[Huawei Developers] Freemind R:
Whether it's online or offline operations, a lack of a steady customer stream can cause you to rethink your marketing strategies. A number of solutions have been proposed, including more ads, better products, and improved user services, but sometimes, even the most diligent efforts aren't rewarded.

So wouldn't it be great, instead, to be able to determine the potential of existing users, and predict return users in advance? This would give you a high-level understanding of your users' habits and needs, enabling you to win them back and convert them into loyal, paying users.

1. Method for evaluating user return potential

From registering an account to completing a second purchase, a user's return potential is highly correlated with their behavior data, which includes page views and active time. Therefore, analyzing user behavior data in your app is of immense help in predicting return users.

HUAWEI Prediction uses a massive amount of experimental data to train a machine learning model and uses this model to predict the probability that active app users will make a payment over the next week with their payment data from the most recent week as inputs. This service can predict return probabilities of users depending on such events as in-app purchases reported to HUAWEI Analytics by your app.

2. Method for extracting the most value from existing users

After determining how users behave within your app, you'll want to predict the audience with the highest return potential, and formulate targeted policies for these users, to maximize efficiency.

For example, some products have a membership system, and continue to attract return users by providing discounts and rewards point redemption schemes. Most current membership systems work in the following way: A registered member earns points by making purchases, which can then be exchanged for coupons and gifts. This type of framework does attract return users to some extent. However, due to a relatively high exchange threshold, limited gifts and complicated rules for using points, users will tend to lose interest in the membership scheme.

Our return prediction can optimize this type of incentive scheme, helping your app attract return users at a lower cost. It is found that the predicted return users tend to be more active, based on either their most recent app usage, or throughout the most recent week. From this information, we can surmise that such users would like to make a second purchase due to the need arising from a specific scenario, and will tend to take multiple products into consideration. Therefore, if your product is not competitive enough, it will likely lose many of its paid users.

3. Method for winning back and converting users

Predicting return users helps identify users with a high return potential. The next area of focus is boosting the probability of return. Some shopping apps will make use of their membership system to provide time-limited bonus points, coupons and cash incentives on weekends and holidays. After the promotion is complete, it is found that the activities attract return users at a higher cost, which is not ideal.

HUAWEI Prediction precisely segments return users, helping you avoid this problem. You can set predicted users with high return potential as the audience, and add the Prediction filter in Remote Configuration, as well as the corresponding parameters for the promotion to ensure that the promotion is only sent out to those users in the predicted audience.

The results from a remote configuration experiment in A/B Testing indicated that the treatment group which contains predicted return users yielded as high of a conversion rate as that from the control group, and at a 60% lower cost.

Predicting and analyzing behavioral characteristics of users with high return potential, through HUAWEI Prediction, enables you to formulate targeted marketing strategies, and achieve sustainably rapid growth, even in the wake of increasingly scarce traffic streams.

To learn more about HUAWEI Prediction, feel free to check out this document.