Machine Learning and data analytics have shown a pronounced effect on various aspects of the commercial world and industries. Enterprises are using innovation in the field of data analytics and machine learning to design better marketing campaigns. It also helps generate pricing and customer-centric recommendations and even plan more effective financial budgets.
However, the rapid advancements of technological innovation is coupled with an equally swift evolution of the business-to-business (B2B) sales channel. Data has now become the bread and butter for a large number of big and small companies, a trend which will only continue to grow. The power of advanced analytics if tapped efficiently can help in improving B2B sales.
A McKinsey&Company report on marketing and sales suggests that customers insist on getting competitive off-the-shelf products along with more complex and customized solutions. They also expect extensive levels of B2B sales support and expertise in the industry and the products from the sales force. Companies are investing in attempts beyond selling their products, in ways like risk-sharing and service-level agreements by customers. This is to ensure that they stay committed to providing real value.
Moreover, companies now need to develop multi-channel sales programs to enable customer targeting in a more effective way. For instance, a logistics and transportation partner can focus on the simpler sales while dedicated sales managers will have to focus on the challenging customers to meet their requirements and provide extensive B2B sales support at all times.
Furthermore, companies servicing large customers are devising new and cost-effective ways to interact with customers and provide sales support to them. For example, in multinational customer and seller networks, customers now communicate over the telephone or through Web and video conferences. This enables sales reps to plan and spend more time on high-value face-to-face selling activities, such as developing complex interactions with current clients and mining for new clients and customers.
B2B enterprises of all scales are adopting ways of business-to-consumer (B2C) retailers like Amazon by using customer data to predict their behavior, drive higher sales, and strengthen customer relationships.
Most B2B companies use analytics to monitor and analyze product quality and performance in the market, customer behavior and health of their overall operations. However, the conventional metrics like revenue and profit margin do not provide a complete insight into the performance of a company. Since there is a lag in the calculation of such performance metrics from the time of actual events, companies now need to design better performance metrics. Real-time metrics enable analyses of fast-evolving industries in shorter sprints of time-period.
Real-time analytics is becoming more and more popular with all B2B companies. They are designing alternative measures like when the customer first engages with the sales team or digital content, their perception of the same and the time when they get back to the company for the actual contract or their first purchase. Such metrics can give one real-time insight into a company’s performance instead of delayed information.
Use of Artificial intelligence and advanced machine learning algorithms for analytics in the B2B space has enabled real-time insights on a company’s performance. It has also ensured that the enterprises receive real-time recommendations on B2B sales and purchase decisions. Retail companies like Tesco and Walmart are using advanced analytics to make more informed decisions on product portfolio optimization and product pricing decisions while procuring goods from their suppliers.
Advanced analytics can be used across industries
Statisticians and researchers have tailored a lot of mathematical algorithms for special use-cases in the B2B retail industry. For example, managers can predict the performance of newly introduced products on the basis of similar products in the past. This would help the managers decide if they want to store the particular product in their stores or not. Furthermore, companies use analytics for designing marketing strategies like cross-selling, up-selling, product-bundling and targeted advertisements for higher customer penetration. Such algorithms are useful across B2B insurance, retail, manufacturing, and even technology industry.
General Motors, a US-based automotive company has a large network of dealerships throughout the world which procures aftermarket auto-parts from the manufacturing giant on the basis of the customer sentiment towards the products. In this scenario, advanced analytics can help optimize storage and purchases for the dealerships and hence, maximize profit for both the dealerships and the OEM (original equipment manufacturer).
Beyond product and customer-base optimization, B2B companies can use advanced analytics for effective lead generation and scoring. This is by far, the most valuable use-case for companies that want to grow their customer base rapidly. Many companies are increasingly using historical market information to view their sales prospects. Besides cold calling and mails, companies use advanced analytics to customize PoVs and sales pitches for the targeted customer.
Some success stories of using advanced analytics in B2B sales
Orica Limited, an Australia-based supplier of commercial explosives and blasting systems, caters to the needs of mining, quarrying, oil and gas companies across the globe. It has invested resources in creating a sound data analytics infrastructure. Instead of drawing insights from paper and white-boards, they have digitized all their data in the form of decision boards and recommendation engines. Data provided by its customers, along with the objectives of the mining projects and blasts, conditions of the equipment and the site, the exact techniques and products used in the blast, and the outcome achieved is used for creating an analytics framework.
The pre-blast modeling and post-blast measurement and analyses are then packaged in a user-friendly online system called Blast IQ. This allows mining customers to simulate and analyze blast outcomes given the information like the geological site data, drilling equipment data and the desired outcome. This has not only helped Orica optimize the mining processes but has also led to them using data analytics for industry-specific intelligent recommendations. This is one example of how analytics can improve the performance of an unconventional industry.
If that is not impressive enough, Monsanto Growth Ventures (MGV), the venture capital arm of the Monsanto Company used data analytics to enhance its chemicals-business with data and analytics-based services.
Post-acquisition of The Climate Corporation in 2013, they together assembled a database using IoT infrastructures like soil sensors and field experiments. Monsato then incorporated the findings in a suite of farmer advisory services. Since then, it has continued to develop and retain a loyal customer base with additional mobile applications and advisory services. The use of analytics in a primitive industry, agriculture is evidence enough that advanced analytics can help any B2B business model grow and advance in a more informed and sustainable manner.
Advanced analytics has not only changed the perception of large businesses towards technology, but it has also reformed the way these enterprises measure their own performance. Organizations are now more receptive to technology and data-driven decisions. This trend is still in its growth-stage and will continue to take over more lines of industries over time.