AI Sales Improvement

AI Predicted Sale Closing

Problem:

Will a deal close and when is the question asked at every sales meeting.  And all the responses are biased “opinions”.  While Sales Reps have an intuitive sense about their deals, they are also driven by pressure and quota, which will skew their prediction.

Cost:

If and when a deal closes impacts everything down-stream, from cash-flow to capital allocation to manufacturing to inventory.  Get it too wrong and the wheels can fly off the bus, with too much or too little cash or product, either of which materially impacts revenue quality and business optimization.  In some cases, someone(s) will lose their job(s).

Aurora Solution:

Through a combination of AI-Enabled analytics and our proprietary scoring methodology we measure the sales prospect and Sales Reps propensity to close a deal by measuring and predicting the cadence and flow of a deal to determine if the deal has a low, medium, or high propensity to close in a defined time frame.

AI Sales Team Performance

Problem:

We all know who our top performing Sales Reps are . . . but are they? Often, the Sales Rep that sells the most is not the most efficient at selling, and the most effective selling Rep is not the top selling or most efficient Rep. These are differences with distinctions and not knowing them means the Sales Team is under-performing its potential.

Cost:

Many Sales Teams rely on a relatively few Sales Reps to produce the bulk of the sales. This is dangerous. Lose one of these key folk and sales can take a dive. The 80%/20% rule often applies that 20% of the Reps sell 80% of the business, meaning 80% of the costs of the Sales Team has marginal utility.

Aurora Solution:

First, we measure sales, sales effectiveness, and sales efficiency for each Sales Rep against several key indicators. Then we learn what parameters make a Rep high selling or efficient or effective so this can be replicated across the team. We assure measurements have dimensional consistency; i.e. a Rep is measured within, say, the same territory or customer type.

Next, we use our AI-Enabled analytics to predict the future of the trend of Sales Reps or territories or regions to learn the future impact to sales and revenue quality assurance.

AI Fair Challenge of Stretch Quota

Problem:

A stretch goal is the norm. No sooner are quotas set when the CEO wants another, say, $10 million more revenue. This stretch is usually apportioned proportionally or equally through divisions, or groups, or Reps. But the stretch is often seen as unfair and disruptive.

Cost:

Sales Team cohesion and morale are impacted when the stretch is deemed unfair or a set-up for failure. Some people who speak-up because their territory can’t support an increase, can find themselves being fired and replaced with someone who will “step-up” to the quota. Often the goal is made, but by exhausting the Sales Team, which leads to Sales Rep turn-over and taking bad or low margin deals.

Aurora Solution:

With AI-Enabled Fair Challenge a user simply enters a “Challenge” value and selects the application of the Challenge across any grouping (e.g. territories, Reps, products, etc.) and receives a prediction of the probability of achieving the challenge and the allocation across the grouping. In Fair Challenge, though the distribution of the Challenge across the group is unequal, each member of the group has the same probability of achieving its Challenge. Thus, the Challenge is fair!

An added value of Fair Challenge is the calculation of the probability of success of meeting the challenge. Here management will have an unbiased assessment of the stretch that will give pause to re-think if the stretch is setting the team up for failure.