![](\"https:\/\/sparque.ai\/wp-content\/uploads\/2025\/11\/warmteservice.webp\" "\\"warmteservice\\"")
<\/span><\/div><\/div><\/div>Warmteservice<\/h1><\/div>Perfectly arranged<\/p>\n<\/div><\/div><\/div><\/div><\/div>
About Warmteservice<\/h2><\/div>Warmteservice is an omnichannel B2B wholesaler supplying heating, sanitization and installation materials – a family business of craftsmen for craftsmen. Relevant recommendations on the product and the cart page by SPARQUE.AI ensure that customers don\u2019t forget to order materials or parts needed for a job. But in addition, if needed, Warmteservice sends a reminder e-mail at the end of a business day. That\u2019s \u201cperfectly arranged\u201d.<\/p>\n<\/div><\/div><\/div><\/div><\/div>
![](\"https:\/\/sparque.ai\/wp-content\/uploads\/2025\/11\/WS.webp\" "\\"WS\\"")
<\/span><\/div><\/div><\/div>How SPARQUE.AI helped Warmteservice<\/h3><\/div>SPARQUE.AI predicts based on order data, as well as the items currently in the shopping cart, which products from complementary product categories are ideal cross-sell candidates. Recommendations based on one product already carry a reasonable probability. But with two more items in the cart, the accuracy increases to around 95%, which makes the recommendations shown on product detail and cart page very likely to convert.<\/p>\n
But there’s more. All orders of a customer on that day are screened, and after checking that ideal cross- or up-sellers are absent in subsequent orders, a special e-mail is sent to invite the customer to make a supplementary order before 11.p.m.<\/p>\n<\/div><\/div><\/div><\/div><\/div>
How SPARQUE.AI helped Warmteservice<\/h3><\/div>SPARQUE.AI predicts based on order data, as well as the items currently in the shopping cart, which products from complementary product categories are ideal cross-sell candidates. Recommendations based on one product already carry a reasonable probability. But with two more items in the cart, the accuracy increases to around 95%, which makes the recommendations shown on product detail and cart page very likely to convert.<\/p>\n
But there’s more. All orders of a customer on that day are screened, and after checking that ideal cross- or up-sellers are absent in subsequent orders, a special e-mail is sent to invite the customer to make a supplementary order before 11.p.m.<\/p>\n<\/div><\/div><\/div>
![](\"https:\/\/sparque.ai\/wp-content\/uploads\/2025\/11\/2405_sparque_customer-images-014.webp\" "\\"2405_sparque_customer-images-014\\"")
<\/span><\/div><\/div><\/div><\/div><\/div>![](\"https:\/\/sparque.ai\/wp-content\/uploads\/2025\/11\/2405_sparque_customer-images-009.webp\" "\\"2405_sparque_customer-images-009\\"")
<\/span><\/div><\/div><\/div>Imagine. Done.<\/h3><\/div>How does it work? SPARQUE.AI\u2019s unique Build-your-own-algorithm feature allows Warmteservice to maximize the use of order and PIM data. The foundation is \u2018frequently bought together\u2019. But it is adding similarities between product properties, brand and model to the algorithm that made the difference between passing grade and the high quality recommendations. Also, recommendations are based in part on what else is in the cart. More products increase the accuracy of the recommendations. Feedback data from Google Analytics (products displayed, viewed or added to cart) is also fed to the algorithm. Relevant recommendations? Done.<\/p>\n<\/div><\/div><\/div><\/div><\/div>
Forgot something? Ordered until 23:00, delivered next day<\/h3><\/div>Every day, SPARQUE.AI predicts from all orders of each customer whether anyone is missing important materials or parts. If there is a plausible case, an e-mail reminder is sent that allows to complete the order in time to still manage next-day-delivery.<\/p>\n
For this kind of recommendations, all orders of that customer on that day are screened in the evening. In a typical use case, a spare part or consumable is ordered in the morning, but the repair also requires a specific tool. A customer may have already ordered that one in a second order later that day. Only when recommended products are not present in a subsequent order, an e-mail completing the set is sent at the end of the business day, so that customers can wrap up their order until 23:00.<\/p>\n<\/div><\/div><\/div>
![](\"https:\/\/sparque.ai\/wp-content\/uploads\/2025\/11\/2405_sparque_customer-images-015.webp\" "\\"2405_sparque_customer-images-015\\"")
<\/span><\/div><\/div><\/div><\/div><\/div>![](\"https:\/\/sparque.ai\/wp-content\/uploads\/2025\/11\/2405_sparque_customer-images-010.webp\" "\\"2405_sparque_customer-images-010\\"")
<\/span><\/div><\/div><\/div>Imagine. Done.<\/h3><\/div>How does it work? With the unique Build-your-own-algorithm feature, Warmteservice can determine what logic is right for their customers. A generic newsletter with offers will have a low conversion rate. This is why the products in a ‘haven’t you forgotten anything?’ e-mail must have a high conversion probability before it is sent. Otherwise, you only teach the customer to ignore your e-mails. SPARQUE.AI is indispensable to get every e-mail right – one by one.<\/p>\n<\/div><\/div><\/div><\/div><\/div>
Perfectly arranged<\/p>\n<\/div><\/div><\/div><\/div><\/div>
About Warmteservice<\/h2><\/div>Warmteservice is an omnichannel B2B wholesaler supplying heating, sanitization and installation materials – a family business of craftsmen for craftsmen. Relevant recommendations on the product and the cart page by SPARQUE.AI ensure that customers don\u2019t forget to order materials or parts needed for a job. But in addition, if needed, Warmteservice sends a reminder e-mail at the end of a business day. That\u2019s \u201cperfectly arranged\u201d.<\/p>\n<\/div><\/div><\/div><\/div><\/div>
<\/span><\/div><\/div><\/div>How SPARQUE.AI helped Warmteservice<\/h3><\/div>SPARQUE.AI predicts based on order data, as well as the items currently in the shopping cart, which products from complementary product categories are ideal cross-sell candidates. Recommendations based on one product already carry a reasonable probability. But with two more items in the cart, the accuracy increases to around 95%, which makes the recommendations shown on product detail and cart page very likely to convert.<\/p>\n
But there’s more. All orders of a customer on that day are screened, and after checking that ideal cross- or up-sellers are absent in subsequent orders, a special e-mail is sent to invite the customer to make a supplementary order before 11.p.m.<\/p>\n<\/div><\/div><\/div><\/div><\/div>
How SPARQUE.AI helped Warmteservice<\/h3><\/div>SPARQUE.AI predicts based on order data, as well as the items currently in the shopping cart, which products from complementary product categories are ideal cross-sell candidates. Recommendations based on one product already carry a reasonable probability. But with two more items in the cart, the accuracy increases to around 95%, which makes the recommendations shown on product detail and cart page very likely to convert.<\/p>\n
But there’s more. All orders of a customer on that day are screened, and after checking that ideal cross- or up-sellers are absent in subsequent orders, a special e-mail is sent to invite the customer to make a supplementary order before 11.p.m.<\/p>\n<\/div><\/div><\/div>
<\/span><\/div><\/div><\/div><\/div><\/div><\/span><\/div><\/div><\/div>Imagine. Done.<\/h3><\/div>How does it work? SPARQUE.AI\u2019s unique Build-your-own-algorithm feature allows Warmteservice to maximize the use of order and PIM data. The foundation is \u2018frequently bought together\u2019. But it is adding similarities between product properties, brand and model to the algorithm that made the difference between passing grade and the high quality recommendations. Also, recommendations are based in part on what else is in the cart. More products increase the accuracy of the recommendations. Feedback data from Google Analytics (products displayed, viewed or added to cart) is also fed to the algorithm. Relevant recommendations? Done.<\/p>\n<\/div><\/div><\/div><\/div><\/div>
Forgot something? Ordered until 23:00, delivered next day<\/h3><\/div>Every day, SPARQUE.AI predicts from all orders of each customer whether anyone is missing important materials or parts. If there is a plausible case, an e-mail reminder is sent that allows to complete the order in time to still manage next-day-delivery.<\/p>\n
For this kind of recommendations, all orders of that customer on that day are screened in the evening. In a typical use case, a spare part or consumable is ordered in the morning, but the repair also requires a specific tool. A customer may have already ordered that one in a second order later that day. Only when recommended products are not present in a subsequent order, an e-mail completing the set is sent at the end of the business day, so that customers can wrap up their order until 23:00.<\/p>\n<\/div><\/div><\/div>
<\/span><\/div><\/div><\/div><\/div><\/div><\/span><\/div><\/div><\/div>Imagine. Done.<\/h3><\/div>How does it work? With the unique Build-your-own-algorithm feature, Warmteservice can determine what logic is right for their customers. A generic newsletter with offers will have a low conversion rate. This is why the products in a ‘haven’t you forgotten anything?’ e-mail must have a high conversion probability before it is sent. Otherwise, you only teach the customer to ignore your e-mails. SPARQUE.AI is indispensable to get every e-mail right – one by one.<\/p>\n<\/div><\/div><\/div><\/div><\/div>
Warmteservice is an omnichannel B2B wholesaler supplying heating, sanitization and installation materials – a family business of craftsmen for craftsmen. Relevant recommendations on the product and the cart page by SPARQUE.AI ensure that customers don\u2019t forget to order materials or parts needed for a job. But in addition, if needed, Warmteservice sends a reminder e-mail at the end of a business day. That\u2019s \u201cperfectly arranged\u201d.<\/p>\n<\/div><\/div><\/div><\/div><\/div>
<\/span><\/div><\/div><\/div>How SPARQUE.AI helped Warmteservice<\/h3><\/div>SPARQUE.AI predicts based on order data, as well as the items currently in the shopping cart, which products from complementary product categories are ideal cross-sell candidates. Recommendations based on one product already carry a reasonable probability. But with two more items in the cart, the accuracy increases to around 95%, which makes the recommendations shown on product detail and cart page very likely to convert.<\/p>\n
But there’s more. All orders of a customer on that day are screened, and after checking that ideal cross- or up-sellers are absent in subsequent orders, a special e-mail is sent to invite the customer to make a supplementary order before 11.p.m.<\/p>\n<\/div><\/div><\/div><\/div><\/div>
How SPARQUE.AI helped Warmteservice<\/h3><\/div>SPARQUE.AI predicts based on order data, as well as the items currently in the shopping cart, which products from complementary product categories are ideal cross-sell candidates. Recommendations based on one product already carry a reasonable probability. But with two more items in the cart, the accuracy increases to around 95%, which makes the recommendations shown on product detail and cart page very likely to convert.<\/p>\n
But there’s more. All orders of a customer on that day are screened, and after checking that ideal cross- or up-sellers are absent in subsequent orders, a special e-mail is sent to invite the customer to make a supplementary order before 11.p.m.<\/p>\n<\/div><\/div><\/div>
<\/span><\/div><\/div><\/div><\/div><\/div><\/span><\/div><\/div><\/div>Imagine. Done.<\/h3><\/div>How does it work? SPARQUE.AI\u2019s unique Build-your-own-algorithm feature allows Warmteservice to maximize the use of order and PIM data. The foundation is \u2018frequently bought together\u2019. But it is adding similarities between product properties, brand and model to the algorithm that made the difference between passing grade and the high quality recommendations. Also, recommendations are based in part on what else is in the cart. More products increase the accuracy of the recommendations. Feedback data from Google Analytics (products displayed, viewed or added to cart) is also fed to the algorithm. Relevant recommendations? Done.<\/p>\n<\/div><\/div><\/div><\/div><\/div>
Forgot something? Ordered until 23:00, delivered next day<\/h3><\/div>Every day, SPARQUE.AI predicts from all orders of each customer whether anyone is missing important materials or parts. If there is a plausible case, an e-mail reminder is sent that allows to complete the order in time to still manage next-day-delivery.<\/p>\n
For this kind of recommendations, all orders of that customer on that day are screened in the evening. In a typical use case, a spare part or consumable is ordered in the morning, but the repair also requires a specific tool. A customer may have already ordered that one in a second order later that day. Only when recommended products are not present in a subsequent order, an e-mail completing the set is sent at the end of the business day, so that customers can wrap up their order until 23:00.<\/p>\n<\/div><\/div><\/div>
<\/span><\/div><\/div><\/div><\/div><\/div><\/span><\/div><\/div><\/div>Imagine. Done.<\/h3><\/div>How does it work? With the unique Build-your-own-algorithm feature, Warmteservice can determine what logic is right for their customers. A generic newsletter with offers will have a low conversion rate. This is why the products in a ‘haven’t you forgotten anything?’ e-mail must have a high conversion probability before it is sent. Otherwise, you only teach the customer to ignore your e-mails. SPARQUE.AI is indispensable to get every e-mail right – one by one.<\/p>\n<\/div><\/div><\/div><\/div><\/div>
SPARQUE.AI predicts based on order data, as well as the items currently in the shopping cart, which products from complementary product categories are ideal cross-sell candidates. Recommendations based on one product already carry a reasonable probability. But with two more items in the cart, the accuracy increases to around 95%, which makes the recommendations shown on product detail and cart page very likely to convert.<\/p>\n
But there’s more. All orders of a customer on that day are screened, and after checking that ideal cross- or up-sellers are absent in subsequent orders, a special e-mail is sent to invite the customer to make a supplementary order before 11.p.m.<\/p>\n<\/div><\/div><\/div><\/div><\/div>
How SPARQUE.AI helped Warmteservice<\/h3><\/div>SPARQUE.AI predicts based on order data, as well as the items currently in the shopping cart, which products from complementary product categories are ideal cross-sell candidates. Recommendations based on one product already carry a reasonable probability. But with two more items in the cart, the accuracy increases to around 95%, which makes the recommendations shown on product detail and cart page very likely to convert.<\/p>\n
But there’s more. All orders of a customer on that day are screened, and after checking that ideal cross- or up-sellers are absent in subsequent orders, a special e-mail is sent to invite the customer to make a supplementary order before 11.p.m.<\/p>\n<\/div><\/div><\/div>
<\/span><\/div><\/div><\/div><\/div><\/div><\/span><\/div><\/div><\/div>Imagine. Done.<\/h3><\/div>How does it work? SPARQUE.AI\u2019s unique Build-your-own-algorithm feature allows Warmteservice to maximize the use of order and PIM data. The foundation is \u2018frequently bought together\u2019. But it is adding similarities between product properties, brand and model to the algorithm that made the difference between passing grade and the high quality recommendations. Also, recommendations are based in part on what else is in the cart. More products increase the accuracy of the recommendations. Feedback data from Google Analytics (products displayed, viewed or added to cart) is also fed to the algorithm. Relevant recommendations? Done.<\/p>\n<\/div><\/div><\/div><\/div><\/div>
Forgot something? Ordered until 23:00, delivered next day<\/h3><\/div>Every day, SPARQUE.AI predicts from all orders of each customer whether anyone is missing important materials or parts. If there is a plausible case, an e-mail reminder is sent that allows to complete the order in time to still manage next-day-delivery.<\/p>\n
For this kind of recommendations, all orders of that customer on that day are screened in the evening. In a typical use case, a spare part or consumable is ordered in the morning, but the repair also requires a specific tool. A customer may have already ordered that one in a second order later that day. Only when recommended products are not present in a subsequent order, an e-mail completing the set is sent at the end of the business day, so that customers can wrap up their order until 23:00.<\/p>\n<\/div><\/div><\/div>
<\/span><\/div><\/div><\/div><\/div><\/div><\/span><\/div><\/div><\/div>Imagine. Done.<\/h3><\/div>How does it work? With the unique Build-your-own-algorithm feature, Warmteservice can determine what logic is right for their customers. A generic newsletter with offers will have a low conversion rate. This is why the products in a ‘haven’t you forgotten anything?’ e-mail must have a high conversion probability before it is sent. Otherwise, you only teach the customer to ignore your e-mails. SPARQUE.AI is indispensable to get every e-mail right – one by one.<\/p>\n<\/div><\/div><\/div><\/div><\/div>
SPARQUE.AI predicts based on order data, as well as the items currently in the shopping cart, which products from complementary product categories are ideal cross-sell candidates. Recommendations based on one product already carry a reasonable probability. But with two more items in the cart, the accuracy increases to around 95%, which makes the recommendations shown on product detail and cart page very likely to convert.<\/p>\n
But there’s more. All orders of a customer on that day are screened, and after checking that ideal cross- or up-sellers are absent in subsequent orders, a special e-mail is sent to invite the customer to make a supplementary order before 11.p.m.<\/p>\n<\/div><\/div><\/div>
<\/span><\/div><\/div><\/div><\/div><\/div><\/span><\/div><\/div><\/div>Imagine. Done.<\/h3><\/div>How does it work? SPARQUE.AI\u2019s unique Build-your-own-algorithm feature allows Warmteservice to maximize the use of order and PIM data. The foundation is \u2018frequently bought together\u2019. But it is adding similarities between product properties, brand and model to the algorithm that made the difference between passing grade and the high quality recommendations. Also, recommendations are based in part on what else is in the cart. More products increase the accuracy of the recommendations. Feedback data from Google Analytics (products displayed, viewed or added to cart) is also fed to the algorithm. Relevant recommendations? Done.<\/p>\n<\/div><\/div><\/div><\/div><\/div>
Forgot something? Ordered until 23:00, delivered next day<\/h3><\/div>Every day, SPARQUE.AI predicts from all orders of each customer whether anyone is missing important materials or parts. If there is a plausible case, an e-mail reminder is sent that allows to complete the order in time to still manage next-day-delivery.<\/p>\n
For this kind of recommendations, all orders of that customer on that day are screened in the evening. In a typical use case, a spare part or consumable is ordered in the morning, but the repair also requires a specific tool. A customer may have already ordered that one in a second order later that day. Only when recommended products are not present in a subsequent order, an e-mail completing the set is sent at the end of the business day, so that customers can wrap up their order until 23:00.<\/p>\n<\/div><\/div><\/div>
<\/span><\/div><\/div><\/div><\/div><\/div><\/span><\/div><\/div><\/div>Imagine. Done.<\/h3><\/div>How does it work? With the unique Build-your-own-algorithm feature, Warmteservice can determine what logic is right for their customers. A generic newsletter with offers will have a low conversion rate. This is why the products in a ‘haven’t you forgotten anything?’ e-mail must have a high conversion probability before it is sent. Otherwise, you only teach the customer to ignore your e-mails. SPARQUE.AI is indispensable to get every e-mail right – one by one.<\/p>\n<\/div><\/div><\/div><\/div><\/div>
How does it work? SPARQUE.AI\u2019s unique Build-your-own-algorithm feature allows Warmteservice to maximize the use of order and PIM data. The foundation is \u2018frequently bought together\u2019. But it is adding similarities between product properties, brand and model to the algorithm that made the difference between passing grade and the high quality recommendations. Also, recommendations are based in part on what else is in the cart. More products increase the accuracy of the recommendations. Feedback data from Google Analytics (products displayed, viewed or added to cart) is also fed to the algorithm. Relevant recommendations? Done.<\/p>\n<\/div><\/div><\/div><\/div><\/div>
Forgot something? Ordered until 23:00, delivered next day<\/h3><\/div>Every day, SPARQUE.AI predicts from all orders of each customer whether anyone is missing important materials or parts. If there is a plausible case, an e-mail reminder is sent that allows to complete the order in time to still manage next-day-delivery.<\/p>\n
For this kind of recommendations, all orders of that customer on that day are screened in the evening. In a typical use case, a spare part or consumable is ordered in the morning, but the repair also requires a specific tool. A customer may have already ordered that one in a second order later that day. Only when recommended products are not present in a subsequent order, an e-mail completing the set is sent at the end of the business day, so that customers can wrap up their order until 23:00.<\/p>\n<\/div><\/div><\/div>
<\/span><\/div><\/div><\/div><\/div><\/div><\/span><\/div><\/div><\/div>Imagine. Done.<\/h3><\/div>How does it work? With the unique Build-your-own-algorithm feature, Warmteservice can determine what logic is right for their customers. A generic newsletter with offers will have a low conversion rate. This is why the products in a ‘haven’t you forgotten anything?’ e-mail must have a high conversion probability before it is sent. Otherwise, you only teach the customer to ignore your e-mails. SPARQUE.AI is indispensable to get every e-mail right – one by one.<\/p>\n<\/div><\/div><\/div><\/div><\/div>
Every day, SPARQUE.AI predicts from all orders of each customer whether anyone is missing important materials or parts. If there is a plausible case, an e-mail reminder is sent that allows to complete the order in time to still manage next-day-delivery.<\/p>\n
For this kind of recommendations, all orders of that customer on that day are screened in the evening. In a typical use case, a spare part or consumable is ordered in the morning, but the repair also requires a specific tool. A customer may have already ordered that one in a second order later that day. Only when recommended products are not present in a subsequent order, an e-mail completing the set is sent at the end of the business day, so that customers can wrap up their order until 23:00.<\/p>\n<\/div><\/div><\/div>
<\/span><\/div><\/div><\/div><\/div><\/div><\/span><\/div><\/div><\/div>Imagine. Done.<\/h3><\/div>How does it work? With the unique Build-your-own-algorithm feature, Warmteservice can determine what logic is right for their customers. A generic newsletter with offers will have a low conversion rate. This is why the products in a ‘haven’t you forgotten anything?’ e-mail must have a high conversion probability before it is sent. Otherwise, you only teach the customer to ignore your e-mails. SPARQUE.AI is indispensable to get every e-mail right – one by one.<\/p>\n<\/div><\/div><\/div><\/div><\/div>
How does it work? With the unique Build-your-own-algorithm feature, Warmteservice can determine what logic is right for their customers. A generic newsletter with offers will have a low conversion rate. This is why the products in a ‘haven’t you forgotten anything?’ e-mail must have a high conversion probability before it is sent. Otherwise, you only teach the customer to ignore your e-mails. SPARQUE.AI is indispensable to get every e-mail right – one by one.<\/p>\n<\/div><\/div><\/div><\/div><\/div>