Case Study

Accelerating Value and Efficiency for E-Commerce in Technical Goods
reduction in time-to-value
boost in price efficiency
increase in overall profit

Before Priceloop approached TechGoods*, an e-commerce retailer specializing in technical products, the company was struggling with inaccurate and time-consuming pricing methods, causing them a great deal of frustration and financial losses.

* (name changed)



First go live of PoC within 5 business days. Full rollout after 27 days.

Quick implementation

Upon the start of the implementation project, a joint workshop between the TechGoods' pricing team and Priceloop was held.

In preparation of this workshop, static input data was provided by TechGoods and loaded into the Priceloop Platform. This enabled the team to implement 90% of the pricing requirements together already during the 3 hour workshop. At the same time, the pricing team left the workshop already trained on how to enhance or perform changes to the rule set on the platform.

As a next step, the Priceloop Platform was connected via API to live systems, so that imports and exports happen automatically. During this phase, TechGoods' pricing team continued to tweak and enhance their workspace according to their requirements.

Priceloop exceeded their expectations by:

Easy & accurate data extraction

TechGoods used to spend hours manually collecting pricing and product data from various websites, and then inputting it into Excel spreadsheets. Their operations were sometimes affected by pricing errors stemming from this manual process.
Priceloop's advanced technology has transformed their process. Now, with a simple paste of the product URL or GTIN/EAN, TechGoods can access most product information within minutes.
Our AI streamlines data extraction and automatically updates pricing details on the dashboard. This means less time spent on data crawling and happier TechGoods' pricing team enjoying the data accuracy and pricing automation.

Effective rule-based guardrails

Priceloop Platform offered TechGoods a robust set of rule-based guardrails that ensured pricing strategies were executed precisely. These guardrails allowed the company to maintain control over their pricing approach while accommodating the flexibility required in a dynamic market.
For TechGoods, factors that impacted their pricing rules were:
⏵ Bundle/Set-Pricing

TechGoods offers bundle deals for several technical products on various e-commerce platforms. Our solution ensures that whenever the price of any component in the bundle changes, the bundled price is automatically updated accordingly.

⏵ Reference pricing

Reference pricing refers to using the price of product A as an input for product A.

In the case of TechGoods, there were private label products that were dependent on prices of comparable A-brand products from their assortment. Parameters and rules were defined to make sure that the private label products were strategically positioned according to their private label brand positioning.

⏵ Competition

Our platform fetches competitor data on daily and weekly frequency (depending on product segment) for TechGoods' products across different e-commerce platforms. After blacklisting a set of competitors, which can be changed by TechGoods on the platform at any time, the competitor crawl data is used to react to competitor moves in a smart way.

For example, TechGoods used the platform to set up rules that attack front positions on price comparison websites and marketplaces when the margin allows it. In case margin is not attractive in front positions, the margin will not stay at the defined minimum margin.

Rather, the strategy is to jump to a significantly higher price point, e.g. by matching the price of the main competitor (which in the case of TechGoods typically significantly above the cheapest prices on the web).

By pursuing this strategy, it is possible to sell at healthy margins to those buyers who are using the main competitor website as a price comparison rather than price comparison websites.

Since “price comparison buyers” would likely not buy anyways even with minimum margin, if the resulting price is not featured in one of the top positions, this strategy prevents margin leakage for less price sensitive buyer segments.

⏵ Weather-based Pricing

As for many e-commerce businesses, TechGoods' business is highly dependent on weather. The Priceloop Platform allows using current local weather data as an input to set up pricing rules and TechGoods used this data to make sure that prices were slightly higher during periods of poor weather within each region.

⏵ Currencies

As a global e-commerce company, TechGoods needs software that automatically updates prices based on the real-time exchange rates for different currencies.

⏵ Geo-Pricing

Priceloop enables TechGoods to set geographically specific pricing rules. When a customer from one region visits their online store, the Priceloop Platform automatically displays prices tailored to that location, ensuring competitive and localized pricing.

⏵ Inventory-based Pricing

TechGoods aims to enhance inventory management with Priceloop's solution. Our advanced AI monitors inventory levels and adjusts prices dynamically to boost sales, optimize turnover, and prevent stockouts.

⏵ COGS, Max prices, Min margins

With our tool, TechGoods can set maximum price limits, maintain minimum profit margin, and adjust prices automatically based on the actual cost of goods sold (COGS).In addition, calculations on costs are performed, for example, to take supplier kickbacks into account.

⏵ Seasonality

We enable TechGoods to schedule seasonal discounts or raise prices on popular products during holiday seasons. The Priceloop Platform ensures timely changes and will automatically return to regular prices when the promotion ends.

Machine learning capabilities

To further refine their pricing strategy, TechGoods leveraged additional machine learning capabilities on the Priceloop Platform. These enabled the company to go beyond rule-based approaches, incorporating dynamic machine learning algorithms that adapted to evolving market dynamics, consumer preferences, and competitive pressures.

In particular, TechGoods used our Price Tester and Stock Optimizer module:
⏵ Price Tester
  • Our Machine Learning Module uses the output prices for the Priceloop Rule Builder.
  • Any other rule-based prices can be used as input.

  • Our module tests systematically different values for deviation from the rule-based price.
  • Product clusters are automatically identified by our Clustering Model.

  • Elasticities are derived from the results and optimal values for deviation from rule-based prices are calculated.
  • Further testing and optimization happens continuously.

  • Whitebox approach: In-depth analytics are provided on generated test results.
  • See how prices deviate up and down from TechGoods' rule-based pricing and analyze its impact on profit and other goal metrics.
⏵ Stock Optimizer
Priceloop's Stock Optimizer covers a variety of scenarios, helping TechGoods find the optimal reaction to different inventory scenarios.

No overstock

In this scenario, inventory and availability are not a concern.


To address this situation, Stock Optimizer lowers prices to encourage sales and finds the best trade-off against profit margins.


Once shortage is detected, Stock Optimizer increases prices strategically to extend the availability of your products. This helps TechGoods make the most out of existing stock while ensuring a steady flow of inventory.

Industry Phase out

In this case, Stock Optimizer analyzes all data and finds the optimal trade-off among profit, revenue and inventory costs.

Timed End of Life

During  product's final phase, Stock Optimizer adheres to the planned date for product removal or sell-off until the end of the season.

With our advanced algorithms, TechGoods crafted pricing strategies that were not only more precise but also highly responsive. The integration of AI-driven insights into their pricing decisions enabled TechGoods to set optimal prices, maximize revenue, and enhance overall performance.
Priceloop Platform: machine learning
This strategic shift reduced the burden of manual pricing adjustments and unlocked new revenue streams, solidifying TechGoods' position as a market leader in the e-commerce sector.

Visualization and Reporting Capabilities

Lack of transparency in their pricing strategy had always been a challenge for TechGoods. Thus, they eagerly sought a solution that could provide them with custom reports and dashboards tailored to their specific needs. Upon partnering with Priceloop, TechGoods experienced a significant transformation in their pricing operations.

Through the advanced analytics of the Priceloop Platform, TechGoods continuously tracked market trends, assessed pricing performance, and gained a deeper understanding of customer behavior. This newfound knowledge empowered them to make informed pricing decisions, optimize their product offerings, and react swiftly to changes in the competitive landscape.

The ability to generate custom reports and dashboards has streamlined their decision-making process. TechGoods' pricing team can now visualize critical data points in real-time, enabling them to proactively adjust pricing strategies, identify profitable opportunities, and mitigate potential risks.
Priceloop Platform: heatmap
Still have questions?
We’d love to hear them! Don't hesitate to reach out if you have any questions or concerns. Our team is dedicated to providing you with the support you need, and we're just a message away.
contact us
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.