Customer Data the proverbial gold mine significantly positions businesses to leverage Big Data and Big Data Analytics. Businesses willing to expand using insights from analytics and Big Data always have opportunity knocking on their doors. The final step is to unleash Big Data insights and not just unlock new avenues of profitability but also enhance customer loyalty.
Advertising agencies and marketers largely vouch for the benefits of Big Data initiatives. Big Data, they claim, allows developing insights from customer experiences to help drive marketing strategy. Consumer data also helps parse customer feedback and identify in-demand products.
In the era of Online Marketing, many businesses face stiff competition with newer players entering the market at a shocking rate. The Internet landscape is playing an ever-increasing role in collecting the raw information needed to fuel Big Data initiatives.
Conversely, the time to react to understanding of consumer psyche or preferences has reducing notably. To stay ahead, businesses need to address consumer demands, understand trends, and offer value to product or services before someone else does. Online businesses need to be competent enough to engage with their customers at the right time using an appropriate channel. While the crawling the web for consumer data is the most popular source for information, many marketers also opt to harvest data from offline systems like call centers, mobile devices etc.
It is a technique to predict future behaviour of customers based on history and saved preferences. Not just grocery stores & retails, but enterprises too are benefitting from Basket Analysis. This helps in cross-selling and up-selling, and plays an influential role in sales promotions, designing loyalty programs, changing store design, and introducing discount plans.
Data entities collected via data extraction and web scraping have direct correlation to business processes. Moreover, data entities share an affinity since they are involved in similar business processes. For e.g. evaluating credit card records gives an idea of typical buying behaviour like when purchases are made, how expenses are distributed etc.
Data Mining helps in Market Segmentation based on factors such as income, age, occupation or gender. It helps in understanding market competition. Companies design and implement strategies based on market segmentation. It helps in identifying target customers thereby helping businesses to design specific approach for launching new products as per customers’ demand.
Database Marketing is another technique which looks into the purchasing patterns of customers along with their psyche and thereby create products that are futuristic. Analysts evaluate database information retrieved from sales, surveys questionnaires and thereby target their prospective customers. Much of this database is also collected by monitoring online purchase activities or extracting information like reviews from blogs, news, or forum sites via mass-scale crawl and then using this information to focus targets.
Sales Forecasting predicts customers’ buying tendencies. It analyzes when the customers last bought some stuff and when are they going to buy again. This analysis can be used to figure out how many such customers are there in the market and out of them how many are actually going to buy the products.
Companies depending on telecommunications can mine customers’ data to look into customers’ usage patterns and create a profile based upon these patterns and preferences. Marketing managers can then build strategies based on the customers’ feedback thereby offering right promotional schemes and enhance customer satisfaction.
Credit Card marketing collects data from customers related to their service usage to identify various customer segments and devise customer loyalty programs that improve acquisition and retention of customers.
Customers are always looking for lower prices, schemes and offers. Social media can prove quite beneficial in this aspect. Social media websites and apps such as Facebook are leveraging “Customer Cluster” technique to track customer purchase data ensuring strategic business benefits. This technique looks into customers’ buying behavior to retain them in future. Social media helps in learning about customers’ sentiments towards a product or service.
Database mining helps in predicting people cashing on the warranty or guarantee scheme. These calculations help larger enterprises dig into more details analyzing data of past guarantees, sales, and profits. In the larger scheme of things, warranties also help in product development.
Michael Kaushansky, EVP, Chief Analytics Officer at Havas Media stated in a recent post about Big Data Analytics, “Data should be the oxygen of any marketing and advertising organization”.
Data mining techniques help in finding new correlations and hidden data. Data mining or knowledge-discovery in databases (KDD) extracts information from the databases to understand consumer behavior and gain useful insights.
Customer Data Analytics enable companies in taking up new marketing initiatives and launching new products in the market. Understanding the impact factor various sources have on customers, analysts are able identify new patterns, potential risks and innovative opportunities.
These insights are the assets that help companies address specific needs of customers and deliver better value resulting in happy customers. Happy customers bring more revenue. Certainly, Customer data is helping companies make targeted offers to a selective audience at just the right time, with the help of data on customer preferences.