We present two updates. The first is full cart last-click attribution. The new attribution model will help compare the effectiveness of Retail Rocket Group recommendations with other tools. The second is a new algorithm call “Checkout Area Products,” which will increase impulse purchases at the checkout stage.
Attribution of the entire order by last click
In addition to traditional attribution, Retail Rocket Group’s product recommendation statistics now include a new model — attribution of the entire order by the last click. The tool helps to more accurately assess the contribution of product recommendations to overall revenue by comparing their effectiveness with other advertising tools in one coordinate system.
New features: last-click attribution
Of product recommendations and the “Checkout area products” algorithm
Traditional attribution model in blue column, new last-click attribution model in green
What has chang?
The conditions of traditional attribution are quite strict. The goal is to ensure maximum accuracy in measuring the real incremental effect of our recommendations.
Traditional Retail Rocket attribution takes
Into account only the cost and quantity of purchas products in recommendation blocks with which the visitor to the web store or mobile application interact through the recommendation block or widget. Other products in the order are not taken into account. Interaction is a click or adding to the cart directly from the product card in the recommendation widget.
New features: last-click attribution of product recommendations and the “Checkout area products” algorithm
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The new attribution model takes
Into account the entire order – if the user interact with one of the products through the recommendation block, then buy kuwait whatsapp number data 5 million all products purchas in this order will be attribut to this block. In this case, any product that was purchas is attribut – from the recommendation block or another.
For example, a customer click on a phone case in the recommendation block and left the store. Return within 24 hours of the click and plac an order for a protective glass and headphones. The phone case is not in the order, but we will take the glass and headphones into account in the new attribution model.
New features: last-click attribution of product recommendations and the “Checkout area products” algorithm
Comparison of attribution models
Traditional Attribution
Retail Rocket Last Click Attribution
Attribution mobile number in window 24 hours 24 hours
What do we count as revenue? Only products that were interact with through recommendations
If the buyer click on a phone case in the recommendations block and in addition to it add a glass and headphones to the cart, only the case is attribut. The entire order, even if it does not contain recommend products.
If the buyer click on a phone case
In the recommendations block and left the store. Return within 24 hours after the click and plac an order for a protective glass and headphones. The phone case is not in the order. In the new attribution model, we will take into account the entire order amount: glass and headphones.
How is it reset? 24 hours 24 hours or any external transition
Peculiarities Reflects the effectiveness of the recommendations Helps to compare more easily with the performance of other tools or companies
New Algorithm – “Checkout Area Products”
The algorithm is design to increase the number of impulse purchases at the final stage of checkout, similar to products locat at checkout counters in an offline store.
How the algorithm works
In real time, the algorithm suggests products to customers that can be add to the cart right before completing the order. The recommendations are select bas on the observation of customer behavior.
Adding products to the cart via the recommendation block immiately before completing the order directly contributes to an increase in the average number of products in the order, and, accordingly, the average check. Without additional recommendations, the buyer usually completes the order and ends the current session, which eliminates the possibility of purchasing goods from the checkout area. Thus, the algorithm helps to significantly increase revenue due to products that would otherwise not have end up in the user’s cart.
Choose software for a loyalty program with Retail Rocket Group
Evaluating the results of the loyalty program
To understand whether a loyalty program is beneficial, it nes to be assess on three parameters:This data can be analyz within 2–3 months after the program has been launch.
We calculate revenue. To eliminate the influence of seasonality, we ne to take the same period of the previous year for comparison: that is, June 2024 should be compar with June 2023, and so on. If there is no revenue growth, then we ne to adjust the loyalty program and change its conditions to more favorable ones for the buyer.
We conduct an audit of the client base. Determine how many new contacts have appear in it. Separately calculate the number of repeat purchases with the partic.
