Why Behavioral Science Is Critical to Marketing and Research

What if we could identify consumers’ underlying emotions or motivations to improve our understanding of whether they were actually going to purchase a product? Over the past few years, marketing and research has been digging into the “why” behind behaviors to get even deeper, below the surface of the insights we deliver. The goal is to help brands better understand the true drivers of consumers’ behavior — and it all starts with behavioral science.

What if we could identify consumers’ underlying emotions or motivations to improve our understanding of whether they were actually going to purchase a product? Over the past few years, marketing and research has been digging into the “why” behind behaviors to get even deeper, below the surface of the insights we deliver. The goal is to help brands better understand the true drivers of consumers’ behavior — and it all starts with behavioral science.

What Is Behavioral Science?

Behavioral science isn’t a new industry, but within the past few years is something of an emerging topic in marketing and research. At its core, behavioral science and the research that results from it, seeks to understand the many aspects related to someone’s habits or decision-making. Most importantly, as we noted, it helps to understand why people make certain decisions.

If you think of that in the context of our marketing and product strategies, it’s clear why behavioral science plays a role in market research. There are a variety of methods that can get close to truly understanding consumer behavior, but much of them can fail to capture empirical evidence — sensory information captured through observations and the documentation of behaviors through experimentation.

As a result, the importance and rise of behavioral science in marketing and research is no small subject. Just in the past year, there have already been numerous events discussing behavioral science specific to gathering and analyzing data to understand why consumers make decisions — but marketers and researchers, by and large, are still figuring out how to leverage it.

Leveraging Behavioral Data

Big data can be used as a possible solution for at least two reasons. First, it gives us access to more data than ever before, including data based on actual behavior from purchasing, web analytics, subscriptions, and more. As a result, big data can reduce the struggles we sometimes have with differences between stated versus observed behavior.

Second, there are big data sources that allow us to understand motivations of our consumers by examining the big 5 personality traits for millions and millions of people. By understanding different personalities, we can begin to realize if being “extroverted” or “conscientious” drives consumers’ purchasing. Some suggest that behavioral science and the resulting data on motivations behind decision making will be the new normal for market research. We agree that understanding what people don’t tell us in surveys is as important as what they do. Together, these two types of data give us a more well-rounded picture of consumer behavior, and with the right methodology, you can gain this knowledge quickly.

In a specific use case, a brand was looking to understand their target audience for a new product innovation. They had hypothesis’ about what this audience would look like, and likely could have gained that knowledge through standard quantitative research. However, by incorporating an approach that combines survey data and big data, they were able to understand who their audience was, but also what would motivate them to purchase this particular new product. The moral of the story? Consumers are more than just the people that buy your product.

Big Data: What It Is and How to Analyze It

What really is big data? Big data encompasses extremely large datasets that can be analyzed to reveal more in-depth insights, patterns, trends and even help predict future outcomes. But what actually makes up these “extremely large datasets” can be much more exhaustive, and understanding them can significantly improve our overall knowledge of big data and how to use it.

What really is big data? Big data encompasses extremely large datasets that can be analyzed to reveal more in-depth insights, patterns, trends and even help predict future outcomes. But what actually makes up these “extremely large datasets” can be much more exhaustive, and understanding them can significantly improve our overall knowledge of big data and how to use it.

Big data is just data: The following types of big data can be used to define any data in today’s world. But the goal of understanding the different types of data is to help determine how they might be used together to provide the answers to the questions marketers are asking.

3 Types of Big Data

First and foremost, big data can be defined based on its structure. The structure of data depends on how organizable it is. In other words, whether it can be formatted into tables of rows and columns. There are three types of big data when defining it by the structure:

  1. Structured: Data that is structured is often already stored in a database or other data management platform, and it can be easily accessed and processed to provide an ordered output.
  2. Unstructured: Usually larger datasets — the majority of big data is unstructured, meaning it can’t easily be organized or classified.
  3. Semi-Structured: As the name implies, semi-structured data isn’t inherently organized at the start, but as it is analyzed or digested it can begin to take on a more structured form.

Both structured and unstructured data can be either human-generated or machine-generated. Human-generated, structured data can be contact information or website form details directly collected from an individual. Human-generated unstructured data can be any form of website activity and social data such as video, audio, or social posts shared by a person.

On the other hand, examples of machine-generated, structured data include GPS tracking, inventory tracking, or transaction data. Unstructured forms of machine-generated data include information gathered through satellite such as images or weather sensory information.

Each of these types of data can be analyzed in many different ways. However, there are certain types of analysis that will serve their own purpose depending on the objectives at hand.

4 Types of Analysis

There are many reasons to look to big data for insights. Whether it’s combining big data and survey data for detailed audience intelligence or combing through it to predict purchase data, they all fit into four types of analysis:

  1. Prescriptive Analysis: Data analysis that provides answers to what actions should be taken.
  2. Predictive Analysis: An analysis of data that can be used to predict what situation or number of situations may results.
  3. Diagnostic Analysis: Data analysis that provides insight into what happened in the past and why.
  4. Descriptive Analysis: Data analysis that can be real-time or leveraged to see what is currently happening.

Mapping your analytics and marketing strategy to the type of big data needed and the type of analysis can help understand what tools and solutions may be best to bring it all together. Specifically, the type of data and analysis will lead you to the type of big data analytics required.

4 Benefits of Applying Marketing Analytics

Marketing analytics is no small subject in today’s world of business. In fact, according to Transparency Market Research, the marketing analytics industry is set to grow by roughly 14% by 2022. Why such growth? Marketing analytics has a tremendous impact on a marketing organization’s activities, but also on a brand’s overall understanding of their entire company’s success.

Marketing analytics is no small subject in today’s world of business. In fact, according to Transparency Market Research, the marketing analytics industry is set to grow by roughly 14% by 2022. Why such growth? Marketing analytics has a tremendous impact on a marketing organization’s activities, but also on a brand’s overall understanding of their entire company’s success.

There are four unique benefits marketing analytics provides, and combined together, these benefits give a holistic view of an organization’s past, present and future.

But First: What Is Marketing Analytics and Why Is It Important?

Marketing analytics is a result of the technology and influx of data we use as marketers. Early on, marketing analytics was a relatively simple concept. It encompassed the process of evaluating marketing efforts from multiple data sources, processes or technology to understand the effectiveness of marketing activities from a big-picture view — often through the use of metrics. Fundamentally, it’s all about quantifying the results of marketing efforts that take place both online and offline.

Today, marketing analytics has become an entire industry that’s changing the way we work and the type of work we do as marketers. 

It’s important to measure the financial impact of not just marketing but of a variety of efforts from product and sales — which marketing analytics also can provide. As a result, knowing and understanding the different types of analysis and the benefits they provide within marketing analytics, can help to identify what metrics to focus on for what objectives — because objectives can be an endless list of how to understand or increase ROI, monitor trends over time, determine campaign effectiveness, forecast future results, and so on.

The 4 Benefits of Applying Marketing Analytics

1. Learn What Happened

Marketing analytics can first lend insight into what happened in the past and why. This is instrumental to marketing teams in order to avoid making the same mistakes. Through descriptive analysis and the use of customer relationship management and marketing automation platforms, analytics bring to light not only what happened in the past but also provide answers to questions on specific topics. For example, you can ask more about why a specific metric performed the way it did, or what impacted the sales of a specific product.

2. Gauge What’s Happening Now

Marketing analytics can also help you understand what’s currently taking place in regards to your marketing efforts. This helps determine if you need to pivot or quickly make changes in order to avoid mistakes or make improvements. Using dashboards to display current engagements in an email track or the status of new leads are examples of marketing analytics that look to assess the real-time status of marketing efforts. Usually, these dashboards are created by employing business intelligence practices in addition to a marketing automation platform.

3. Predict What Might Happen

Some could say the predictive aspect of marketing analytics is the most important part of it. Through predictive modelings such as regression analysis, clustering, propensity models and collaborative filtering, we can start to anticipate consumer behavior. Web analytics tracking that incorporates probabilities, for example, can be used to foresee when a person may leave a site and when. Marketers can then utilize this information to execute specific marketing tactics at those moments to retain customers.

Or perhaps it’s marketing analytics that assesses lead management processes to prioritize leads based on those similar to current customers. This helps identifies who already has a higher propensity to buy. Either way, the goal of marketing analytics for the future will be to move away from a rear-view strategy to focus on the future. Luckily, the influx of data, machine learning, and improved statistical algorithms mean our ability to accurately predict the likelihood of future outcomes will rise exponentially.

4. Optimize Efforts

This last benefit only comes when you combine your analytics with your market research objectives — but if you do so you could see the greatest impact. In fact, if you’re not ensuring your marketing analytics and market research work together, then you could be missing out on a lot of opportunities. Essentially, it’s about translating marketing analytics findings into market research objectives. A common mistake marketers make in conducting marketing analytics is forgetting to gather real customer feedback. This activity is important to bridge the gap between analytics insights, a marketing strategy and activation.

In addition to the first three benefits or approaches, brands should use marketing research as a tool to push their marketing analytics from just learning about lead generation and sales metrics to actually understand customers in the context of their marketing opportunities.

Understanding and Leveraging Big Data for Audience Insights

Big data can be defined as extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions — it isn’t just data for data’s sake. But how big is big data?

Data mining, big data
Creative Commons license. | Credit: Flickr by KamiPhuc

Big data can be defined as extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions — it isn’t just data for data’s sake. But how big is big data? Some data management platforms (DMPs) have over 900 million consumer profiles globally with 10,0000 different data points associated with it. To get even close to that amount of data from consumer surveys, you’d have to run about half a billion surveys.

So while big data has been most impactful to programmatic advertising and media, it also provides a lot of opportunity for other marketing efforts and market research. Due to the insight it provides into consumer behavior, as budgets continue to shrink and the speed of decision making continues to increase, big data is necessary. 

The 4 V’s of Big Data

Today, big data is bigger than ever with more people engaging and utilizing tools that offer big data integration. However, incorporating big data is a marathon, not a sprint and companies have to take the right steps before making the leap. If you’re not already using big data, before getting started, you’ll want to familiarize yourself with the 4 V’s of big data:

  • Volume: the amount of data available
  • Variety: the different types of data
  • Velocity: how frequent, real-time, or up to date the data is
  • Veracity: how accurate and applicable the data is

The biggest challenge when it comes to data is the “veracity” of it. Because there is so much and such a variety of data, it can be difficult to assess its accuracy and application to your business. Discerning the signal from the noise is where most innovation teams will spend their time interpreting the data. In other words, veracity helps to filter through what is important and what is not, and in the end, it generates a deeper understanding of data and how to contextualize it in order to take action.

Data Veracity: The Most Important “V”

Data veracity, in general, is how accurate or truthful a data set may be. In the context of big data, however, it takes on a bit more meaning. More specifically, when it comes to the accuracy of big data, it’s not just the quality of the data itself but how trustworthy the data source, type, and processing of it is. Removing things like bias, abnormalities or inconsistencies, duplication, and volatility are just a few aspects that factor into improving the accuracy of big data.

Unfortunately, sometimes volatility isn’t within our control. The volatility, sometimes referred to as another “V” of big data, is the rate of change and lifetime of the data. An example of highly volatile data includes social media data, where sentiments and trending topics change quickly and often. Less volatile data would look something more like weather trends that change less frequently and are easier to predict and track.

The second side of data veracity entails ensuring the processing method of the actual data makes sense based on business needs and the output is pertinent to objectives. Interpreting big data in the right way ensures results are relevant and actionable. Further, access to big data means you could spend months sorting through information without focus and without a method of identifying what data points are relevant. As a result, the velocity of data and agile methods come into play here — big data should be analyzed in a timely manner, as is difficult, otherwise the insights would fail to be useful.

Big data is highly complex, and as a result, the means for understanding and interpreting it are still being fully conceptualized. While many think machine learning will have a large use for big data analysis, statistical methods are still needed in order to ensure data quality and practical application of big data for better marketing activation. For example, you wouldn’t download an industry report off the internet and use it to take action. Instead you’d likely validate it or use it to inform additional research before formulating your own findings. Big data is no different; you cannot take big data as it is without validating or explaining it. But unlike most market research practices, big data does not have a strong foundation with statistics—luckily, integrating it with survey data can help.

Integrating Big Data With Survey Data for Market Insights

While big data can answer when, where, and what, it can’t answer why. Integrating primary research, particularly with an agile methodology that can keep up with the velocity of big data, can help to analyze and connect the dots — easier said than done.

The obvious benefits of using big data in marketing includes gaining a better understanding of people, content, and media. By combining big data with survey data, you can identify a market opportunity, understand your target audience, and incorporate findings into your messaging and creative execution. Infusing survey research with big data also means the volume of questions that need to be asked are reduced as big data provides more answers. So our understanding of consumer behavior is going to grow exponentially over time as we bring these two worlds together. The ability to incorporate big data to use fewer questions can also deliver more speed and value.

The result is unique audience intelligence. The benefits of this approach mean removing the guesswork during activation, letting the audience identify the opportunity for you, creating more effective messaging, and ultimately increasing value on ad purchases. There are certainly challenges to infusing survey research with big data before organizations can reap the benefits. Since DMP’s were originally meant for advertising, they haven’t been made research-grade, so oftentimes there are errors or inconsistencies in the data related to an audience. However, there are solutions out there — and more to come for sure — that will be able to overcome the challenges and provide an accurate depiction of audience insights through big data.