A type of customer intelligence, consumer lifestyle data measures attitudes, habits, hobbies, and interests of individuals.
This data comes from national or private surveys, customs and tourism statistics, social media profiles, and various mobile and online data collections.
These online and mobile data sources provide information about mobile apps downloaded, amount of time spent on them (passive app data), how many devices a user owns, and the location and movement patterns of the user.
Additionally, companies can enrich their consumer lifestyle datasets with transaction and purchase histories.
This data is most often organized into customer segments based whichever segments the data user wants to target. Most often, these are age range, location, gender, income bracket, and employment status. However, additional demographic features, like marital status, hobbies, and political affiliation may appear. In fact, many data providers, such as the ones you can review on our site, allow for custom customer segmentation.
This data lends itself to marketing and customer care uses. Companies use this data to conduct market research and to create targeted marketing campaigns. Then, once leads become customers, companies can focus new product or service launches on their customers or reach out to individuals who used to purchase from them but no longer do so.
With the range of data sources and the number of devices tracked per customer, the most important aspect of a good consumer lifestyle dataset is identity validation. In short, focus on clean yet comprehensive data that accurately tracks each individual across all channels and devices.
StartUp Nation: Why Consumer Lifestyle Data is the Key to Successful Marketing
Core: Effects of Consumer Lifestyles on Purchasing Behavior on the Internet: A Conceptual Framework and Empirical Validation
“We see consumers experiencing and making lifestyle changes, driven by COVID-19, that suggests the arrival of a sustained shift in eating habits,” said Sean M. Connolly, president and chief executive officer, during an Oct. 1 conference call to discuss first-quarter results. “We also know from prior recessions that an economic downturn typically leads to a permanent increase in at-home eating, even when economic growth returns.”
Food Business News: Conagra capitalizing on consumer lifestyle changes | 2020-10-02
VR therapy is the use of virtual reality equipment and programs within a therapeutic setting, sometimes as the primary therapy used, to address both mental illness and physical injury. In many cases, VR headsets are used in combination with biometric sensors like heartbeat or electrodermal activity sensors.
CCBT stands for Computerized Cognitive Behavioral Therapy and refers to the use of apps or other programs to do CBT therapy on a personal basis.
Cognitive behavioral therapy is, essentially, a method of training people to recognize fallacious beliefs or self-destructive behavior in themselves and then working with them to change their beliefs and behaviors to more healthy, productive ones. For example, someone prone to catastrophizing (assuming the worst outcome of a situation) may need to learn how to identify more likely outcomes.
CBT works best with mild to moderate emotional disturbances, especially depression and anxiety. Patients may use these programs in concert with their therapists or general practitioners, but cCBT has also proven itself very effective when used on its own.
Machine learning models help identify, analyze, and predict stress in individuals and larger populations. Stress management programs use these models to help people improve their responses to stressors and thus reduce their overall stress levels.
With the expanding online fitness and wearable health device industries, there is an increased interest in health-related apps and devices; stress management programs that integrate with these apps and devices should continue to grow in size and number.
Companies post content to their websites, newsletters, and social media accounts based on social media metrics that indicate when posts receive the most engagement. This engagement varies by social media site, industry, content type (promotion, article, etc.), and the time of posting: post engagement varies by weekday and even time of day. A content calendar, then, is the scheduling of content publication to increase engagement and conversion.
Companies also schedule site maintenance on these calendars, to ensure they only occur during times of least engagement.
Other terms for content calendar include editorial calendar, social media posting schedule, and other variations of these. An editorial calendar, however, focuses on content planned for company-managed websites plus social media accounts. Social media content schedules, on the other hand, focus on social media posts, as the name indicates.
In the current era, consumers expect the firms they engage with to provide personalized service and offers. They believe that companies have the technological tools to analyze their specific needs and can perform this task with minimal effort, so businesses should develop such ability.
One may imagine that this kind of mass operation will need some resources and financial investment but by implementing artificial intelligence and automated processes, this capability can be readily available for any business.
Traditionally, personalization was focused on a set of rules based on existing data. The firms used to collect data in advance without any consideration for real time data but now better and much faster results can be obtained by using AI that allows businesses to conduct profiling and real-time analysis to optimize each conversion. This process is defined as Predictive Personalization and is driven by Machine Learning.
Promotional planning is the process of optimizing marketing tools, strategies, and resources to promote a product or service to generate demand and meet set objectives. Artificial intelligence (AI) can be used to effectively plan promotional events, measuring their outcomes, and adjusting as necessary to achieve growth.
Remarketing campaigns show ads to people who have visited a business’s website or downloaded one of its apps. Remarketing is designed to get potential customers who have shown interest in your product or service to recall your business and their intent to buy, therefore enlarging the odds of them converting.
Nowadays, the abundance of data and the advances in Machine Learning and big data applications reduce the need for top-down segmentation of customers. Smart customer clustering based on many commonalities help companies better address customer needs to provide the right experience and divide resources efficiently.
Credit scoring is a statistical analysis performed by lenders and financial institutions to assess a person’s creditworthiness for mortgages, credit cards, and private loans. Credit scoring is used by lenders to decide whether to extend or deny credit.
Traditionally, a person’s credit score determined by credit bureaus is a number between 300 and 850 with 850 being the highest credit rating possible. As new types of lenders and insurers emerge, however, the traditional credit score becomes just one parameter joined with a large variety of alternative data that helps determine a person’s creditworthiness.
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Quantcast Advertise helps marketers, publishers, and others reach their target audiences. They track interest, behavior, and brand awareness, enabling companies to prospect and target customers. Quantcast claims their data are highly accurate and able to scale to any level.