Last updated - September 7, 2021
The retail landscape is undergoing a drastic change at the moment. This change has greatly affected the way of doing business. Retailers used to sell products without collecting customer data or competitor data because retailers can’t find value in the data due to immature data collection and analysis technology. In short, data can’t help them increase profits.
Now with the fast development of big data, data collection and data analysis is easier to dig out value than before. Some visionary retailers have already embraced these new profit growth ways to increase their competitiveness and ROI. How do they leverage the data? Let us look at the methods that visionary retails use.
Set a Goal
The first thing on the to-do-list is to define a goal. If we are not clear about our goal, then there is no way to find value in the data. The following goals are the 5 most common goals that retailers set for analysis:
- Competitor’s product category and product development strategy analysis
- Competitor’s pricing & promotion plan analysis
- Competitor’s inventory status analysis
- Competitor’s customer feedback analysis
- Collecting customer feedback from a competitor analysis
Once we define our goals, we then need to retrieve data from our own database and collect open data from our competitors. Review data from our own database is easy because everyone of our customers or visitors will leave their tracks. Since our competitors won’t share their data with us, we have to find a tool to help us get legal data for analysis. You may think it is difficult but collecting data from competitors is not as hard as you thought. I will explain more in the last session. But now, let’s focus on how to do a successful data analysis. When the goal is set and data is collected, it’s time to choose a path for analysis.
Choose a Dimension
After defining our goal, we need to find the right dimension to analyze the data. Usually, we will start from product price, product type, product review, inventory, and product bundling. These dimensions are the most important ones.
Product Price
Competitors’ product price information can give us a lot of important insight. For example, when we compare our product and price with theirs, we can estimate their profit based on our revenue. Besides, we can find out our competitors’ advantages and ours so we can avoid selling goods that have low margin goods in our store but have a high return on theirs.
To sum up, we need price information to do the following:
- Find out on which product we can start a price war
- Estimate competitors’ profits
- Analyze competitors across-platform price strategy
- Find another supplier that can provide the same product at a lower price
- Seasonal and promotional price strategy
Product Category
Analyzing the product category is an important dimension to dig deep into specific sections. Analyzing product category alone can’t give us much value. We need to use this dimension with price, sales and regions. By collecting product category information from our competitors, we can roughly get an answer from the following:
- Product categories that contribute significantly to the total sales
- Product categories that are not performing well
- The performance between us and our competitors
- Categories that are going to be popular in this product category
- Geographic regions that have more margins(If your competitors are operating a global business, you should also collect data from those regions.)
Product Review
Product review is the best source to find hidden opportunities. It is undeniable that we can learn the strengths and weaknesses of a product or the way they treat customers. The information about the strengths and weaknesses of a product can unveil the QA situation of their suppliers. We bring this question to our suppliers to make sure such problems won’t happen to us. Competitors’ customer servicer performance teaches us how to deal with unsatisfied customers. We can learn from them and tailor them for us.
To wrap it up, there is something we can use for our advantages:
- Minimize the odd that product quality problem occurs
- Find ideas to improve current products
- Provide good experience to customers
- Identify a good logistic company, manufacturer, and supplier
Product Bundling
Collecting and analyzing competitors’ product bundling can help us understand more about our competitors’ product strategy. Usually, we can use bundling to raise the profile of a new product, allowing us to test the product on your customers and get feedback. Besides, we also can use this method to clear old or slow-moving inventory. Use these methods backward, we can learn the following information from their moves:
- Find out whether or not they are launching new products
- Know what products you have overstocked
- Analyze whether or not they offer real value to their customers
- Comprehend how they influence customers’ decision
Find a data collection solution
There are two popular methods to capture competitors’ performance data. One is using a programming language such as Python, Node.js, etc. The other one is using a web scraping tool. They both have their advantages and disadvantages. Let’s find out which solution is more suitable for you.
Coding language
Using a coding language has many advantages such as a high degree of customization and flexibility, the capability of bypassing CAPTCHA, to name a few. These main advantages can help us get data from hard-to-scrape websites. Its disadvantage, however, prevents most of us to use this type of crawler. We have to be familiar with one of the programming languages mentioned before to create the crawler. Otherwise, we have to pay a developer to build the crawler for us, which will cost a lot.
Web scraping tool
The web scraping tool is much easier to use for no coding skill people, though it can help most of the non-programmers to extract the data. Let’s take Octoparse as an example. Octoparse can extract text, number, image, and link from a website, which meets most common users’ data format requests.
To help its users to easier extract the data, Octoparse makes dozens of ready-to-use templates and crawler auto-creation features. We just need to input parameters into Octoparse, then Octoparse will automatically start extracting data for us. If we have the crawler auto-creation, we just need to input the URL into it, then it will lead us to complete the crawler creation process with a few clicks. Besides, it’s able to perform multi-tasking, which can help us save a lot of time.
