This post illustrates how incremental improvements along the Customer Lifecycle of a SaaS Business Model can result in massive changes of outcome for revenues as well as profitability. In Customer Lifecycle models revenues are not the sum of deals, but the product of conversion and expansion rates, creating "multiplicative" instead of "additive" impact. We describe which time, conversion, volume and financial metrics are required to capture all assumption for the business model. Learn how you can more than double your revenues by improving key metrics only between 10% and 15%.

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Jacco Van Der Kooij (CEO of WinningbyDesign) and Fernando Pizarro recently published the book The SaaS Sales Method: Sales As a Science. The SaaS Sales Method nicely exposes the math which underpins each stage of the customer relation lifecycle from getting a customer to keeping and growing a customer. It provides a framework to understand and improve the entire sales process, organization and training, shifting from what the authors call a superstar culture to a science culture.

Obviously, I like this approach a lot as it introduces a realistic framework to model the logic of the customers lifecycle. This is very much in line with the process we use at Lean-Case to create robust and defendable business models based on Unit Economics. They focus to answer if Customer Lifetime Value (CLTV) is significantly larger than Cost of Customer Acquisiition (CAC).

Simple spreadsheet models are adding up numbers and model a world where doubling sales would require twice as many leads, or doubling the number of salespeople. These types of assumptions are rarely realistic as they typically ignore time dimensions and require twice as much money to be spent.

In models applying Customer Lifecycle Logic, revenues are not the sum of deals, but the product of conversion and expansion rates, creating "multiplicative" instead of "additive" impact. Massive changes in outcomes are therefore possible by making marginal improvements on the performance of single steps. For example, if you look at a 5-step sales process and improve the performance of each step by 15%, your results can double as 1.15 to the power of 5 equals 2.

To create an example model which illustrates the power of customer lifecycle models, let's take a look at the SaaS Business Model Framework and related Performance Metrics introduced by WinningByDesign. It breaks down the Get-Keep-Grow Customer Lifecycle in 2 major phases

The **Getting Customer Phase** splits up into 6 stages. It starts by acquiring prospects (S1 - Prospect), turning them into Marketing Qualified Leads (S2 - MQL), handing them over to the Sales Team as Sales Qualified Leads (S3 - SQL). SQLs must be accepted as Sales Accepted Leads (S4 - SAL) and are then converted into Committed accounts (S5 - Commit) to deploy a solution and on-board them as live clients (S6 - Live).

The **Keeping and Growing Customer Phase** breaks down into 2 stages securing the initial recurring revenue stream (S7 - MRR) and maximizing revenues of the account over its lifetime including expansion and churn (S8 - LTV)

To measure impact on performance in a customer lifecycle model, the SaaS Sales Method introduces 3 types of metrics per process step (Volume, Conversion and Time Metrics). We have modified this model slightly and added Financial Metrics as a 4th element:

Let's now get hands-on and define the assumptions for a sample SaaS Model. The figure below summarizes our assumption for Volume Metrics, Time Metrics and Conversion Metrics. Assumptions must be taken for both phases - the Get-Customer and the Keep-and-Grow-Phase. To keep things simple and transparent, we only model the economics of one customer cohort (or even better one campaign).

In the following sections, we take you through all assumptions for time, conversion, volume and financial metrics and explain basic calculations. At the same time, we show you how these assumptions are set up In Lean-Case.

For the Get-Customer Phase, we assume that converting a prospect into Live Customer takes a total duration of 6 months (= 1 + 1 + 1 + 2 + 1). For the Keep-&-Grow-Customer Phase, we assume that customers can renew contracts on a monthly basis, i.e. there is churn and expansion on a monthly basis.

We have picked conversion rates close to real life rates but also simplified them for the sake of our case study. The overall conversion rate from Prospect to a Live Customer is 1%. Check the table below to understand how this overall conversion rate is calculated.

The WinningByDesign Book actually gives you conversion rate benchmarks for each stage to compare against. They are useful and dependent on Annual Contract Values. ** **

In the Keep-and-Grow-Phase, conversion is determined by churn and expansion. For live customers, we assume a revenue expansion rate of 3% and a churn rate of 1.5% which results in a customer lifetime of 67 month (=100/1.5%) . Combined, churn and expansion yield a monthly net expansion of 1.5% (=3%-1.5%)

We assume **1.000 prospects at the beginning of the process. At an overall conversion of 1% and with a sales cycle of 6 months, there will be 10 new customers after 6 months. **

Financial Metrics cover the Cost-of-Customer-Acquisition (CAC) in the Getting Customer Phase as well as revenues and Cost-of-Goods-Sold (CoGS) in the Keep-and-Grow-Customer Phase.

CAC include the Cost of Selling and the Cost of Marketing. Typically, you would include the cost of your Sales Teams. For this case study, we assume CAC to be fully variable. They occur when you create prospects and at every transition from one stage into another when converting prospects into a live customer. We assume that the cost per "lead" (=prospect) is €100. The cost to convert a prospect into a MQL, a MQL into a SQL, ... is €250 at each stage. The figure below shows the calculation in quite some beauty. By dividing the Total CAC along the process by the number of New Customers, we get to a Cost-of Customer Acquisition of €19.625 per new customer.

With average MRR of €1.000 per customer, 10 new customers will result in **New MRR** of €10.000 after 6 month. Every Month MRR will decrease by the monthly churn rate of 1.5% and increase by the monthly expansion rate of 3.0%. The monthly growth rate is therefore 100% - 1.5% + 3% = 101.5%, i.e. that MRR in Month 2 is 10.000 * 101.5%, MRR in Month 3 is 10.000 * 101.5% * 101.5%, ...

The table below shows the details of this calculation and also shows that Annual Recurring Revenue (ARR) for the first 12 month is calculated as the sum of all MMR.

Cost of Goods Sold are relevant to calculate the Customer Lifetime Value. Revenues subtracted by CoGS equal the Gross Profit. If you sum up the Gross Profit for all month of the lifetime you will get Customer Lifetime Value. According to SaaS benchmarks which we have compiled, we assume Cost of Goods Sold to be 20% of revenues

Let's now turn to the fun part. We have set up the model and want to learn which impact incremental "multiplicative" improvements have for one customer cohort of the SaaS Business Model:

**Now let's increase the conversion rate for each of the 5 sales pipeline stage by 15%**. This means that a 25% conversion rate turns into 28.8% (= 25% * 1.15). A 80% conversion rate turns into 92% (= 80% * 1.15). These changes result in an overall conversion of 2% (= 28.8% * 28.8% * 92% * 28.8% * 92% ) . This also means that 1000 prospects convert into 20 live customers. Compared to the bases case, the number of new customers doubles from 10 to 20. Thereby, new MRR in the first month double as well from 10k to 20k.

Further positive impact on revenues can be achieved by increasing revenue expansion and decreasing churn. By improving both by 10%, increasing monthly expansion from 3% to 3.3% and decreasing chrun from 1.5% to 1.35%, we achieve incremental improvements month over month.

1st year ARR grow by 3% from 232k to 269k and 5-year ARR by 16% from 2.423k to 2.808k (see chart below).

On top, cost improvements can improve the Customer Lifetime Metrics. Decreasing CoGS improves the Gross Profit and thereby the Customer Lifetime Value. Decreasing CAC has direct impact on unit economics. Let's check this for our example. By improving both - CogS and CAC by 10%, - the LTV/CAC ratios grows by 14% from 3.7 to 4.2. The CAC Payback in Months decreases by 14% from 20.2 to 17.7 month (see chart below).

We can now summarize what the overall impact of incremental improvements across the entire value chain looks like. The two tables below show the results for 1st-year and 5-year metrics - Revenues, CogS, Gross Profit, CAC and Net Profit. They list results for the Base Case as well as for the incremental improvement scenarios - improving all conversion rates by 15%, improving churn and expansion by 10% and improving CogS and CAC by 10%.

While overall 1st year revenues grow by 105% (compared to base case from 131k to 269k), 5 year revenues grow by 131%.

While 1st year net profit is down by 47% from -89k to -133k (obviously because the entire CAC for all 20 customers occur in year 1), 5-year Net Profit even grows stronger than revenues - by 147% from 791k to 1,949k.

The video below visualizes the case results based on Lean-Case simulations - how many new customers you acquire, what the impact of increasing churn and expansion is and how reduction of CoGS and CAC can improve unit economics.

Impressed by the power of incremental improvements?

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CEO and Founder Lean-Case - Eckhard is a Serial Entrepreneur co-founding cyber-security startup accells acquired by Ping Identity and m-payment startup paybox acquired by Sybase/SAP. As a Business Angel, VC Partner and Investment Advisor, he has realized that turning business models into numbers is a major challenge and must professionalize.