E-commerce is a significant internet industry, which also makes one of the most competitive ones. Companies are looking to maximise their sales in any way they can. The data present on the web is one of the primary sources that they are using for this task. Getting social media data from different sites and social media platforms and using proper recommendation systems on them to provide the most appropriate and targeted suggestions to the consumers can go a long way in increasing the probability of a sale. This makes Web Scraping and Data Analytics one of the most significant growth drivers of an e-commerce business.
The need for Web Scraping in E-Commerce
We don’t even need to go out shopping now. With the growth of e-commerce, one can now shop for almost anything from home itself, be it clothes, furniture, electronics, groceries or even fruits and vegetables and get it delivered to the doorstep as fast as in a day.
As simple as it sounds, e-commerce is also one of the riskiest and most competitive sectors out there. With a large number of variables governing the delivery of a good or availability of a service, the probability of successful transaction between the company and customer decreases exponentially. With increasing sales policies like free delivery, try before you buy by Myntra, full refund return, etc. to stay in the market, the probability of failure of the transaction has further increased.
In such a scenario, increasing this probability of a successful sale to minimise the losses has become the top priority for the companies. To continue in the market, companies need to make the costumers feel as secure while they are shopping as they would in a brick-and-mortar store to get the maximum footfall. Therefore, policies like free delivery or full refund on returns need to continue to exist. The companies thus need to find an alternative way to increase their sale per customer visit.
This is where Web Scraping comes in. It has been established from time to time that in today’s internet-dependent world, data can solve every possible problem that the companies could come across. For e-commerce companies, the data can be mined in a structured format and then analysed to identify the aspects of the business responsible for most substantial loses or monitor the product prices to stay competitive in the market. Data can also be used to determine the most poorly reviewed product or seller by the customers or even to recommend products based on location and previous buys of the customer. Scraping data off of Web has now become one of the most significant operations of all the e-commerce companies.
How does Web Scraping help?
- Real-time Monitoring Competitor Prices
Keeping track of competition’s policies and prices is essential to ensure that the consumers don’t get a better deal for the same product from any other player in the market. Using web scraping, competitor price monitoring can be achieved with ease. We can get data from all the competitors’ sites in a structured format and placed beside our own to be compared and analysed. Apart from product prices, the deals that are on offer can also be scraped to provide a competing deal that would ensure the consumer traffic to not deviate from our own site.
For example, Flipkart’s Big Billion day, Myntra’s End of Season Sale and Amazon’s Prime Day tend to be around the same time, within a week of each other, to keep up with the market sales and keep the consumer interested in one’s own site. Companies like Flipkart, Amazon, etc. keep themselves up-to-date with their competitor’s announcements and update their market strategy accordingly.
2. Product Reviews and Ratings
Reviews and Ratings go a long way in convincing the buyer of the authenticity and security of the product. A consumer tends to believe more the other consumers who have bought the product and experienced the company’s services than the claims of the company itself. Thus, it is crucial to provide unbiased and truthful reviews and ratings of the product to the consumer to help him feel secure about purchasing the product.
They also help the retailer to keep track of how the product is doing in the market and make market strategy decisions like which product to shelf, increase the price of which product, etc. But keeping track of reviews across different platforms and manually tracking down each review is not feasible; and thus, Web Scraping can be utilised in such a situation.Using the reviews and ratings across different sies, we can not only see how the product is doing in general in the market, if it should be shelved or not, but can also keep track of which retailer is selling the product most profitably and use this information to modify the price tags and delivery conditions on the product.e
For eg., if the competition is selling the same product, but it is poorly reviewed then the chances of a consumer buying the product from you increases and vice versa.
3. Identifying Target Audience and recommend accordingly
E-commerce is one of the most consumer-dependent sectors. How successful is your business entirely depends on the number of sales being done. Therefore, finding the right consumer and recommending the right product becomes one of the most critical aspects of any e-commerce business.
One can simply use the data of the previous purchases made by an individual and use it to recommend the products of the price and the category he is most likely to buy again. For example, a person who has previously purchased five t-shirts between the range of Rs.800 – Rs. 1000 should be recommended clothing of the same range as he is most likely to buy these than any other product like a TV worth Rs. 30K or a hairpin worth Rs.50.
Identifying the target audience and recommending the right products thus becomes integral in increasing sales. Web Scraping helps the business to get this data of each individual customer in a structured format that can be fed to a recommender system to get the most appropriate, individualised suggestions.
How can E-retailers benefit from Social Media Data?
Social media is the means for e-commerce companies to directly monitor and influence their consumer activity and provide the right recommendations based at the right time, thereby increasing the chances of a sale. Sites like Instagram and Pinterest provide the companies with the options to link their account with their shopping website. This helps the consumer go directly to the purchase page on the site from a product posted by the company on its Social Media account.
By linking social media accounts like Facebook, the company can monitor the user activity timings, browsing and likes history, events going to be visited etc.
1. Monitoring Activity Timings
Each and every person have a different schedule in their life. This means that their social activity timings would differ based on place, age etc. The companies can obtain this data by tracking the user activity and profiling them based on their most likely activity timings and amount of time being spent on the social media platforms. This data can be utilised to offer the best content at the most appropriate time to get maximum visibility
2. Monitoring Activity History
Different people have different interests. Showing the same content to every consumer is not a feasible method. Social Media acts like a bucket of information of likes and areas of interests of the consumers. The data can be obtained easily from their activity on social media platforms and can be used to suggest the products that lie under the consumer’s interests. For eg., If a person has liked several photos or posts about Video Games, then he can be suggested with Games and Gaming Products. Similarly, plant seeds and equipment can be recommended to someone who has liked posts about gardening.
3. Monitoring Location
People tend to update their location and events they are visiting on social media. Social media data based on locations can be used to suggest the products that are bought mostly around the area that the consumer is in or will be going to. For eg., if someone uploads a photo and tags the place to be at a hill station, then woollen apparel can be suggested or if someone is going to a marathon event, in that case, sports shoes, socks, sweatshirts etc. can be recommended.
4. Monitoring Friend Activity
Social media revolves around the concept of friends, which means that the concept of Collaborative Filtering can be used to suggest products. This means that if a person A and person B are friends and have bought a similar product in the past, then person A can be suggested a new product that person B recently bought. This works because of the idea that the friends generally have similar interests and thus, they tend to buy the same thing more often than not.
With web scraping, social media data that is needed for above-mentioned recommendations can be obtained from numerous social media platforms, with right permissions, automatically and efficiently. This data can be updated periodically and maintained in a structured format all without the need of any human involvement. Processing this data accurately to get the best results could easily be the difference between the company and its competition.
DataHut provides one of the best solutions to this problem of obtaining the data. We can extract the data from multiple sources and present it to you in a structured and readable format, ready for further processing. Contact Datahut to know more about how you can leverage social media data for your retail needs.