What’s Math Got To Do With Sales and Marketing?
Imagine a salesperson sitting down at their desk on Monday morning and being recommended the “Most Awesome Deck” because the sales call their colleague used it in last week turned into closed-won business!
Granted, enterprise sales today is more complex than this example, it encompasses lots of touch points, evolves over time, and has an infinite number of moving parts. But by aggregating the wealth of data, both structured and unstructured, spread across enterprise systems it is now possible to provide highly relevant search, recommendations to those who don’t know what they need to seek, and unified content analytics to marketers responsible for contribution to revenue. But how?
As you know reps have to do more activities and do them faster than ever before. More conversations, meetings, followups, with more people. Thousands a day across your team. With this in mind a Sales cycle can be described using the mathematical tools of probability and statistics to paint a picture of a graph that at it’s simplest looks like this:
Social, financial, transportation, communication networks garner much more press but the Sales Cycle is an equally as complex network. Just as you see with Kim Kardashian on Twitter or Carl Icahn with board seats – when you look into the nodes in a sales graph you start to see preferential attachment.
Preferential attachment is the process whereby some quantity, e.g. money, followers, retweets, is distributed among a number of individuals or objects according to how much they already have, so that those who are already wealthy receive more than those who are not, or the more popular someone is on Twitter the more likely it is that a newcomer will follow them.
Now let’s apply this thinking to the sales cycle. You know who the best sales reps are, but what is it they are doing that makes them great? And what is it they are doing that you can replicate across the rest of your company?
A sales motion can now be graphed and we can identify the nodes (documents, action, opportunities, salesrep, campaigns, teams, CRM data, etc..) that have the most influence in said motion. Preferential attachment results in clustering amongst the most important nodes in a way that enables go-to-market leaders to not only construct highly accurate maps of their deals, but to track top performers and what they do, identify exactly where and why wins and losses occur, and when coupled with artificial intelligence techniques these methods can:
- Predict where the greatest revenue opportunities can be captured
- Increase the probability of winning a deal by suggesting next best steps
- Provide suggestions on how to reduce the length of a sales cycle
- Act as a coaching tool for front line sales managers by showing them the validity of a salesperson’s forecast
Examining the graph we see nodes that are working well (big nodes). And these are the activities, or documents, or people that are adding the most value. When you see this you want to drive more opportunities through this node, and you also want to learn from this node so you can replicate it across the rest of the team. The graph also shows us nodes that are not performing as expected, be it a person or a campaign, with this information you can decide to nurture and improve that investment or to cut losses.
Harnessing the Power of the Sales Graph
Intuitively, sales and marketing leaders know that sales cycles are not like uniform, assembly line widgets where each one is identical. Instead, they’re more like snowflakes where each is one unique. The graph shows us how reps behave early in sales cycles where there’s limited knowledge either of the prospects or of what they’re trying to achieve, and as the sales cycle progresses, getting more complex, it shows us the salesperson-prospect interaction become more complex as well.
Significant insights can be obtained and become more predictable through the real time evolution of the graph as sales reps alter their behaviors based on previous recommendations. Using opportunity-document linkage and Markov Chain theory it is possible to determine both the direct and indirect revenue contributions of a piece of content within a sales cycle and the best marketers will seek to understand the contribution of each of their investments — as well as the indirect value of the same investments — to uncover their best campaigns and revenue contribution.
Go-to-market leaders have much to look forward to from AI in the years to come. As more data sources such as calendars, intranets, and phone conversations are connected to the sales graph our ability to provide incredible search, activity based recommendations, and analytics improves.
A network-level understanding of sales behaviors provides countless benefits for CROs and CMOs. Modeling top rep behavior and understanding marketing contribution to revenue being two important examples.
Modeling top rep behavior – sometimes this is referred to as THE LAST MILE PROBLEM. While B2B companies now largely have a good grasp of how they are driving activity at the top of the funnel, the rest of the funnel is very murky at best. This is ironic since opportunities and deals at these stages have higher probabilities of closing and therefore represent more dollars to the business. It’s also ironic because most marketing departments invest a huge amount of resources on supporting sales and deals at this stage. But what is really happening when a rep is working 1:1 with a client?
Understanding marketing contribution to revenue – Today’s methods for measuring marketing’s revenue contribution still leave much to be desired. In a world where sales and marketing are much more aligned the conversion from MQL to closed business is too easily gamed and just doesn’t cut it for progressive CEOs.
If you would like to learn more about what preferential attachment, activity based relevance, and the sales graph means to commercial leaders, we are offering a complimentary session to qualified companies facilitated by our chief data scientist Adam Duston. APPLY HERE.