End the Bickering Between Marketing and Sales

Firms can earn big returns by effectively measuring each step in the sales and marketing process

Do you hear your Sales leaders complain to Marketing that their teams are not getting enough high-quality leads?  Are the Marketing folks pointing to the high volume of leads that fit the Ideal Customer Profile criteria and have shown sufficient intent to trigger a qualification as a Marketing Qualified Lead?

There is a rich bounty of published work telling us that Sales and Marketing must run in lockstep.  In an account-based world, a common language is clearly required to understand and account for the contribution of the sales and marketing teams to the revenue generation process.

Marketing Contribution Measures the Value Added by Marketing

They say Marketing is from Venus and Sales is from Mars. Whether you recognize it or not, the performance of a sales team depends in large part to the amount and quality of the leads sent to it by the marketing team. While measuring lead volume might be easy, assessing quality is not.

Typically, the value of leads generated by marketing is calculated based on average values such as average customer lifetime value, average lead conversion rate and the average cost per lead. As with total sales volume by sales professional, this simplistic measurement is done because more precise assessment is not easy.

This traditional way of measuring the value of a lead doesn’t provide any color on the quality of the lead, however. It fails to account for a lead’s propensity to close, the prospecting effort required to convert it, and the expected deal size for the lead. This simplistic accounting excludes important operational intelligence for optimizing sales performance.

The Cien Value Chain employs a complex set of AI models that derive a true value for each lead and opportunity.  Using hundreds of machine learning and natural language processing models, the Value Chain calculates what the real value of any given opportunity is TODAY. Think of it as calculating the expected value of a lead that may not close for weeks or months.  What the Cien Value Chain gives you is a starting point value that all parties; Marketing, Business Development, and Sales can agree on as representing the real dollar value of leads attributed to Marketing.

Sales Contribution Measures the Value Added by Sales

If you can’t measure it, you can’t improve it.

Why did Bob sell more than Sue last year? Was it due to the quality of the leads he received from marketing, the territory he was assigned, his work ethic, or simply a winning smile?

Historically, sales representatives were measured by the total revenue of sales signed. This is still a commonly used indicator of sales performance in many organizations.

Using total sales revenue as a measurement for sales performance can be misleading at best and counterproductive at worst.

This is because the total sales revenue per sales rep is a simplistic measurement that doesn’t reveal the underlying dynamics of where marketing and sales are strong or weak. Marketing may be handing weak leads to a strong sales professional, for instance, making the seller look worse than his skills allow. Or a rep might be closing sales and generating a high total sales volume, but half the cross-sell opportunities for his prospects go unrealized while another sales rep could better uncover these hidden opportunities.

Total sales revenue simply does not capture enough operational intelligence for sales performance optimization.

Many sales managers know this. The problem is that measurement beyond sales quota achievement is hard; firms must account for lead, pipeline, human and market factors that come into play. The data for this operational intelligence is there in CRM systems, but it often is incomplete, inaccurate or insufficiently structured. So sales teams tend to measure on a few key performance indicators, and CRM data is used primarily for forecasting purposes.

Getting beyond these simplistic metrics is important for the operational intelligence that businesses need for improved sales performance. This requires looking at both marketing output and sales performance.

Let’s take an example.

Imagine two sales reps Bob and Sue who work in the same team. At the end of the quarter, Bob has signed $1,000,000 worth of bookings, and Sue $800,000. Without a clear picture of the team’s effectiveness, Bob appears to be the highest performing seller.

When a firm is able to take into account the true dollar value generated by lead generation and sales development efforts, it is able to compare the lead value received by a sales professional with the added value the seller has contributed to it.

In Bob and Sue’s case, it’s possible to calculate the value of leads they received and compare them with the actual value they signed. And it turns out that Bob actually received $500,000 of leads and pipeline value from marketing, while Sue only received $200,000. Bob actually contributed less to the business than Sue, even though at a superficial level his numbers were higher.

As this example shows, the best seller on the team isn’t necessarily the one who has signed the most contracts, it is the ones who have added the most value to the leads and opportunities received by the marketing and sales development teams.

It may be that other sales professionals appear to sell more because they’ve received more valuable leads from marketing, or because they work a territory that has greater demand than another rep. External market factors such as a local competitor leaving the market might also distort the picture of the seller’s performance.

Artificial Intelligence Makes it All Possible

Improving sales performance requires better operational intelligence. The problem, as stated above, is that most firms cannot effectively analyze their business data comprehensively enough for this insight.

The good news is that advances in statistical science, machine learning, natural language processing, and AI allow businesses to exploit sales data and CRM records for new levels of understanding previously not possible.

With AI, companies are now able to measure and account for the exact value created by marketing campaigns, the value generated by a sales development team’s prospecting, and the qualification efforts, distinguishing them from the value created by the sales professional who converted those qualified opportunities into signed contracts.

By looking at the multiple factors that make up a sale, artificial intelligence allows firms to break out the individual performance of every seller in the organization. From this, companies can immediately generate more revenue by allocating higher value leads to the most effective sales reps and train those reps who are underperforming.

Cien delivers the intelligence needed for this more sophisticated analysis. The first step to measuring sales and marketing’s contribution is to request a Hidden Revenue Assessment.