Instagram female mature users are becoming more expensive, why this type of data is becoming more and more

existIn Instagram user screening, a recent obvious change among female mature users is that the cost of acquisition is rising. Not only is the cost of launching higher, but the data itself is also becoming more and more difficult to obtain. When many teams are doing accurate customer acquisition on Instagram, they will obviously feel that such users are becoming more and more "expensive". What’s expensive here is not only the price, but also the difficulty of acquisition, screening and stability costs.

If you only look at it from the surface, it is easy to attribute the reason to increased competition, but in fact the more core problem is that the value of this type of users themselves has been re-amplified. As traffic becomes more and more dispersed, the conversion efficiency of general users decreases. Female mature users, who have both consumption power and stable decision-making ability, will naturally become the focus of competition.

WhyInstagram female mature users have become increasingly popular recently

The reason is straightforward. This type of users perform more stably on multiple indicators.

The first is spending power. Compared with young users, mature female users are more likely to make actual purchases in many categories, especially in fields such as beauty, home furnishing, health, and light luxury.

The second is decision-making stability. This type of users will not change their interests as frequently as some young users. Once they recognize a product or brand, they are more likely to make repeat purchases.

Another is that the content acceptance method is clearer. They pay more attention to practicality, experience and real feedback rather than pure visual stimulation.

The combination of these characteristics makes this type of users more valuable in the conversion link.

Why this kind of data is getting more expensive

From the perspective of supply and demand, it is easy to understand.

Demand is increasing. More and more teams working on high-end single products, private domain operations, and brand transformation are actively screening mature female users.

Supply is falling. It is not that the number of users has decreased, but that the proportion of high-quality users that can be filtered, identified, and stably reached has decreased.

Plus the cost of screening itself is rising. In the past, simply using age stratification or gender filtering could cover a part of the population. Now, without the addition of active user filtering, device identification, and abnormal account filtering, it is difficult to guarantee data accuracy.

The superposition of these factors has resulted in the data of mature female users becoming more and more expensive.

Which industries are best suited to prioritize screening this group of people?

Beauty and skin care is the most typical scenario, and these products are inherently highly dependent on mature female users.

The same goes for home life products, because they involve long-term use and experience judgment, and this group of people is more likely to form stable conversions.

Health and light health products are also more suitable for this type of users.

There are also some high-customer service businesses, such as education, consulting, etc., which will also give priority to the screening of female mature users.

What these industries have in common is that they value long-term value more than short-term clicks or interactions.

What dimensions should you look at when screening mature female Instagram users?

If you only look at gender and age, the accuracy is no longer enough.

It is usually necessary to add at least active user screening, because even if such users meet the age structure, they are difficult to reach if they are not active.

Device identification also has auxiliary value in some scenarios and can help determine the user level.

Another one is account quality, such as abnormal account filtering to prevent low-quality data from being mixed in.

If conditions permit, content interactive behavior can also be combined, but this layer usually requires more data support.

Overall, this type of user filtering is more suitable for multi-dimensional combinations rather than single tags.

Why female mature user screening increasingly relies on data combinations

Because single tags are increasingly prone to failure.

Age tags can be easily obtained, but the quality is not guaranteed.

Active tags can determine behavior, but do not represent crowd matching.

Only by combining the dimensions of gender, age, activity, equipment, and quality can we get closer to the real target group.

This is why filtering out female mature users is now more and more like making a data structure rather than simply filtering.

The role of digital planet in screening such high-value groups

In actual operations, many teams will use Digital Planet to do thisScreening of female mature users on Instagram. By first completing gender screening and age stratification through Digital Planet, and then superimposing active user identification and account quality filtering, the target group can be separated from the original data. Digital Planet supports free trial screen number detection, and is more suitable for multi-dimensional label combination screening, allowing mature female users to screen not only by label judgment, but also by structure judgment.

The advantage of this approach is that the filtered data is closer to the available population rather than the population that appears to be eligible.

Why is it increasingly difficult to obtain mature female users?

In essence, it is not that there are fewer users, but that the screening threshold has become higher. In the past, simple screening could cover some people, but now more detailed screening logic is needed to lock in the target.

Coupled with the increase in competition, everyone is competing for this group of people, leading to an increase in acquisition costs. So it is difficult, not absolutely difficult, but more difficult than before.

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