Our Leadership

Buks Etsebeth

Founder & Managing Director

Erick Stroh

Director of Operations

Form a Community


Personalized advertising

Brands are increasingly leveraging data and technology to create personalized and targeted ads for individual consumers. This includes tailoring content based on an individual's preferences, behaviour, and demographics.

Native advertising

Native ads are designed to seamlessly blend into the format and style of the platform on which they appear. This non-disruptive approach is gaining popularity as it provides a less intrusive way to engage with consumers.

User-generated content

Brands are leveraging user-generated content (UGC) to engage with consumers effectively. User-generated campaigns encourage people to share their experiences with a brand or product, which helps build brand trust and authenticity.

Interactive advertising

Interactive ads encourage active participation from consumers, increasing engagement and enhancing the experience. These ads may include quizzes, polls, games, or 360-degree videos, making the advertising more interactive and memorable.

Programmatic advertising

Programmatic advertising uses advanced algorithms to automate ad buying and targeting. It enables brands to reach their target audience more efficiently and in real-time across various platforms.

Sustainability-focused advertising

As sustainability and environmental concerns become more important to consumers, brands are aligning their advertising with sustainable practices and messaging. Ads that promote eco-friendly products, recycling, or responsible consumption are gaining traction.

4 Types of DATA


Descriptive analysis

Descriptive analysis in marketing unpacks data, revealing key features for marketers. It organizes information into a clear overview, helping decipher patterns and trends. Utilizing statistical measures, charts, and graphs, it enhances decision-making by presenting marketing metrics, consumer behaviour, and market conditions in a concise and easily understandable manner. Example of descriptive analysis in marketing: RippleAdz analyses its recent campaign using key metrics like open rates and feedback. By creating charts and graphs, they identify trends, audience reactions, and areas for improvement. Descriptive analysis paints a clear picture of the campaign's success, guiding RippleAdz to refine strategies for future success. This snapshot serves as a foundation for diagnostic and prescriptive analyses to optimize upcoming marketing efforts.

Diagnostic analysis

Diagnostic analysis in marketing delves deep into data to pinpoint root causes of performance issues. Unlike descriptive analysis, it uncovers why outcomes occur. Marketers use it to reveal factors impacting success, enabling informed adjustments to optimize strategies, address weaknesses, and capitalize on strengths. This process provides crucial insights into the factors driving marketing performance, empowering effective decision-making and strategic adjustments. Example in marketing: RippleAdz notices a drop in online sales. Through a diagnostic analysis, they explore feedback, user behaviour, and conversion rates, discovering a decline in organic traffic. Armed with insights, RippleAdz optimizes website content, runs targeted ads, and enhances product visibility, addressing the root cause. Diagnostic analysis empowers marketers to make informed adjustments, improving overall performance and tackling specific factors affecting trends or issues.

Predictive analysis

Predictive analysis in marketing utilizes data, statistical algorithms, and machine learning to forecast future outcomes and trends. By analysing historical data, marketers make informed predictions about consumer behaviour, market trends, and campaign success. This forward-looking approach empowers marketers to optimize strategies, allocate resources efficiently, and make data-driven decisions, enhancing overall efficiency and success. Predictive analysis enables proactive responses to potential market changes, ensuring marketers stay ahead and continually improve their efforts. Example in marketing: Predictive analysis in marketing helps businesses anticipate future trends and behaviour’s, allowing for more strategic and proactive decision-making to optimize marketing efforts and improve customer retention. Predictive analysis example in marketing: RippleAdz predicts customer churn by analysing historical data and utilizing machine learning algorithms. The model identifies at-risk customers, enabling proactive strategies like personalized offers and customer support to prevent churn. This anticipatory approach optimizes marketing efforts and enhances customer retention.

Prescriptive analysis

Prescriptive analysis in marketing recommends future actions based on advanced analytics. Unlike descriptive and diagnostic analyses, it goes beyond summarizing past data. Using algorithms, it guides marketers in making strategic decisions to maximize outcomes, such as increasing sales or improving ROI. This proactive approach aligns with business objectives, driving positive results. An example in marketing: After using predictive analysis to forecast customer preferences, RippleAdz employs prescriptive analysis to tailor content, optimize timing, and allocate the budget efficiently. This data-driven approach guides decision-makers to implement targeted strategies, aligning with predicted customer behaviours and achieving desired business outcomes.

Data privacy and transparency

In light of increasing concerns over data privacy, advertisers are emphasizing transparency in data collection and usage. Brands are ensuring they comply with data protection regulations and are transparent about the use of consumer data in their advertising practices.