- Muskan Goel
6 Reasons why a Retailer Must Embrace a Data-Driven Retail strategy
With words like big data, predictive analytics, and descriptive analytics infiltrating the retail market, it is clear that retailers recognize the need to adapt and change. So, is it critical to become data-driven, given that many companies are not? The short response is yes, but there are innumerable advantages of executing a data-driven approach that your whole team will learn from!
What is a Data-Driven Retail strategy?
A data-driven enterprise uses data analytics to perform any vital planning and policy. Competition in the retail sector, in particular, uses data to think more critically about customer engagement, product growth, pricing, and so many more. Even relatively insignificant information, such as the location of a commodity on a store shelf, may be affected by data-driven perspectives.
Why use a Data-Driven approach in Retail?
We've seen numerous once-successful stores slim down then go out of business for decades. Despite this, the products sold in these stores continue to prosper and grow. With such fierce rivalry, it is impossible to deny that data-driven companies have a clear edge over their rivals. Let's take a look at some unique advantages of a data-driven retail model:
1. Yield a More Personalized Consumer Experience
Today's consumers have access to information from all over the globe. As a result, they yield more leverage in the buyer-seller relationship than it has ever been. They can only purchase and choose to buy from brands that provide satisfying opportunities they cannot find anywhere.
If you're orienting your brand and goods to your consumers' preferences or making real-time decisions based on actual actions, accurate consumer data is essential for successful interactivity.
2. Forecasting Future Consumer Behaviour
What if you can somehow accurately predict who would buy a commodity, where they will acquire it, and how they might buy it? Although consumer data will not grant you superior powers, it will empower you to forecast the future as accurately as possible practically.
Brands can identify any aspect that leads to a buying decision by studying patterns, historical actions, purchase history, and a multitude of other data points. You will use this data to reliably forecast potential consumer activity and change the marketing strategies accordingly.
3. Create Pricing Decisions Strategically
We should thank the Web for the need for brands to be very proactive in their sales prices. Why is this so? Placed, a buyer might walk into a shop, find just what they're searching for, and then leave empty-handed—all because they took out their smartphones and discovered that one of your competition sells the same stuff at a lower cost.
Setting prices based on your quality levels, manufacturing costs, and sales targets are no longer adequate. To wisely set retail prices, brands must actively nurture competitive insight, consumer reviews, and other first- or second data forms. Data-driven brands will take a somewhat more systematic approach to price, coupons, deals, and other immediate price drops in addition to the original price of a good.
4. Cost Efficiencies
Part of being a data-driven business is examining the productivity of each department and marketing platform. This helps minimize spending because underperforming areas can be streamlined, changed, or removed. This includes everything from marketing platforms to underperforming stores. Irrespective of the technologies, services, or communications in place, retailers will use data to scrutinize any aspect of their company to ensure that both tools and techniques are optimal for their performance.
5. Adapting to industry dynamics
Utilizing web data extraction methods, innovative retailers track stock level and competitor behavior. This empowers them to adapt to market changes automatically, allowing them to take appropriate action to expand the company. Retailers can use data analysis to get answers to queries such as:
Did rival 'X' launch any new products?
What is the cost of product 'Y' in competing stores?
How many rivals 'Z' change their price in a particular segment?
These answers will aid in the development of a winning plan.
6. Recognize New Opportunities and Act Quickly
New platforms, tactics, and brands emerge on a regular basis. Using a data-driven approach, marketers can identify trending opportunities before they become popular. Retailers should track the performance of their software, techniques, and goods over time to identify any changes or transitions in these areas and avoid losing out on useful opportunities.
How to Become a Data-Driven Retail Business?
Step-1: Identify your primary data sources
The challenges of data acquisition are what hinder many brands from adopting a data-driven approach. After all, new technology makes it simple to monitor a customer's digital presence and collect massive amounts of data. One of these approaches can not provide you with any of the information you need.
a) Data from Points of Sale (POS): A point-of-sale (POS) mechanism is the hardware used to complete a transaction with a customer. It is, however, the most important source of consumer info. More specifically, POS data reveals critical product and consumer perspectives that can influence your decisions.
b) Web Analytics: Whether it be a comprehensive internet shop or a simple company website, in any case, the Internet is a vast pool of data that can provide valuable insights into your target demographic and how they communicate with your brand.
c) Competitive Intelligence:: Due to a rise in new brands, it is crucial to remain informed about the rivals. This involves doing observational studies, collecting business data information from official agencies, and conducting win/loss analysis to determine whether consumers were losing to a rival.
Don't restrict yourself to the data points you're always using, such as your CRM and POS. The objective is to adopt a data-driven mindset. Acknowledge and accept what the data will tell you, and then use it to your benefit.
Step-2: Capture and integrate the details
First, ensure that you are collecting as much data as possible at each possible data collection stage. To do so, you must add value to your customers' lives by giving them an incentive to send you their info. Giving consumers deals or special member rewards for trading in their data is an example of this.
Next, gather all of the data in one place. In the same way, as we must break down organizational silos and search through all networks for consumer data, we must also break down data silos and merge it all into a single point of reality.
Step-3: Analyze and forecast
The next step is to look for patterns and create propensity models. You take the consumer attributes and match them to what you want them to do next. Here are a few ideas for shifting the focus from analytics to a real-time data approach.
Managed inventory analytics will help you find the correct product balance and inventory solutions to reduce the risk of sales loss due to over and understocking.
CRM data and insights are improving so that desired consumers with a buying background can be contacted promptly with new offerings focused on previous expenditures, loyalty, and patterns.
Misappropriated hiring ratios are becoming a thing of the past as workforce management, and personnel statistics are merged with traffic flow data.
For decades, pricing vs. purchase data has been on every distributor's radar, and it is continually changing to optimize turnovers.
Traffic and browsing trends are continually being monitored in new and creative ways to make the shop visit a smooth flowing and fun experience to catch each transaction.
The benefit of developing a propensity model is that you can automate the next step while being confident that it is supported by accurate data. The simplest example is providing tailored feedback based on the most recent action taken. Examine the consumer profile base for the particular store and the bias of that base to buy particular items.
The retail sector has undeniably shifted, but so has any other industry in our post-digital market world. If you mainly sell in retail, don't be concerned about the adverse effects of digital transformation on your company. Instead, think about how data will help your company exceed its targets and experience unprecedented success.
Datahut scrapes e-Commerce websites to provide online store owners with valuable data to make this decision. Contact Datahut now to know more.
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3. Scrape Product Information from Walmart using Python beautifulsoup