How to make mobile phone number filtering more efficient, 3 methods suitable for e-commerce
The amount of data for e-commerce teams usually grows rapidly, and advertising leads, old customers, active users, and private domain accumulation will continue to accumulate. With too much data, if there is no fixed filtering process, the back-end customer service will become busier and busier, but effective conversions may not necessarily increase simultaneously.
Many times, the problem is not the traffic, but the number that was not processed in advance. The earlier the number filtering is done, the easier the subsequent operations will be.
Why e-commerce teams rely more and more on number filtering
The e-commerce scene has an obvious characteristic: the source of users is very diverse.
Common ones include:
l Advertising data
l Social media attracts users
l List of old customers
l Event registration data
When these data are mixed together, what usually appears is:
l The proportion of empty numbers increases
l There are more and more duplicate numbers
l The proportion of low active users is relatively high
If it is directly imported into the system and used, the pressure on customer service will increase significantly.
The first method: do basic availability filtering first
This is the most basic and important step.
Confirm first:
l Does the number really exist?
l Whether it can be reached normally
l Is it an abnormal number?
Because if the number itself is not available, all subsequent actions are meaningless.
The problem with many teams is not that they don’t know how to market, but that a lot of resources are wasted on invalid numbers.
The second method: Stratify according to active status
After the number is available, the user status needs to be further differentiated.
Usually it can be simply divided into:
Highly active users
Suitable for priority private messaging and focused marketing.
Common active users
Suitable for continuous reach and content cultivation.
Low active users
Reduce the sending frequency to avoid wasting resources.
After handling this, customer service will not be slowed down by a large number of low-value users.
The third method: further screening based on consumption tags
In the e-commerce scenario, just looking at the number status is not enough.
A more effective way is to combine:
l age group
l area
l Device type
l Spending power related tags
For example, high-customer single products are more suitable to prioritize high-consumption age groups and highly active users.
This method will be more stable than indiscriminate mass sending.
Before batch processing, it is more labor-saving to do a number check first.
If all numbers enter the operation process directly, repeated problems will continue to occur later.
In actual operation, you can first use Digital Planet to do screen number detection to filter out unavailable numbers and abnormal data, and then combine active and consumption tags for further stratification. Digital Planet supports free trial screening test.
This allows the data to complete basic cleaning before entering the system.
After the filtering process is fixed, the team efficiency will change significantly.
When number filtering becomes a fixed step, the most obvious changes are usually:
l Customer service communication is more focused
l Response rate is more stable
l Data analysis becomes clearer
Because low-quality data has been processed in advance.
Why e-commerce teams are more suitable for hierarchical operations
E-commerce users themselves vary greatly.
Some users are suitable for immediate transactions, some are suitable for long-term cultivation, and some have almost no possibility of conversion.
If all are processed uniformly:
l High-value users are easily ignored
l Customer service time will be occupied by low-quality users
l The overall efficiency will become lower and lower
Therefore, layering is more important than scaling.
A common misunderstanding is to only pursue data volume
Many teams will continue to expand their list in hopes of increasing sales.
But if the data quality gets worse:
l Shipping costs will increase
l Conversion rates will drop
l The backend will become increasingly difficult to manage
The really effective direction is to clean the existing data first.
The core of number filtering is not to reduce data, but to improve operability
After filtering, the amount of data may be reduced, but the proportion of users that can actually be operated will be significantly increased.
This means:
l Each mass message is more valuable
l Every private chat is easier to progress
l Every customer service follow-up is closer to real customers
E-commerce operations increasingly rely on front-end data processing
In the past, many teams focused more on back-end conversions, but now they pay more and more attention to front-end data quality.
Because front-end data determines the efficiency of all subsequent actions.
Putting number filtering at the front is essentially reducing the proportion of invalid operations.
The same amount of data, as long as the processing logic is different, the final results will be much different.
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