If you are running a business and have tried multiple marketing strategies but still haven’t seen a substantial impact, fret not! You are not alone.
Despite several fancy whitepapers and theories available all around, the underlying fundamental is pretty simple! The effectiveness of a marketing and pricing strategy depends on its profitability or its potential to maximize the revenue. While providing a wide range of good quality products and services improves a brands’ reputation, an effective pricing strategy and discount programs hugely impact the market share and customer loyalty of a brand.
How does that work? An effective pricing strategy would not only incentivize your customer to buy the products but would also optimize your revenue and profit model. It gives your company an edge over its competitors while increasing market share and brand reputation. Many companies have now adopted data analytics to design effective pricing strategies and seasonal discount programs.
How can Data Analytics help you design a Competitive Pricing Strategy?
Let us understand how data and analytics can help you improve your pricing strategy.
Predictive data analytics for designing a pricing strategy is one of the growing examples of scenarios where data science and business strategy can intersect. Organizations are leveraging analytics in applications where they want to make quick revenue gains in the next 4-6 months or where they want to discover a latent opportunity for revenue or profit maximization.
A few other applications of data in designing a pricing strategy are:
- Conduct pricing post audits: You can utilize the large sales and campaign data reservoirs you store and update regularly to investigate how your last pricing decision fared
- Unveil latent opportunities: Data can help you check your old and new products or services for any pricing anomalies. Imagine an automotive manufacturer selling a left chassis part for 100 US dollars and the right part for 80 USD. Given the right part performed well and no other factors come in to play, the company can gain on revenue and profit by pricing both parts equally.
- Pricing experiments: You can design your own pricing experiments with patented products or services, discount campaigns, and targeted selling.
How can you achieve all this with data?
All of this is possible with a wide variety of data at your disposal. Of course, a lot of analytics and business domain knowledge will also come in to play.
A lot of companies study their own historical sales trends, banner campaigns and market performance to predict the most optimum price for a given product at a given point in time. However, data extracted from their own performance seldom work in isolation. You can club this with alternative data from multiple sources.
What can those sources be? Analytical studies like competitive pricing or pricing for specific target customers require you to extract data on the same. A lot of firms like technology giants (Dell, Samsung etc.) sell their products through multiple channels. They can easily find competitor prices from eCommerce sites.
These sites also provide information on what the customers think of the prices through the comments and reviews section. You can also find your consumers’ perception of the existing products on social media websites and dedicated complaint forums. Companies can scrape this data from eCommerce and social media websites to design their winning pricing strategy.
The scope for using data for pricing strategies does not stop there. You will be impressed where else data extraction and analytics can help you. Datahut offers exemplary data extraction and web scraping services to help you use data for all the above-mentioned purposes.
Data scraping in a competitive pricing strategy
A regular influx of good quality data can help you automate various pricing processes. You can create dashboards that can help you compare the performance of various products across different price bands. It can help you dynamically change product prices on various forums. Furthermore, pricing strategies also depend on innovation and development in the industry.
Moreover, a lot of companies now require real-time analytics. Various industries now have dynamic pricing and need to revise their product prices in real-time. Such a market will heavily rely on a constant stream of high-quality information from reliable sources. For instance, airline companies and hotels need to keep updating prices according to competitor performance and their own sales trends. Retail companies also use these tools for product portfolio optimization.
Essentially, there are three key elements you can scrape to price your products and services more effectively. They are:
- Product Attributes and Prices
- Customer Reviews
- Real-time data
Let’s see how this can be put to application.
Scraping Product Attributes and Prices for competitive pricing strategies
Customers are increasingly becoming more aware and wise when it comes to purchasing decisions. They compare prices across platforms and then make an informed decision of buying a product or a service. Due to this, comparison shopping engines are a growing and popular trend. You can extract product attributes like the price, discounts and offers, descriptions, product guarantees, purchase modes, and even immediate competitors and alternatives. This would help buyers weigh their options and choose the best deals available.
A lot of comparison shopping engines gather product details either through web scraping or by having retailers submit their own product details. You can then store this data in well-structured formats like CSV files. Retailers can thus, pull comparative data through web-scraping to develop effective pricing strategies.
ECommerce giants like Amazon and Flipkart use this method to gauge the performance of their competitors. Web scraping can also provide information on shipping times, product availability, brand and market perception. This can help various industries design good marketing strategies for better user experience.
Using Scraped Reviews in a pricing strategy
Have you ever observed how a lower-ranked product eventually disappears from store shelves? A lot of companies monitor their product performance regularly.
In the United States, automotive companies like General Motors constantly monitor customer complaints and remarks on government websites. GM then tweaks the vehicle performance or prices it suitably. Similarly, firms in the real estate sector to do so. Brokers and house-owners use this strategy to quote an optimal price for their property. Retail uses scraped data to find the average and median prices of products across categories in the market.
Studies reveal that social media reviews are an essential component of the customer journey. Facebook influences both online and offline buying decision of over 50% of all consumers. Web scraping becomes a vital tool to capture relevant reviews from these social media sites and other suitable web-pages.
Using Real-time data for Dynamic and Reactive Pricing
Industries like travel, tourism, and eCommerce are rapidly evolving every minute. Here, products are put up on sale fast and pulled out of the market just as rapidly. The prices are also very reactive owing to how the market and the competitors are playing. This is why having real-time information about your own performance and the competitors’ trends is essential.
Real-time data can help you check if there are products that have high viewer traffic but low conversions. These products might need a price revision to improve their sales performance. To have access to up-to-date information regularly would be a great aid to companies for the best strategic decisions. This is possible with web scraping. Web scraping engines can run at a predefined interval to extract accurate and real-time data.
Success stories across various industries
A lot of eCommerce companies assess and price their product portfolio in a reactive manner. This decision is based on their competitors’ prices, product reviews and customer sentiments. This also pushes OEMs (original equipment manufacturers) to their toes for creating the best pricing strategy.
AS Watson, a Hong Kong-based retailer has been pushing for implementing advanced scraping mechanisms to procure product and competitor data. They want to solve problems like new product recommendations, assortment optimization and strategic pricing problems using this data.
Dynamic pricing is a staple most retailers swear by. Online retail giants like Amazon are doing so already, which gives them an edge over the rest of the market. Predictive analytics, along with a good data scraping infrastructure can help companies design an exemplary strategic pricing model.
Furthermore, companies from automotive and heavy machinery manufacturing are using data extraction methods and predictive analytics to up their pricing strategy game. Since the space is competitive and conservative, it becomes necessary for the industry players to keep the market share stable with good data-backed pricing solutions. This approach is not limited to a particular industry. It can be extended to various industries like travel and tourism, media, entertainment, and even banking services.
A lot of companies dedicate their resources to extracting data from other firms, pre-process it and prepare it for further analytical investigation. Organizations like Datahut provide web-scraping services that have further helped enterprises across industries to improve their performance.
Getting into Web Scraping to create the right pricing strategy can be a good starting point for getting into the full-fledged analytics space. Web scraping coupled with predictive analytics can help you to price your products and services effectively.