Forecasting Customer Growth With A Growth Simulator Web App
How you can build a quick estimate of customer growth behavior using a set of simple assumptions with the help of Kwik-Look’s free web-tool
Revenue does not magically occur by itself.
Revenue growth is a direct result of actions taken in your business. Do you know what growth hacks are required for your business’s year on year growth? Do you know how growth levers impact your top and bottom line?
This article outlines the design of a simple web-tool that I developed for a recent freelance engagements. (Look me up on Fiverr!) This tool is targeted for SME Start Up companies who want to quickly forecast their potential customer growth and Customer Lifetime Value under different scenarios and assumptions.
Outline
- Introducing Kwik Look
- Modelling Customer Growth
- Converting Customer Base Estimates Into Cashflows
- Customer Lifetime Value & Cost Of Customer Acquisition
- Conclusion
1. Introducing Kwik Look
Kwik Look is a cash flow consultancy company that specializes in helping SME startups make informed better decisions by understanding the potential cash flows, value drivers, risks, mitigation strategies and value of equity when negotiating with potential investors.
2. Modelling Customer Growth
Kwik Look’s core products & services offering is a Cash Flow & Risk Analysis tool and a set of business advisory services structured along that tool.
However a critical element of any successful growth plan is a good understanding of how the customer base could develop over time . Most start ups tend to focus on new customer acquisition as their key growth lever. However customer retention is equally important as the negative effects of customer attrition adds up !
For example one study found that 5% increase in customer retention produces more than a 25% increase in profit. Another study states that it costs five times more to attract a new customer than to keep an existing one !
Therefore the Kwik Look team was looking to develop a complementary tool that their clients could use in tandem with their main Cash Flow & Risk Analysis tool.
There are a number of established methods to model customer retention and purchasing behavior such as the Buy Till You Die BTYD model that describes and forecasts the evolution of purchasing behaviors of groups of customers over time by simulating different aspects of customer behaviors (i.e The “RFM” dimensions of Recency — How long ago has it been since the customer last purchased, Frequency — How often will the customers purchase and Monetary — What is the average value of each purchase?) using different statistical distributions.
Unfortunately , although these models are able to better reflect natural variability in customer behaviours, they are conceptually complex. Especially for fledgling SME founders who have other competing priorities to get their start ups off the ground, we clearly needed something a bit easier to understand and explain.
Also to forecast growth , these BTYD models need to be calibrated against a meaningful amount of historical customer transactional data —yet another challenge given that most of the intended users would be new start ups and not established firms with large back logs of CRM data.
Therefore we had to come up with something more fit for purpose that was simple and quick to use but still captured the aggregate effect between how new customer acquisitions and existing customer attrition drive changes in the total customer base estimates. Ultimately, this would then be converted into cash flow terms to provide startup founders the necessary insights into how best to shape their growth plans.
A good way to understand the nature of the problem is to begin with a “toy” model with some simplified conditions.
At the start ( i.e time step 1), the initial number of customers joining is just the estimated total leads generated multiplied by the conversion rate.
Now the interesting bit is when we move forward a time step because now there would be a few different things going on.
First you have new customers joining- again this is just based on the total leads generated and conversion rate. However there are also new customers being referred by existing customers from the previous time step based on an assumed referral rate. Finally there are also customers who choose to leave (i.e stop buying) as part of natural attrition*
(*For the moment , let’s just assume a fixed rate of attrition over time and allow customers to drop out at any time (i.e There is no contractual/subscription based purchasing commitment ☺). In practice though , the attrition rates for groups of customers who join at the same time (sometimes termed cohorts) tend to start high and level off over time)
This process repeats in the third time step except now you have to consider that the attrition of existing customers would be different between the the two different cohorts of customers who joined in time step 1 vs those who joined in time step 2.
This interaction makes it slightly complicated to calculate this in a spreadsheet as you would need to ensure the calculations across the different time steps and cohorts are consistent.
You could use a series of array formulas to do this but it would be a bit finicky whereas in the web tool, this is taken care of.
3. Converting Customer Base Estimates Into Cashflows
The next step is to convert these estimates of customer figures into monetary figures. This is done by assuming some associated per customer costs for marketing, sales, servicing and retention , sales values per customer and purchasing behaviors (For simplicity’s sake let’s assume every active customer will make a single monthly purchase) to translate customer figures into cashflows.
4.Customer Lifetime Value & Cost Of Customer Acquisition
Repeating this calculation across multiple time periods will allow users to create a quick estimate of profitability and two key measures- the Customer Lifetime Value and the Cost Of Customer Acquisition
The CLV (Customer Lifetime Value) is a prediction of all the value a business will derive from their entire relationship with a customer and it’s value is driven by three factors:
- How long a customer stays on as a customer
- How frequently a customer makes purchases
- How much (i.e the $ value) they buy during each purchase
It is a useful metric to know because it gives an indicator of how valuable each customer is to a company also takes into account how good a company is at retaining customers and ‘improving’ their purchasing behaviors.
In the free version of the web tool, a simpler metric called 1 Year Customer Value is estimated based on the average revenue for each customer over a 12 month period.
The Cost Of Customer Acquisition (CoCA) provides an estimate of the amount of investment needed to attract new customers and drive sustained growth. This metric is calculated as the cost of Sales and Marketing divided by the total number of new customers acquired from converted leads and referrals (i.e Note that the CoCA changes depending on what time period chosen)
For the free version of the web tool, the 1 year average is calculated across the 12 time periods.
The ratio of the these two metrics provides a very useful insight. If the CLV/Ave CoCA ratio is less than one it effectively means that the business expects to spend more to attract new customers than the value that can be generated back for each customer.
If you are interested, go and try out the simple FREE webtool for yourself at https://www.kwiklookgrowthsimtool.online
5. Conclusion
Planning for growth is challenging.
However what can help is being able to very quickly understand the relative impacts of the various growth levers in terms of how different buying behaviors and customer acquisition & retention rates can influence the potential growth of your customer base and the impact on your bottom line cashflows and profitability.
If you are interested in finding out more , contact Kwik-Look team (info@kwik-look.com) for a free initial consultation
There is a beta version of this tool that also allows you to explore different scenarios with more complex assumptions such as …
- Rather than assuming customers only buy once every time period , you may want to see how outcomes change if they may buy at a different frequencies ?
- Perhaps rather than a fixed monthly lead generation you want to model a more aggressive marketing campaign in the early stages of the product/service launch ?
- Maybe you’d like to understand the aggregate results of a portfolio of multiple services/products with different launch dates rather than only a single product, etc
