CEO of Regalix Inc.

B2B buyers today expect a customized and seamless buying experience backed by value-added insights from the seller. A recent study shows that 89% of business buyers rank the experience provided by a company at the same level as its products and services, and 82% are ready to pay more for a great experience. Given this reality, artificial intelligence (AI) and machine learning (ML) have the potential to transform sales forever, as they offer organizations the power to build strategic, adaptive sales processes that can be tailored to enrich each customer’s buying experience.

The Power Of AI

As a McKinsey research shows, companies that have pioneered the use of AI in sales have seen benefits, including an increase in leads and appointments of more than 50%, cost reductions of 40% to 60% and call time reductions of 60% to 70%. Here are four important ways AI and machine learning can transform sales:

1. Tapping the right customers with improved prospecting and lead qualifying: Increasing quota attainment and win rates start with building a better pipeline. Prospecting is one of the most challenging parts of the sales process. AI-powered sales enablement solutions can simplify this process by creating better customer profiles and buyer personas through pattern matching with existing customer data profiles, thereby helping sales reps discover potential high-value prospects to target.

AI tools can also automate lead qualifying and nurturing to the point where they are ready for interaction with a salesperson. This frees up sales teams to focus on highly qualified leads.

2. Boosting the value of sales content with enhanced content management: Due to physical distancing and lack of in-person engagement, 67% of buyers are relying even more on digital content to inform their purchase decisions than they did last year. But responding to this demand requires a deeper understanding of how customers engage with different content assets that goes beyond simple vanity metrics such as “number of views,” “likes” and “shares.”

Using consumption and engagement metrics that show how content is received, consumed and acted upon by the prospect — such as scroll depth, time spent and the number of content pieces consumed — and linking such metrics to measures of pipeline velocity and conversion rate, AI technology can provide a deeper understanding of the ROI of different content assets and thus guide content production.

3. Supercharging sales teams with optimized onboarding, training and coaching: Even as the sales landscape is changing rapidly, sales teams seem less than satisfied with the nature of their training. In one study, for instance, only 41% of sales reps rated their sales training as always or almost always effective. AI and machine learning can fill this gap by powerfully tailoring learning experiences for greater effectiveness and efficiency.

AI-powered training solutions can optimize onboarding and training by creating progressive learning journeys that are customized to the needs of each sales rep. Algorithms tracking the consumption and activity patterns of sales reps provide insights on how best to guide them through product, company and industry knowledge, using bite-sized learning modules.

4. Focusing sellers on actually selling by automating ancillary tasks: At the everyday level, sellers often struggle because they spend too little time selling and too much time doing other tasks. A recent study found that sales professionals spend just 34% of their time selling.

AI is emerging as an effective solution in automating many repetitive administrative tasks that do not necessarily require human involvement, such as data capture, various scheduling and administrative tasks, and routine engagement and follow-up. This enables sales reps to spend more time on the actual selling process.

Challenges With Implementing AI And How To Overcome Them

While AI-enabled solutions have gained wider acceptance, there are some challenges that organizations need to address while implementing such solutions. Roadblocks can arise in any of the following areas:

1. Data: Data is the fuel on which AI and ML tools run. Organizations should ensure that they have the necessary data streams to gain a 360-degree view of customers. However, seeking the elusive state of having perfect data availability can also be a problem. Working with specific data subsets for modest process goals when accompanied by efforts to improve data collection and data quality can be a useful stepping-stone.

2. Processes: Building a comprehensive view of the customer requires breaking down silos between customer-facing departments and developing data-driven processes among sales teams. AI-enabled platform vendors can provide the infrastructure and consultative expertise that contributes to such alignment and behavior change. Organizations should make ease of integration and adoption a priority while researching potential solutions. They should also invest in the training needed for sales teams to adapt to more data-driven, AI-enabled processes. 

3. Management: An insufficient budget and lack of executive buy-in can be major obstacles to successful AI/ML implementation. Thus, it’s necessary to build demonstrable use cases that can serve to convince skeptics in the organization. Beginning with a well-planned pilot that demonstrates measurable benefits can go a long way toward securing buy-in and budget.

4. People: Any talk of AI is inevitably accompanied by fears of job loss and redundancy. Even where such fears are minimal, sales teams may show reluctance to restructure their processes significantly. Overcoming these obstacles requires being transparent and participative in decision-making and ensuring that sales reps have the necessary resources to learn and adapt to their transformed roles.

AI-Enabled Sales Is Here Already

Highly optimized sales processes backed by AI and machine learning technology aren’t a wishful future; they are already here. Using effective algorithms and comprehensive analytics, smart, integrated AI-enabled sales solutions can improve decision-making, increase the productivity of sales reps, and improve the effectiveness of sales processes to provide a superlative customer experience.


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