这种情况其实是有两种原因:
第一,产品本身的转化率不行;
第二,店铺人群不精准;
怎么样判断到底是什么情况,以及怎么去解决?
第一步,先确定到底是什么原因导致流量不精准,点击率低。
方法如下:
进入抖音小店后台电商罗盘商品概览,找到经常出单的几个品:
点击“人群画像”,选择最近一个月的成交人群,看店铺的访客属于哪一个类型的岁数段,地域以及人群特质。
多选择几个经常出单的商品,去对比一下这个商品曝光人群画像。
如果人群画像对不上,说明你店铺人群不精准。
导致店铺流量不精准,有曝光没有点击没有转化。
想要矫正的话,有两个方式:
第一个直接找商品类目对应的垂直达人带货,去抖音小店后台精选联盟达人广场去联系达人带货。
佣金比市场平均高一点(不然达人不愿意带,沟通话术也很重要。
如果不想这样操作的话,也可以直接去找买过这个产品的老客户做复购,并且上新与这个单品人群相关的商品;用老客户给你去直接一拖五破零。
第二步,检查一下商品的标题是否有和产品不匹配的关键词。
毕竟系统第一步给流量搭建模型,是根据商品的标题、主图、详情页的同款数据,给这个商品去匹配人群的。
如果你的商品标题里有不符合这个产品的关键词,把不相关的关键词删掉,用巨量算数去找这个产品相对应的关键词。
There are actually two reasons for this situation:
First, the conversion rate of the product itself is not good;
Second, the store crowd is not accurate;
How to judge what the situation is and how to solve it?
The first step is to determine what causes the inaccurate traffic and low click-through rate.
The method is as follows:
Enter the e-commerce compass product overview in the background of Douyin store and find several products that are frequently ordered:
Click "Crowd Portrait" and select the transaction crowd in the past month to see which type of age group, region and crowd characteristics the store visitors belong to.
Select several more frequently ordered products to compare the exposure crowd portrait of this product.
If the crowd portrait does not match, it means that your store crowd is not accurate.
It leads to inaccurate store traffic, exposure but no clicks and no conversions.
If you want to correct it, there are two ways:
The first is to directly find vertical influencers corresponding to the product category to bring goods, and go to the Douyin store backstage to select the alliance influencer square to contact the influencer to bring goods.
The commission is slightly higher than the market average (otherwise the influencer is unwilling to bring, and communication skills are also very important.
If you don’t want to do this, you can also directly find old customers who have bought this product to make repeat purchases, and launch new products related to this single product group; use old customers to directly bring you one to five to break zero.
The second step is to check whether the title of the product has keywords that do not match the product.
After all, the first step of the system to build a model for traffic is to match the crowd for this product based on the same data of the product title, main picture, and details page.
If there are keywords in your product title that do not match this product, delete the irrelevant keywords and use huge arithmetic to find the corresponding keywords for this product.