The service competitiveness index (sci) is the core indicator for determining the exposure of small programs/life numbers, the ranking of searches and the tilting of official resources - the higher the sci scores, the easier it is to get into the first page of the payment, the first screen, and the cost of getting paid can be reduced by more than 50 per cent. Most operators, however, face the pain of “failing uv data” and “deficit mau growth” and “stagnate collections”. This paper will tighten the payment logic of the csi algorithm, from the three core dimensions of uv daily activity, mu monthly activity, collection collections, to the step-up strategy of dismantling the dropables, and will help you to achieve a three-month leap in the sci score and take over the payment of the bonus. +baby75484
I. Understanding the rules first: the central equipment corrective corrections of paying sci
If the sci is to be promoted efficiently, it must first define its bottom rating mechanism. According to the official disclosure of the payment treasures, the sci is calculated mainly through the four dimensions of user activity (40 per cent), quality of service (30 per cent), commercial value (20 per cent), ecological synergy (10 per cent), in which the user activity is the basis - while the uv's daily (daily independent visitors), the mau's monthly (independent visitors) and the collection is the core data of the user's active dimension - the “low limit” of the sci fraction。
Particular attention needs to be paid to the strict determination of “real user behaviour” in the payment algorithm, the real-time monitoring of brushes (e. G. Forgery of uvs with simulators) and the direct reduction of sci by more than 50 per cent once violations are triggered, with a recovery cycle of up to six months, and the need to adhere to the principle of “compliance”。
Ii. Step one: strengthening uv days and building sci bases
The uv daytime activity is the sci-upped “knocking bricks” - only if the daytime is stable enough to reach the target will there be room for the mau and the collection. The goal is to move the uv from “scattered fluctuations” to “stable growth on a daily basis”, and it is proposed that the first-month target of the new account be 500-1,000 uv/day, with mature account impact of 2000 + uv/day。
(i) 3 major compliance channels, low cost new uvs
1. Payment for the flow of the inner scene
- entry to the payable lifeline, release of dry content related to the high functionality of a small program, jump-by-cards in the article and direct users to click (path: reading the article clicks on the card into a small program to complete uv statistics)。
- participation in official “situation events” for the payment of treasures, such as “consumer coupons” for people's days of service, where eligible mini-programs can be recommended by banner on the first page, with a single event bringing 5,000+uv precision. For example, the participation of a contribution-type small program in the “privileged utility contribution” activity increased threefold in three days by uv and the user retention rate reached 40 per cent (more than the normal new channel)。
2. Private traffic conversion
Private users, such as the micro-trust community, corporate micro-credit clients and others, are directed to the name of the paid treasure search applet (the phrase: “pay the treasure search `xx fee' and pay the electricity fee reduced by five dollars”) and the user is encouraged to perform the search for entry through “small benefits” (e. G., vouchers, credits). Attention needs to be drawn to the avoidance of direct delivery of applet links (the payment of treasures weighs less heavily on the flow of external links) and to the fact that “active searches” are more easily judged by algorithms as “real needs uv”。
3. Underline scenes
Underline stores/cooperative scenes can be used to post a 2-d code for the payment of a small program, with a combination of “scanning benefits” (e. G., a small program such as catering “scanning points at $3 per person”) to attract a lower-line user scan. This type of uv has a higher probability of subsequent transformation (e. G. Consumption, collection) due to the "high match " of the scene, and has a positive effect on the sci more than cross-line traffic。
(ii) uv quality optimization: avoidance of “ineffective daily life”
Algorithms look not only at the number of uvs, but also at the “uv mass” - if the user exits within 10 seconds after entering the applet (a jump rate of over 80%), they are judged to be “unvalid uvs” and cannot upgrade the sci. Optimizing direction:
- the primary screen highlights the "core function", such as the contribution class, which directly displays the "water/electricity/gas" entry point, so that the user does not find the target function
- the first entry was when a “new-man guided missile window” (e. G., a “click on a $5 voucher”), which extended the user's stay beyond 30 seconds and increased the uv quality rating。
Step two: breaking the mau moon and pulling up the sci fast
Mau is the sci's “core growth engine” - once the uv has stabilized, the “unilateral uv” needs to be converted into “live users” by “retire operation”, with the goal of having mau reach 15-20 times the uv's daily life (in the case of 1,000 uvs per day, the mau needs 15,000 to 20,000)。
(i) category 3 retention strategy to enhance user access
1. Cyclical demand awakening
For the " life-cycle " function of a applet, send payment money template messages (subject to user authorization), such as:
- sub-procedures such as contributions: “your electricity bills are about to be defaulted and the value of `xx contributions' paid for the search can be filled quickly, minus $2”
- financial-type applet: “the fund of your concern has increased by 3 per cent today and click to see details”。
Template messages need to be “exactly reached” to avoid high-frequency harassment (recommended to send 2-3 times a month), which could lead to the cancellation of the authorization by the user and affect the mau。
2. Membership binding
A member system of “centre-grade” members is set up: users can obtain credits for a specified action (e. G., one contribution, one financial information) and credits are fully convertible, and gifts are given in kind. For example, a sub-procedure for living services has increased the rate of mau visits by 35 per cent and directly pushed sci up by 12 points, “100 points per month for three times a month, 100 points for five coupons”。
3. Exclusive monthly benefits
The introduction of a monthly mau-specific activity, such as “the third entry to a small program in the month, which can receive an eight-dollar non-threshold voucher”, encourages users to make multiple visits within the month. Activity entrances need to be visible on the front page of the applet (e. G., at the top of banner) and to enhance the perception of “revisit benefits” by opening windows when users first enter。
(ii) mau data monitoring: alert to “loss risk”
A weekly “data centre-mau analysis” section in the back office of the paying merchant to see the “average of weekly visits” “user loss rate”:
- retention activities need to be optimized if the average number of weekly visits is less than two (e. G., enhanced benefits)
- if the rate of loss exceeds 50 per cent (i. E., up to 50 per cent of mau last month continues to be visited this month), the reasons for the loss of users (e. G., inadequate functionality and low welfare attractiveness) need to be analysed and the strategy adjusted in a timely manner。
Iv. Step 3: enhancing collections and consolidating the sci high score advantage
Collections are the key indicator for the payment of “user recognition” - the higher the collection, the higher the sci score under the equivalent uv, mau (data show that for each 1% increase in the collection rate, the sci increases by about 2-3 points). The goal is to reach 5-8% of the collection rate (number of users/uv daily activities) (for example, 1,000 uvs per day, 50-80 new collections per day)。
(i) 4 collection triggers to guide the active collection of users
1. Functional value tips
On the core functionality page of the applet (e. G., contribution success page, financial gain page), the pop-up hint is: “to collect small programs, the next contribution/receipt will be easier”, combined with the incentive to “take 20 points after collection” and lower the user collection threshold。
2. Site orientation
Design the narratives in conjunction with users, such as:
- hand-outs: “separate me, no more night search”
- travel-type applet: “after collection, next round-trip point, save 3 minutes”。
The scenery jargon allows users to perceive the “practical value of the collection” more than twice the conversion rate of the simple “indication collection”。
3. Exclusive interest in the collection
The launch of the collection order campaign: the user will receive an "exclusive coupon" after the collection (e. G., "the collection will be reduced by $3 and will be valid next time") and the coupon will be set up for a "seven-day period" which will force the user to return to the collection for a short period of time, while increasing the collection and mau。
4. Page design optimization
Place the collection button at the top of the front page of the applet (e. G., the top right icon), or place " easier after the collection " next to the functional entrance to ensure that the user can quickly find the collection entry. Avoids hiding the collection buttons deep in my page, reducing user operating costs。
(ii) collection risk warning: avoidance of “ineffective collection”
The algorithm filters the "unusual collection" (e. G. Multiple collections of the same device, cancelled immediately after collection) and therefore requires attention:
- prohibiting “compulsory collections” (if the collection is not functional), which would otherwise be considered an offence
- regular clean-up of the “zombie collection” (users who have not returned to the collection three months after the date of the collection), which can alert the sleeping users and increase the activity of the collection users by sending information on the “friendly benefits of the collection”。
V. Guide to pit avoidance: 3 common sci promotion error zones
Reliance on the “out-of-site” trap: while some operators are heavily guided through platforms such as tremors and fast hands, the weight of the money is lower on out-of-station traffic and is prone to “high uvs, low mus, low collections” and lower sci ratings — priority focus on inside-of-site payments and conversion of the private domain, making it easier to draw points。
Ignoring the “quality of services” dimension: the sci looks not only at user data, but also at “complaint rate, speed of client response” - if the user complaint rate exceeds 1 per cent, the sci score is directly deducted, thus ensuring a 24-hour response and timely resolution of user problems (e. G. Failure of payments and unavailability of coupons)。
Data “unusual growth” is not desirable: at the end of the month, some operators “brush uvs, collections”, resulting in excessive data volatility (e. G., flat-day uv100, surge of 10,000 at the end of the month), may be judged by algorithms as “unusual data”, triggering a downscaling — data “smooth growth” needs to be maintained (e. G., 10-15 per cent growth of uvs per day), consistent with true user behaviour patterns。
In short, the payment promotion is a step-by-step project on “uv foundations, maura growth, solid collection advantages” and requires long-term fine-tuning operations in conjunction with payment ecological rules and user needs. The current payment package is tilting the resources of the high sci applet (e. G., recommendation on the front page, search weighting) so that from now on, the above-mentioned strategy will result in a three-month leap forward of the sci score from the “flow edge” to the “core exposure area”。
