Predictive AI

Stim Evaluation

We use stimulus (stim) evaluation to predict responses to stimuli, including visual, auditory, or other forms of sensory input. AI predictions for the future using AI tools for business. Langoor.ai tried to execute Predictive AI based ideologies in our client's project, which are listed below as use cases.

Advertising and Marketing

Use Case

Evaluating consumer responses to the advertisements.

Diagnostic AI identifies product features and brand attributes most valued by consumers, helping marketers tailor campaigns that highlight these elements.

Outcome

AI can craft personalised email copy based on customer data and behaviours. By analysing user preferences, browsing history, or past purchases, AI generates unique tailored subject lines, body content, and CTAs that resonate with individual recipients, improving open rates and conversions. The copy can be adjusted for tone, offers, or recommendations, making mass campaigns feel more personal.

Product Design and Development

Use Case

Predicting customer feedback on product prototypes.

Before launching the physical product, AI predictions can evaluate digital or physical prototypes by simulating customer experiences and preferences. Based on the data and trends, AI-driven sentiment evaluations can predict how users might respond to design, features, or usability.

Outcome

Healthcare and Cognitive Response Testing

Use Case

Assessing neurological or emotional responses to various therapeutic stimuli.

AI models can predict how patients with neurological conditions or emotional challenges respond to specific stimuli, such as music therapy, visual stimuli, or virtual reality environments. This helps in personalising therapy based on anticipated cognitive or emotional responses.

Outcome

Concept Evaluation

Concept evaluation involves assessing abstract ideas, strategies, or product concepts to predict their potential success, effectiveness, or audience reception. Langoor.ai leverage executing Predictive AI in a few projects and below are some of the use cases.

Innovation in New Product Development

Use Case

Evaluating new product concepts before prototyping.

AI models can analyse preexisting consumer data and market trends to predict the potential success of a new product launch. It includes factors like market demand, competition, and consumer sentiment based on similar past products or concepts. AI in market research helps each of the new products that come into the market with a huge impact based on the pre-prediction.

Outcome

Creative Content Ideation

Use Case

Testing creative concepts for media, entertainment, or advertising.

AI can predict audience engagement with creative concepts for films, TV shows, or ad campaigns by analysing past content performance and audience preferences. It evaluates plot themes, character appeal, or visual styles to forecast potential viewership or engagement. Generative AI also helps in finding the better versions of creating creative content.

Outcome

Business Strategy and Policy Planning

Use Case

Evaluating new business strategies or public policies.

AI models evaluate data from similar past strategies or policies to forecast their potential impact. For example, businesses test pricing, expansion, or sustainability, while governments can evaluate the likely outcomes of new policies by simulating public reactions or economic effects.

Outcome

Ad Evaluation

Ad evaluation involves AI ideas for business to predict how advertisements perform in audience engagement, effectiveness, and return on investment (ROI). Langoor.ai works with a few clients’ projects, and we share the use cases where Predictive AI enhances ad evaluation in those projects.

Pre-Launch Ad Testing

Use Case

Predicting the ad performance before a campaign goes live.

AI models explore recorded data from similar ads, target audience behaviours, and market trends to predict the success of new advertisements. It includes assessing metrics like click-through rates (CTR), engagement levels, or conversion rates for various ad creatives.

Outcome

Audience Segmentation and Targeting

Use Case

Testing crePredicting how different audience segments will respond to specific ad creatives.ative concepts for media, entertainment, or advertising.

AI analyses demographic, psychographic, and behavioural data to forecast the diverse responses of target audiences to a specific advertisement. It includes predicting engagement rates, purchase intent, or emotional responses based on segment preferences.

Outcome

Creative Optimisation

Use Case

Optimising ad creatives for maximum impact.

AI models evaluate multiple versions of an ad (different headlines, images, copy, etc.) and predict which combination will perform best based on past performance data and audience preferences. It can simulate A/B testing by predicting which creatives will drive higher engagement, better brand recall, or higher conversion rates.

Outcome

Packaging Testing

Packaging testing uses AI models to predict the performance, appeal, and functionality of product packaging before it is manufactured or distributed. At Langoor.ai, we have successfully implemented some use cases executed with significant impact.

Consumer Appeal and Design Optimisation

Use Case

Predicting the purchasing decision of the consumer based on the package design.

AI models analyse visual elements of packaging (such as colour, shape, and typography), and past encounters with consumer preferences help to predict the appealing package for the target audience. The AI can simulate how different designs influence consumer perception, brand recall, and purchase intent.

Outcome

Sustainability and Material Testing

Use Case

Evaluating the environmental impact and durability of different packaging materials.

AI can predict the sustainability performance of various materials by analysing factors such as recyclability, carbon footprint, and life cycle impact. It can also predict the durability of the packaging during shipping and handling, minimizing product damage and waste.

Outcome

Supply Chain Efficiency and Cost Optimisation

Use Case

Predicting the cost and logistical efficiency of packaging in the supply chain of the product.

AI models simulate how different packaging designs and materials will impact shipping costs, warehouse space, and overall supply chain efficiency. By predicting package dimensions, weight, and stackability, AI helps optimise packaging for reduced shipping costs and improved storage capacity.

Outcome

Competitor Benchmarking

Competitor Benchmarking under Predictive AI involves using AI ideas for business to predict how a company's products, services, or strategies will perform relative to competitors in the market. The below-listed use cases are tried by Langoor.ai for their client's projects using Predictive AI to understand the competitor benchmarking.

Market Share Prediction

Use Case

Predicting future market share relative to competitors.

AI in market research helps to examine historical sales data, consumer behaviours, market trends, and competitor performance to forecast how a company’s market share will evolve. It can predict shifts in customer loyalty, competitor moves (e.g., pricing changes or new product launches), and the potential impact of external factors like economic conditions.

Outcome

Product Performance Benchmarking

Use Case

Comparing product performance and customer satisfaction against competitors.

AI can evaluate a company's products against competitor offerings by analysing customer reviews, social media sentiment, and usage data. Predictive models can forecast how future product upgrades, new features, or price adjustments will perform relative to competitors in customer satisfaction, sales, or retention.

Outcome

Pricing Strategy Optimisation

Use Case

Benchmarking and predicting competitor pricing strategies.

AI models examine competitor pricing data and market trends to predict future pricing changes, discounts, or promotions. It enables companies to anticipate how competitors will price similar products or services and optimise their pricing strategy to remain competitive without eroding margins.

Outcome

Internal Knowledge Model

An Internal Knowledge Model under AI predictive analytics involves using AI to organise, examine, and predict outcomes based on a company’s internal knowledge assets—such as documents, databases, expertise, and workflows. Here are three use cases in which Langoor.ai uses Predictive AI to enhance internal knowledge models.

Employee Expertise Matching

Use Case

Predicting the best internal resources or experts for a specific project or task.

AI models peruse internal documents, employee profiles, and past project data to predict which employees or teams are best suited to handle a particular project based on their skills, past performance, and expertise. AI can also anticipate the likelihood of project success based on team composition.

Outcome

Knowledge Retrieval and Workflow Automation

Use Case

Predicting and retrieving relevant internal knowledge for decision-making or task automation.

AI models can predict the most relevant internal documents, reports, or insights based on the task or a specific query. AI can proactively provide relevant information or automate repetitive tasks such as report generation by analysing previous queries, decision patterns, and workflow data.

Outcome

Predictive Maintenance for Knowledge Assets

Use Case

Predicting when internal knowledge models or databases need updates or maintenance.

AI models can examine the usage of knowledge assets, such as internal databases or guidelines. They also track performance issues or outdated information. AI models can predict when to update information based on market changes.

Outcome

Predictive AI simplifies business and marketing in a new way of understanding. To gain insights on analysing your business, foreseeing its performance, devising creative strategies, and maximizing overall business potential.

Connect with us to learn more about how predictive AI can enhance your marketing effort.