What is Consumer Behavior Data? Examples, Datasets and Providers

Consumer Behavior Data Use Cases in Detail

Consumer behavior data is used in product development, product management, marketing, data analysis, and post-sales customer service, and is vital for companies that are:

• Expanding geographically, particularly into unfamiliar markets
• Launching new products or services
• Customizing products or services for specific target markets
• Expanding their demographic reach
• Seeking to increase customer retention/loyalty

Understanding and being able to predict customer decision making, purchasing, and loyalty behavior helps companies tailor their product offerings as well as marketing and promotional activities to maximize both sales and retention.

Here of some typical uses of consumer behavior data:

Product recommendations:
Consumer behavior insights are used to generate the recommendations that we are familiar with from online retailers and streaming services, in this case by finding patterns in purchase histories and product affinities. For example, over 80 per cent of the TV shows and movies people watch on Netflix are discovered via the platform’s recommendation system. Netflix uses behavior intelligence to tailor recommendations to the user’s personal interests. Amazon is also a leading user of consumer behavior data to promote products to customers based on previous purchases.

Content optimization:
Consumer behavior data is used to generate the recommendations that we are familiar with from online retailers and streaming services, in this case by finding patterns in purchase histories and product affinities. But you don’t have to be an industry giant to leverage the up- sell and cross-sell opportunities that consumer behavior data makes possible.

For example, you might know what your customers want to buy, but when are they most likely to buy? What are the most common triggers? What kind of interactions, via which channels, typically precede a purchase? Data showing how and when customers interact with your brand — via social media, first or third party websites, mobile apps, email marketing etc. — can guide decision making about the timing and personalization of your marketing interventions to maximize sales.

Customer retention:
Companies can also use consumer behavior feeds to develop ways of reducing customer attrition or churn. Customer attrition, or churn, simply means the loss of customers. This is extremely important for a business to avoid as retaining existing customers can be much more cost-effective than acquiring new ones. In fact, it costs fives times as much to attract a new customer than to keep an existing one.

Companies can also use consumer behavior data to develop ways of reducing customer attrition or churn. Clearly, data on the timing and frequency of negative reviews or inbound contact with customer service can help companies predict the likelihood of customer defection and act preemptively. But consumer behavior analytics can also help uncover patterns that predict defection for the vast majority of unhappy customers who never express negative sentiment, thereby improving retention and the revenue it generates.

What Other Types of Consumer Data Are There?

Various different kinds of data can be collected on a user which tell us more than simply how they behave. Some others include:

Basic audience data: This type of data simply tells you who your audience is. It can include contact data such as the user’s name or email address, as well as demographic data about their age, gender, location and income.

Attitudinal data: Attitudinal data tells you what consumers think from their first-hand account. This is a kind of declarative data and can be obtained from the user directly by looking at surveys, online reviews or social media comments. This kind of data is useful for understanding the honest opinions of your customers, however it may not be representative of your entire audience as some consumers are more willing to share their views than others.

Interaction data: Interaction data is fairly similar to behavior data in that it shows how an audience interacts with your brand but at various stages of the consumer journey. This can include clickstream data, for example, to show how a customer arrives at a website.

Each type of data has its own advantages and disadvantages, but combining different types of data can be a useful technique for digital marketing strategies as it allows you to understand various aspects of your audience.

What Are the Advantages of Behavior Data?

Audience data can tell you who your audience is, such as age, location, gender, income, but this alone may not be enough to make assured business decisions. Behavior datasets tell you how your audience actually responds to content, which can give you a more accurate depiction of their choices and actions as a consumer. Drawing conclusions based on descriptive data can involve making assumptions about certain demographics, whereas behavioral insights give a much clearer picture on the actual desires of a consumer, as it is supported by evidence of their actual actions.

Behavior data intelligence is often seen as more reliable than declarative data, which is data that users give willingly themselves, as it is based on real behaviors rather than how a user considers themselves as a consumer. Declarative data such as surveys can easily be influenced by the mood or preconceptions of a consumer, whereas behavior data is true to how they actually respond and interact with the stimulus. Behavior analytics can also be more instant and can be used in real time in marketing strategies.

A survey of businesses found that, on average, 93% of businesses with an advanced personalization strategy experienced revenue growth, showing that using consumer behavior insights allows for high level personalization of content to ensure an increase in revenue.

What Are Typical Consumer Behavior Data Attributes?

In raw form, consumer behavioral data is pretty much illegible. Lots of unstructured data and paradata is collected which needs processing in order to make sense of it. Thus, the raw data is often organized into clickstream files.

Consumer behavior data has expanded beyond its traditional reliance on the RFM model (recency, frequency, monetary spend) to encompass a variety of information collected throughout the customer decision journey. There are many different kinds of consumer behavior data at different levels of granularity. Some of the most common include:

What is an Event?

Consumer data is usually stored in the form of an ‘event’. An event refers simply to an action taken by an individual, such as clicking on a page. This data is stored alongside metadata with ‘properties’, which describe the event, such as the device type.

How is Behavioral Data Typically Collected?

Consumer Behavior Data is collected from a variety of sources, including websites, help desks, CRM systems, and mobile apps. Some of these sources — purchase information, CRM and help desk data — are internal to the company, while others are external. Traditional market research like surveys and questionnaires also yield consumer behavior datasets.

Some of the main methods of behavioral data collection include:

Cookies: Browser cookie tracking is the most common way of capturing audience data from websites and almost every website uses them. Cookies are small text files that save user-specific data in your browser. They store events such as when a user visits a site, the actions they take, the time they remain on a page and the products they buy. They reveal exactly how consumers navigate your website, which features they interact with and which they don’t, and what paths lead to successful sales.

Analytics tools: Services such as Google Analytics use JavaScript code to capture data from the user’s browser and count the number of page visits.

Clickstream files: As we’ve seen, clickstream files are a way of collecting raw behavior data and making sense of it by compiling it into a series of events which show a customer’s journey through a site. Analytics tools can provide the infrastructure for this. Clickstream files include an ID to identify the individual user, an action such as the website visited, and a timestamp of when the action took place.

Market research: Surveys and questionnaires can also be used to obtain a different kind of behavioral data, declarative data. They provide an easy way to find out what your customers think about your products or services and can be of high quality since it comes directly from the user. This data provides information about brand recognition and affinity during the initial consideration and active evaluation phases, while data on how customers arrive at your digital platforms helps evaluate and focus marketing expenditures.

What is Behavioral Analytics?

Behavioral analytics involves looking at consumer behavioral databases to draw conclusions on the reasons for consumer actions.
Behavior analytics can be used for a variety of purposes. It is important for businesses wanting to understand their current and potential customers better and adapt their marketing strategies accordingly. It can be used in retail for product recommendations, in app and game development to predict usage trends, in social media to show recommended posts or even in political campaigns to help attract potential voters.

How to Perform Customer Behavioral Analysis?

Collect audience data: It is useful to get to know your customers with qualitative data such as demographics, location, interests. This can help to define who your audience is and which customers you should target.

1. Audience segmentation: If you have a large dataset it is hard to draw useful conclusions; this is where audience segmentation is important. Audience segmentation is the process of dividing an audience into smaller subgroups based on common characteristics, such as age, gender, location, interests, online habits. These groups can then be observed in their actions to analyze the way consumers interact with a business.

2. Acquire quantitative data: Behavior data can be collected from within the company (first party data), with usage reports and insights for example, or bought from external vendors (third-party data). By combining data from different sources you can get a good picture of how different customers behave.

3. Compare quantitative and qualitative data: Combining both data types allows you to see how different kinds of customers interact with your company in different ways. For example, which customer group buys more products, or which group returns frequently to your site.

4. Apply this information to your marketing campaign: Insights on how different types of consumers behave allow you to optimize your marketing strategy. For example, you can personalize content to target the right customers.

5. Evaluate the results: Collecting data on sales, number of website visits and revenue, for example, will help to understand if your analysis was correct and effectively used in your marketing strategy.

What Types of Behavioral Analysis Are There?

Behavioral analysis can be conducted in various ways using different tools. There are various kinds of behavioral analysis tools:

Funnel analysis: Funnel reports show a user’s journey in stages, helping to visualize conversion funnels to see which consumer journeys lead to purchases and which do not. Funnels can then use testing to see what small changes can increase conversion and customer retention.
Segmentation tools: Segmentation allows you to identify KPIs and consumer trends. For example, it can be used to analyze the effectiveness of different marketing strategies by comparing the number of site visits as a result of each of them. This allows companies to identify strong and weak points in their campaigns.
Cohort analysis: Cohort analysis involves looking at specific groups of users and how they behave over time. This can be used to discover user retention rates or investigate what brings customers back for repeat purchases.

How to Assess the Quality of Consumer Behavior Data?

Consumer behavior data should be targeted to your needs, up to date, and accurate. In some cases too much data can be as bad as not enough. Determining up front in consultation with a data provider what kind of data and what quantity of data will best benefit your company can save costs and frustration in the long run.

Consumers are always consuming and always generating data, so you need to ensure that your data is keeping up. Data collection is a quickly expanding industry, in fact, 90% of the world’s data has been created in the last two years alone. Only data that is updated regularly — or even in real time — will help you stay on top of your markets. Frequency refers to the regularity with which data is collected, while latency measures the delay between collection and distribution. High- frequency low-latency data will keep you up to date. Data also needs to be accurate and representative in order to be relevant to your customer segments, and data providers must ensure that appropriate sampling techniques are being used to ensure accuracy.

Some things to consider when purchasing data:

• Completeness - are any important attributes missing? Rarity - is this data unique?
• Usability - how easily can the data be turned into insights? Volume - how detailed is the data?
• Latency & frequency - how up to date is the data? Accuracy - how accurate is the data?
• Privacy - does the data comply with data protection laws?

What Are the Common Challenges When Buying Behavioral Data?

Given the potentially unlimited scope of consumer behavior — which can include everything people do in their roles as consumers — and the vast quantities of data being generated and captured digitally, it can be difficult to know which kinds of data best suit your needs. Speaking directly to data providers is a good place to start.

Consumer behavior can also be quite volatile at times, and this volatility is reflected in the data. So it is important to make sure you’re getting the right data covering both a representative audience and the right time frame, up to and including the present.

It is also important to understand where the data comes from to ensure its reliability. Research shows however, that over [50% IT leaders (57%) and IT professionals (52%)]((https://bigdata-madesimple.com/exciting-facts-and-findings-about-big-data/) report they don’t always know who owns the data they use in their analytics. Not knowing the source of your data can put you at risk of making misguided marketing decisions based on inaccurate or irrelevant data.