Enhancing Marketing Strategy with Predictive Keyword Analytics for a Leading U.S. Shingle Manufacturer

Background

A leading manufacturing organization sought to improve its marketing effectiveness and enhance brand visibility by leveraging data-driven insights. While it had a significant digital footprint across various online channels, there was little clarity on how search behaviors, keyword trends, and customer engagement patterns correlated with brand performance. The company wanted to capitalize on emerging digital signals to craft more targeted and profitable marketing strategies.

Challenges

  • Limited ability to track and anticipate fluctuations in keyword popularity across time and geography.
  • Inadequate insight into how keywords related to brand visibility, competitors, and broader industry trends.
  • Difficulty predicting which keywords would drive the most profitable user traffic.
  • Fragmented analytics data from different sources (Google Trends, website analytics, keyword planners) that was hard to consolidate into actionable strategies.
  • Absence of a user-friendly tool to visualize data, analyze insights, and generate prescriptive marketing recommendations.

Solution Delivered

A comprehensive analytics and forecasting framework was designed and deployed, focusing on keyword-based intelligence:

  • Google Trends: Leveraged Google Trends and associated tools to:
  • a. Understand seasonal dependencies of the searched items across various geographical regions and comparative keyword research to discover event-triggered spikes
  • b. Capture visitors’ demographic data on the website
  • c. Conducted behavioural analysis to track visitor journey including new or returning users, etc.
  • d. Get insights on nature of type of devices used, approximate location, channel-specific information and cookie logs (all with user consent)
  • Time-Series Keyword Analysis: Leveraged statistical models (SARIMAX, Prophet, LSTMs) to assess and forecast keyword popularity, identifying seasonal and regional trends.
  • Correlational and Causality Mapping: Applied techniques like Granger causality and Pearson/Spearman correlations to uncover dependencies, lead-lag effects, and patterns among key search terms.
  • Data Integration and Enrichment: Combined Google Analytics, user behavior data, and third-party keyword tools (Google Keyword Planner, Ahrefs, Mangools) to gain deeper insights into user demographics, devices, traffic sources, and keyword volumes.
  • Dimensionality Reduction & Clustering: Employed PCA, ISOMAP, and clustering techniques (e.g., K-Means) to simplify keyword sets and segment them into brand-affiliated, competitor-affiliated, and neutral terms.
  • Prescriptive Strategy Engine: Built a recommendation layer that used predictive and segmentation data to propose high-profit keyword sets for targeted ad campaigns.
  • Interactive Dashboard: Deployed a UI-based dashboard allowing stakeholders to explore descriptive, predictive, and prescriptive keyword analytics with temporal and geographic filters.

Outcomes & Impact

  • Enhanced Brand Awareness Analytics: The organization gained clear visibility into how its brand compared to competitors across regions and time.
  • Data-Driven Marketing Decisions: Marketing teams received actionable recommendations on keyword investments, boosting ROI on digital ad spend.
  • Improved Forecast Accuracy: Keyword trend predictions helped anticipate market interest, improving campaign timing and relevance.
  • User Engagement Optimization: Insights into visitor behavior enabled personalized marketing strategies and improved website responsiveness across devices.
  • Streamlined Decision-Making: The interactive dashboard allowed marketing leaders to dynamically explore insights and make informed decisions quickly.
  • Scalable Framework: The modular approach enabled reuse for other digital forms and keyword sets, streamlining future marketing initiatives.

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