China’s manufacturing industry is transforming from a “manufacturing power” to a “manufacturing power”. As the backbone of the industrial system, the equipment manufacturing industry is facing unprecedented challenges.
According to data from the Ministry of Industry and Information Technology, the scale of my country’s equipment manufacturing industry has ranked first in the world for many years. However, behind the huge macro market, many machinery and equipment TOB companies are generally trapped in the dilemma of weak growth and high customer acquisition costs.
The traditional sales model relies heavily on the salesperson’s personal abilities and network resources. Exhibitions, industry magazine advertisements, and phone calls are the main means of acquiring customers.
This model once played an important role in the era of information asymmetry, but its disadvantages have become increasingly prominent under the wave of digitalization: single customer acquisition channels, low lead conversion rates, blurry customer portraits, and difficult to quantify the marketing process.
Mechanical equipment products usually have complex technologies, long decision-making chains, and long procurement cycles, and the target customer groups are highly vertical and dispersed.
How to accurately identify potential customers with real purchasing intentions in massive corporate information and establish effective connections with them has become a core obstacle on the road to corporate growth.
There are four core pain points in machinery and equipment sales.
First, the sales cycle is long. The sales cycle of equipment manufacturing companies generally spans several months to several years. The average sales cycle for the purchase of industrial equipment worth one million or more exceeds 6 months. Each sales team member needs to manually screen 300+ corporate information every day, but the effective lead conversion rate is less than 5%.
Second, the threshold for professional knowledge is high. Sales to B2B major customers in the manufacturing industry involve complex decision-making chains, high technical thresholds, and long cycle spans. Sales personnel often find it difficult to meet customer needs.
Third, the conversion rate is low. The average customer acquisition cost through traditional channels in the machinery manufacturing industry has increased by 47%, while the lead conversion rate is less than 2%, and customer follow-up lacks standards.
Fourth, there are serious data islands. Customer information is scattered in Excel tables, personal notebooks, and different department systems, forming serious data islands.
Facing the above pain points, 53AI provides enterprises with a complete set of four-stage intelligent sales assistance solutions.
The first stage is the construction of knowledge base, where professional experience is structured and precipitated through the 53AIBrain knowledge base.
The second stage is to refine the sales crown SOP, transforming excellent experience into a replicable thinking chain.
The third stage is the creation of intelligent agents to achieve the deep integration of artificial intelligence and professional knowledge.
The fourth stage is CRM integration, completing the last mile of the entire AI-enabled sales process.
The implementation effect verification shows that intelligent sales assistance can significantly increase the sales conversion rate, accelerate the replication of the sales team’s capabilities, improve customer satisfaction, and reduce operating costs.
The sales conversion rate has increased from less than 12% to 28-32%, the independent operation cycle of new employees has been shortened to 2-3 months, customer satisfaction and repurchase rates have also been greatly improved, and operating costs have been significantly reduced.
In the future, the new sales paradigm empowered by AI will promote the digital transformation of the entire industry. Sales assistance agents will evolve from “advice providers” to “strategic partners”, reshaping the competitive landscape and innovating talent training models.
For machinery and equipment companies, embracing AI intelligent sales assistance has become a must-answer question related to future survival and development.
