferences to dictate what they see next and where. In theory, demonstrating such customer intimacy should lead to better experiences, higher engagements, brand stickiness, and more conversions – a goal that healthcare organizations have had for some time now.

But have we made progress?

Here‘s where we are today

In 2021 alone, pharma companies have invested millions of dollars in building digital experience capabilities to improve customer journeys.

More than $30 million were invested by top-tier firms in 20211

However, despite the significant focus and investment, companies have often struggled to translate robust customer insights into integrated, impactful, and consistent experiences.

HCPs are not entirely satisfied

Patients too echo the same sentiment

Patients did not rate any type of pharma content to be good or excellent 3

Clearly, there is a misalignment between how organizations in the industry push information and how patients and HCPs want to receive them, leading to poor experiences and engagements.

Reasons behind the content experience gap

Siloed data

Healthcare organizations need data, particularly data based on historical and present interactions with the brand to build an in-depth understanding of the customer journey. But oftentimes, such data exists in siloed internal and external third-party systems that are challenging to amalgamate and access, preventing organizations from gaining (and profiting from) a holistic view of the customer journey. Without centralizing such customer data, insights gathered from one channel cannot immediately be applied to the engagement on another channel - limiting customer personalization opportunities to a large extent.

Low content re-usability

As HCP and patient expectations evolve, the number of different ways to address their needs increases exponentially - raising the demand for new pieces of contextual content. However, producing large volumes of content like this is seldom easy - in which case - organizations should look at repurposing content that has already been created. However, without a well-defined process to identify and re-use relevant and existing content across customer segments and personas, organizations cannot meet the volume, velocity, and variety of goals that personalization demands.

Generic content

A ‘cast a wide net into the lake, and you‘re bound to get a bite‘ approach to content creation and distribution can lower customer engagement levels because of the lack of relevance. Such content is rarely ever contextual to the customer journey as it focuses more on what the company wants to say versus what the customer wants to hear.

Sub-optimal review process

Sub-optimal content review and approval processes can sometimes be a bottleneck that can delay customer campaigns and cause a logjam in the publishing process. Such roadblocks in the review phase can stall the project to the point where it is no longer relevant to the customer - another reason why engagement levels can suffer.

Slow adoption of advanced technical capabilities

Slow progress on building highly relevant and advanced technical capabilities like customer engagement analytics, content creation, predictive models, and more is another cause for concern standing in the way of achieving personalization goals quickly. It slows down content operations, affects the quality of content produced, and eventually dampens customer experiences.

The need for advanced content analytics capabilities

Content analytics uses modern technologies like artificial intelligence, machine learning, and natural language processing to extract hidden insights into the content consumption habits of customers. These gold nuggets of insights can give commercial teams a clear view of what their HCPs, patients, and other customers care about, highlighting what they like, and don‘t like, and what resonates with them the most. This enables organizations to personalize content at scale for specific audiences while also helping them decide what content to keep, what to do more of, and what to get rid of. This allows them to do less of what isn‘t working and focus more on improving the aspects of their customer experience efforts that bring real results.

What the personalization journey looks like with content analytics

Six ways how content analytics can help

It feeds your omnichannel customer strategy with insightful recommendations on content use
It benchmarks content based on customer engagement levels to personalize campaigns
It improves HCP engagement rate by 10%-15%
It streamlines operations from content creation to content deployment
It enhances productivity in the overall process of content creation by at least 10%
It garners cost savings of 30%- 45% by improving the quality and reusability of existing content

Building a content analytics strategy from scratch

Conduct an AS-IS process analysis

Review the current data sources within your organization, analyze visualization capabilities, and identify transformation steps
Assess the existing data model and data dictionary to ascertain the depth of data collection
Conduct discovery workshops with cross-functional stakeholders (Marketing, Brand, Creation, and IT Teams) to understand the challenges in your existing process

Design an appropriate analytical framework

Summarize your findings from the discovery phase analysis
Design a suitable KPI framework across the entire content cycle (planning, development, and deployment stages), highlighting the need for each parameter based on your research
Gather feedback across all functions and finalize your KPIs
Design an integrated data model based on the KPIs finalized

Set up a data engineering and data warehouse layer

Based on your KPI framework, tag and segregate every piece of customer content created. This will help you understand your content library at a macro level
Identify the required content data sources and store them in a data lake along with other existing data points
Create an ETL pipeline to convert your data sets into structured, filtered, and analytics-ready data marts stored in a data warehouse
Design dashboard wireframes to visualize your KPI framework
Develop the final wireframes in a visualization tool of your choice such as Tableau, Power BI, Qlik

Activate AI and ML Models

Connect the data marts to your dashboards and set them up on auto-update as per your required cadence
Develop and deploy AI and ML models to track content performance in real-time and provide next best action content recommendations
Design a content attractiveness model to determine the success rate of the content right before it is deployed

Leveraging analytics across the content operations journey

Content strategy enablement

You can‘t really tell the story of your brand if you don‘t know who you are telling it to. That‘s why your first step is to identify the personas you are attempting to target with your content and their content and channel affinities. Predictive AI-based algorithms can mine customer data in a way that it fetches the hidden correlation between different content and channel variables based on their co-occurrence between personas in your dataset. This will help you accurately capture, classify, and track your content across channels, empowering you to capitalize on customer affinities at scale.

Content development

Personalization demands relevant content - lots of it and at an accelerated pace. When working with high volumes, it can be difficult for teams to prioritize the content piece that needs to go out first. Here‘s where advanced analytics can help. Advanced analytics and predictive models can help you forecast the quantity of content required for a specific customer journey or campaign, giving you a head start on all your content planning and creation efforts. It can also help you optimize development time by prioritizing all high-impact content assets that typically take the lowest time to develop. Additionally, leveraging metadata and content re-use techniques in this phase is essential as it helps your team find, categorize and manage content using tags and tagbased permissions. By combining reusable artifacts and trans created assets with a robust analytical framework, it becomes simple and efficient to categorize, file, and automate content for later use.

Content review and approval

By activating operational metrics on centralized dashboards to reflect data such as time taken to develop content, time taken to review, no. of content pieces reviewed, no. of content pieces approved, no. of content pieces rejected and reasons behind the rejections, the progress of content across stages, and more - you can predict reviewal times, approval rates, and forecast delays. This allows you to prioritize your review requests effectively by focusing on the content that would require the longest time to review - e.g.: technical-heavy content passing through the medical review stage.

Content deployment and insights generation

Set up a centralized analytics-driven data dashboard to measure the performance of your content once it is deployed. Analyze whether your content has reached your HCP or patient on a day and at a time that mattered most. Capturing more data like this helps you extract critical insights that directly answer questions like:

How well is your content helping you reach new audiences?
How engaging is your content?
How much is your content contributing to goals and conversions?
What sources are driving traffic to your content?
How well your content is doing on specific channels (like email, social media, website)?

Feedback loop

Design and automate an AI-based feedback loop linked back to your first step - content strategy enablement. Feedback loops use the post-deployment insights generated on content effectiveness as critical inputs to dictate future content operations. It enhances real-time dynamics and orchestration of Next Best Actions. Feedback loops can be either negative or positive. Negative feedback loops are self-regulating and useful for maintaining an optimal state of content quality while positive feedback loops help you mirror the most effective content actions from the past to amplify desirable outcomes.

Here‘s an example:

The process of applying advanced analytics across content operations is not universal. Organizations must customize their approach to content analytics for different customer segments. Take patients for example: Data on a patient‘s historical engagement and content consumption habits typically sit on multiple third-party systems operating in siloes. Hence, identifying the patterns in their engagement may not be as simple as the process for HCPs. The application of content analytics, in this case, will largely depend on the data accessibility and transferability aspects first, before running it through advanced analytics and generating insights for personalization. Hence, factoring in these requirements and optimizing your content analytics strategy to suit each customer segment is paramount.

Maximizing outcomes

Many global organizations have already started letting content analytics sing a song of success for every customer marketing campaign they execute. Here‘s a story of one such pharma company that not only generated winning customer content in record time but also optimized its process and operations along the way.

How a global pharma improved content efficiencies by 15%

A top 5 global pharma company had two goals:

Create a global centralized reporting dashboard to capture critical insights across the content operation journey and optimize the MLR process
Enable access to quick customer content data summaries (with documented insights and calls to action) to support strategic discussions among key stakeholders

Here‘s what they did:

Identified relevant business metrics and corresponding data sources
Built a robust ETL data process
Applied business rules in the decision support system
Created and automated preliminary summaries
Developed a self-serve, analytics-based Tableau dashboard with visual reporting enabled for easier consumption of insights
Enabled option to filter reports by geography, brand, or business unit
Documented actionable insights and generated comprehensive data summaries


↓ 15-20%
Overall content cycle time
↑ 5-15%
Operational efficiencies
↑ 5-15%
On-time monthly submissions
↑ 5-15%
Speed to completion


With the rapid pace of digitization, the amount of data generated by patients and HCPs will only increase with time. As they spend more time interacting on online platforms, they will generate more data indicating their interests, behaviors, and preferences - calling for the need for companies to invest increasingly in technologies that can interpret different forms of content. The use of natural language processing and artificial intelligence systems are crucial to do this more effectively. Although content analytics is currently at a nascent stage in the industry, it shows great promise as we move towards a future state of customer experience where content analytics can potentially become the primary source of customer intelligence for companies, providing field force teams with key insights and contextual discussion points during their online and offline interactions with customers.


The state of digital excellence in the global pharmaceutical industry, 2021 https://dt-consulting.com/the-state-of-digital-excellence-in-the-global-pharmaceutical-industry-2021/
The state of customer experience in the pharmaceutical industry, 2022: patient interactions https://dt-consulting.com/the-state-of-customer-experience-in-the-pharmaceutical-industry-2022-patient-interactions/


Nellai Srinivasan
Nellai Srinivasan
Peeush Goel
Peeush Goel
Vikrant Ghai
Vikrant Ghai
Vikas Mahajan
Vikas Mahajan