Finding the perfect recreational vehicle (RV) can be a challenge. Customers often search for something highly specific — a vehicle that fits their travel goals, budget, amenities, and space needs — only to encounter irrelevant or unavailable options. For significant purchases like RVs, simply showing exact matches often leads to dead ends.
This highlights the need for marketers to adopt a smarter approach: one that prioritizes relevance while offering crucial flexibility. With personalized product recommendations, marketers can surface not just the perfect match when available, but also the next best alternative, saving customers the frustration of starting their search from scratch.
A leading RV retailer in the United States recently faced this unique challenge. Unlike typical ecommerce platforms, this retailer does not stock multiple units of the same vehicle in one location. Customers often travel across cities or states to complete a purchase, with each decision involving complex variables, from vehicle type to price.
To navigate this complexity, the retailer adopted Blueshift’s Customer Engagement Platform (CEP) to build a scalable and flexible personalization engine. The result? Intelligent campaigns with recommendation flows that balance customer preferences with marketer control, delivering relevant, timely, and high-converting communications at scale.
How Personalized Product Recommendations Improve RV Shopping
Blueshift’s recommendation strategy helped the retailer deliver more relevant inventory based on customer preferences, behavior, and campaign goals. Instead of relying only on exact matches, the system made it possible to surface the next best RV options when inventory was limited or unavailable.
This approach is especially important in RV retail, where inventory is dynamic and every purchase involves more consideration than a standard online transaction. By using a flexible product recommendation engine, the retailer could improve the customer experience while still giving marketers control over campaign logic.
Dynamic Personalization in a Non-Traditional Ecommerce Landscape
Blueshift’s CEP provided the crucial balance of precision and flexibility by integrating multiple customizable “master recipes” that harmonize user input with marketing agility.
User-Preferred Recipes: One master template identifies preferred dealer states and vehicle types to find matching RVs.
Marketer Control: Teams can filter results by state, RV type, or a combination of both.
Campaign Intent: Allows for product selection based on intent, such as highlighting new arrivals, popular models, or specific years.
Behavioral Recipes: These recipes provide dynamic recommendations based on browsing history and core specs like length and sleeping capacity.
This combination allowed the retailer to deliver personalized product recommendations in a way that reflected both customer intent and business priorities.
How the Personalized Recommendation Strategy Works
The recommendation engine, powered by Blueshift, transforms individual preferences into refined, shoppable results. By using different strategies depending on campaign intent, the system ensures customers see the most relevant inventory available.
For example, if an exact vehicle match is not available, Blueshift can surface similar options based on attributes such as RV type, price range, length, sleeping capacity, or location. This ensures customers are still shown relevant choices instead of reaching a dead end
A Customer Journey Example in Action:
Results: Personalized Scale with Human-Led Precision
By utilizing specifically designed custom recipes within Blueshift, the retailer has successfully powered over 220 campaigns. The data demonstrates that integrating recommendations doesn’t just increase volume—it drives a disproportionate lift in customer response:
| Metric | Performance Impact |
| Channel Reach | Recommendations power 57.47% of total communication volume. |
| Engagement Dominance | These campaigns account for a staggering 72% of total clicks and the overall strategy led to a significant 109% increase in click rate. |
| Efficiency Lift | Personalized content drives the vast majority of customer engagement despite representing only half of the total sends. |
This demonstrates not only the scale enabled by Blueshift but also the high-performance impact of blending automated personalization with marketer oversight. By surfacing the “next best” vehicle when an exact match isn’t available, the retailer ensures that every send provides maximum value to the recipient.
Conclusion: Personalization That Moves with the Customer
This use case demonstrates how Blueshift’s CEP excels in non-standard ecommerce environments where traditional personalization models often fall short. By offering a hybrid model of AI-driven intelligence and human-led curation, Blueshift provides the flexibility to manage dynamic inventory landscapes and complex purchase variables with ease.
As customer journeys become increasingly nuanced, Blueshift’s platform ensures that marketers can deliver high-value relevance and scale through personalized product recommendations, regardless of industry complexity or inventory constraints.
