Data Strategy: The Importance of Looking Forward, not just measuring the past

People are fascinated with trying to predict the future.

In many aspects of life, it’s not possible, but within business, predictive modeling is becoming an increasingly required capability to find competitive advantages and execute critical strategic decisions. Many businesses today fail to grow or survive because of a lack of focus on forecasting. Failing to forecast market trends and customer behaviors can lead to strategies that don’t adequately address areas of opportunity brands can take advantage of. When done right, however, you can make more well-informed decisions designed to drive results.

In healthcare and life sciences marketing, predicting the future and knowing where to concentrate energy, resources, and budget can significantly impact brand success, whether for a launch brand entering a crowded market, or for a brand reaching the end stages of its product lifecycle. The clearer our vision of a path forward, the better we can make informed decisions that can make or break a brand’s success.

These informed decisions all start with data. Data can be an incredibly valuable asset, but data alone is useless. Analytics is what makes that data valuable and beneficial, so it can then be applied to inform business needs in multiple ways, including efficiencies, performance, and strategically guided decisions. Analytics are what can take data and help it predict those areas of opportunity.

Predictive analytics is a discipline that concentrates greatly on data modeling and machine learning techniques. It uses historical, even real-time data to predict and forecast possibilities and outcomes, which brands can use to help make critical decisions. Predictive analytics can identify customer segments, predict customer behavior, and develop personalized marketing campaigns.

Predictive Modeling in Healthcare Marketing

Amidst growing adoption of predictive analytics in healthcare marketing, brands are excited, curious, and sometimes intimidated by what can feel like the ‘black-box’ nature of predictive modeling. Clients are increasingly seeking guidance on how to effectively harness data science to drive their marketing efforts while remaining aligned with their brand’s core strategy and trusting the validity and utility of the results. Areas that predictive analytics can be used to help inform and guide include:

  • Untapped customer journey points – Predictive analytics can forecast care gaps, decision barriers, and other moments that impact treatment and outcomes. The “standard” playbook for healthcare marketing no longer guarantees success; finding a competitive advantage to deliver timely solutions with surgical precision is not only possible but can be scalable like never before.
  • Finding high value customers – Predictive analytics allows life sciences companies to identify which customers are most likely to be receptive to their products and which customers are likely to generate the highest revenue. Analyzing past customer behaviors can help develop targeted sales and marketing strategies that are more likely to be successful.
  • Optimizing customer targeting – Predictive analytics can help optimize sales territories by identifying which areas have the highest potential for sales growth. By analyzing data on physician prescribing patterns, “in-office” or clinical behaviors, referral patterns, procedural data, and patient demographics, companies can hone in on the right audience of physicians taking the most promising actions and allocate resources more effectively to maximize potential impact.
  • Personalizing brand interactions – Predictive analytics can help sales reps and media teams tailor their engagements with individual customers by providing insights into their preferences and behaviors. Analyzing customer interactions and looking at data like HCP diagnosing, treating, prescribing, coverage, and other factors such as social determinants of health can enable a personalized and segmented approach and increase the likelihood of success.

However, as companies turn to more predictive analytics and automated next best action recommendations, how can they best ensure these findings are being properly implemented in sales and marketing strategies?

Healthcare data science cannot operate in isolation of other functional teams.

Marketing predictive models should incorporate the minds of both internal and external strategy counterparts and even engage key opinion leaders within the healthcare industry to ‘gut check’ the process and ensure strategic alignment.

It starts with hypothesis-driven thinking. This is fundamental at the beginning of data analytics for marketing, as it helps shape and then build predictive models using customized and targeted healthcare-specific algorithms and techniques. But this can’t happen in a vacuum and requires the right input from the right stakeholders.

At DiD, our data science and analytics team collaborate with brand strategists and medical consultants throughout every stage of project development. Project briefs for healthcare predictive models should mirror the process already in place for creative and media briefs, which already benefit from full team collaboration. Having cross-team strategic collaboration is key to addressing potential client concerns.

In the situation where predictive analytics help inform brand tactical and engagement strategies, close collaboration with our media team enables them to proactively prioritize detailed customer targeting efforts, better inform campaign channel selection, and create a more sophisticated marketing ecosystem designed to deliver maximum impact.

The collaborative approach ensures that, from initial project brainstorm to model development, the results of any predictive models align with real-world healthcare challenges, comply with regulations, and tackle critical issues in patient care that matter to our brand teams. This ensures that the resulting work is not only statistically robust but also practically valuable in enhancing patient and brand outcomes.

Harnessing Predictive Modeling to Make Better Informed Brand Decisions

Predicting the future is not a guarantee, but it is possible to more meaningfully look toward data to find the right opportunities to invest in based on a higher likelihood of impact or success. Incorporating predictive analytics can help deliver better ROI, create stronger brand understanding and connectivity, and help optimize marketing budgets by moving beyond simple awareness only campaigns.

To learn how you can leverage data-driven intelligence, predictive modeling, and strategic scenario planning to help deliver results, visit us at

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