Currently, AI and machine learning have been used in various industries. There is also much hype on sales intelligence where most of the companies are using AI and machine earning technologies to extract and learn from the data to improve the sales. Studies show that 40% of the time now spent on sales tasks can be fully automated.
Problems and implications:
Following are some of the problems faced by sales sectors:
Time consumption: Tasks such as gathering customers and product information, processing transactions, and preparing contracts to become tedious and require heavy man-power. Analysing the data also consumes a lot of time manually.
Forecasting: Reading through the past deals that were lost or won, making up a predictive model for the current variables becomes complicated when done manually.
Customer satisfaction: Due to lack of proper response from the salesperson, lack of connection between customer needs and company products customer satisfaction decreases.
Companies like Digiconnectt, Absolutdata provide AI and Machine based solutions for the above problems in sales through the following ways:
Real-time personalized experience: AI and machine learning have a large impact on customer satisfaction as they analyze the data and provide the best solution to attract more customers. Features like conversation cheat sheets to keep buyers engaged with relevant conversation topics, KSPs and offer customized for a specific deal opportunity, personalized suggestions for each dealer based on the individual history etc improve customer/buyer experience.
Automation: Through automation of AI into CRM databases and sales force automation software tedious tasks such as manual CRM data entry could be removed. Automation offers both job satisfaction would increase and stronger relationships would be built. This frees up the sales force’s time and energy, allowing them to focus on more important, nuanced tasks such as strategizing, coaching other reps and building relationships with prospects and customers.
Predictive modeling: Predictive analysis and modeling leverage AI and machine learning technology to identify learn and replicate the best solution for the sales team. Features like New lead navigator where a successful selling path for fresh new leads is mapped for the sales team, Next best action guidance where the specific direction of tasks is provided to the sales team are few of the examples using predictive analysis.
Analysis and suggestions: Through features like product/service recommendations, purchase drivers analyzer the history of the products and leads would be analyzed and an intelligent solution would be suggested that would provide which products or services individual contacts are likely to purchase.
Optimization: Features like the determination of price elasticity help to scale price optimization in various product and services pricing scenarios.