If we are not actively engaged in industries related to technology, we may fail to fully appreciate how we might already be influenced by artificial intelligence in our day-to-day world. Everyone is talking about self-driving cars, seemingly inanimate objects conversing with you about your personal preferences, someone somewhere already seems to recommend your shopping list armed with the knowledge of what you like or dislike.
From the viewpoint of the business world, all companies today are looking to adopt AI in some form or the other to improve business processes, achieve efficiency, so on and so forth. I recently read an article about Softbank’s Masayoshi Son and his vision “for an AI-powered utopia where machines control how we live”. While this may sound like an unreal possibility, one could relate to this thought better if one were to ponder over David Fano’s (Chief Growth Officer, WeWork) words, “Basically, every object will have the potential to be a computer”.
If we were to give every function in an organization the vision to assume AI as a way of functioning, we may well be on our way to a future-ready organization. In this blog, I will focus on the technology sales function and expound five areas in which AI can be used to advance the boundaries of traditional analytics.
Speedy Wins – Certain Enterprise technology products have a sales cycle time of about 8 – 10 months, which means there is a lot of sales time and effort spent without a logical sense of the outcome. With AI, the fail-fast system can be reinforced in the sales engine. If logistic regression was used to predict win/loss probabilities of a deal, an AI system can be taught to study thousands of such predictions and actual outcomes, almost amplifying traditional analytics-based predictions. This can be used to better prioritize deals to pursue within a quarter.
Smarter Insights – There exist several sales reporting dashboards and applications built by various teams to deliver insights to both end users and executive decision-makers. Commercial BI applications allow integration with chatbots and speech recognizing AI systems, so we are already in the world of smarter “delivery” of insights. In order to actually make the insights themselves smarter, AI can be used for causal inferences. This differs from identifying key drivers through statistical modeling or decision-tree based methods that it hinges upon drawing relationships in data even in non-ideal situations.
Improved Business Planning & Resource Allocation – Predicting the pipeline gap and its impact on sales attainment is an important part of business planning. Predictions are often delivered at the most meaningfully granular level of the product family which often means hundreds of forecasts delivered per week. With artificial intelligence governing the attainment gap, it can help identify business segments where the attainment gap may be large but still not affecting the bottom line in a big way. This way, business planning can better focus resources to support and win deals in segments that will contribute to the overall profitability.
Enhanced Analytics Governance – Over the past twenty years, every technology organization would have built hundreds and thousands of analytics predictions with varying sales objectives in mind. Over time, objectives may change but statistical models being sensitive to data changes (which is nothing but changes in sales patterns) are not always decommissioned to re-align business objectives. If an AI entity were to govern all statistical models, it would be able to predict which model is likely to fail and when depending on how the key drivers to the models are evolving over time.
Traditional analytics has set the base for sales estimations and predictions to be made possible from the CRM and sales data that has accumulated over several decades. Now that we know how to use the data in a better way, one needs to gauge how to employ AI systems to deliver insights such that deals can be converted faster and smarter.