Let’s face it: time is money.
And if you’re not measuring the time between your customer orders, you’re leaving money on the table.
Here’s why TBO – Time Between Orders – can become the game-changing metric, helping you unlock powerful insights into your customers’ purchasing habits, optimize inventory management, and supercharge your marketing efforts.
Today’s blog post takes a deep dive into TBO: from its definition and calculation to practical applications and limitations.
Whether you’re a business veteran or just getting started, get ready to harness the power of TBO and take your business to the next level.
Let’s ride!
Time Between Orders – Definition
Time Between Orders (TBO) refers to the time that passes between two consecutive orders placed by a customer from the same brand.
Companies use TBO to understand customer behavior, customer distribution (in terms of RFM), purchase frequency, and overall annual demand patterns for a product or service.
For example, suppose you acquire a new customer who orders on April 1st, then returns for a repeat purchase on April 15th. In that case, the TBO for that particular customer would be 14 days.
Other terms for this metric are Time Between Purchases (TBP) or Repeat Purchase Interval (RPI).
However, the terms are used synonymously to refer to the very idea of measuring the time between two consecutive purchases. The specific terminology used may vary depending on the industry or context in which it is being used.
Brands highly value TBO to gain insights into customers’ purchase patterns and adjust their marketing and inventory strategies accordingly.
At the same time, TBO is often combined with other data sources (such as Customer Lifetime Value (CLV) and Average Order Value – AOV) to provide a more complex understanding of customer behavior.
This combination can inform data-driven decisions for improving customer retention and increasing revenue.
How to Calculate the Time Between Orders
So, how do you calculate the Time Between Orders? Is it a simple process, or would you need a data analyst to calculate this metric?
To calculate the TBO for your business, you would first need to track the dates of each order placed by a particular customer. These dates are visible in your Shopify App or any other CRM.
After tracking the dates, you would calculate the average time difference between each order to determine the TBO.
Here’s how an online retailer will calculate their TBO:
Suppose you run an online fashion store, and one of your repeat customers, Jane, has made three purchases from your store in the past six months.
Jane’s order dates and order numbers are as follows:
Order #1001: March 1st, 2023
Order #1017: April 15th, 2023
Order #1032: May 8th, 2023
To calculate Jane’s TBO, you would first find the time difference between the first two orders, then between her second and third orders.
Here are the calculations:
TBO 1 = April 15th, 2023 – March 1st, 2023 = 45 days
TBO 2 = May 8th, 2023 – April 15th, 2023 = 23 days
So, Jane’s TBO is 45 days for the first two orders and 23 days for the last two orders.
Knowing Jane’s TBO gives insights into her purchase behavior and helps you tailor your marketing and promotional efforts accordingly.
For example, you might send her personalized product recommendations or promotions to encourage her to make another purchase within a specific time frame.
Or you can ask for feedback when you know she has time to use the product and assess its effectiveness to get valuable product information.
This way, you avoid becoming a spammy retailer, flooding Jane’s inbox with offers before the natural moment when she would’ve ordered, demanding feedback before she even got the product, and overall annoying her with irrelevant messaging.
Speaking of calculating the TBO – let’s look at other Time Between Orders formulas you can use:
- Calculating the time between buy and sell orders
This metric will vary depending on the trading strategy and market conditions.
Some traders may hold onto a position for just a few minutes, while others may hold a position for weeks or even months.
- Calculating the average time between orders.
To calculate the average time between orders, you would divide the total time elapsed between orders by dividing it by the number of orders.
For example, if you had 10 orders and the total time elapsed between those orders was 100 hours, the average time between orders would be 10 hours.
The average time between orders
=
Total time elapsed between orders / Number of orders
- Calculating the expected time between orders.
To calculate the expected time between orders, you would use statistical analysis based on historical data to determine the average time between orders.
This can help you forecast future trading activity and plan accordingly.
- Calculating optimal time between orders.
The optimal time between orders would be determined by analyzing historical data and identifying patterns in market behavior that align with the trading strategy.
The Importance of Time Between Orders
Keeping track of the TBO metric is a critical piece of the puzzle every savvy business owner should pay attention to.
Not only will it help you boost customer happiness and streamline inventory management, but it also unlocks valuable insights into the buying patterns of your target audience.
Let’s look at each facet of your business that can be impacted by the TBO – when tracked regularly and used tactically:
- Customer retention
Retaining your existing customers can increase profitability by up to 25%. Moreover, some companies even reported an astounding 95% profit increase simply by keeping customers they had already acquired.
Seeing how customer acquisition is becoming increasingly challenging (for some companies, it even costs them money instead of bringing in profits), your retention strategy needs to be impeccable.
Looking at how the TBO evolves for your customers will help you quickly identify customers at risk of churning.
In the RFM segmentation, we’re talking about the “about to dump you segment” – people who used to bring in significant and regular revenue are now about to stop being your customers.
The point of tracking TBO and identifying the About to Dump You segments is becoming proactive instead of reactive to customer churn.
Targeting customers at risk of churning with personalized marketing campaigns and promotions helps you improve customer retention and loyalty.
- Inventory management
There are few situations more annoying than wanting to purchase a product you really need only to find it’s out of stock. It won’t matter how much you love the brand – if you need the product, you’ll buy it elsewhere.
In fact, a frequent reason for startups going out of business is caused by inventory issues such as a stack overflow, late replenishment, or overall poor inventory management.
TBO helps brands avoid losing customers due to poor inventory management by enabling them to forecast product demand.
Anticipating customer orders helps you adjust your inventory levels, stock up on popular items, or prepare for busy shopping seasons.
With TBO, you can not only prevent stockouts but also prevent overspending on excess inventory.
Both situations can be costly for your business, so having a way to prepare better and manage your inventory more efficiently is a great asset.
- Marketing and promotions
Remember Jane, our hypothetical fashion retail customer?
We mentioned spamming and annoying her with irrelevant messages and emails.
In fact, spamming isn’t an uncommon practice – nearly 50% (45.37%, to be precise) of all emails sent worldwide are flagged as spam, leading to people unsubscribing and even altogether abandoning a brand.
Measuring the TBO helps you avoid becoming part of these stats by empowering you to optimize your marketing and promotional processes.
When you track the Time Between Orders, you can spot customers most likely to make another purchase within a specific time frame and target them with relevant offers and incentives.
Consequently, you’re encouraging repeat purchases and boosting your revenue without annoying customers with too many emails.
- Customer segmentation
At Omniconvert, we’re die-hard fans of RFM segmentation. We believe in it so strongly that we’ve even integrated it into our very own CRM software, Reveal.
Analyzing each customer’s transactions’ RFM (Recency, Frequency, and Monetary value) empowers you to tailor your marketing efforts to specific segments and improve your overall marketing ROI.
The “R” in RFM refers to Recency, which measures the time since a customer’s last purchase – where the Time Between Orders comes into play.
TBO plays an essential role in RFM, as it helps you determine which customers are due for a follow-up purchase and which ones might be at risk of churning.
Combining TBO with RFM metrics provides the insights you need to identify high-value customers, create targeted marketing campaigns, and ultimately drive revenue growth.
- Insights into Customer Behavior
Tracking the time elapsed between each customer’s orders reveals trends and patterns in purchase behavior, informing your marketing strategies, product offerings, and overall customer experience.
For example, a short TBO reveals a loyal customer who enjoys frequent purchases. This person could join a loyalty program and become a brand ambassador.
On the other hand, you have long TBOs, which usually signal a bargain hunter or a customer who is still threading the waters or might need more time to test products until you can win him over.
Since personalization and relevancy are crucial to creating loyal customers, the TBO metric will deliver the needed insights to make informed decisions about how you treat each customer according to purchase frequency.
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As you can see, the Time Between Orders metric opens the door to maximizing revenue and taking your business to the next level.
So, measuring it is necessary if you want to earn benefits such as improved customer retention, more revenue, and optimized operations.
How to Use Time Between Orders
So, you’re sold on TBO – excellent! What’s next?
Well, our dear padawan, here’s a roadmap (or framework) for using the Time Between Orders metric and leveraging it to earn all benefits discussed above.
- Identify your customer segments
Use the TBO metric to segment your customers into groups based on purchase behavior.
Even if you’re not using the RFM segmentation as a whole, the TBO is still an excellent place to start.
TBO-based segmentation might include customers with a short TBO (i.e., purchase frequently), those with a long TBO (i.e., purchase infrequently), and those with a medium TBO.
Remember that each customer has a unique place within your organization, and it’s your responsibility to ensure they are appropriately categorized.
- Develop targeted marketing campaigns
You should use the customer segments identified in step one to develop targeted marketing campaigns.
For example, you should approach customers with a short TBO with promotions or loyalty programs to encourage repeat purchases. At the same time, you could use reactivation campaigns for customers with a long TBO to entice them back to your store.
Consider one specific approach for each segment, united under the TBO metric, which rules them all.
- Optimize pricing strategies
You shouldn’t leave your price points to chance or allow competition and market trends to dictate your prices.
Instead, use TBO data to optimize your pricing strategies.
For example, you could send discounts to customers with a long TBO to encourage them to purchase. Since customers with a short TBO might not need as much incentive to make a repeat purchase, you can adapt your prices and align them with the reality of your customer base.
- Improve customer experience
Now that you have your customer segments and pricing strategies, it’s time to think about retention – and the key to retention is experience.
Your TBO data will highlight areas where you can improve customer experience.
For example, suppose customers with a long TBO report a poor experience on your website. In that case, you should improve the UX or add new features to the website (such as AI assistance) to make the shopping experience more enjoyable.
- Personalize product recommendations
Another critical component in customer retention is represented by product recommendations.
We must return to the spamming warning – no one loves a flooded inbox.
Use the TBO metric to personalize customer product recommendations based on their purchase history.
For example, you can recommend new (or complementary) products to customers with short TBO who don’t need so much convincing.
For customers with medium TBO, you can send recommendations on using the product or even promotions for other popular products inside their demographic.
- Test and refine strategies
Evidently, you shouldn’t take anything you read at face value. While we shared common sense ideas that worked for our clients, you should always consider the particularities of your products and customer base.
Regularly test and refine your strategies based to optimize your results. This might include A/B testing marketing campaigns, adjusting pricing strategies, or improving the overall customer experience.
Limitations of the Time Between Orders Metric
Speaking of not taking things at face value – we would be remiss if we didn’t take a second to consider the limitations of the TBO metric.
One of these limitations refers to seasonal factors (such as Christmas, when people tend to purchase more, versus the summer when orders drop). TBO might not be reliable during certain times of the year, so always consider the larger context in which you measure it.
At the same time, TBO is useless for analyzing one-time purchases (for luxury items or non-cyclical products), as it requires at least two orders to calculate the time between them.
Moreover, the Time Between Orders metric can be skewed by outliers. People whose behaviors are too random to fit into a pattern.
Outliers might be customers who make large purchases infrequently or those who make multiple small purchases in a short period.
Another limitation of TBO is the date on which it relies.
TBO calculations use complete and accurate data, so if there are gaps in your data, the TBO results may be inaccurate.
Last, TBO isn’t providing enough context around customers’ behaviors. With this metric alone, you won’t know anything about order quantity, monetary value, or customer satisfaction. For example, a long TBO for a particular customer may be due to a specific event not reflected in the data.
This is why we’re advising you to combine this quantitative data with qualitative insights obtained from talking to your customers (through interviews, focus groups, or even an open-ended customer or developer survey.)
Despite these limitations, TBO can still be a valuable tool to gain insights into customer behavior and optimize their operations.
You’ll need to combine with other metrics and strategies to gain a more comprehensive view of customers’ purchasing behavior and make data-driven decisions to drive growth and profitability.
Wrap-Up
As you can see, TBO is a simple yet powerful metric, providing valuable insights into customer behavior, informing inventory management decisions, and driving revenue growth.
At the same time, remember that TBO is just one piece of the puzzle. It would be best if you combined it with other customer data (quantitative and qualitative) to make truly informed decisions.
So if you’re striding to stay ahead of the competition and maximize your business’s potential, start tracking TBO today and reap the rewards.
Happy tracking!
Frequently Asked Questions about Time Between Orders
What is time between customer orders?
Time between customer orders refers to the length of time that elapses between a customer’s first order and their following orders.
How do you calculate time between orders?
To calculate the time between orders, you would subtract the time of the previous order from the time of the current order.
For example, if a customer made an order on January 1st at 10:00 AM, and then made another order on January 3rd at 2:00 PM, the time between orders would be 2 days and 4 hours.
How to calculate TBO?
To calculate Time Between Orders (TBO), you would use the same method as calculating time between orders – by subtracting the time of the previous order from the time of the current order.
TBO can be used to analyze customer behavior and identify trends, such as the average length of time between orders or the time of day when customers are most likely to make a purchase.
