PH Deck

Users struggle with managing and optimizing their multi-level marketing (MLM) processes, leading to inefficiencies, low engagement, and suboptimal profits.

Users need insights into the healthy buildings sector but rely on fragmented or outdated market reports. Fragmented or outdated market reports lead to inefficient decision-making and lack of actionable data.

Users currently provide their employees periodic wages, typically in bi-weekly or monthly intervals.

This can lead to financial stress for employees as they can't access earned wages whenever needed, causing reliance on payday loans or credit cards.

Users in multi-level marketing face challenges managing complex structures and transactions.

The current situation involves the use of outdated or manual systems to manage multi-level marketing (MLM) businesses. These methods cause inefficiencies and errors when tracking sales, commissions, and managing member networks. The drawbacks of this old situation include tracking sales, commissions, and managing member networks, which can lead to errors, inefficiencies, and lack of transparency.

Farmers' markets and craft fairs currently rely on manual processes like spreadsheets and paper-based systems for vendor management, payments, and customer engagement. Manual processes lead to inefficiencies, errors, and limited scalability.

Marketers often struggle with fragmented resources across various aspects of growth marketing, such as SEO, UX, and others, leading to inefficiency and a lack of cohesive strategy. The drawbacks include time wasted searching for quality resources and difficulty in staying updated with the best practices.

In the current situation, users are manually managing multi-level marketing (MLM) businesses, which involves overseeing commissions, tracking referrals, and organizing network growth activities.

Manual efforts can lead to inefficient management, errors, and limitations in tracking business growth and commissions effectively.

Users manually analyze prediction markets, which is time-consuming and prone to human error due to reliance on fragmented data sources and outdated methods.

Users struggle with traditional at-home workouts that lack structured progression and consistency, leading to suboptimal muscle growth and inconsistent results due to absence of progressive overload programming.