Big Data Analytics strategy to analyze and visualize multi-source data including sales, e-commerce behavior, browsing activity, and IoT device data.
Predictive model to deeply profile customers and deliver relevant product recommendations, improve engagement, cross-sell and up-sell.
Redefined customer segments through clustering by purchase behavior, revenue contribution, region, and industry, alongside market basket, affinity, and CRM analytics.
Interactive Tableau dashboard showcasing key insights for cross-selling and upselling across different customer segments and business units.
In today’s hyper-competitive marketplace, understanding consumer behavior isn’t just valuable, it’s essential.
Dyson, known globally for its innovative technology products, recognized the need to sharpen its consumer engagement strategy across the entire lifecycle: from acquisition to retention.
Fugu partnered with Dyson to deliver a Big Data-powered predictive model, offering Dyson deeper insights into its customer base and unlocking powerful opportunities for personalized marketing, cross-selling, and upselling.
Dyson’s data ecosystem was vast and complex, collecting information from multiple sources:
However, these valuable data streams existed in silos. Without a unified view, it was challenging for Dyson to:
What Dyson needed was a consolidated, intelligent solution that could analyze, predict, and visualize customer behavior in a meaningful way.
Fugu designed and implemented a comprehensive Big Data Analytics Strategy that combined:
We built machine learning models to analyze past behaviors and predict future purchase tendencies, creating actionable intelligence for marketing and sales teams.
Using clustering algorithms, we redefined customer segments based on:
We mapped Dyson’s product lines and accessories, running market basket and affinity analytics to uncover the best product combinations for cross-sell and upsell campaigns.
We delivered a rich Tableau-based dashboard that offered Dyson teams:
The deployment of this solution had a transformative impact on Dyson’s sales and marketing effectiveness:
Dyson could now tailor offers based on predicted customer needs.
Better identification of cross-sell and upsell prospects across multiple product categories.
Personalized engagement strategies helped strengthen loyalty and reduce churn.
Interactive dashboards allowed teams to derive insights and act in real-time, rather than waiting for traditional reporting cycles.
In essence, Dyson moved from reactive marketing to a proactive, predictive engagement model — powered by data.
The success of the Dyson project underscores the power of combining big data analytics, predictive modeling, and advanced visualization to drive real business results.
By turning fragmented data into unified, actionable insights, Fugu helped Dyson not just understand its customers better, but anticipate their next move.
Big Data Analytics strategy to analyze and visualize multi-source data including sales, e-commerce behavior, browsing activity, and IoT device data.
Predictive model to deeply profile customers and deliver relevant product recommendations, improve engagement, cross-sell and up-sell.
Redefined customer segments through clustering by purchase behavior, revenue contribution, region, and industry, alongside market basket, affinity, and CRM analytics.
Interactive Tableau dashboard showcasing key insights for cross-selling and upselling across different customer segments and business units.
In today’s hyper-competitive marketplace, understanding consumer behavior isn’t just valuable, it’s essential.
Dyson, known globally for its innovative technology products, recognized the need to sharpen its consumer engagement strategy across the entire lifecycle: from acquisition to retention.
Fugu partnered with Dyson to deliver a Big Data-powered predictive model, offering Dyson deeper insights into its customer base and unlocking powerful opportunities for personalized marketing, cross-selling, and upselling.
Dyson’s data ecosystem was vast and complex, collecting information from multiple sources:
However, these valuable data streams existed in silos. Without a unified view, it was challenging for Dyson to:
What Dyson needed was a consolidated, intelligent solution that could analyze, predict, and visualize customer behavior in a meaningful way.
Fugu designed and implemented a comprehensive Big Data Analytics Strategy that combined:
We built machine learning models to analyze past behaviors and predict future purchase tendencies, creating actionable intelligence for marketing and sales teams.
Using clustering algorithms, we redefined customer segments based on:
We mapped Dyson’s product lines and accessories, running market basket and affinity analytics to uncover the best product combinations for cross-sell and upsell campaigns.
We delivered a rich Tableau-based dashboard that offered Dyson teams:
The deployment of this solution had a transformative impact on Dyson’s sales and marketing effectiveness:
Dyson could now tailor offers based on predicted customer needs.
Better identification of cross-sell and upsell prospects across multiple product categories.
Personalized engagement strategies helped strengthen loyalty and reduce churn.
Interactive dashboards allowed teams to derive insights and act in real-time, rather than waiting for traditional reporting cycles.
In essence, Dyson moved from reactive marketing to a proactive, predictive engagement model — powered by data.
The success of the Dyson project underscores the power of combining big data analytics, predictive modeling, and advanced visualization to drive real business results.
By turning fragmented data into unified, actionable insights, Fugu helped Dyson not just understand its customers better, but anticipate their next move.
