11 Implementations Of AI And Machine Learning That Truly Benefit Sales Teams

11 Implementations Of AI And Machine Learning That Truly Benefit Sales Teams

11 Implementations Of AI And Machine Learning That Truly Benefit Sales Teams

Artificial intelligence (AI) and machine learning (ML) form two of the most powerful emerging technologies to enter the world of business today. Both of these advancements can be used in tandem with one another to benefit sales teams in the short and long term.

AI enables a system to determine the most efficient method of doing something, given a framework of parameters. ML extends AI, making it able to learn from its missteps. Below, 11 members of Forbes Business Development Council chime in on the ways sales teams can leverage AI and ML to increase the efficiency of their operations and overcome the most common hurdles they may encounter. 

1. Process Scaling And Standardization

Use a predictive data tool like InsideSales.com to allow you to standardize and scale your processes, creating a more consistent performance from your sales reps. Utilize predictive data around current and past customers to determine the best time to make contact. Let the data do the work for your reps, so they can focus on learning about the business.

2. Pipeline Scoring

We have been experimenting with AI capabilities in scoring prospects via our CRM platform. While I personally feel there are continued improvements to be made in the data quality and weighting of the data by industry, I see great potential for this to assist high-volume salespeople to better manage their pipeline and prioritize their sales activities using this information. – Jen Tadin, Gallagher

3. Having Set Answers

We have to make it easy for our customers to order products from us, and with AI we have developed a set of questions and answers that help the end user receive quick technical answers to their questions. In many instances, a potential customer becomes more interested in our procedurally-based products and they reach out to our sales consultant and order more products from us based on need. – John McCoy, Komet USA

4. Front Loading Quality Into The Sales Funnel

The best place for AI in the sales process is in helping fill the sales funnel with higher quality leads based on the characteristics of the target customers and personnel being contacted, then refining the message to be the best fit. Automation can go a long way to make this more efficient. This also forces companies to really study and understand the characteristics of their customers. – David Friedman, Ayla Networks

5. Identifying Best Areas To Sell

Our sales team goes door to door, which can take a lot of time. To make the process more efficient, we use AI to predict where the best areas are to sell, which connects us with those who are truly in need of our services. While not every company sales model is like ours, AI can be a valuable resource when identifying the right audiences for your product or service. – Vess Pearson, Aptive Environmental

6. Predicting Likelihood Of Winning A Deal

Salespeople can leverage the concepts of artificial intelligence and machine learning by using them in their customer relationship management. By using the data from previous opportunities, AI and ML can predict the likelihood of winning or losing a deal based on previous outcomes. When leads come in, the AI and ML will rank your leads on most likely to win, providing salespeople a stronger win propensity. – Tyler Bowman, Velosio

7. Filtering Out Bad Leads

ML and Al are great tools to save salesforces time by screening initial data, ensuring the most serious clients get called first and get the most attention. They are not a silver bullet that negates the importance of building a relationship with clients. But they should make the prequalification process quicker and easier, leaving time for seasoned salespeople to speak with clients. – Joseph LaForte, Par Funding

This article originally appeared on forbes.com To read the full article and see the images, click here.

Nastel Technologies uses machine learning to detect anomalies, behavior and sentiment, accelerate decisions, satisfy customers, innovate continuously.  To answer business-centric questions and provide actionable guidance for decision-makers, Nastel’s AutoPilot® for Analytics fuses:

  • Advanced predictive anomaly detection, Bayesian Classification and other machine learning algorithms
  • Raw information handling and analytics speed
  • End-to-end business transaction tracking that spans technologies, tiers, and organizations
  • Intuitive, easy-to-use data visualizations and dashboards

 

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