How do I pick the right AI solution for Intelligent Selling?

By Ram Jayam

Intelligent selling is about nurturing opportunities and automatically recommending products of interest proactively while keeping up the productivity.

Actively engaging opportunities with the buyer’s enablement mindset will elevate the sales discussions into an advocacy conversation. Proactive engagement will result in transforming complicated sales process simple by engaging with the right information at the right stage. The sales rep of the future must be able to fully anticipate and fulfill customer needs before contacted. So, AI has to be part of the selling strategy!

The question is: What criteria should I be using while picking an AI-Machine Learning solution?

Look for one that,

  1. Provides actionable intelligence: Pick an artificial Intelligence solution that can generate actionable results that can enhance sales productivity. If it is about engaging with the customer, then the actionable results should lead to customer engagements. For example: If an event has triggered the AI engine to act, then the AI-engine has to bring up the actual email thread that you currently have with the customer. Unsupervised Learning and Reinforcement learning techniques may be good choices considering that the client conversations are relatively transactional. Some of these algorithms can also learn accurately from enterprise behavioral patterns.
  2. Doesn’t force a process change and it integrates well with the current tool set: pick a solution that will not require a significant process change or that will require retraining of sales teams. We have often noticed that new processes and tools not in line with the current operational process will be soon be abandoned. Make sure the AI engine will integrate seamlessly with Outlook or SFDC
  3. Uses a rich dataset for machine-learning – pay attention to the dataset that the AI engine will use to learn about customers: this dataset has to be rich with information, and it has to be dynamic and accessible in real-time. Machine learning needs a massive amount of observational data to make accurate predictions. Make sure that the dataset is part of the daily routine and in line with the active current process, and does not require any manual intervention. In most cases, this could be your email interactions with customers, or content repositories like SF-Files, Dropbox, or Box. Let it learn from your client interactions like email exchanges rather than from outdated information in the CRM system.
  4. Use closed looped systems – Self-learning techniques used for AI-Machine learning engines are more accurate when they can learn from the actual user incidents and fine-tune automatically. Central to machine learning is the idea that with each iteration, the algorithm will learn from the data. Algorithms iterate thru data until convergence is met, i.e., the user acts on the suggestions made in this case.