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Mastering Market Segmentation in Pharma

Mastering Market Segmentation

Mastering Market Segmentation in Pharma

Best Practices to Increase Reach and Engagement

In essence pharma marketing targets several kinds of audiences within its market range, including patients, healthcare professionals (HCP), and other decision-makers. So, segmentation is mandatory to better serve both ends.

Following specific criteria to aggregate data pointers, pharma marketers can sort the prospects and customer segments to tailor the message. Furthermore, personalizing the interactions and creating high-value campaigns that are fit to size.

For the most part, it’s the general consideration that pharma market segmentation revolves around 4 major categories: demographic, psychographic, behavioural, and geographic. Through this division, a suited and targeted approach can be pursued for each segment with higher chances of resonating with the audience’s needs and expectations.

With personalized and added value interactions, pharma companies can differentiate themselves. While developing relationships and nurturing prospects and customers through seamless experiences that match their journey touchpoints. 

Divide to Conquer: Market Segmentation Process

Mastering Market Segmentation

For both B2C and D2C audiences, pharma companies must:

  • Identify unmet audience needs;
  • Develop products/ solutions; and
  • Use marketing activities to promote them successfully through added-value campaigns. 

All with the aim of targeting high-level accounts and prospects with the highest probability of revenue. 

The process begins with understanding which are the biggest consumers of the product in the D2C audience. Who is going to use/ prescribe to the B2C audience? Who is going to evaluate the real implementation? Generally speaking, all the dimensions of the segments must be considered including, organizations’ sectors, job roles, and potential revenue probabilities. So as to better identify the most valuable segments to target in the campaign.

On the whole, understanding and having data-driven knowledge from past interactions with the existing customers is a good starting point to set the right direction for a productive and accurate segmentation. On the whole, the buyer personas must resonate with the targets through customer-related team inputs combined with data-driven insights. 

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The categories must consider all relevant and decisive details, sub-dividing themselves into smaller segments that don’t overlap, enabling the future performance measurement and analysis is effective in the long run with room for ongoing optimization.

GeographicsDemographicsPsychographicsBehavioural
CountryAgeLifestyleBenefits search
CityGenderActivityPurchase
Density IncomeInterest Usage
LanguageEducationOpinionIntent
ClimateSocial status ConcernsOccasion
AreaFamily Personality Buyer stage
PopulationLife stageValuesUser status
Occupation Attitudes Lifecycle stage
Engagement

In addition, knowing what pain points to address, which needs are unmet and the solutions offered, enables a clear vision of the type of segment. While establishing differentiation from the competition. Pharma teams must look further in detail to identify the perfect fit for the organization in the short and long term.

Also, an essential practice is to acknowledge which are the audience’s preferences and behaviours. This helps identify the channels to communicate through and media type to use when creating added value personalized content. So, pharma research-based segments have to be durable, not created based on guesses or short-term trends, for marketing activities and tactics to work.

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Mastering Market Segmentation

Tech Innovation: Attitudinal Research

Technological advances made new tools available. Overall this (combined with the traditional research-based market segmentation) can enhance the pharma teams’ outreach and effective audience engagement. 

By taking advantage of Machine Learning, the attitudinal approach mixes old and new empowered by big data collection. Not only that, but also act upon insights to better understand the targets, their journey touchpoints, and tailor the interactions.

Enabling the traditional model efforts optimization, the mixed framework used in attitudinal segmentation can help identify opportunities that were not visible before. All through segments’ extrapolation based on secondary data from the whole universe of pharma’s audience to unravel individual motivations and best-suited targets.

Furthermore, with machine learning usage and predictive insights collection, pharma companies can identify faster the high-value accounts for each product. Increasing, consequently, effective engagement through hyper-personalized messaging with fit-to-size added value content and at the same time discovering new opportunities.

All things considered, pharma teams can focus their efforts and attention on prospects and customers that really wish to engage with them. Rather than wasting time and resources in attempting to connect with targets that aren’t suited for the campaign/product/stage.

In the long run, to validate the predictions and continuously optimize the engagement model, typing tools based on machine learning can be integrated into the post-launch performance measurement strategy. This is used during the interactions and for market research purposes. 

Mastering Market Segmentation

The Takeaway

All in all, collaboration and data are the key elements for a sustainable, long-term, cost-effective market segmentation in pharma companies

In short, all customer-related teams’ efforts and expertise must be combined to achieve a 360º understanding of the prospects’ and customers’ pain points. As shown above, this can be done by using the data to narrow down the segments that add value to the business.

Embracing an agile targeted research-based marketing strategy that is data-driven, enables continuous improvement. Such helps to further tailor and match segments, products and stages.

In conclusion, measuring the performance is crucial to understand if the predictions enabled by adding machine learning to the tech stack used were accurate. Also to get valuable insights for future interactions.

Overall, with the aid of these predictive insights, pharma teams can identify more rapidly the high-value segments/accounts and successfully engage through hyper-personalized messaging. In essence, this can be enhanced with fit-to-size added value content that will enable new business opportunities.  

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