Calculate the ROI of Building or Buying Data Solutions

This article explains how to make an ROI case for either building or buying data solutions.

We will start with the concept of understanding value and then define fixed and ongoing investment costs. We will show how value is a function of revenue as well as fixed and variable costs at the individual customer level. Finally, we will connect the two concepts to understand expected incremental value based on an investment.

Figuring out the value of building or buying data solutions data solutions is difficult. An executive at a large CPG company recently mentioned the pain of calculating the ROI of buying third-party data sets. I used to feel this pain when I ran Strategy and Operations for Facebook’s business marketing team. Twice a year, we evaluated investment cases for multi-million dollar projects.

Since then, I’ve advised executives on how to evaluate platforms like Segment, Databricks, Snowflake, and Looker. I will share my findings and framework from a customer or account point of view. This methodology centers around the incremental value of those platforms to your accounts or customers.

Before we get started with any investment decisions, we must understand the economics of our business as it exists. Let’s start with the following example.

Input Variables / Data Points

– Average Order Value Per Customer (AOV) per mo (in $ and >0) = $40
– Customer Churn Rate (r) per mo (between 0-100%) = 10%
– Gross Margin (GM) in % (0-100%) = 40%
– Customer Acquisition Costs Blended (CAC) (in $ and > 0) = $56
– Total Active Customers (TAC) per mo = 100,000
– SG&A Costs per mo = $5M

Assumptions

All business cases start with assumptions that help us frame and think about the ROI equation. Here are the assumptions I start with:

– Average order values are good representation for all orders and customers
– Retention rates (or churn rates) are steady over customers, cohorts, and time
– Gross margins per order are stable
– Blended customer acquisition costs are consistent over customers and time
– We will not take into account the time value of money and will exclude discount rates

Note: Each one of the data points above probably has a distribution (which could be wide or narrow).

Basic Calculations

\[Average\ Lifetime = \frac{1}{r} = 10\ months\] \[Lifetime\ Revenue = {Lifetime}*{AOV}=$400\] \[Unit\ SGA = \frac{Total\ SGA}{TAC}=$50\] \[CLV={Lifetime\ Revenue}*{Gross\ Margin}-({CAC}+{Unit\ SGA})\] \[CLV=({$400} * {0.4})-({$56}+{$50})={$54}\]

The basic calculations show that you are making $54 per customer on average. Use the calculator below to determine your original CLV.

Investment Hypothesis

Now that you have a basic understanding of your customer lifetime value, you should see how an investment in a data solution would increase or decrease the different parts of this equation.

For example, if you are considering buying third-party data from a company like Experian to help narrow your targeting and reduce your customer acquisition cost, you should determine what impact that might have on customer level profitability. Similarly, if you are evaluating a tool that helps you optimize marketing budgets between channels, you should calculate the impact on either CAC, AOV, or Retention.

Building a build-vs-buy investment case

Let’s consider an example where you are looking at a data solution like Looker. You can choose to either build an in-house tool or buy Looker.

The Case for Building

When considering a data investment, you should account for three types of costs:

– Build effort (people hours to hire, train, develop) vs. Integration cost (cost of integration + people hours for orientation + training)
– Ongoing people costs vs. maintenance cost
– Ongoing compute costs (in-house or vendor costs)

Let’s start with estimating what it would take to build a tool internally. You estimate it will cost you six full-time developers to build for six months at $120K/year each (blended to $20K/mo). Once deployed, you want two of them to manage and maintain the internal tool. That brings you to a monthly cost of $10K. You also know that you’ll have cloud compute costs. You estimate this cost to be $3 per month per customer. Since the solution is custom built for your team, you expect decision quality to be higher and reduce churn by 10%.

So, recalculating the above math:

\[New\ Churn\ Rate = {0.1}*({100}{\%}-{10}{\%})={0.09}\] \[Average\ Lifetime = \frac{1}{r} = 11.11\ months\] \[Lifetime\ Revenue = {Lifetime}*{AOV}=$444.44\] \[Added\ Cost\ of\ New\ Tool = {20}{K}+{10}{K}\ per\ month\] \[\frac{Unit\ SGA + Expected\ Costs}{Customers} = \frac{Total\ SGA}{TAC}=$50.3\] \[CLV={Lifetime\ Revenue}*{Gross\ Margin}-({CAC}+{Unit\ SGA}+{Per\ Customer\ Cost})\] \[CLV=({$444.44} * {0.4})-({$56}+{$50.3}+{$3})={$68.48}\]

You can use the calculator below to calculate your new CLV.

Your average customer value is $68.48. From here, the Expected ROI calculation is simple:

\[ROI = \frac{Net\ Return\ on\ Investment}{Cost\ of\ Investment} = \frac{{$68.48}-{$54}}{3} = 4.83x\]

Use this calculator to find your ROI.

The Case for Buying

The tool you choose will not directly improve the value of orders or decrease churn. However, it will enable you to make better decisions by having visibility into every single customer.

For example, such an investment might enable marketers and product managers to look at which product purchases lead to better average order values or higher retention.

So let’s hypothesize that Looker will add cost ($100K integration + $2/customer) to the SG&A side but will reduce average monthly churn by 10%. We can assume that the solution can be broadly used by multiple teams and has a similar output.

So, recalculating the above math:

\[New\ Churn\ Rate = {0.1}*({100}{\%}-{10}{\%})={0.09}\] \[Average\ Lifetime = \frac{1}{r} = 11.11\ months\] \[Lifetime\ Revenue = {Lifetime}*{AOV}=$444.44\] \[Added\ Cost\ of\ New\ Tool = {100K}\] \[\frac{Unit\ SGA + Expected\ Costs}{Customers} = \frac{Total\ SGA}{TAC}=$51\] \[CLV={Lifetime\ Revenue}*{Gross\ Margin}-({CAC}+{Unit\ SGA}+{Per\ Customer\ Cost})\] \[CLV=({$444.44} * {0.4})-({$56}+{$51}+{$2})={$68.78}\]

You can again use the new CLV calculator for the buy option:

The average customer value is $68.78. Therefore, the Expected ROI becomes:

\[ROI = \frac{Net\ Return\ on\ Investment}{Cost\ of\ Investment} = \frac{{$68.78}-{$54}}{2} = 7.39x\]

Use the ROI calculator to compare the ROI of building vs. buying.

Based on this, you can see that buying Looker has a much higher ROI than trying to build something similar internally.

At Retina, we build custom models to calculate customer lifetime value early in the customer journey. Our expertise and focus allows us to save costs and boost the ROI for our clients. Get in touch if you’d like to learn more.