Introduction

In the fast-paced realm of digital marketing, staying ahead of the competition requires more than just an online presence; it demands strategic insights derived from meticulous data analysis. In the context of Google Shopping, where product placement plays a pivotal role, understanding the correlation between price and performance is crucial. To better understand this, we will explore how consumers behave in relation to pricing and how we can strategically adjust our pricing to profitably and sustainably grow our business. In this article, we will delve into how saving and harnessing pricing data for marketing purposes and explore how strategic pricing, combined with profitability analysis, could be a real game-changer. We will also provide a guide on how to log pricing data, which will allow us to easily combine pricing data with other marketing performance data such as profitability data and performance metrics from Google Ads.

The Digital Shelf and Consumer Behavior

In the digital landscape, Google Shopping serves as the virtual shelf where products vie for attention. Much like a physical store, the placement of a product can significantly impact its performance. In a world where consumers have the power to compare products at their fingertips, price becomes a paramount factor. For sellers, especially those dealing with identical products from various suppliers, offering a competitive price often becomes the deciding factor for success.

A product where the price was raised before the benchmark caught up, which led to significantly lower sales and lower profitability. GP2 (Gross Profit) and GP3 (Gross Profit after Advertising Spend)

Navigating Price Adjustments

While it's evident that adjusting prices can boost sales, the true magic happens when this is coupled with profitability analysis. Beyond merely lowering prices, understanding the impact on the bottom line provides invaluable insights. For instance, a price reduction may lead to higher impressions & click share which increases sales, and when balanced against the decrease in profitability per sale, the overall result can be a net positive. This delicate equilibrium is where success lies.

A product where pricing was adjusted to be lower than benchmark

Here is an example of a product's performance in comparison to the benchmark price. With the profitability of a product in mind, we are able to increase sales of a product while improving overall profitability. As seen in the graph above, GP3 (Gross Profit after Advertising Spend) greatly improved with lower pricing compared to the benchmark. Doing so allows us to sustainably and profitably develop our presence in this highly competitive market.

While competitive pricing is vital, we emphasize a well-rounded strategy that includes loyalty programs, brand awareness, shipping options, and pricing. At our core, we have seamlessly integrated price benchmark data with product profitability performance. This practical approach allows us to clearly demonstrate the impact of strategic pricing on both sales and profitability, ensuring our clients navigate the path to success with confidence.

Sustainable and Profitable growth

In a world where customers wield the power to compare and choose, mastering the art of strategic pricing is a non-negotiable for digital marketers. Leveraging the data-rich environment of Google Ads and Merchant Center, adding profit data on top, not only allows marketers to sustainably and profitably grow their business but also to gain a comprehensive understanding of the impact on profitability.

To gain access to this invaluable data and empower your e-commerce growth with informed decisions, we leverage Kuvio for profit data and BigQuery for logging price competitiveness data. Integrating profit data in bidding and reporting is no easy task, that’s why we use a third-party solution like Kuvio, using Price Benchmark data in your analysis, however, is pretty straightforward. Below follows a guide on how to analyze your marketing performance by making your Price Benchmark data available for blending with other metrics.   

Logging of Price Competitiveness report

Price Competitiveness report from Google Merchant Center is a report that shows how other retailers are pricing the same products that you sell. We will find an average price for each product, which can help us understand the price at which other retailers are successfully attracting traffic. This report is not only essential in helping us set a pricing strategy but also tremendously useful in understanding the performance of our marketing effort. By default, this report is not accessible outside of Google Merchant Center and the date field is not available when accessing through third-party solutions, making it difficult to analyze and act on this insight.

Since the Price Competitiveness report does not have a date field associated with it, we need to log the price benchmark data from Google Merchant Center through Data transfer combined with scheduled queries. We leveraged the capabilities of Google Cloud Platform to accomplish this, where the following steps outline the process:

1. Create a BigQuery Dataset

Start by creating a BigQuery Dataset and make note of the dataset ID, which is essential for the subsequent steps. Ensure that the data location is set to the same location as our scheduled queries. In our case, we opted for the Multi-region setting (EU) for reference.

2. Set Up Data Transfer in BigQuery

Initiate a Data transfer in BigQuery, choosing Google Merchant Center as the data source. Provide details such as Display name, Destination Dataset, and Merchant ID. Schedule this data transfer to run at a frequency of once every 24 hours.

Assign a distinctive display name for easy identification.

For the Destination Dataset, either select from a dropdown menu or paste the previously prepared dataset ID.

Specify the Merchant ID, corresponding to the Google Merchant Center account ID which is intended to extract price benchmark information from.

In the data transfer settings, choose the "Products" and "Price Competitiveness" reports. This selection facilitates a more detailed breakdown of product performance, allowing price competitiveness analysis based on custom labels or product categories. Note that viewing price benchmark data on a category level may be limited due to Google's restrictions on competitor sales or click data in this report.

3. Combine and Enrich Data

Once the data transfer is configured, the "Products" and "Price Competitiveness" reports will be available in the specified dataset. Create a new table within this dataset, combining data from both reports using a query. Introduce additional dimensions based on the data to enhance its richness.

We chose to join the "Price Competitiveness" report with the "Product" report based on offer_id.

Some fields can be removed if deemed unnecessary.

The table we used features the following fields:

• product_id

• merchant_id

• offer_id

• title

• availability

• availability_date

• brand

• google_brand_id

• custom_labels

• gtin

• item_group_id

• mpn

• price

• sale_price

• sale_price_effective_start_date

• sale_price_effective_end_date

• google_product_category_ids

• google_product_category

• google_product_category_path

• product_type

• price_micros

• benchmark_price_micros

• product_type_level_1

• product_type_level_2

• product_type_level_3

• product_type_level_4

• product_type_level_5

• category_l1

• category_l2

• category_l3

• category_l4

• category_l5

• above_benchmark

• at_benchmark

• below_benchmark

• timestamp

The fields above_benchmark, at_benchmark, and below_benchmark are dynamically generated by the query based on whether price_micros is lower or higher than benchmark_price_micros. To address the absence of a timestamp in the "Price Competitiveness" report, a timestamp based on the current date is created and added to the table.

4. Schedule Query

To ensure we consistently access the most up-to-date price competitiveness data, it is essential that our query, responsible for extracting information from the "Price Competitiveness" and "Products" reports, is executed on a daily basis in alignment with our data transfer schedule.

If the query already defines the destination table, there's no need to re-specify it within the Scheduled query settings. Be mindful to match the data location setting with that of your dataset and table location.

Additionally, as a cost-saving measure, consider incorporating a clause in the query to delete data from the "Price Competitiveness" and "Products" reports after successfully populating the destination table, reducing associated expenses.

In summary

As we outlined in this article, we emphasized the importance of pricing in Google Shopping, where customers can quickly and easily compare and choose, which is crucial for digital marketing success. How understanding the dynamics between performance, pricing, and profitability could bring sustainable growth in this highly competitive marketing landscape. In this article, we mentioned Kuvio, a third-party profit data integration tool, and how we used BigQuery to save and integrate price competitiveness data in our analysis. If you have any questions regarding our strategy or the tools we use, please do not hesitate to reach out and book a demo.

Article and knowledge brought to you by our Sr. Solution Specialist, Zehua Shan, and Digital Strategist, Tobias Glaving.