AT A GLANCE
- Discover how big data improves inventory management.
- Learn how analytics drive personalized customer engagement.
- Understand the role of data in accurate demand forecasting.
- Gain insight into big data as essential for retail competitiveness.
Here are the top things you should know when it comes to the importance of big data in retail:
We all know that data-driven research and development is the key to success in an ever-growing digital age. And this is no different in retail.
Since the start of the online retail boom, brick-and-mortar stores have often found it difficult to keep pace with the speed and convenience of online deliveries.
However, big data analytics gives physical stores a unique springboard to success, opening the door to improved customer experience and upselling opportunities that even digital competitors can’t match.
Now, both business models are thriving and enjoying greater efficiency and profitability – namely because of the many retail analytics solutions now available.
What is big data?
Put simply, big data analytics is the collection and interpretation of information on a grand scale.
Computer algorithms identify patterns and trends in retail data, which can then be used in conjunction with qualitative data on typical human behavior, interactions and experiences.
This gives individuals and companies tangible data that—with the right software, resources and knowledge—can be used effectively to reveal more about the habits of their customers.
Big data can also be defined as a mass increase in the volume, variety and velocity of data coming in. This is known as “the three Vs” of big data:
Volume – Retail data is often vast and unstructured. Without relevant resources, staff can be left to draw findings from this data manual, which is often inefficient and can be inaccurate. Big data analytics solutions automate this responsibility, generating quick and accessible findings that drive actions.
Variety – With great strides in technology in recent decades in how and where we can collect information, retail data takes many more shapes and forms than ever before, so businesses must be wary.
Velocity – The speed at which data arrives is also faster than ever before. This means dedicated teams need to react quickly to extract value from that data and act on it in real time.
How big data is being used in retail
Big data analytics provides retailers with so much valuable and actionable information that it’s now critical for companies in almost every decision.
To start, big data analytics help retailers understand customers. In brick-and-mortar stores, this means everything from which POS displays are selling the best to the directional shopping habits of customers.
Online, big data analytics helps predict upcoming trends and which SKUs each regional store will need to stock to remain competitive year-round.
Whether it’s monitoring social media trends for the latest “buzz” or making sure stock matches seasonal demand, big data analytics reveals the exact stock businesses need, and how much, ahead of time.
As well as helping businesses to improve the customer experience, big data analytics in retail is used to drastically boost efficiency. Many companies use cloud data solutions to track inventory levels and sales figures in real time, and they also use these solutions to predict future demand more accurately.
Big data is increasingly used to personalize the online shopping experience, too. For example, online retailers use data-driven algorithms to provide shoppers with product recommendations – based on their purchase history – to add to their baskets pre- and post-checkout.
How do retailers collect data?
With so much data now available to retailers, it needs to be collected in many ways. Retailers can either ask for data directly – via email address and phone number forms for marketing purposes – or go through more indirect channels.
When consumers click on a website, they’ll be asked to accept tracking cookies. These are chunks of data that attach themselves to the user’s unique browsing ID, giving websites an idea of how long they’re browsing, which pages and products they’re looking at and what they buy. This information then helps companies tailor their marketing efforts.
Retailers can also tap into third-party data from suppliers. This providesinformation on consumer habits to streamline the online experience.
Brick-and-mortar stores can also collect internal data. Point-of-sale data collection is key in managing which products need to be in specific regions to match consumer demand year-round.
Big data analytics can be used to streamline the order process as well, primarily in EDI systems to provide more data points to shipping teams throughout the supply chains.
EDI software helps keep everything from orders and invoices to shipping notices in one easy-to-use hub. Big data analytics provides more data on any external factors that could interfere with anyone of these processes, letting companies respond quicker.
