Predicting the Path Most Taken

Predictive Path to Purchase studies and methodologies for customer purchase decisions

By Chelsea May

Companies that embark on Predictive Path to Purchase studies often report some combination of increased confidence in their existing marketing strategies coupled with valuable, previously unconsidered insights that become extremely beneficial competitive advantages.

For an in-depth explanation of why Predictive Path to Purchase studies are so important in the modern media landscape, please see our article, “How to Apply ‘Predictive Path to Purchase’ to Better Understand Customer Behavior.”

In this article, we’ll examine Martec’s approach to this methodology.

Gaining Insights Every Step of the Way

There are four primary timelines involved in documenting the Predictive Path to Purchase:

  • Pre-purchase 
  • During the purchase
  • Post-purchase
  • Predicting future purchases

Martec applies distinct methodologies at each of these milestones to effectively learn, document, and predict the path to future purchases for a given product or service.


Because so much consideration of a purchase decision happens well in advance of that discrete purchase, it’s important to study the conversations and analyze the expressed opinions and considerations being offered by the potential buying audience. The best way to do that is to “listen in on” conversations that are being had in public about brands and product categories. Martec often will suggest methods such as web-scraping and monitoring open public dialogues on social media platforms. Technology is leveraged to disseminate sensors to see how a brand, product, or category is being talked about.

It’s important to not only study actual customers of a brand, but also potential customers as well. We target very specific types of people who have very specific types of buying behaviors and motivations. 

During the Purchase

It’s critical to capture behaviors and sentiments expressed during the purchase process itself. We must capture information at every touch point along the customer journey. This may include working with the client brand to contact existing and potential customers using traditional survey techniques at various points along their journey to identify markers that suggest a likelihood to complete the purchase.

It’s again at this point during which we engage back-end analytics tools to understand those inputs and cross-analyze them against various customer persona types to best understand what may cause customers to either divert, stay the path, or ultimately trigger a purchase. During this point, we often utilize our Emotion Intelligence platform to learn about how customers feel during the purchase process, both positive and negative emotions, whether active or passive, and how intensely.


There is “listening” that needs to occur after a purchase is made as well. Our process is to request data from the brand client regarding metrics captured throughout the purchase process, such as the percentage of people who drop off at different points (e.g., cart abandonment) versus those who completed the purchase process. We also can gather video diaries immediately following a customer experience event or gain access to the online fora that categories of customers gravitate to in order to share opinions and make or receive recommendations, both before and after purchases.This allows us and the client to fully analyze the purchase process throughout the entire sales funnel, including Phase Four (Evaluation) discussed here.

Martec prides itself on an ability to leverage the numerous and deep relationships we have to identify and reach practically any profile of prospect or customer, no matter how “niche” or specifically defined.

Predicting Future Purchase

Studying and understanding data gathered in these three phases empowers us to apply the inputs to be predictive about future customers.The most effective Predictive Path to Purchase studies combine both quantitative research (e.g., surveys) and qualitative research (e.g., conversations, discussion groups, etc.) to best understand big-picture insights as well as specific, emotion-driven decisions of the actual individuals in the broader market, which quantitative studies alone don’t capture as well.

The optimal approach is known as “modeling,” using quantitative insights, but this requires a lot of data and customer behavior metrics that many companies don’t have today. In such instances, we can use more qualitative methods to perform the predictive aspect of Predictive Path to Purchase, if need be. This results in a data-informed personification of the customer base, which informs predictions that are tied directly to customer segments in a very granular sense (e.g., a 20-year-old purchaser isn’t likely to behave the same way a 60-year-old will).

More Data In, Better Insights Out

The key throughout all of this is to work hard to understand the language nomenclature of the market you’re looking to influence. Small nuances in vocabulary, style, and voice can be all it takes to attract or repel specific audience types, even if your intentions are well-placed and somewhat rooted in a general understanding of the market. There is a big difference between speaking to a customer segment and speaking with a customer segment.

Marketing strategies and tactics have evolved rapidly in the past few decades. The stakes are high, as even stalwart companies we grew up with (like Kodak, Sears, and Kmart) can suddenly disappear based on evolving market preferences and behaviors.

The modern path to purchase is a many-traveled route. To predict that pathway — and subsequently to optimize for it — you should consider all of the potential touch points that can impact prospective customers…even those you don’t currently know about.

Chelsea May serves as Project Manager for Martec, with specific emphasis on quantitative research and initiatives.

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