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3 Steps to Unlock the Power of Behavioral Data

Behavioral data describes what customers do minute by minute, second by second. And, because it’s granular, contextual, and explanatory, it provides the strongest predictor of customer intent.
Mar 3rd, 2023 11:00am by
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The use of behavioral data to create the most descriptive views of a customer journey continues unabated. The most competitive organizations in every industry are using behavioral profiles to deliver differentiated customer experience and consider it as their most valuable data asset. Proponents of behavioral data analysis understand its ability to enhance services — from personalization to product development.

Despite this, today’s data teams are experiencing a disconnect between their vision and reality. They want to pursue innovative, value-creating projects using best-quality behavioral data — for example, building scalable data platforms, feature-engineering for data-intensive AI and ML apps, powering critical reporting and building composable customer data profiles (CDPs).

Yet, in reality, their days are spent finding, cleaning and preparing data. Trying to force results from poor-quality data. In other words, doing time-consuming, manual tasks that don’t utilize their full skills and capabilities. No wonder, as a recent research report from Snowplow confirms, seven out of ten data practitioners plan to leave their jobs this year.

In order to help these data teams overcome these challenges and unlock the power of behavioral data, here are the steps for setting an organization up for success.

But first, what is “behavioral data”?

Behavioral data describes what customers do minute by minute, second by second. And, because it’s granular, contextual and explanatory, it provides the strongest predictor of customer intent.

Organizations have a huge opportunity to use behavioral data to enhance services so that their product is personalized. This is because data is predictive and contextual, so it is the best possible fuel to power advanced analytics and AI applications.

Step 1: Establish a Strong Data Culture

Given how valuable behavioral data is to decision-making, organizations across different sectors need to recognize its importance and establish a strong data culture.

In practice, a strong data culture is a “decision culture” according to McKinsey research, which is a culture where an organization can accelerate the application of advanced analytics, powering improved business performance and decision-making. Furthermore, Forrester found that organizations that use data to derive insights for decision-making are almost three times more likely to achieve double-digit growth.

So why is it such a challenge to create this type of culture? One issue is that 35% of organizations are being held back by an immature approach to data — even though they have high confidence in its value — according to recent research from Snowplow. These organizations still have work to do in building data maturity, yet this can be a time-consuming process that requires a reevaluation of culture, leadership and technology.

To solve today’s issues and boost the retention of valuable, skilled data practitioners, organizations need to audit their specific technology needs and invest in the right platform to collect, build and manage high-quality behavioral data.

Step 2: Optimize Data Teams

Optimizing data teams is one of the most critical decisions an organization can implement to access the power of behavioral data. In today’s environment, data teams face a plethora of issues. The data used for projects may be incomplete, inaccurate, messy or limited in utility.

In fact, research from Snowplow shows that 88% of data professionals at the vice-president or director level claim that a lot of their time is spent cleaning, finding and preparing the data. Many data teams are so busy grappling with incomplete or inaccurate data that they can’t pursue innovative, value-creating data projects.

In addition, data practitioners also feel bogged down with last-minute requests and other administrative duties and therefore cannot use the full scope of their analytical skills. As a result of these issues, 63% of data professionals cite stress as a reason they want to leave their jobs, according to research from Snowplow.

All of these issues faced by data teams could be lessened by ensuring they have the tools and technology in place to optimize their data and avoid having to spend so much time preparing it. This in turn will free data teams up for more time for projects focused on transformative AI and advanced analytics with rich behavioral data that’s simple to use.

Step 3: Introduce a Data Creation Strategy

A large portion of organizations is looking for unique and innovative ways to establish a new way for behavioral data analysis to better do its job. Despite this, many organizations though are facing challenges that could cause a number of painful delays. A growing frustration is that organizations could be investing more in the right technology. Poor management and lack of proper technology only adds to the increasing stress faced by data teams.

In fact, only Snowplow’s research found that 24% of organizations say that their behavioral data is centralized, well-governed and highly trusted which leaves a lot of people dealing with governance and trust issues. However, those frustrations experienced by data teams could be alleviated by implementing a data creation strategy.

Data creation is the process of creating high-quality, contextual behavioral data to power AI and other advanced data applications. Instead of working with the data exhaust which happens as a result of SaaS applications and black box analytics tools, data creation allows a choice of metrics that would best reflect the organization’s needs. The great thing about this is that it saves data teams quite a lot of time as it continuously delivers a highly trusted real-time stream of data that evolves with the business.

Even though there is ongoing frustration and an ever-changing industry, the silver lining is that teams can use data creation which is the process of deliberately creating high-quality behavioral data to power AI and advanced data applications. Fortunately, for those that choose to implement a data creation strategy, according to Snowplow’s recent report, organizations can give data teams more time to innovate and help organizations unlock the power of behavioral data.

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