Case Milange: Google Ads for selling branded swimwear

Direction – sale of branded swimwear

Location – Ukraine

Development start date – June 2021

Leads – purchase request, transaction created.

Site –  https://milange.ua/ 

Introduction:

We received a site, an online store, with a wide range of swimwear, pareos.

Site audit. Analysis of competitors. Forecast.

• We conducted a site audit and competitor analysis, identified "weaknesses" in competitors' ads and sites, and determined for ourselves the directions we will develop and the advantages of our site's offerings.
• Made a preliminary forecast by directions, taking into account the location, in order to determine the amount of the bid in different cities of Ukraine, in the TOP 3 display. After the development and collection of semantics, a more accurate forecast was made.

Development:

Developed search campaigns:

• by site categories;
• brand campaign;
• smart shopping campaigns by site categories;
• campaign with Ukrainian semantics;
• campaign on competitors;
• remarketing campaign.

What was done during the operation:

• compiled semantics that most closely match the target user request:
• divided by ad groups;

• written cross minus and collected minus words to lists. attached to campaigns.

• bids are placed, according to the forecast, taking into account the location.
• created ads - search responsive, text, dynamic ads.
• Added KeyWord, UTP and call to action to text and responsive ads.
• it is important to monitor the quality of ads and constantly improve it.
• an extension has been developed (we always work out the account to the maximum under the extension).

Shopping campaigns

• Shopping campaigns were developed according to the priority areas of the site (from the advertiser's sales experience).
• Accordingly, in the campaigns, the correct budget was set to the size of the direction's demand.
• Also paid attention to the trade feed, checking for the correct description.

Analysis and works:

• Initially, after launching the campaigns, we observed where the target users came from, analyzed the data.
• For example, what percentage of target users come from mobile devices and computers, the conversion value of these devices; who orders the service more often: man/woman, their age; what are the common interests of the target users.
• After the analysis, we carried out a number of works related to rate adjustments (lowering/increasing) depending on the received data, as well as work related to the expansion of the semantic core.