Competition pricing and activity screening

Competition pricing and activity screening

The use of web-data analytics to assess competition pricing and activity screening is becoming increasingly important in the modern digital age. This technology offers businesses the ability to gain valuable insights into their competitors and make informed decisions on pricing and marketing strategies. Competition pricing is an important component of any business strategy. By monitoring the pricing of competitors, businesses can gain an understanding of the market forces driving the prices of their products and services. This can be done through web-data analytics, which provides detailed insights into the pricing of competitors, including their sales volumes and pricing strategies. Web-data analytics can also be used to assess the activity of competitors. Businesses can use this technology to monitor the activity of their competitors, such as their advertising campaigns, website visits, and product reviews. This information can be used to inform a business’s own marketing and promotional strategies. Activity screening is another important aspect of web-data analytics. This technology can be used to track the activity of competitors, such as their interactions with customers and their use of social media. By monitoring competitor activity, businesses can gain valuable insights into how their competitors are engaging with their customers and the type of content they are creating.

The Problem

Tracking pricing and promotional and social media activities of 100s of competitors across 100s of locations in European countries on HoReCa market.

Our Solution

Cloud-based architecture that consists of scheduled / on-demand data gathering pipelines, storage and social media listener that monitors activity of competitors. As part of the analytics, we use statistical and ML techniques that can i.a. perform entities clustering, detect  keywords in reviews. Processes are packed into end-to-end automated pipelines.

Outcome

Pricing and activity data for a given location and competitor enables to identify market dependencies, influence of promotional actions and sentiment related to the brand. The solution is customised so that it can easily integrate with user’s proprietary data and deliver analytics once a day or upon request.

Key Takeaway

Competitors' activities analysis is 50x more granular within distributed location network.

Technology Keywords

Python, DL/Transformer, ETL, Cloud-based, Forecasting

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