Today, we’re bringing you the latest in AI-powered marketing and business strategies. Here’s what’s inside:

🚨 AI Top Story: How top brands leverage AI to drive up to 20% revenue growth.

📊 AI Use Case of the Week: Predict business trends with AI: Centralise data, build models, and optimise insights.

🎯 Killer Marketing Prompt: Craft ad copy that captivates, converts, and maximises ROI with proven strategies

🌟 Creator Spotlight: Meet Vivienne Onwuocha, a business consultant sharing expert insights on financial management, business strategy, and organisational growth.

The AI Approach That Drives A 20% Revenue Increase

Proven AI Marketing Strategies From Leading Brands

As AI reshapes the marketing world, new research from Boston Consulting Group (BCG) reveals a clear blueprint for success—and the results speak for themselves. Companies leading the way with AI-powered marketing are seeing up to 20% revenue growth while improving efficiency and reducing costs.

BCG’s study analysed over 70 case studies across industries, including retail, financial services, and consumer goods, to understand how top-performing brands are using AI to gain a competitive edge. The research highlights that these companies don’t just adopt AI—they embed it deeply into their operations. They align their data infrastructure, integrate AI into workflows, and foster collaboration between marketing, data science, and creative teams.

The study uncovered three areas driving the most impact: predictive analytics, which anticipate customer needs and behaviours; hyper-personalisation, delivering tailored experiences at scale; and AI-driven creativity, enabling brands to test and optimise campaigns faster than ever.

What sets these leaders apart is their ability to use AI not just for automation but for smarter decision-making and innovation. By combining advanced technology with human expertise, they’re identifying new opportunities, refining strategies, and creating campaigns that connect more meaningfully with their audiences.

The takeaway is clear: adopting AI isn’t enough.

To drive measurable growth, brands must embed AI into every stage of their marketing process while ensuring it works hand-in-hand with creativity and strategy.

For those that get it right, the rewards are substantial, and the future of marketing will belong to them.

🔍 ChatGPT’s AI Search Engine Is Rolling Out To Everyone - OpenAI is bringing its AI search engine to free users, rivalling Google.

🏭 How AI Is a Modern-day Industrial Revolution In The Marketing World - AI is revolutionising marketing, changing how brands connect and grow.

✨ Jasper Announced The Launch Of AI Studio - Jasper unveils AI Studio, helping brands create custom, on-brand AI content.

🍪 The Classic Oreo Is Getting Tastier with AI - How AI is helping Oreo refine its recipes.

UIpath

UIPath is a market leader in robotic process automation (RPA), offering advanced AI-driven tools to automate repetitive tasks and streamline workflows. Its user-friendly interface and robust capabilities make it a go-to solution for businesses of all sizes looking to improve operational efficiency and reduce manual workloads.

Here are some of its key features:

AI-Powered Automation: UiPath leverages machine learning to handle complex tasks such as data extraction, invoice processing, and email parsing, significantly reducing human intervention.

Process Mining and Insights: The platform identifies inefficiencies and bottlenecks in workflows, providing actionable recommendations to optimize operations.

Seamless Integration: UiPath integrates effortlessly with enterprise applications like SAP, Microsoft, and Salesforce, ensuring smooth implementation across existing systems.

Intelligent Document Processing: It uses AI to process unstructured data from documents, including invoices, receipts, and contracts, enhancing accuracy and speed.

Scalable Solutions: UiPath supports automation for individuals and businesses, scaling from single-task bots to enterprise-grade deployments.

Low-Code Interface: The platform's low-code environment enables users with minimal technical expertise to design and deploy automation workflows easily.

VIVIENNE ONWUOCHA  - AI In Business Analysis Cheat Sheet

AI-POWERED BUSINESS FORECASTING

Tools Needed:

• Google Sheets - for organising data.

• ChatGPT (GPT-4 Turbo) - for generating predictive insights.

• Power BI - for advanced data visualisation.

• Zapier - to automate data flows.

• BigML - for machine learning-based forecasting.

Step 1: Centralise Historical Data

What to Do:

1) Consolidate data from relevant sources into Google Sheets using Zapier for automation:

- Sales data (e.g., revenue, unit sales).
- Marketing performance (e.g., ad spend, click-through rates).
- Seasonal trends or external market data.

2) Structure the sheet with columns like:

- Date/Time Period.
- Performance Metrics (e.g., Revenue, ROI).
- External Factors (e.g., Market Growth, Seasonality).

Step 2: Identify Key Metrics Using ChatGPT

What to do:

Input your structured data into ChatGPT to identify metrics that impact performance the most.

Sample Prompt:

Our business operates in [specific industry], targeting [audience demographic, e.g., small businesses or individual consumers] with [product/service type, e.g., SaaS solutions, retail products]. 

The primary revenue streams are [describe, e.g., subscription plans, eCommerce sales].

Here is the historical sales data:

January: Revenue $50,000, Ad Spend $10,000, Market Growth 2%.

February: Revenue $XXX, Ad Spend $XXX, Market Growth XX%.

The primary goal is to understand which factors most influence sales in this context. 

Key metrics include [list additional metrics, e.g., conversion rates, average order value].

Analyse this data to identify patterns or correlations and recommend which metrics we should prioritise for future forecasting. Provide actionable insights tailored to our industry and target audience.

ChatGPT will generate actionable insights based on the data provided.

Step 3: Build Predictive Models With BigML

What to do:

1) Upload your organised data from Google Sheets into BigML to build machine learning-based forecasting models.

2) Select key drivers identified by ChatGPT (e.g., Ad Spend, Market Growth) as input variables.

3) Train predictive models in BigML to generate forecasts:

- Predict sales, market trends, or other outcomes based on historical data.
- Simulate different scenarios (e.g., increased marketing budget or economic changes).

4) Export the predictions back to Google Sheets for reference and further processing.

Step 4: Visualise Forecasts In Power Bi

What to Do:

1) Import the forecast data from Google Sheets into Power BI.

2) Create interactive dashboards to visualise:

- Predicted vs historical performance.
- Key drivers’ impact on outcomes (e.g., how Ad Spend affects Revenue).
- Risk factors and growth opportunities.

3) Add filters for dynamic exploration by time periods, markets, or segments.

Step 5: Automate Data Updates And Forecasting

What to Do:

1) Use Zapier to automate workflows between tools:

- Sync new data from your CRM or analytics tools to Google Sheets.
- Automate updates to BigML models and Power BI dashboards whenever new data is added.

2) Schedule recurring workflows to ensure forecasts are updated in real-time.

Step 6: Monitor Accuracy & Iterate

What to Do:

1) Compare actual results against forecasted values in Power BI to identify discrepancies.

2) Input deviations into ChatGPT or BigML for refinement:

Sample Prompt:

"Here is the forecasted vs actual data for the last quarter:

January Forecast: $XX, Actual: $XX.
February Forecast: $XX, Actual: $XX.

Analyse discrepancies and suggest adjustments to improve forecasting accuracy."

3) Update BigML models based on these insights to enhance predictive capabilities.

Ad Copy Optimisation

You are a copywriting strategist tasked with optimising ad copy for [brand name]’s digital advertising campaigns. 

The goal is to craft persuasive, engaging copy that resonates with [target audience demographic] and drives conversions on [specific platform(s), e.g., Google Ads, Facebook, LinkedIn].

Start by analysing the key value propositions of [brand name]’s [product/service] and the pain points or aspirations of the target audience.

Highlight how the ad copy should reflect the brand’s tone and messaging, whether that’s professional, playful, or aspirational.

Develop multiple ad copy variations, each with a unique hook or angle. For example, emphasise urgency with time-sensitive offers, address specific customer needs, or showcase social proof through testimonials or success metrics.

Include guidance on crafting compelling headlines, concise yet impactful descriptions, and clear calls to action (CTAs) that align with campaign objectives (e.g., “Buy Now,” “Learn More,” “Sign Up Today”).

Propose strategies for A/B testing the ad copy, focusing on metrics such as click-through rates (CTR), engagement, and conversions. Offer insights on refining copy based on performance data, ensuring continual improvement.

Finally, suggest tips for aligning ad copy with visuals, ensuring cohesive messaging that grabs attention and reinforces the campaign’s objectives. 

This strategy should elevate [brand name]’s ad performance, maximising ROI and audience engagement.

Asking AI What Rock Legends Would Look Like If They Were Alive in 2024

Alper Yesiltas's project, featured on Bored Panda, showcases a captivating series of AI-generated images envisioning how celebrities might look today if no significant life changes had occurred.

He employed Remini, an AI photo enhancer, to achieve the desired aging effects and textures. Additionally, he used Adobe Lightroom and VSCO for refining lighting and colours, ensuring the images appeared photorealistic.

The project, titled As If Nothing Happened, delves into themes of time, legacy, and human curiosity, sparking thought-provoking discussions about the power of technology to reimagine alternate realities.