Getting personalized CX right with content analytics​

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.

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.

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 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 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.

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.

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.

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.

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.

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:

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.