Sales funnel management for technology and SaaS companies.
Clients often come to us with concerns about sales forecasts and sales funnel management. Anybody in charge of a business needs to know their sales funnel is reliable. When you’re in the middle of September you expect your sales team to provide you with a 95% accurate sales number for the month and a pretty reliable estimate of October.
Too often this isn’t what’s provided and it’s a source of frustration to owners and directors. How can you plan, invest and sleep at night if Sales don’t know what they’re doing?
This technology company brought us in to improve accuracy of sales forecasts and funnel management.
What we found.
So often the problem a client sees is the symptom rather than the cause. The underlying problem in this case was customer service. Not bad customer service, in fact there was a healthy focus on keeping customers happy. The real issue was that all customers were treated the same, regardless of their size or potential. This meant that all customers received personalised time and attention with the sales team at their beck and call.
What did this have to do with forecasting and funnels? Not surprisingly the biggest £ opportunities came from the largest clients. But with limited time to spend nurturing relationships and networking the account, the nuance of decision-making was being lost. There will always be some surprises in forecasting so if you’re going to focus on accuracy then do it for the top 50% of revenue.
The second financial implication of this was that on many occasions a customer visit was losing money. The sales team were celebrating gaining “access” to decision makers and the business were delighted that the team were busy. But busyness is not always good business.
How we helped
One of the key principles of understanding the cost of sales is to work out how much a sales person costs. Not just the salary, but the bonus, car allowance, National Insurance costs and so on. You should add to this a proportion of costs for management and overhead. Let’s say you have twenty people in the business and one HR manager. Without any employees you wouldn’t need HR, so really each employee costs you 1/20th of the cost of the HR manager. The same applies to sales managers.
Then you have to add fixed costs like a proportion of rent for the space their desk takes up, the cost of your IT service contract and so on.
With all this done you have the “fully loaded” cost of a sales person and can see what level of £ sales they need to achieve to pay for themselves.
The second angle on this is to work out which customers have the most potential. It’s not as simple as those who spend the most.
The exercise can be as simple or as complicated as you wish. We’ve seen complicated Excel models with numbers graded to the nearest percentage point. Or you can do it on a flipchart in an hour.
What we did here was to look at size and profitability and then we overlaid margin and discounts. Finally, we took a more subjective perspective and thought about the strength of the relationship, what share of a customer business we had and our prospects for increasing that.
With this understanding of customer attractiveness and cost to serve we helped the client separate customers into Key Accounts and Others. A virtual, self-serve portal was created to service the long tail of Others, reducing cost and management time. Importantly, this also created capacity to handle a longer list of Others at lower prices.
We then created training materials for Key Account Managers to handle two different types of conversations: Those critical customers who would grow with the business to define tomorrow’s sales line, and those who were sizeable now but likely to decline over time. It’s really important to keep this last group happy without sacrificing margin.
Focusing the team on key accounts improved call numbers on these customers by almost 20%. Sales leads coming into the team were better qualified because Marketing could assess earlier on what category the Prospect might fall into. Finally, there was a double-digit increase in forecast accuracy because more time was being spent on the big revenue customers.