ECommerce businesses, like any other business today generate large amounts of data. It got accelerated with digital transformation and extensive social media experiences. Besides, e-commerce firms are exhaustively investing in advanced technology initiatives such as Big Data Analytics. As it captures huge volumes and a variety of data of varying complexity. The reason firms are relying on big data is to decipher unidentified patterns within the cluster of data. These patterns could aid in better strategic decisions to propel revenue growth. Moreover, big data analytics offers eCommerce businesses the opportunity to foresee trends, uncover actionable insights from raw data and produce consequential business conversions.
With data growing from terabytes to zettabytes, B2B e-commerce firms are on the lookout to solve some of their core challenges. It is hard for the inventory leaders to find optimal price points for millions of SKUs (stock-keeping units). Marketers are often confused in targeting the niche customer segment. Moreover, it is difficult for sales leaders to pinpoint the exact customer touchpoints to make the best offers and uncover other obscure business insights. As a first step to solving the challenges, segment the data into structured data and unstructured data. However, the significant question lies in decoding meaningful, actionable insights from unstructured data.
Why Should B2B Ecommerce Firms Adopt Big Data Analytics?
Businesses are opting for online purchases more than ever before. There are more transactions in B2B e-commerce due to the variety of raw materials, and component purchases involved before developing the final product. As a result, firms are looking for faster and more effective ways to collect, manage, dispense and control structured and unstructured product information across all channels.
In the B2B e-commerce world, accurate product content informs customers in a better way, builds trust, and helps improve brand perception. However, if product data is vague, incompatible, or hard to find, customers start looking for resorts, affecting sales and brand credibility with long-lasting impacts. Therefore, B2B companies are focusing on developing better customer experience with the help of Big Data Analytics. The following are the areas in which Big Data Analytics can impact B2B e-commerce businesses:
Understanding Pricing Patterns
B2B e-commerce is a volatile market, due to the constant changing prices on millions of SKUs on a daily basis, fierce competition, ever-increasing product demand, contractual pricing based on volume discounts, etc. In order to accurately determine the prices of the product components and their assemblies, big data analysts depend mainly on their historical data and competitive pricing data.
Firms can further estimate price elasticity (i.e., price elasticity answers questions like if there is a hike in product prices, does its demand reduce) or segment markets based on different set attributes seen in different sales points. In the case of B2B eCommerce businesses, price elasticity and segmentation are bigger challenges because of the variety of products and their subsets.
Reviving Sales Opportunities
Now more than ever, there are a plethora of channels for selling B2B e-commerce products. For starters, the sales team refers to Customer Resource Management Systems (CRMs) data to make strategic action plans. Moreover, social media platforms are used as a primary channel for customer engagement and can help provide marketing insights and opportunities for lead generation.
These insights can help sales folks market the brand more effectively. For example, the list of people who abandoned the cart just before the final purchase. The insights could be used by sales executives to send follow-up messages, this boosts the chances of completing the sale. Sales representatives could use historical data and real-time updates that indicate customer behavioral patterns. This enables salespeople to touch base with their customers at the most receptive times with the most optimal pricing options. Further, it provides opportunities to upsell and cross-sell.
Fine Tuning Supply Chain Planning
Demand forecasting has transformed supply chain management. Its vital insights and up-to-the-minute forecasts enable leaders to avoid supply chain pitfalls and improve their inventory management. Moreover, demand forecasting utilizes data analytics in the B2B space for real-time demand forecasts and deliveries.
A vast amount of data silos come from distribution hubs, warehouses, transport, freight, and suppliers. Supply chain leaders can leverage silos data to optimize storage space in warehouses and transport, and reduce costs. For buyers, it brings visibility with real-time supplier collaboration and traceability of the exact status of their orders.
Indeed, big data is altering the B2B e-commerce space by enhancing the customer experience. Leaders are able to offer personalized content with rich and interactive product displays and dynamic pricing to the prospects. The insights from big data analysis help with customer acquisition, and retention as well as cultivating brand loyalty. Data Analytics provides firms with sales opportunities and insights for process improvements across their network of buyers and sellers. Moreover, it helps capitalize on their data. As it saves costs through self-service options and highlights where they can do better in terms of chasing sales opportunities.