The combination of Big Data, Artificial Intelligence (AI), and Machine Learning (ML) provides a strategic advantage to companies in improving services and building innovative products. Tools for deriving & measuring customer needs are shortening the sales life cycle by enabling organizations to reach the target audience faster.
We have evidently seen powerhouses like Uber and Amazon improving the outcome of their B2C operations with Big Data, AI and ML. Still, a lot of scepticism prevails that whether the same technologies would work in the case of B2B as its buying cycle is comparatively larger. On the contrary, IT experts are hopeful that these technologies will deliver same cadence for B2B scenarios.
- Lead Generation: In B2C scenarios, machine technologies have helped evidently in optimizing pricing, checking for fraud, incentivizing target customers, rerouting transport, maintaining machines and restocking inventory. In similar lines, deep learning can help B2B companies in making dramatic breakthroughs in generating leads. By bringing advancements from ML and AI, business users can search contacts faster from company websites and social channels. Moreover, predictive algorithms and models help in identifying and reaching the target audience faster.
- Key Account Management: Retention science experts believe that teams can derive actionable information from offline, online, behavioral and geographic data to understand customer pain points and retain key accounts. The solutions can address the problems associated with real time data sets and streamline marketing initiatives. The users can identify patterns and provide custom suggestions to target clients.
- Customer Behavior Analysis: Machine learning is providing its users entirely new abilities to track customer behavior. Advanced solutions suggest statistical patterns to address distinct customer needs. The solutions can be programmed to understand who the customer is and what role does he/she plays in the ecosystem. This unique capability positions companies to respond faster to customer needs.
- Unprecedented Business Agility: Machines transform the business model by offloading work from human counterparts. WIth ML & AI, mundane repetitive tasks and real time errors can be avoided to free business teams and allow them in doing more productive work. Teams can overcome IT inefficiencies by managing unstructured data and processing it in an agile manner.
- B2B Marketing Success: Conventional CRM machines doesn't provide a standardized way to differentiate between an executive or a counterpart sitting in another location. Sales teams struggle to find the potential prospect who makes the buying decisions. Machine tools know how to fix these long standing problems. They provide predictive algorithms to give a clear picture about the prospect, his/her designation and hierarchy.
A Total Addressable Market (TAM) predictive analysis distinguishes the potential ROI of a lead and reduces chaos from the sales funnel. Another edge which the sales team gets is professional activity feed of prospects from social signals. This function equips sales team with more information on preferences to engage confidently with prospects and finalize deals with them.
- Sales Call Statistics: There is no doubt that sales call analysis helps organizations to sell smartly. Graphical call reports tell the whole story about customers journeys and improve the contact centre efficiency. During conversations with clients, teams can flag the time when they talk about pricing and core business problems. Sales teams can build much better picture of the target customer and sell him solutions faster using all this valuable information. Without machine learning this job can be highly laborious.
So, How do You Get There?
For these compelling advantages to come to fruition, enterprises also need the right integration solution to ride smartly on machine technologies. There is sheer lack of stability in the nature of these technologies which disrupts data efficiency and data movement between AI, ML, and Big Data. When disruption is sweeping across all sectors, it is important to interconnect these processes and technologies for infrastructural efficiency. To achieve higher cadence and improve data delivery, it is important to integrate every layer of these technologies with a B2B integration tool.
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