Google Smart Shopping and Campaign Segmentation

Google Shopping is an effective platform for attracting potential customers and buyers of goods and services. Therefore, it is important to know how to work with it.

Google Shopping offers a type of marketing campaign that allows users to obtain all necessary information before clicking on a product, including photos, store ratings, product names, prices, discount programs, etc. Ads are published on several sources: search results, Google Shopping, “Goods”, YouTube.

Google Smart Shopping – Distinctive Features and Key Concepts

Smart Shopping is one of the newest types of Google advertising campaigns. It uses machine learning not only to reduce the number of man-hours needed to optimize campaigns but also to do much more work in a short period of time. Advertisers just need to upload the campaign logo and product information, set the campaign goal and budget, and Smart Shopping Campaigns will handle the rest with automated bidding and ad placement. Thus, smart product listings are designed specifically for advertisers with smaller budgets and less time for strategic ad management.

Note! Smart Shopping Campaigns are an effective and popular subtype of the shopping campaign platform. Its main distinguishing feature is its use of machine learning. It is not possible to directly influence the functioning of smart campaigns, but it is possible to prepare them as much as possible before launching.

Segmentation of Smart Shopping Campaigns

Studying this topic raises many questions. Many do not understand whether it is necessary to combine all products into one campaign, or if it’s better to choose segmentation by categories or other parameters. That’s why it is important to study all the features of smart campaigns and their algorithms.

One Smart Campaign for All Products

Before combining products into one campaign, you need to ensure that there are no other options related to the niche’s specifics. Simultaneously, such a method, as partial launch of the online-store products, might not always be suitable. This is due to the fact that with a large variety of assortment, using products in one campaign is considered incorrect and inappropriate. Google itself holds this view in marketing settings. Under such circumstances, the advertisement will not be distinguished by the maximum relevance score. Therefore, segmentation plays a major role.

Several Campaigns with Diverse Parameters

Creating smart campaigns in Google Ads tools is limited. The maximum number is 100. However, the effectiveness of such volumes will be achieved only in one case – if each campaign has its own specific product or product group, and they must not overlap.

For example, for testing and analyzing different strategic directions in bidding, it is recommended to use 5 types of products and no more than two campaigns. This option is possible under the strategy of maximum numeric value assigned to a defined conversion for further study of its impact on business. Testing is acceptable also to study the target profitability.

The solution is simple, yet one feature is important – campaigns are similar to competitors’ firms. Such competitive struggle affects the reduction of profitability. Competition will be high when campaigns even have one product type in common. If they all function simultaneously, then they will be limited in displays. Such a problem can arise in one campaign or in two immediately.

What are the consequences of low volumes of displays or clicks? Google has several secrets that are known to a very small percentage of publishers. Thus, for instance, the situation may develop in several directions.

• If the volume of clicks and the ratio of the volume of resource users who performed target actions to the total volume of users visiting the site (percentage metric), differ by large values, then the advertisement will be distinguished by high efficiency and relevance for the user.< /li>
• With unfavorable indicators, the system negatively affects the account. Competitors will have the advantage. For example, the amount of ad displays will be reduced, and competitors’ will increase. The same algorithm applies to other indicators.< /li>

Such arises due to a significant decline in the efficiency metric. If intelligent bidding is used, then all actions are automated. If manual management is applied, then all bidding actions are done manually. However, both options influence the increase in bids.

Attention! The standard Google Shopping service will be an ideal solution to problems in cases where it is necessary to activate two or more campaigns with different parameters for identical product categories. This won’t cause competition with smart campaigns.

Segmentation Depending on Categories

This option is the easiest and most effective when separating products for Google Smart Shopping is necessary. As each product has its own parameters, like target efficiency and profitability metric, this approach is particularly in demand. It can be used in many strategies, including Target ROAS.

If a business or company deals with the sale of computers and their components, then each product from the assortment has its distinct cost. Because of this, they will relate to different price categories. However, they differ by their own margin rates. Under such circumstances, one Target ROAS for all is unacceptable.

For example, it will be ineffective to focus on marketing components, as the profit will be minimal. Ads will not show computers, which will severely impact profitability. The reverse understanding can be considered with this example. If the focus is only on computers, then the ad will not include components. As a result, the effectiveness of sales will be only from the first type of products.

Advice! To exclude all listed problems, make sure one category of goods corresponds to one campaign.

Segmentation Based on Product Potentials

An effective way to segment smart campaigns is based on product conversion rates.

In some situations, it may turn out that some of the products perform better than the others. In this case, there is no efficiency in advertising the second group. In such circumstances, products with a conversion rate should be moved to a separate campaign. This algorithm applies when after marketing analysis, it becomes clear that an advertising campaign incurs significant losses due to the rare appearance of some part of the goods in the advertisement.

Conclusion: Key for Learning

Currently, smart shopping campaigns that operate based on machine learning are leading. Thus, if the selection of products in the online store is small, using Google Smart Shopping, the best solution may be to stop the functioning of other campaigns. This will prevent any interference in the learning process.

In other circumstances, it is necessary to choose a specific product group, which does not belong to any campaign type. This rule applies to standard, smart, and remarketing types. The only nuance is that such requirements do not apply to search-type marketing.