The retail industry is expanding at a rapid rate! Isn’t that a term that we’ve all heard before?
Let me tell you, it is not only growing at a rapid rate, but it is also flourishing! Just to give you an idea of how big it is – Sales in 2017 totaled USD 3.53 trillion, and they are expected to expand at a rate of 4% in 2018. It’s also on the verge of a technological transformation.
Retailers have been able to create cutting-edge experiences for their customers because to the power of mobile and digital technology.
The retail industry’s current customer is notoriously fickle. Brand loyalty is low among the younger generation of buyers.
It is in fact correct. According to a research by the CMO council, 54% of consumers would consider switching for better content and offers.
Aren’t we prepared to move to brands that provide a better shopping experience and a wider selection of products? Of course we are; the adage “Customer is King” has been continually pounded into our skulls.
Retailers are also observed investing in a variety of unknown companies that can provide comparable quality to the bigger names at lower prices.
There’s also the entire world of e-Commerce, which has exploded in popularity, forcing many merchants to choose between a brick-and-mortar and a digital presence.
All of these developments are being pushed forward by the retail industry, thanks to insights into customer data such as purchasing and browsing habits.
Industry analysts can also obtain input more quickly and design a fresh trip for their clients.
Big data is the technology that enables these great insights or fuels the new age consumer journeys.
Simply put, your last batter in the ninth inning hits a home run, and Big Data is the batter!
Big data is the technology that allows extraordinarily massive data collections to be crunched and analyzed.
The volume (size), velocity (speed of incoming data), and variety of the data sets are used to determine whether they are ‘big data’ (the different types of data).
When used to retail, big data has the potential to alter it in unfathomable ways. Here are a handful that I found to be particularly pertinent and impactful:
1. Understanding Customers & Learning Shopping Habits
Now that the customer is in charge, retailers should concentrate on getting to know each customer individually in order to provide a more personalized experience. By analyzing transactions, browsing behavior, preference for certain products, buying trends, and social media behaviors, big data can help merchants better understand their customers. This allows merchants to provide more tailored service to customers through focused advertising, product recommendations, and pricing.
2. Analyzing Brands & Providing Better Product Recommendations
It is critical for merchants to understand each brand separately in order to deliver better product recommendations to customers. Using social media monitoring and brand website traffic analysis, big data may analyze a brand’s social acceptance. Conversions from a product’s landing page to checkout can also reveal the extent of brand appreciation among consumers. This information can also assist shops in making correct product recommendations to customers and boosting conversions.
3. Building Promotional Strategies
Retailers spend a significant amount of money on advertising and other promotional initiatives in the hopes of increasing sales. However, if these adverts reach the correct customers, sales could be increased. Big data can assist in evaluating the demands of a client segment by taking into account a consumer’s purchasing habits as well as other significant but frequently overlooked information, such as anticipated weather conditions. This information can be used to target relevant adverts to potential purchasers, resulting in increased sales. For light cancellation analysis and weather circumstances, Red Roof Inn, a US hotel business, used big data approaches. The company then used this information to send out appropriate lodging offers. This method eventually resulted in a 10% increase in revenue.
To summarize, big data has enormous potential to help retailers stay ahead of the competition and provide better customer service. Apart from the aforementioned big data use cases, there are numerous other particular big data applications in retail that can increase sales income and client retention.
Retail Analytics To Retail Intelligence
Retailers can now use data science technology to think beyond retail analytics and into retail intelligence. These technologies can assist merchants in making significant strides by collecting and analyzing data as well as generating self-learning models. AI and machine learning can help merchants derive valuable insights and information from unstructured data, photos, and videos. It can help them not only comprehend but also predict patterns with greater precision.
Let’s have a look at how Retail Intelligence is shaking the retail industry and opening up new possibilities-
Micro Customer Segmentation
Customer segmentation in traditional retail is done through macro-segmentation. Customers are segmented by age, gender, demographics, and other factors. By exposing several layers of consumer data to create a 360-degree buyer persona, retail data intelligence will enable businesses to embrace micro-segmentation.
Smart Product Recommendation
Product suggestions will evolve to include context-aware recommendations in addition to those based on purchase history and browsing behavior. Machine Learning-based recommendation engines will self-learn from data to extract deeper insights into a buyer’s demands, including what, why, and how.
Retail intelligence will allow merchants to use Predictive Pricing, in which prices are determined at the shop level depending on market conditions. Weather, time, demand changes, inventory, and other internal and external sales forces can all be considered in to adjust product prices in real-time.
Predictive Inventory Management
By evaluating customer behavior, market trends, weather patterns, and other factors, Data Intelligence will assist businesses in monitoring stock levels in real time and predicting inventory requirements.
Retail Intelligence powered by AI and machine learning has huge potential for retailers to create smart, context-aware processes, deliver compelling experiences, produce cost savings, and empower their employees.
The way merchants do business has already been affected by data. It will be fueled even more by data intelligence. Retailers who can make the most of their data will gain a competitive advantage.