Most leading global enterprises today have assessed various applications of machine learning. Organizations spearheaded these practices to understand how revenue can increase, especially considering the data generated from sales. Operations leaders and sales managers looking to strategically implement data to drive business growth and reduce sales costs have countless chances to implement machine learning for significant results.
One of the most significant advantages machine learning provides in today’s corporate sector is that businesses can automate decision-making processes without adding to their business risks. This automation has predicted $59.8 billion in corporate sector revenue through AI and machine learning by 2025. An increasing number of small, medium and large organizations are therefore choosing machine learning development companies as partners to drive sales and boost revenue.
Taking certain steps will provide faster or better results than others. Implementing machine learning takes several forms within the organizational structure.
Steps to Increase Revenue Growth using Machine Learning:
Sales Data Makes a Deep Impact:
One of the most common unofficial sources of customer data and behavioural insights for an enterprise is CRM (customer relationship management) platform. Generally, an enterprise uses several CRM platforms together, thanks to technical debt accumulated from uncoordinated teams and units and acquisitions that don’t integrate entirely, among other reasons. This truth can be highly frustrating for sales operations leaders or managers, especially when the need of the hour is data-driven decision making.
Actionable Intelligence from Sales Cycles:
Most downstream activities and sales funnel activities are usually linear, moving from one document process or stage to another. Consider the MQL (Marketing Qualified Lead) that grows into outbound emails and then into proposed IPCs (initial product configurations). If the MQL continues maturing, it could become a quote soon, and if it’s completely extraordinary, it could become a permanent contract.
There are several hundred or dozens of specific activities and stages throughout a sales cycle where individual managers and contributors make critical transactional decisions. These decisions generally depend on a person’s skills when processing large data volumes manually, either mentally or using spreadsheet software. For instance, consider a sales professional using MS Excel or even Tableau to process variables and records, looking through large volumes of data and situations to identify potential leads. This helps them understand who to reach out to, when to contact them and which pricing plan will appeal to them the most.
By partnering with a machine learning development company, businesses can bring down any entry barriers that get in their way as they try to leverage machine learning solutions. Businesses can now enter large volumes of raw data into machine learning models that provide helpful suggestions or even automate the most favourable next steps in the cycle to impact revenue positively. With such a solution, enterprises get to work smarter instead of harder.
Leveraging machine learning solutions can help businesses answer key questions easily. Which leads require greater attention and have higher chances of converting? Which new prospects will provide the highest lifetime value (LTV) for the enterprise? and business partner has a higher chance of winning the opportunity if it’s source to them? What are the best product configurations, outreach channels and product discounts that will help close the sale?
Identify Opportunities:
In the sales domain, machine learning is as relevant as it is to any other business area. Teams are only limit by their imaginations while developing or scaling these solutions and their applications. Sales companies looking to use machine learning and AI to increase their revenue today need to focus on certain areas:
– The opportunities and problems that are known or believe to be worth investing the teams’ resources and time, which can help avoid experimentation without a scientific basis
– Fundamental data needed for training machine learning models with enough granular and historical observations in easily accessible databases and systems
– All the downstream processes that will be impact by the predictions of the model, such as suggesting optimal decisions to managers and sellers or automating business insights
When you partner with a credible machine learning and mobile app development company, you get an end-to-end learning platform that supports a wide range of users. In today’s ever-evolving market, having a robust enterprise AI platform makes all the difference for both businesses and customers.
