+58% profit within the 1st months of Performance Max implementation

Preparation for the Google Performance Max Launch

As part of the project, the client questioned the feasibility of the Performance Max launch because it required the development of new creatives for each of the Small Appliances subcategories, which needed additional resources.

During the Performance Max preparation period, we launched the Smart Shopping campaigns. After 2 weeks, the indicators for the campaigns stopped changing and growing, which meant that they had reached their maximum effectiveness for the allocated budget. Following our approach, switching to the new tool should have improved campaign performance results by category.

Smart Shopping campaigns were running and collecting statistics for benchmarking right before the launch of Performance Max.

Setting Performance Max

Performance Max's machine learning models allow predicting more accurately which combinations of ads, audiences and creatives will be most effective in achieving the goal. To get the desired result, it’s important to provide the campaign with the maximum amount of data (e.g. the number of conversions and traffic).

‍We were able to provide the required amount of data through a non-standard campaign format: instead of separate campaigns for each subcategory, we launched one campaign for all subcategories of the section. As a result, we received a substantial amount of data, which allowed the campaign to learn quickly.

Algorithm for Setting Performance Max

• Formed groups of objects (a linear analogue of a group of ads in the Google Search campaign) based on the principle:

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1 group of objects = 1 group of goods

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9 groups of objects

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Campaign with breakdown by category

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CAMPAIGN STRUCTURE BROKEN DOWN BY CATEGORY

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• Uploaded ads with subcategory landing pages, banners and videos for each subcategory.

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• Adjusted audience signals.

Audience signals is a set of data about the behaviour and interests of potential users, on the basis of which pattern of behaviour is built. Focusing on the created pattern, Google automatically targets the audience which is necessary to fulfil the set goals.

Taking into account the configured groups of objects, we created unique audience settings for each group:

• Audiences that visited or showed interest in subcategories of a group of objects were specified in the remarketing lists (e.g. customers who visited the coffee machine category in the last 30 days; customers who have made a purchase or who have added a category item to their cart in the last 30 days).
• Custom segments and a pool of audiences for remarketing were set up for each group of objects.

INDIVIDUAL SEGMENTS

Customers who have previously entered keywords:

Expanded the pool of keywords of the search campaign of each of the categories at the expense of words that are not quoted in search advertising (e.g. buy an electric kettle Tefal or Bosch with home delivery to the Dnipropetrovsk region).

Customers who have previously visited certain applications or domains:

Added URLs of only niche competitor sites (e.g. the Delonghi website for the Coffee Machines subcategory), because adding marketplaces, even when using subcategory filters, would not create accurate audience building signals.

Intermediate Results

Since the start of the campaign, we have been measuring the dynamics of Performance Max indicators every day. After two weeks, the first slice of the results was made and compared with the data of the Smart Shopping campaign for the same period.

RESULTS OF SWITCHING FROM SMART SHOPPING TO PERFORMANCE MAX CAMPAIGN AFTER TWO WEEKS:

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Even according to the interim results, we could track the effectiveness of the new tool: without increasing the budget, the cost per click decreased, which gave a 17% traffic growth. CRR decreased due to 15% increase in CR and 25% in average check. In addition, the rest of the campaign indicators improved significantly.

RESULTS

The results of the first month of Performance Max work confirm the effectiveness of the selected approach of replacing the advertising tool and combining subcategories within the framework of one campaign.

RESULTS OF SWITCHING FROM SMART SHOPPING TO THE PERFORMANCE MAX CAMPAIGN BASED ON THE RESULTS OF THE FIRST MONTH:

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Performance Max indicators significantly exceed the results of Smart Shopping for a similar budget. We managed to improve the efficiency of the Small Appliances category's CRR by 36.56%, and the category's revenue by 57.87%. As a result of the decrease in the average price per click, we managed to increase the volume of traffic in the category by 30.67% without increasing the budget.

As a result, the Performance Max advertising approach opens up new opportunities for the brand to achieve its goals concerning improving all performance indicators.

WHAT'S NEXT?

The decision to switch to Performance Max in the chosen campaign structure was justified. At the time of publication of this case, the process of transition of other categories of goods to the new tool has started. We are already seeing great results and possible options for campaign optimization.

We will reveal more insights and capabilities of the Performance Max tool with the Eldorado.ua team in the next case.

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Promodo are experts in their field, so we had no worries about the success of transition to PMax. The issues with the preparation of creatives occurred because the tool was quite new at the time. There was a lack of examples. We coped with it, due to the detailed technical task from Promodo and Google cases from other countries. We are 100% satisfied with the results. Mariia Perekhozhykh. Senior Marketing Specialist, Eldorado.ua

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