We have previously introduced member activity grading, which uses past clicks, interactions, consumption behavior records, etc. to divide user activity into five levels. One is the lowest interactive group, and fifth is the highest interactive group. This allows brands to For users with different levels of activity, push wake-up messages, or promote shopping guides and other messages for different purposes. However, a watershed moment that marketers are even more looking forward to has emerged. In the past, we analyzed historical data and divided it into groups. But now for “potential purchase groups”, we have to conquer “prediction” and amplify the benefits of first-party data . .
In order to improve the conversion rate, the “potential purchasing audience” is not only based on more complex behavioral analysis, but also adds the factor of time, allowing the brand to directly screen out the members who are most likely to purchase in the next two weeks.
We consider that over a long period of 60 days, users who add to the shopping cart, have the same number of clicks, and spend the same amount will be identified as having the same degree of interaction with the brand. However, over a short period of two weeks, the potential consumption probabilities adidas integrates online and offline virtual can still be very different. For example, users whose last purchase date was two weeks ago, who have added to the shopping cart in the past three days, and whose browsing time has increased are predicted to be users with a higher probability of short-term purchase. In comparison, users who just purchased three days ago may not have even received the package. Even if there are leftover items in the shopping cart, they are still less likely to purchase in the short term.
Starting from the three dimensions of users, products, and time, Crescendo Labs uses deep learning to automatically predict yeezy 350 boost v2s behaviors and provide intuitive products based on all accumulated behavioral data in the past. “Potential purchasers” not only use machine learning to eliminate the need for Reduce labor costs, allowing brands to achieve more precise segmentation in less time, and make full use of the data accumulated by our backend MAAC.
“Potential Buying Users” also provides sufficient flexibility, allow