Why Your Data Isn’t Driving Growth — And How AI Can Change That 

Many companies today are sitting on a goldmine of data, but struggle to extract the most value from it. Despite investing heavily in data acquisition and storage, they struggle to translate raw information into actionable strategies or measurable growth. In fact, for many brands, data has become a cost center rather than a revenue driver—a problem that undermines both marketing effectiveness and overall business performance.

Companies that invest in data maturity and AI-enabled analysis, on the other hand, can demonstrate a clear, positive return on investment from their data stack. Strategic integration of artificial intelligence enables companies to generate deeper, more valuable analysis, hyper‑personalize their offerings around customers' behaviors and preferences, predict the behavior of future customers, and more.

Let’s dive into the specific AI-powered strategies we use at Brooklyn Data to maximize the value of our customers’ data stack.

7 AI-Enabled Strategies to Achieve Business Goals

RFM (Recency, Frequency, Monetary) Analysis

• Recency, Frequency, Monetary Analysis is the process of studying behavioral data to segment customers based on engagement and spending patterns. Deploying AI tools to conduct this analysis can help you develop a deeper understanding of customer value tiers and plan marketing spend appropriately.

MBA (Market Basket Analysis) & Recommendations

• Market Basket Analysis suggests products that are often bought together and surfaces personalized recommendations, and is one of the most ubiquitous applications of AI in modern marketing. Utilizing AI for MBA and recommendations enables you to drive incremental purchases from customers and increase their lifetime value over time.

MMM (Marketing Mix Modeling)

• Marketing Mix Modeling provides an aggregate view of channel performance in relation to defined business outcomes. This provides a holistic view of your marketing spend, where it’s working, and where it isn’t. Supercharging this analysis with AI can assess marketing effectiveness and optimize budget allocation.
  
  
  

Churn Prediction

• Churn prediction seeks out customers who are likely to lapse, allowing for proactive retention outreach. Churn is a brand killer, and capturing new customers is far more expensive than retaining existing ones. Applying advanced AI to this analysis can result in massive marketing savings.

pLTV

• Predictive Lifetime Value utilizes historical spending patterns to forecast future revenue potential. A practical application of AI to this analysis enables you to uncover the unique attributes of your current customers and use those to determine the value of prospects, allocating marketing dollars to those with the highest potential value.

Lead scoring

• Lead scoring predicts conversion likelihood based on the similarity of a prospect to high-value past customers. Utilizing AI to enhance this process can help you better prioritize acquisition efforts and avoid wasting valuable resources on leads that are unlikely to convert.

Cluster analysis

• Cluster analysis groups customers based on their intrinsic attributes and behaviors. This can reveal hidden customer segments that manual data analysis can typically miss, but adds incredible value to your marketing strategy.

Implement Advanced AI Applications that Drive Growth with Brooklyn Data.

At Brooklyn Data, we’re experts in designing and deploying AI applications that maximize the value of your data. From enhancing customer insights to optimizing marketing spend, we build our solutions to deliver tangible business outcomes.

But we don’t stop at AI integration. We also help you develop the data infrastructure and maturity necessary to support these technologies in the long term.