By Nelson Ikechukwu Nworie
In the olden days, art was thought to be the essence of great marketing. But recently, big data has introduced a more scientific element to marketing campaigns compelling smart marketers to depend heavily on their data analytics to understand customers’ needs and preferences to serve them better.
Big data is changing the face of retail business, offering the customers a personalized experience and helping businesses become more profit-oriented. Consequently, it is easier for companies to forecast accurately, which helps them manage their businesses more effectively.
Artificial Intelligence, Machine Learning, Natural Language Processing, and Deep Learning are some of the advanced concepts used to make smart decisions.
While data and analytics can never replace the creative minds behind the best marketing campaigns, they can provide marketers with tools to help enhance their efforts.
The use of data and Analytics has made it simpler for brands and retailers to understand their customer’s shopping habits with the factors that influence them. These insights help them to tailor their campaigns to the needs of the customers.
At the moment, customers have immediate access to a wide array of information at the tip of their fingers, thanks to digital technology. Having instant access to products and pricing makes it easier than ever for shoppers to search, compare and make purchasing decisions at a go.
Practically, retailers attempt to find innovative ways to collect insights from the ever-increasing amount of information about their customers, using data-first to plan their strategies for the future
Big Data analytics has become a key component to the smooth running of a retail business. It starts by predicting the products that will be most popular using key BI tools. Next, they identify customers who are likely to be interested in these products and what else they might need – before actually reaching out with personalized offers.
Businesses can use big data analytics to their advantage in the following ways:
Customer journey analytics: Retailers can see patterns of different customers by correlating their purchasing histories with the already available information in the database. This enables them to predict what a customer is likely to purchase next based on their previous purchases, how much they spent before, and how often.
Decision-making: Companies need to consolidate data regularly, which allows them to review important information about their products and customers. Dashboards can help give business owners everywhere an accurate picture of their competitors’ pricing and catalog movements.
Utilizing market basket analytics: A technique often used by retail shop owners to figure out what products their customers may want to purchase together; the insight helps retailers make millions in sales by providing clients with items they didn’t even know they needed!
Social Listening: It’s important to listen to what your customers say on social media. One way of doing so is to use a data platform such as Hadoop. Natural language processing or NLP can also be used to extract information from social media sites, and machine learning uses the data so that retailers can gain an edge over their competition.
Predicting Trends: Marketers are like political analysts. They routinely gauge the sentiments of the masses and determine their thoughts on certain topics that affect many people’s lives. Data gathered is then used to predict which products or services will be top-sellers in specific categories.
Customer satisfaction: Retail analytics are making it possible for companies to predict and properly satisfy customer demands, creating a seamless experience and thereby improving customer satisfaction.
In conclusion, big data analytics can help businesses better understand their customers, engage with them and deliver better products.As the digital age expands, the retail industry is affected by an ever-changing landscape. To remain competitive, retailers must adapt to digital trends and implement new technologies.
