12.2.19 Clark tells his Black Friday experience; Personal loans for presents!?; Tenant screening goes futuristic
Stores were not crowded on Black Friday this year. Clark visited Costco, Target, Walmart, Microcenter, Ollie’s & Five Below just to get a feel for retail that day. There were no real crowds. Patterns have changed. Around 23% of holiday shopping during Black Friday month will be done online, up significantly from last year. So much of shopping has shifted to click and collect. It’s convenient for shoppers to order online and pick up their merchandise. Costco stepped up promotions this year with an emphasis on Costco.com, but marketing got ahead of operations, resulting in a massive computer failure, crashing the site.
It’s gotten too easy for consumers to take out personal instant loans right on their smartphone. This is a booming business, with solicitations everywhere including on social media. The average loan is over $15K and as much as $30K. 20 million have done so thus far. These loans carry a moderately high interest rate. Many of these loans are being taken out for lifestyle. An Experian survey finds that 1 in 3 Americans acknowledge taking out a personal loan for Christmas shopping. Anytime you borrow for lifestyle, it comes back to burn you because it leaves no cushion in your life for the unexpected. Remember what really matters is time spent with loved ones. Bake or do something nice for someone in lieu of spending, because going into debt to keep up appearances is unwise, creating anxiety and financial pressure in your life.
More than 1 in 3 Americans rent. The screening process is important to this 37% of U.S. adults. Historically a landlord would run a credit check and maybe check references. But today, large scale landlords are adopting more sophisticated tools using AI and deep data to come up with a renter score. Being judged on broader criteria can be a plus for those in the gig economy, no longer limited by traditional income ratios, and for those without traditional credit. The algorithms vary, but all use an array of factors – totally different than how screening has been done in the past. One model considers the type of debt an applicant has, weighing student loan debt less than credit card debt. Different factors are in play to determine suitable tenants. Small landlords generally are not using these models. Large corporate apartment complexes may be using several. One complex may reject you or require a large deposit, while another may give the go ahead.
Learn more about your ad choices. Visit megaphone.fm/adchoices
Available Results
Generated results are saved to your library for reuse and search.
Choose Template
Pick the result you want. You can review provider and model before generating.
A concise first-pass summary for understanding the episode quickly.
A comprehensive, source-grounded extraction of the reusable knowledge in an episode.
A comprehensive extraction focused on opportunities, strategy, markets, and company building.
Explicit actions, next steps, habits, recommendations, and things to avoid.
Repeatable methods, frameworks, mental models, processes, and systems.
A dedicated inventory of concrete resources named in the episode.
A concise first-pass summary for understanding the episode quickly.
A detailed readable summary organized by chapter or topic.
A navigable map of subjects, topic flow, and suggested chapters.
A comprehensive, source-grounded extraction of the reusable knowledge in an episode.
Reusable atomic knowledge units extracted from the episode.
A comprehensive extraction focused on health practices, protocols, claims, and safety caveats.
A comprehensive extraction focused on opportunities, strategy, markets, and company building.
Explicit actions, next steps, habits, recommendations, and things to avoid.
A dedicated inventory of concrete resources named in the episode.
A dedicated analysis of warnings, limitations, trade-offs, weak evidence, and uncertainty.
A concise first-pass summary for understanding the episode quickly.
A detailed readable summary organized by chapter or topic.
A navigable map of subjects, topic flow, and suggested chapters.
A comprehensive, source-grounded extraction of the reusable knowledge in an episode.
Reusable atomic knowledge units extracted from the episode.
A comprehensive extraction focused on health practices, protocols, claims, and safety caveats.
A comprehensive extraction focused on opportunities, strategy, markets, and company building.
Scientific findings, mechanisms, studies, hypotheses, and the limits of the evidence discussed.
Technologies, AI models, technical methods, capabilities, limitations, and adoption implications.
Investment theses, assets, catalysts, valuation reasoning, time horizons, and risks.
Chronologies, actors, causes, consequences, turning points, and competing historical interpretations.
Policies, proposals, stakeholders, arguments, implementation constraints, and predicted effects.
Career paths, skills, hiring signals, workplace decisions, transitions, and limitations of the advice.
Behavioral mechanisms, biases, motivation, habits, emotions, interventions, and evidence limitations.
Economic mechanisms, incentives, indicators, market structure, forecasts, and uncertainty.
Leadership principles, team systems, organizational design, culture, feedback, and failure modes.
Audience, positioning, messaging, acquisition, retention, experiments, metrics, and failed approaches.
Teaching methods, learning strategies, practice, feedback, assessment, and effectiveness evidence.
Theses, premises, arguments, objections, values, thought experiments, and unresolved questions.
Communication patterns, conflict, boundaries, expectations, repair methods, and contextual limitations.
Books, papers, authors, courses, and other learning resources mentioned in the episode.
Repeatable methods, frameworks, mental models, processes, and systems.
Explicit actions, next steps, habits, recommendations, and things to avoid.
Memorable statements and important claims with attribution and source context.
A dedicated inventory of concrete resources named in the episode.
A dedicated analysis of warnings, limitations, trade-offs, weak evidence, and uncertainty.