Custora 4.0: Your complete “Retention Marketing Lab”

Our latest suite of upgrades includes one of the most frequent requests we’ve heard over the last 6 months: One-Time Campaigns.

Now, in addition to testing and automating trigger-based lifecycle marketing campaigns, it’s just as easy to test virtually any idea— online or offline— within a unique customer segment on a one-time basis.

Throughout the design and development process we worked with Revolve Clothing to gather valuable input and feedback. They used the tool to evaluate a holiday catalogue campaign that was sent to various high-value customer segments. Now Revolve has insight into how different customer segments respond to different catalog versions and promotions, and this knowledge will help shape Revolve’s mailer strategy in upcoming seasons.

Within the new tool, you can select any customer segment — using our segment builder or by uploading a list. Zoom in on high-value customers who first ordered something in your Pants department. Pick a group of customers who live in the Northwest. Choose a group of customers who just made their 5th purchase last week.

Then, once you have your desired customer group, Custora helps you set up a marketing experiment so you can measure how each of your marketing ideas impacts the bottom line.  Custora ensures you establish the proper control groups and runs all the A/B statistical analysis on your campaign.

Beyond the One-Time Campaign tool, we’ve also made refinements to how we display Lifecycle Trigger Marketing results, and overall performance should be zippier across the board.

We’re looking forward seeing how all our customers use our new marketing lab.  It’s one more step in our quest to help brands can easily and effectively test marketing ideas that resonate with their customers and drive results that have an impact on the bottom line.

Our ears are always perked for new feature suggestions. Keep them coming.

-Team Custora.


Customer Segmentation in Retail: free online class, from basic to Bayesian

Customer segmentation has been a hot topic with our clients over the past few months: where to start, how to identify segments, and how to apply segmentation to deliver more relevant marketing experiences.

Many tools enable marketing teams to import a variety of “custom attributes” for each user that can be used for customer segmentation. For example, an email provider might enable the team to upload segmentation fields for attributes such as gender, age, spend to date, and more. However, deciding which fields to include is a difficult challenge. The goal of segmentation is to deliver more meaningful experiences to customers, yet there are an almost infinite number of segmentation approaches a firm can take.

This class will be use-case driven. We will introduce common retail marketing challenges, from driving the first repeat purchase to winning back customers who have faded away. For these situations, we’ll discuss techniques that range from simple demographic segmentation to more advanced forms of behavioral segmentation.

Thursday, 2-3pm EST

The agenda is as follows:

1. An introduction to e-commerce segmentation

What is segmentation and why it matters.

2. Segmentation strategy

What defines a “good” or “bad” segment, and common mistakes to avoid.

3. Discovering segments

Techniques and best practices to identify segments.

4. Techniques and case studies

A range of demographic and behavioral segmentation approaches, from the simple to the scientific: pros, cons, and use cases.

5. Q+A

The class will be held from 2-3p EST on Thursday, Feb 28th.



Custora (YC W11) online class: An intro to Customer Lifetime Value

The most common question that online retailers ask us at Custora is, “How can I increase my Customer Lifetime Value.” And not far behind is, “So what exactly is CLV?”

This makes a lot of sense. After all, CLV is by far the most important metric that online retailers should be measuring. Yet despite it’s apparent simplicity — it’s literally just the average amount of profit generated from each user over their lifetime as a customer — there is a lot of hidden complexity.

That is why we’ve decided to host an online class this Thursday, Jan. 31th, at 2PM EST. This class will be a basic introduction to Customer Lifetime Value, along with the opportunity afterward to ask the founders questions about your specific business.

The class will be about one hour long, and will cover the following:

  1. A Brief CLV Primer

    Customer Lifetime Value explained, and why it’s the single most important metric for online retailers.

  2. Measuring CLV

    We’ll discuss the pros and cons of various methods — with a special focus on the benefits of the new Bayesian statistical approach developed by academics at The Wharton School and Columbia.

  3. Predictive vs. Historical CLV

    Why you can likely spend more to acquire each new customer and still increase profitability.

  4. Customer Segmentation by Source

    Know exactly what to spend to acquire each new customer from Google, Facebook, Groupon, etc.

  5. Case Studies

    Learn how retailers like Fab and Etsy have used CLV analysis to their advantage.

Afterward there will be a Q&A with the opportunity to ask questions specific to your business.

For anyone at a startup (or larger e-commerce company) that sells stuff online Lifetime Value is  invaluable to understand, so we’re excited to give this a try. If you’re able to join us, please fill out the form below to RSVP:

We’ll send you an email before the event with instructions for how to join.

Be careful how you average – a retail example

Companies often want to track things like the size of first orders and the size of repeat orders. For example, you might put a plan in place to increase the amount your customers spend per order—that’s one way to grow revenue. As a result, you might monitor the average order value of repeat orders (often described as AOV). Also, you might inspect the AOV of different types of customers to learn how different customer segments interact with your business.

But, as simple as AOV sounds, there is a common mistake that people sometimes make. There are a few different ways to calculate AOV, and, as usual, it can be dangerous for companies to just throw everything together and find the average. The important thing to keep in mind here is that there’s a subtle (but important!) difference between “average repeat order size” and “the average of each customer’s repeat order size.”

Here’s a simplified example to illustrate the distinction. Imagine you have two customers:

Customer A makes 2 repeat orders, each for $10.
Customer B makes 10 repeat orders, each for $20.

If we want to determine the average repeat order size across all repeat orders, regardless of customer, we’d divide $220 (the total spent on repeat orders between the two customers) by 12 (the total number of repeat orders) and get $18.33.

But what if what we want to know is the average repeat order size for a given customer? That’s a different story. In the example above, our answer would be $15 (One customer averages $10 and the other averages $20).

So why does it matter which method you use? On the surface, the difference may seem inconsequential, but consider another example. Let’s say the marketing team at Socktown Sampleshop is working to increase revenue with their existing customers. To get started, they want to understand how their customers are currently behaving.

Socktown knows that customers spend $40, on average, on their first order. They follow the first approach above – calculating the AOV across all repeat orders, regardless of customer – and learn the AOV on repeat orders is $50. This looks promising, and the team might conclude that customers are spending a healthy amount on their repeat visits.

However, when they normalize things, and first take each user’s average—then average that value—they see the AOV per customer is $30. This paints a very different picture! So why would this be the case? This is where things get interesting.

One possibility is that Socktown customers who order often tend to have increased repeat purchase sizes – just as we saw in the first example above. These two factors (frequent repeat orders and increased size per order) actually “weigh up” the pool of repeat orders, which gives us the first, and higher, value. However, that higher number obscures the important fact that majority of Socktown customers are in fact spending less on their repeat orders.

By focusing on per customer statistics instead of overall average statistics, the Socktown team learns some interesting and perhaps counterintuitive insights about their customers. First, customers who purchase frequently also purchase more per order. A sensible next step would be to drill in deeper with analysis on these customers. Which acquisition sources attract these customers, and what types of products do they buy? Second, a large percentage of the population has a relatively low average purchase size. The team might begin to brainstorm strategies to encourage these low-AOV customers to increase their shopping cart basket size.  More importantly, the team also can run marketing experiments targeted towards these low-AOV customers to learn which strategies resonate.


If your retail marketing team is interested in these forms of customer-level analytics and targeted marketing experiments, give Custora a try!

Every customer has a story. Make the most of it.

Infographic: How different are mobile customers?

It’s no secret that mobile commerce is exploding.  As a result, many of our customers have been using Custora to understand the shopping behavior of their mobile customers.  Looking beyond aggregate metrics, we’ve been surprised to see just how much “the mobile shopper” varies across clients and verticals.  We put together the following infographic to highlight some of the stories we’ve seen (click to view in full size):

If you’re curious to understand the customer lifetime value and ordering behavior of your mobile shoppers, let us know!