Turning small numbers into big revenue
In an effort to provide a more detailed look at how data can inform planning, and make an impact on bottom line numbers, we’re publishing in-progress case studies that show stages along the way, rather than just a finished project.
Picture yourself as a small business owner, and then consider these two numbers: 0.05% and $50,000. One of these is a rounding error, one of them is an employee salary. And yet, as we show in this case study, the secret to making significant impact on bottom line revenue comes from the tiny numbers.
Marketing response numbers don’t look particularly sexy. They generally involve a lot of digits after the decimal point and they can seem infinitesimally small. (e.g., 0.125% conversion rate). I’ve heard more than one marketing person describe digital response numbers as “dismal” when they first look at them. But don’t let the smallness of the numbers fool you, there’s gold in them thar hills.
Incremental improvement in these small numbers can translate into enormous value. Consider the following planning case study that we’re conducting with an innovative domestic services company. (Note: Some of this information has been simplified and obscured to improve understanding and maintain the privacy of internal company data and strategy.)
Company X provides its services on a monthly basis, and earns an average monthly revenue of $50 per customer. They use digital advertising to reach new customers, and also send out a weekly email about their solutions to existing and prospective customers. Each month, they are able to retain 80% of their customers, a lower retention rate than they’d like.
In this scenario, there are several things that Datileo looked at to measure the performance of their customer engagement strategies:
- The rate at which digital ads drove people to buy the service (ad conversion rate)
- The rate at which digital ads drove people to join the email list (email list conversion)
- The rate at which people receiving the email chose to buy the service (email conversion rate)
- The percentage of customers that chose to repurchase the service each month (retention rate)
Company X is currently spending $2,000 a month on digital advertising, which is yielding around 50,000 views. Of these 50,000 views, 0.1% of viewers are electing to make a purchase. Meanwhile, 0.5% of ad viewers chose to sign up for the company’s email list. Additionally, the company sends out 4 emails a month, and of the people that receive that email, 0.1% of those people choose to make a purchase. Two percent of the people on the company’s email list unsubscribe each month. Finally, as noted above, 80% of customers return each month. (See below a chart with the details of this scenario.)
But imagine what would happen if they improved each area of their customer engagement strategy incrementally.
- What if they could raise the conversion rate on their ads by improving the targeting and the resonance of the creative?
- What if they could increase the performance of their emails and reduce the number of unsubscribes by varying the content based on subscriber interest area?
- What if they can increase customer retention by a few percentage points by identifying indicators of dissatisfaction or by providing additional value at crucial times?
How much impact might the answers to these questions have on overall revenue?
Scenario B are the high-level goals for this plan. In this Scenario, the Company has improved its advertising conversion rate from 0.1% to 0.125%, and 1% of ad viewers are now signing up for the email list, rather than 0.5%. By improving the quality of their emails and the relevance of the message, the Company increased email conversion rate from 0.1% to 0.125%, while reducing their unsubscribe rate down to 1.75% from 2%. Finally, by identifying factors that predict churn and responding quickly, the company increases retention rate from 80% to 85%. (See chart.)
These numbers by themselves may not knock a CEO’s socks off. But taken together, the overall impact of these improvements, over the course of a year would increase revenue from new sales from $128,800 to $181,650 a difference of over $50,000 and a 71% improvement in total revenue, all on the same advertising spend.
nd over time, the difference in value keeps growing. If the company can maintain this higher performance, the difference in annual revenue would grow to over $100,000 in the second year, as more people convert from the ads, read emails, and fewer people churn.
The promise of big data in business is not to ensure that everything you do works 100% of the time. We are nowhere close to having that much precision in our models, and one shouldn’t expect it. Instead, it’s to find ways to create incremental improvement over time in a variety of areas, and in that improvement is substantial value.