A changing buyer landscape
It is coming to the end of 2018 and without doubt, in the B2B commerce world the buzz word of the year is Artificial Intelligence or AI.
This is the year that all the large ERP and CRM vendors announced AI initiatives following on from 2017 Dreamforce and the Salesforce announcement of Einstein.
So why is everyone suddenly talking AI and what does it mean to sales organizations?
For field sales organizations, one of the most sobering statistics to come out in recent years is the steady decline in average quota attainment for B2B field sales organizations. There have been many suggested causes, but no doubt today’s B2B purchasers have done their research on-line prior to ever engaging with your sellers. In addition, according to research undertaken by the Harvard Business Review “The number of people involved in B2B solutions purchases has climbed from an average of 5.4 two years ago to 6.8 today.”
Sellers are facing more informed buyers and more of them involved in each sale.
AI can help
Although there is no simple panacea, there is a way for sellers and sales organizations to improve their odds. By using AI.
AI can help sales organizations prioritize opportunities, identify deals that are not currently a focus but should be, and surface important reasons as to why deals are either on track or off track.
But not all AI is the same. AI succeeds when you have three key ingredients:
- a richly descriptive set of data
- domain expertise to be able to define problems sets and interpret answers
- machine learning (ML) infrastructure to “learn” from the data.
It is not just about the the algorithms as most have been available for over 30 years. And it is not just about having the data. It’s the intersection of all three.
What machine learning can do is look at vast amounts of say opportunity data and discern patterns in the data. By having a large enough data set that contains both opportunities that won and opportunities that lost and stalled. By looking at patterns across a sufficient set of opportunity data, the machine learning will identify what does “good” look like and what does “bad” look like in terms of winning and losing.
Once the machine has determined this, you have a very useful model. Good AI predictive models will have the ability to identify the key influencers that are evident in won and lost opportunities.
How to use AI for Opportunity Management
Armed with these models, forward looking sales organizations are deploying them via applications like ours. It is important to realize that AI does not replace human judgment, but augments it. In fact to take out human judgement will almost guarantee low adoption throughout an organization as sales professionals have tribal knowledge and emotional intelligence insights that AI can not yet replicate.
An opportunity probability win score should aid in understanding the totality of a pipeline, and should triangulate with human judgment, either reinforcing through agreement, challenging through disagreement or uncovering hidden insights into overlooked opportunities.
In our product, we headline this through our Opportunity Map, where we take all opportunities with close date in a particular quarter and group them into four buckets:
- Deals that both the sales organization and Aviso agree are strong candidates to close win (committed deals)
- Deals that the sales organization believes are strong candidates to close win (committed deals) but the Aviso Score indicates otherwise
- Deals that both the sales organization and Aviso agree are weak candidates and are not likely to close this quarter
- Deals that the sales organization believes are weak candidates to close win (committed deals) but the Aviso Score believes are good candidates to close won and are worth another look.
Additionally, we provide insights or reasons as to why we are calling these deals the way we are, either confirming that deals are on track and are progressing at the right pace through the deal process or highlighting the reasons why the deal is off track and suggesting areas that need addressing.
Armed with this information, sales organizations and leadership can better:
- Understand where reps should focus to avoid costly, drawn out sales processes while pursuing deals that otherwise might be overlooked
- Help coaching conversations and guide next steps
- Identify which reps are repeatedly over optimistic
- Spot sandbaggers
- Improve data hygiene
AI for Forecasting
Opportunity scores are not just for helping to decide where to apply scarce sales effort, but in addition can be used to create more accurate revenue forecasts earlier in the quarterly cycle. With deal level scores, a good forecasting system will combine an aggregation of projected opportunity win amounts with a top down view of deals that are projected to open and close (new won) in that quarter that are not currently in open pipeline.
A good system will be accurate from day one of the quarter, constantly re-evaluating both projected opportunity win amounts and new wins.
This does not necessarily replace more traditional judgment based rollup forecasting but it adds an extra dimension to triangulate the different methods. Typically, early call deals are less likely to be committed, meaning that judgment contributes to a higher percentage of the sales forecast earlier in the quarter as opposed to later in the quarter. AI is more willing to commit early stage deals and will compute new wins with better accuracy than judgment alone.
It would be a mistake to judge all current AI marketing as hype, believing it will play a future role, but not this quarter as you focus on making the number.
Companies that embrace the right AI tools for Sales today will have a leg up on their competitors. These companies will learn earlier how to deploy AI to derive maximum value. These efforts will pay off in terms of shorter and more predictable successful sales cycles, more effective deployment of resources and Day 1 visibility into where the Quarter will land.