Sales performance is becoming more scientific. Is your sales and marketing team?
Sales performance is becoming more scientific. Is your sales and marketing team?
It's time to start reforming your sales and marketing operations to reflect this trend towards AI and machine learning enabled decision-making
Sales performance is no longer just an art form, gift, or talent.
With the advancement of data science and technology, we can now use data and analytics to predict and improve sales performance at the individual level.
By implementing and capturing data from robust lead management systems
By setting clear and achievable goals and targets built on predictive AI/ML models
By fostering a positive and supportive sales culture with sales enablement technology
We can increase our sales team members’ probability of success and drive predictable revenue growth.
Are you using data to improve your sales performance?
Stop leaving your sales forecasts and revenue projections to chance.
I once worked for a very recognizable and revered consumer direct mortgage brand.
During that era, we had an algorithm we used to evaluate, in 90 days, whether a salesperson could “make it” or not.
The model was effective.
We quickly cut underperformers and reclaimed those resources to invest in the next batch of promising recruits. Our data indicated that most of these folks would continue to underperform.
Think professional sports and salary caps.
However, the only data we had to consider back then was how many leads we gave them and what percentage of them turned into closed deals.
The “art of the deal” between the lead receipt and the closed deal was a mystery.
This is the story of hundreds of sales organizations, thousands of sales leaders, and millions of sales agents. From this experience, sales performance has traditionally been considered more of an art form, gift, or talent.
Accordingly, executive and sales leadership assume (attribute) most of their opportunity to improve sales production and revenue growth to effective hiring and training programs – more hiring than even training.
Remember, we assume it’s a gift.
Having been on the inside of several considerable sales operations efforts to leverage the mountain of data that is now spewing out of a robust sales tech stack, sales performance is swinging rapidly toward the scientific.
Let’s explore areas where data and intelligent systems can improve sales performance on the individual sales agent level.
Everything starts with and is contingent on capturing and managing sales activity data.
For any of this to work, you must implement robust lead management systems to track and manage leads, contacts, and sales activity.
Fortunately, this has never been easier. CRM systems, sales and marketing automation, dialers, email service providers, and texting platforms capture and organize every little activity. Even more exciting, all this data is easily accessible for export or real-time using no-code integration middleware, such as Zapier.
Once you have the data, it becomes a simple exercise to organize and maintain it in a way that is usable for decision support systems and AI and machine learning models.
Use data and analytics to identify patterns and trends in sales performance.
When consistently highlighting patterns and trends in our data, we can spot opportunities, detect weaknesses, and make informed decisions to leverage the good and hedge the bad.
In the past, this was a laborious exercise of slogging through data, generating reports, and then pouring over them in hopes of eyeballing exciting insights.
Sales operations can now layer AI and machine learning technologies into their sales and marketing tech stack. These technologies are trained on our historical data and can proactively predict opportunities and highlight deficiencies, allowing us to build data-driven strategies quickly.
No more using intuition or personal experience to lock in decisions with hard-coded lead distribution or sales cadence rules. Instead, we can use real-time data and mathematically validated models to make dynamic decisions in lead and sales management systems.
Set clear sales goals and targets based on individual past performance and predictive aptitude.
Sales quotas are often set by evenly distributing the unit or revenue goals across the sales team.
Setting sales targets in this way can cause two significant challenges.
This approach can set unrealistic expectations of what the sales organization can produce, given the aggregate aptitude of the team.
Setting targets without considering individual performance can also miss a significant opportunity to optimize across the sales team – giving low performers opportunities to succeed and challenging high performers to achieve even greater success.
Again, using past performance data and predictive modeling allows you to dynamically grade individual sales agents and distribute leads and opportunities for maximum production.
By intelligently grading and predicting production outcomes at the individual level, you can create a highly differentiated sales performance management system – one that gets smarter and more accurate over time.
Your CFO will love your pinpoint forecasting accuracy and continuous improvement.
Match leads and sales agents perfectly.
After grading sales agents, lead scoring is the other half of the performance equation.
But be cautious about how you score leads.
The most common (and worst) system is assigning lead quality or score to a marketing channel or lead provider brand. Remember that the characteristics of the consumer/business and circumstances behind that lead determine if that lead will close or not – not the lead generation tactic or company.
More sophisticated lead scoring systems often weight lead attributes based on desirability (revenue potential). Of course, the fallacy in this approach is that it assumes that more desirable leads convert at a higher rate.
That simply is not the case. There is either no correlation or often a reverse correlation.
Instead, you must score leads by considering predictive attributes and then use those attributes to create a predictive lead score – one that indicates the likelihood of closing.
In situations like these, AI and machine learning can again bring science to the sales process.
Custom machine learning models are increasingly accessible and straightforward to build. Built on historical sales operations data, they can predict the probability of each lead closing in real time.
Given this kind of lead scoring and sales grading, you can design data-driven lead distribution strategies within any lead management system.
Match a lead to a lower-grade salesperson as a training opportunity or confidence builder. Match a lead that is unlikely to close to a telemarketing team or lead nurturing program. Shape your lead distribution strategies to fit your overall sales operation and business objectives.
Monitor and measure the performance of the sales team and your models to detect changes and the need to retune.
Changes in the market, lead generation tactics, lead buying, and sales processes can cause your models to become less predictive. Therefore, like any process or system, monitoring and actively iterating is essential.
As datasets continue to get more extensive and nuanced, AI and machine learning are becoming an inevitable and essential part of any effective sales operation.
Fortunately, AI and machine learning are becoming very accessible and even productized so that you can quickly integrate and dynamically design and train effective models.
Identify sales agents that could benefit from additional training and coaching.
Don’t worry; machines are not taking over sales.
However, these technologies are becoming potent sales enablement tools.
Using the same data fueling your sales performance models, you can pinpoint the exact skills and knowledge that will allow the individual sales agent to perform and grade better.
You can also design lead distribution strategies and sales prompts that will give underperforming agents more high-quality at-bats and higher performing agents assistance in focusing only on the highest-quality leads.
Foster a positive and supportive sales culture that attracts and retains top sales talent.
Investing in systems and processes that enable sales agents to scale their engagement with leads and prospects makes it easier for them to succeed.
Give them the data-driven, intelligent tools and resources to help them spend more time with the right leads – leads types they’ve historically been successful with and are predicted to close.
Intelligent systems will make sales performance much more predictable and the sales profession much more fun.
The perfect combination of increased availability of data, cheap and highly efficient data management and processing power, and rapidly advancing data science makes it easier to make sales performance more scientific.
What are you doing to make your organization's sales performance and operations more scientific? Are you considering introducing AI/ML technologies into your sales and marketing operations?