This is Part Two of a two-part blog (read part 1 here) about how to improve accuracy in lead/revenue projections in organizations with a complex sales process:
To recap part one, simple lead generation calculators miss these four things:
- They don’t take critical metrics (like last year’s revenue) into account
- They have too much confidence in inbound-only strategies
- They devalue the importance of outbound nurturing
- Outbound still supplies a majority of leads for your company (from Aberdeen Research)
Comprehensive Lead/Revenue Calculator for 2016
We have developed the following comprehensive Lead/Revenue Calculator that factors in metrics frequently ignored in the planning process, and addresses other shortcomings often found in these sorts of tools. This is a model you can adjust to fit your company; so multiply, add zeros, adjust percentages or make whatever other changes you feel will shed light on your lead generation situation.
The Lead/Revenue Assumptions
The assumptions embedded in the example above are industry benchmarks OR based on our actual experience over twenty years with clients. For example, we estimate that inbound marketing efforts will produce about 35% of the gap revenue. Some of the other assumptions are estimates from SiriusDecisions Demand Waterfall metrics on the “Average Company” vs. their best-in-class averages. While many industries estimate that sales reps source 60% of their own business, the reality is that each company should provide much more support. Hence the 35% used in this example.
Additional Things to Consider
Most lead/revenue calculators start with the desired revenue and take an arithmetic approach to calculating the number of leads needed to hit the number. There are two problems with that approach:
- While I recommend backing in to the number of leads needed, many companies have a tendency to overestimate average deal size or oversimplify the value of a deal, hence underestimate the number of leads required to meet revenue goals. Here’s an example:
A deal in January is better than a deal in any subsequent month; and, a deal in Q1 is at least three times better than a deal in Q4 from a revenue standpoint—especially in a recurring revenue model. If the average deal size is $15,000 per month, a close in March yields $135,000 in the current year while a close in September yields $45,000. Even if the deal is not recurring, closing early rather than later allows for acceleration of add-ons, upgrades, consumables, etc. A true understanding of the impact of deal size and deal value is essential to arriving at lead predictions that will hit the number.
- One lead/revenue calculator I’ve seen uses the term “lead” loosely. It calculates the number of MQLs (Marketing Qualified Leads) that need to be generated to hit the number rather than SQLs (Sales Qualified Leads). If, for example, it takes 742 MQLs to generate 8 deals; the deals will be won ONLY if sales accepts responsibility for following up on the 742 MQLs; or their organization does that for them. Because if you dump 742 raw MQLs on any sales force there is a better chance they will ignore all of the leads rather than work any of them. Why? Because few sales executives are going to work leads when only one out of ninety-two are actually perceived as worth their time. Sales has been conditioned to expect poor quality leads from marketing and rarely follows up on raw, unfiltered so-called leads. The calculator you use needs to arrive at the number of SQLs and your process needs to assure that those are the only leads turned over to sales.
The problem of dumping leads on sales is not new, but seems to get worse every year. Many marketing departments have a quantity versus quality view of their job. Each year their budget is cut. At the same time they are required to generate more leads than the previous years “quota.” So, they might look at marketing automation, for example, to score leads and end up being able to send more, poor-quality leads to sales faster than ever before.
Or, they might go the route of using content aggregators to identify hand-raisers (e.g., whitepaper downloaders) and send those hand raisers to the field as qualified leads. In one experience (with a very large software company) content aggregator leads cost $23.15 “raw,” but $2,660 per qualified lead (only 1.8% of them were actually qualified). If you spend $92,600 to generate 4,000 so-called leads (4,000 x $23.15) most of the raw leads will be lost as the field is unlikely to follow up 100 leads to find 1.8 qualified accounts. In short, you will be throwing money away.
What should you do? Run the numbers for your company using the downloadable calculator (note that it is the same as the model in this blog but it will calculate based on your input.) Or let us do it for you. We would be happy to walk through the numbers with you so that you have the most objective goals possible for the balance of 2016.
I guarantee that you will be surprised by your actual metrics (or lack thereof), how you stack up against best-in-class companies—and that you will look at the challenge of making the 2016 number a whole lot differently than before.
This post was originally published on this site