Product data is incredibly important in the products industry. Product data can be found all over the Internet—from e-commerce websites to social media websites, product reviews, and catalogs. It can be used to study existing products or to learn about new products that are still being designed, manufactured, or conceived. Product data gives us insights into product management, design, marketing, and procurement.
Product portfolio management refers to the act of optimizing a product mix and its attributes so as to maximize returns in the form of revenue or profit. The above data can be procured from multiple sources. As mentioned above, sources like social media sites, product reviews, and other technical papers can be obtained using one tool- web scraping!
What is Product Portfolio Management?
When someone tries to optimize a product portfolio to maximize revenue or profits, they can do it in multiple ways. One could shuffle the product mix-up or market product bundles together. Product cross-selling is the act of selling an additional (related) product or service in addition to the main product. Product upselling, on the other hand, encourage the customer to purchase more expensive items, upgrades, or other add-ons in order to make a more profitable sale. Pricing the products right, designing effective campaigns, and appropriate marketing for the products is just as necessary to manage your product portfolio.
It can also include making the right product recommendations to your customer by studying their past purchase trends, their sentiments towards different products, or even the existing market trends. For instance, when companies were manufacturing and marketing fidget spinners because of the rapid upward trend of the market at the right point of time, the likelihood of sales of a fidget spinner was extremely high. The recommendations on most sites worked at that given point in time. All these exercises need a lot of effort and can only be done if you have enough amount of the right kind of data.
Portfolio optimization begins when you start analyzing the required data. This means, that after you collect the necessary data to determine the potential as well as the strengths and weaknesses of particular products, various kinds of descriptive, inquisitive, predictive, and prescriptive analytical tests can be run to design effective portfolio management recommendations. The analysis is multidimensional i.e., it includes the study of the market trends, competitor strategy, product characteristics and potential, sales channels, as well as consumer and merchandise planning, supply chain, and logistics. This analysis gives outputs in the form of numbers and raw data. One can then use this to formulate conclusions and recommendations. Following are a few examples of the kind of questions you can answer using the various analyses mentioned above:
How can you exploit the potential of your strongest (best-performing) products to gain a higher margin or a higher turnover?
What kind of new products should you introduce in the market? Who will be its competitors? This will be based on a bigger potential and higher forecasted sales.
Which products should you withdraw from the market based on low potential or profits?
How should you place goods on the shelves at the different sale points so as to optimize product placement, shelf storage, the potential of purchase, and shelf-life?
Why do you need data for Product Portfolio Optimization?
A typical product company follows more or less the same procedure to convert an idea to a market-worthy product. The first stage involves finalizing the concept or the idea behind the product. This is the research and development phase where the function, design, and usability of the product are put in place. What follows next is the engineering and design phase, wherein we get to see the prototype and blueprints of the products.
This is then followed by the development and testing phase, where various industry standards, product safety requirements, and functional constraints are checked for after the actual product has been manufactured. The commercialization and launch phase after testing helps manufacturers make the product available to the market and hence, assess its early performance. One can monitor the early sales trends of the product at this stage.
Having stated the various stages of the manufacturing process, it is a safe claim to make that data analytics can improve the efficiency and productivity of each of these stages. It is important to note that advanced analytics might not be required to glean insights from the data available for each phase. However, a few tweaks to the data and some basic analysis can help unveil the scope of improvement of multiple processes. You need multiple sources of data that reveal all kinds of information about a product in all the stages of the product lifecycle.
Web Scraping for Product Portfolio Optimization
To answer all the above questions, you need the appropriate data in the appropriate amount. Every question or use case can be dealt with a different kind of data. Web scraping can help you obtain these different kinds of data. Let us look at the different problems and how web scraping can aid in those spaces.
1. Dynamic Product Pricing
Business Intelligence teams across the enterprises try to price products effectively so that they can earn profits and maximize revenue. They collect pricing data from various sources and run various analytical tests. For instance, one can scrape pricing data from e-commerce websites like Amazon to get the competitors' pricing data. This can feed into a comparative analysis and help enterprises price their products accordingly.
There are several advantages to this:
Scraping automates the data collection process: This means you can get large amounts of data in a small amount of time. It also reduces the possibility of human error. If the data collection process is automated, the analysts can then focus on answering the more important questions.
Scraping can help you create dynamic pricing modules: Since the volume of data that can be gathered with scraping spans over 1000s and 10000s of products, this will give you a large number of data points across products and categories. This can feed into an analytical pipeline to create several pricing modules. Some of these are- the price-elasticity module, competitor price analysis module, initial pricing module, price optimization module using various key performance indicators (KPIs), and even the pricing audit modules where you can analyze past pricing decisions.
Pricing data can give you a competitive edge: If you scrape pricing data of products in the relevant category and market from an e-commerce website or social media sites, you can also learn about a consumer's sentiment towards these products. You can learn about the market performance of these products and, indirectly, your competitors' performance data.
2. Competitor Analysis
You can scrape advertisement data, social media websites, and e-commerce websites to get a sense of what your competitors' pricing and marketing strategies are. This will help you understand your competitors and maybe get an edge over them. There are several advantages to using web-scraped data for this use case as well.
Scraped data can help you monitor their products and market trends- Scraping competitor's product data can help you learn new insights into product positioning. It can also help you forecast future product launches and sales strategies.
Scraped data can help you learn about advertising strategies- If you scrape advertisement data, you can learn about competitors/ marketing strategies.
It can help you learn about social media strategy- Every market player has devised a social media strategy. You can learn about it if you scrape data from social media websites. It can also teach you about how consumers feel about a particular product.
3. Product Trend Monitoring
You can scrape data from product listings, catalogs, technical reports, publicly available manufacturing details, or even retail sites. All these data sources will reveal the underlying performance trends of products. It can help you forecast the performance of a product that has not been launched yet. For instance, if you can scrape information from apparel merchandise catalog sites and play in the same industry, it can help you understand what patterns, fabrics, and designs you should be investing in. This exercise has several advantages to it:
Scraped data can help speed up market decisions- If you scrape product data from social media websites, product listings, retail websites, or even technical reports and papers, you can quickly launch and market successful products while mitigating the risk of failures. Developing this behavior can lead to consistent, rapid market launches.
You can design an effective advertising strategy using scraped data- Enterprises across industries use machine learning and web scraped product images to design an effective way to portray, describe and advertise products in the most profitable manner. This can help firms improve their market share, boost search rankings, and enhance profitability.
You can optimize your supply-chain processes- If you scrape product data of all forms belonging to a particular market segment, you can learn about the true demand for their products, you can use that to analyze your inventories and, thus, your supply chain processes.
4. Designing Brand Strategy and Plan
If you scrape web-based branding data like market trends, news articles, or marketing strategies, you can monitor companies' omnichannel strategies. It will also help you design your branding strategies and plans to play in the market. Following are the advantages of the same:
Design and manage your own targets more effectively: If you scrape your own product data from various channels, you can get a very quick input for your sales performance on a real-time basis. You can also use this to check if your sales strategies are meeting the compliance requirements across channels. If you scrape the MAP (minimum advertising pricing) data, you can also learn about the violations and analyze if and how you need to re-align your branding strategies.
Scraped data can help you grow your brand: Enterprises across the industries are using web scraped product data to check for violations, industry norms, and even their own performance. For instance, FMEA reports, post-manufacturing tests, and audit reports can be scraped to study compliance trends in the manufacturing industry. All these data inputs can then help you analyze your operational processes and thus correct them if needed. This will help mitigate losses and, thus, hone your brand.
5. Investment Decision Making
Alternative data is a huge bonus in the financial industry. A lot of investors use web scraped product data for financial products to conduct several analyses. The results of this analysis can be used to make investment decisions and even analyze historical investment decisions. There are several reasons that scraped product data can be used to make investment decisions in industries like finance, banking, and hedge funds.
Scraped data can help you analyze company performances and fundamentals: If you scrape YoY category share trends from online retailers, you can learn about powerful predictive indicators of revenue and stock performance. This can be done while aggregating pricing histories and then learning about sell-through trends and inventory.
Minimize risk and maximize potential: Web scraped data or alternative data can be plugged into machine learning algorithms. These algorithms can be designed accordingly to determine asset allocation decisions while greatly reducing risk.
Learn about public sentiment and market trends: You can scrape news articles, social media sites, and product reviews. This can then feed into complex AI solutions that can determine the general public's sentiment toward a product with implications for its firm's revenue. Access to such data enables investors to quickly reevaluate their positions if there is a poor product launch that can threaten their volatility.
Conclusion
Product companies have a huge dependency on data. There are several kinds of data sources that can feed into mathematical and analytical processes that can be used to solve real-world business problems in the product industry.
Although there are a lot of tests and mathematical proofs for various stages of the product lifecycle, it is necessary that firms acknowledge the need for such steps in the first place. Firms then need to either build an analytical muscle within the organization or sign contracts with firms that are capable of solving such problems. To build the necessary infrastructure for obtaining this data, web scraping can be employed as a powerful and reliable tool.
With the ever-growing market, the amount of product data being generated is also growing. This opens new opportunities for businesses to improve their product-based processes. Therefore, it’s critical for today’s businesses to integrate product data into their product management systems. This will help them learn about hidden insights that can greatly inform directional strategy and ensure your company's upward performance. It will help them design an effective product portfolio optimization strategy.
Datahut prices web scraping services at a reasonable and nominal mark without adding any hidden costs. We have a transparent system that scrapes the required information for you and stores it in the desired format. We also provide continuous support and ensure that you receive your data without a glitch. You can then focus on using that data to make more significant decisions and improve your performance.
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