The landscapes of brick-and-mortar and online retail are being transformed by retail analytics.
From Amazon drones transporting your preferred cupcake just as your sweet hunger tickles to your local store selling the newest GoPro right before you go on a new journey, the future is rapidly approaching.
This article will discuss the guiding principles for using data to enhance your retail company.
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What Are Retail Analytics?
Retail analytics is the method of gathering retail data (POS, stock and inventory, market dynamics, customer reviews, etc.) and translating it into insights - trends, projections, and other deliverables that enable you to make better business choices for your retail firm.
When applied properly, retail analytics may help you attract more consumers, carry the most popular goods, enhance your vendor network, and increase your store's sales and profitability.
How Is Retail Analytics Implemented?
There are four stages critical to the success of retail analytics:
Determine which business component you want to enhance
Do you see a reversible trend in product returns from customers? Do you need more customers in your store? Which facet of your retail enterprise do you want to enhance?
Beginning data collection
Generally, merchants only gather some of the data they currently create in their shops. POS and e-commerce transaction data, customer feedback, in-store (physical or online) browsing habits, etc. In addition, be sure to gather any extra information that will assist you in answering your business query. For instance, if you want to estimate which garments would sell better during the next weekend, you may gather meteorological data to construct a model of winter vs summer items that require replenishing.
Modelling your data to get insights
This stage may need some technical proficiency. Model your data using data engineering techniques to concentrate all of your data in a single database. Utilize modern statistical and analytic tools to get trend and prediction insights that will assist you in answering your business-related concerns.
Implement the knowledge gained
Knowing what to do is essential to victory. The second part is execution.
The Benefits of Retail Analytics
Benefit 1: Your retail operation is more productive
Retail analytics may be crucial in enhancing operational efficiency in day-to-day company management. And when everything is handled well, you can keep track of everything, from stock supply to store behaviour to market trends and demand.
But with accurate baselines of your current performance, it is possible to construct sophisticated models to anticipate whether client acquisition will increase or decrease. With a properly integrated product catalogue and warehouse data, you can predict if you will run out of skis shortly before the winter season.
The first stage is to gather and verify all data into a single truth source.
These gathered data may be utilised to create business intelligence reports, ranging from basic Excel graphs displaying stock data per product SKU to complicated Power BI dashboards.
Benefit 2: Enhance your marketing effectiveness and return on investment
Retail data is not only a building block for business analytics. It may also be utilised as a growth driver.
Certainly, BI may be used to get insights that increase productivity and, therefore, the bottom line. However, you may use data to inform your marketing strategy. And it may also be fed straight into advertising software to save advertising expenses and boost marketing return on investment.
Benefit 3: Get ready for demand (by optimizing supply)
Management of inventory is a balancing act.
If your inventory levels for a product in high demand are sufficient, you retain a sales opportunity. If you keep less of a product in stock and it does not sell, you divert warehousing resources away from other goods that benefit your bottom line.
Retail analytics is an excellent tool for anticipating future demand and optimising your supply chain of providers to accommodate the upcoming spike.
Using previous customer data, seasonality impacts, market trends, and consumer behaviour, it is possible to construct models that estimate your near- and far-term stock requirements.
Benefit 4: Delight your customers (by understanding them better)
Typically, client complaints are received after the fact. The client has already decided against doing business with us.
With the multichannel strategy of internet merchants, it is difficult to maintain track of each interaction. Customers provide us with feedback by responding to marketing emails, submitting technical support requests, writing product reviews, sharing their (dis)satisfaction with friends, and, eventually, voting with their hard-earned cash.
However, it is difficult to maintain track of these touchpoints when operating on a large scale. Even more difficult to distinguish between the trees and the forest. The volume of client feedback gathered from various sources might soon become excessive.
The collection of data from different sources is facilitated by retail analytics. However, you should also develop composite indicators that cut through the noise and provide immediate insights into consumer (dis)satisfaction.
Once we comprehend what makes the consumer pleased, we will be able to enhance the operation that makes them happy. If we analyse and monitor customer satisfaction measures, we can rapidly detect when a fire is started and put it out before it consumes other consumers.
How To Automate Retail Analytics
Step 1: Identify the existing gaps
Consider both your online and brick-and-mortar shops. What are the essential business data you need to operate it more effectively? Write your questions down. These will serve as the foundation around which retail analytics may be constructed.
Step 2: Gathering data
Collect all internal (SOP, transactions, customer feedback, etc.) and external (market studies, weather prediction, etc.) data in a centralized repository. Use technologies such as Keboola (check it out, there's a free tier) to automate the laborious tasks. No requirement for data engineers or developers.
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Step 3: Data analysis
Analyze the data acquired in the previous stage to answer your most pressing business concern.
Your research might be as basic as combining data from many spreadsheets to assess the overall company trends or as sophisticated as constructing a machine learning system from scratch.
Featured image: Image by snowing
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