Dealership
Sales
Execution
Peraison helped Harley-Davidson improve dealership sales execution by rebuilding a slow, disconnected sales data experience into fast, workflow-aligned analytics that support staff through the real steps of a sales day.
Sales associates needed quick access to customer history, model comparisons, test ride activity, quotes, follow-up tasks, and performance against daily targets, but the existing experience took hours to refresh and interpret.
Peraison observed how dealership teams actually sell, then redesigned the analytics flow around the natural rhythm of execution: greeting and qualifying, identifying the right fit, managing test rides and follow-up, supporting deal decisions, and tracking daily performance.
The result was a simpler, faster set of data views that helped staff act on opportunities in the moment, run more informed sales conversations, and follow up with greater consistency, while managers gained clearer visibility of pipeline strength, conversion, and attach performance across the dealership.
More time selling, less time searching
Refresh and analysis time dropped materially, removing daily friction and giving sales teams usable information during live customer interactions.
Higher quality execution across the full sales day
Staff could qualify faster, recommend the right motorcycle with more confidence, manage test rides and follow-up more consistently, and run more effective conversations using clearer customer and inventory context.
Stronger management control of pipeline & conversion drivers
Managers gained real-time visibility into pipeline health, quotas versus actuals, conversion and follow-up discipline, enabling faster coaching and intervention to protect targets.
Our Stories & Insights on Consumer Products & Retail
Explore the real‑world impact of our work across consumer products & retails, with stories and insights that reveal how data, AI, and technology deliver measurable outcomes.
Modern Solutions for a changing
Consumer Product & Retail landscape
Helping Consumer Product & retail with data & AI
Unify loyalty, POS, eCommerce, and behavioural data to understand customers and shoppers end to end. Build segments, propensity models, and next best action journeys that lift acquisition, basket size, retention, and lifetime value, while improving personalisation and measurement across channels.
Improve category and range decisions with price elasticity, promo effectiveness, and markdown optimisation. Identify what drives volume versus margin, reduce promotional waste, and strengthen trade investment decisions with consistent measurement by SKU, store, region, and customer segment, while protecting brand equity.
Turn demand data into forecasts, replenishment actions, and allocation decisions that improve availability and working capital. Reduce stockouts, overstocks, and markdown exposure with store and DC level visibility, safety stock optimisation, and exception-based alerts that keep planners focused on what matters.
Connect store, eCommerce, and marketplace journeys to understand conversion drop-off and the drivers of basket growth. Use testing and journey analytics to improve search, product discovery, checkout, and fulfilment choices, lifting conversion and satisfaction while reducing friction across digital and store experiences.
Create end-to-end visibility from supplier to shelf with performance tracking across lead times, fill rates, and service levels. Detect risk earlier, manage disruptions with scenario views, and improve supplier and carrier performance, enabling continuity of supply and more reliable delivery to stores and customers.
Improve store execution with labour planning, task prioritisation, and operational dashboards that link workload to demand. Optimise rosters, reduce wasted effort, and increase on-shelf availability and service quality by giving managers clear daily actions and visibility of compliance and performance drivers.
Build modern data foundations that make retail data trusted, governed, and ready for scale. Accelerate platform modernisation, improve data quality and lineage, and enable reusable data products that support analytics, automation, and AI use cases across merchandising, supply chain, store operations, and customer functions.
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