The 5th and last article of our series "KPIs for Product Managers" is about the costs that the product manager has to consider.
As many teams will probably confirm, sales people suffer from management's "forecasting mania", especially in American or stock market-oriented companies.
In order to be able to forecast the sales of one's team or the entire company (usually on a quarterly basis), it takes experience, the appropriate use of key performance indicators (KPIs) and, in addition to knowledge of the market, a good knowledge of the respective employees.
You should know whether your employees tend to forecast optimistically and exaggeratingly or pessimistically in "sandbagging" mode in order to avoid annoying questions from management.
In large sales units, experienced CFOs often forecast far more accurately than their own sales units.
Why is that?
They take emotion and wishful thinking out of the numbers and analyze the key metrics with past experience.
The average sales cycle indicates the duration from initial contact to sales closure.
It is important to precisely define and know the starting point, i.e. which point in time is taken as the basis for the first contact. Is the first contact in cold calling selected as the start time or the time from which the lead has a corresponding quality? Or is the point in time from which a project enters the forecast selected as the start time? The latter is often the only possible time in companies, because hardly any documentation about the customer is available beforehand. It makes sense to measure the sales phases individually and to calculate the sum of all phases as an average for all customers.
The average sales cycle differs depending on the market segment, size and industry of the customers and, of course, it depends on the product itself.
Product managers should know these values very well, because if sales are expected from a point in time x, measures corresponding to the length of the sales cycle must be started beforehand.
The sales funnel, the sales pipeline, must be well understood and divided into appropriate phases.
Many business centric models use phases such as identification, initial contact, first personal contact (collectively often referred to as "hunting"), decision phase, which is usually divided into sub-phases, such as problem identification, solution, POC (proof of concept) phase or "technical concept", proposal presentation, order, order booked phases.
In such models, percentage win probabilities are assigned to the phases (5%, 10%, 20%, ...) so that CRM (Customer Relationship Management) systems can automatically calculate the forecast. This is done by multiplying the estimated value of the order in the current project by the probability and works well for a large number of deals.
However, if a sales representative only has one mega-deal from major customer Y in front of him, he will hardly be able to compensate for a project with a win probability of 40% with a project value of 10 million euros (= 4 million EUR forecast sales = forecast) if this one deal is not closed.
Other forecasting systems use fewer phases, but deposit concrete criteria behind each phase:
- Are the decision-makers known and involved?
- Is the necessary budget available and approved?
- Are the decision criteria and competitors known?
- Is the decision and purchasing time within a certain time window?
- Etc.
No matter which model is chosen, it should be used constantly over a long period of time in order to be able to make appropriate derivations, predictions, metrics and measures and make them comparable over several years.
In cold calling for new customers, the term lead is used. This refers to a pre-qualified contact from an end customer.
Nowadays, the final decision maker does not always have to be known, and depending on the complexity of the solution, different criteria are defined for a lead.
BANT criteria, which have been used for a long time, are still used to categorize leads.
B = Budget means whether the customer has budget available. Ideally, one gets knowledge about the amount of the budget.
A = Authority: has the customer spoken to the decision maker, or is at least the final decision maker known.
N = Need: does the customer have a need or a problem that can be solved with the own products and services.
T = Time: is the time of purchase or the implementation of a project within a certain time window.
We do not want to go into more detail here about the relevance of these criteria, but rather about the classic development of such leads, since key performance indicators are derived from them. A detailed execution on leads and corresponding key performance indicators will be described in the article KPI Examples and Explanations as lead generation is usually located in marketing.
A derived key figure, which can also be defined as a marketing key figure, is the "lead to project" conversion rate.
In the following, the specific project is referred to as an opportunity.
An opportunity essentially differs from a lead in that it is much more specific, concrete and, above all, qualified. Leads from cold calling, e.g. through TeleSales, website downloads, trade fair contacts, roadshows, etc. cannot be qualified as deeply for complex solutions as must be done by specialist sales.
Only after the customer's requirements and problems are well understood along with the time horizon should a lead be converted into an opportunity. Depending on product complexity, I have seen lead/opportunity ratios in the range of 3:1 to 10:1 in my career. Where 3:1, i.e. 3 leads turn into one opportunity, is a very good value.
The reverse ratio is often referred to as lead conversion. In the 3:1 example, the lead conversion is 33.3%, and in the 10:1 example it is 10%.
If we go back one step and consider the lead ratio - the "quick turnover" dilemma reveals itself. The lead ratio is the ratio of the number of leads divided by the number of contacts or addresses processed.
In classic cold-calling projects, i.e. identifying potential customers in new segments, new contacts, simply addresses of companies that have never been in contact with your own company before, depending on the company brand, the solution and the value of the solution for the target segment, lead quotas of 3-6% are already a very good result.
An increase beyond this is usually only possible through better target group pre-selection or an increase in address quality.
What does this mean for the product manager, some readers will ask?
The simple answer is: high costs!
If we take the last example as a basis: cold calling succeeds in achieving a lead rate of 6%, the conversion rate lead to opportunity is 3:1 or 33.3%.
This means that out of 100 companies 6 leads (6% of 100) are generated and out of these 2 opportunities (33.3% of 6).
In order to generate a won project (equal to sales) from two opportunities, the opportunity close rate or win rate would have to be 50%. Sporty
The question for the product manager is now: How high are the costs for six leads, i.e. two opportunities and consequently one won project? Does the sales or marketing program used pay off?
This requires the next KPI, the Average Selling Price, the average revenue generated per project or new customer. It makes sense to determine the Average Selling Price over a large number of projects and time periods in order to compensate for individual positive and negative outliers.
Viewed over a long period of time, this KPI also provides very good information about price developments. As with many sales KPIs, it makes sense to determine the Average Selling Price separately by segment and sales channel.
As a product manager, you should know very well how expensive the acquisition of new customers may be in order to achieve corresponding sales, hence back to the example. The Average Selling Price of the product in the above example is 10,000 EUR. The Win Rate is 50%, Leadconversion is 33.3% and the Lead Rate is 6%.
The 100 cold addresses generate a turnover of 10.000 EUR!
Accordingly, the cost of buying the addresses, processing all 100 addresses must not exceed 10,000 EUR. The distribution costs for the conclusion not included, just as little the product development costs etc.!
For the sake of simplicity, we will leave it at the lead costs to finish the example. In other words, 6 leads may cost a maximum of 10,000 EUR, or one lead a maximum of approx. 1.600 EUR.
Much too expensive everyone will hopefully say. True, because no other costs were taken into account. Nevertheless, does the product manager know the cost per lead.
In my time, when a booth at CeBIT was still mandatory for most software companies, CeBIT leads cost the equivalent of at least 1.600 EUR.
If we now additionally measure the dwell time in the respective opportunity phase and the conversion from phase to phase, good optimizations can be made.
For example, if most customers are lost in the closing phase, after a successful concept phase, completely different measures must be taken than for the project loss from lead to opportunity. Good CRM systems provide this data automatically, but of course only if the data is entered correctly.
By looking at the sales funnel, it is also possible to make very good forecasts about future business performance and take countermeasures in good time. If, for example, the average sales duration is 9 months and the conversion rate of opportunity to won project (win rate) is 30%, it is already possible to see 6-10 months in advance how the business is likely to develop and what additional measures should be taken.
As Jeff Bezos, CEO of Amazon said: "If we have a good quarter, it's because of the work we did three, four and five years before. It's not because we did well this quarter.“
I can only agree with this and hope that the comments help you to work well beforehand, not just in the current quarter!
Overview: More articles and information for product managers
