One of the most common approaches to tracking customer groups over time is through the use of cohort analysis. The goal behind such analysis is to segment your customer base into time-based groups (e.g., cohorts based on when they became customers) and track how they spend, how loyal they are, and so on.

While there are numerous approaches to cohort analysis, the most common is to track average spend, revenue, and order size across cohorts. What is particularly beneficial about such an analysis is that every business has similar goals: if your business is growing, you are likely trying to increase your average spend over time, while also increasing total revenue.

A very common strategy in email marketing is using images in the body of the email, or even turning the entire email into an image. Doing so serves a few purposes: the email content becomes less likely to fail depending on the browser being used, and data collection becomes easier — tracking email opens requires enabling images. These facts, and others, have led many companies to adopt image-based email marketing campaigns. Below, we present a case study related to using images to encourage open rates — and whether or not it is a good idea.

Context and A/B Test

If you run an online store, you likely know about the importance of analytics and tracking your web visitors. Unfortunately, most tools today provide a high level, aggregated view of your visitors. These tools tell you how many individuals visited each page, but it is often impossible to tell what each individual did and how this relates to their purchases. Today, we show you how you can view your customers’ purchases and email activities alongside website visits in the Canopy Labs customer profiles.

One Customer…

One of the best parts of the Canopy Labs platform is the ease in which you can build custom reports and dashboards. To make this even easier, we have created a short tutorial video to walk you through the process! Click below to watch the video tutorial.

A very common approach to building and understanding customer segments is through the use of clustering techniques such as Principal Component Analysis (PCA). These clustering techniques will analyze your customer data and see if customers tend to cluster by certain features, or combinations of features. Through such an approach, a marketer can use clusters to define specific segments. For example, running a cluster analysis could end up showing two clusters: one with customers who have high values for variables related to “engagement” (e.g., emails, comments, etc.) while another could be a cluster with lower values for engagement variables, but mid-sized value for purchase-related variables (e.g., number of purchases, number of products, etc.). In this case, the marketer can conclude that two segments, “Engaged Leads”, and “Slightly Engaged Purchasers”, exist within the customer base.

The goal of this tutorial is not to discuss PCA in detail, but rather to show a basic example of testing whether clusters of engaged or purchas-oriented customers exist. For those interested in running in-depth clustering algorithms, numerous tutorials exist. Instead, we show how you can perform such analysis in the Canopy Labs R Console.

Most businesses understand that creating segmented email campaigns can be a very effective way to improve conversion and generate sales. However, it’s not always obvious how to go about choosing the right segments and developing messaging that resonates with this group. Today we have compiled a list of 12 segmented e-mail campaign ideas that our customers have found effective.

If you’re an ecommerce business or selling any sort of products to consumers, you’ve likely come across product recommenders. Anyone making a purchase from bigger online brands like Amazon or NetFlix has likely been recommended books to buy or videos to watch. Indeed, many marketers and sales consultants have written about the effectiveness of product recommenders increasing sales.

What do product recommenders do?

Most companies that Canopy Labs work with sell their wares online. More generally, most major retailers do so as well, especially as they invest in digital content and sales processes. If you’re such a retailer, than you already know that tracking your online traffic is crucial to understanding the effectiveness and eventual success of your website. Indeed, every online business we meet obsessively tracks their Google Analytics dashboard, and they’re in good company: over 12 million websites use Google Analytics.

The Situation

A fundamental decision email marketers face is choosing what offer to include in a campaign. Will offering a % discount entice customers to buy, or will a small gift card be more effective? More importantly, which will drive greater sales performance?  Today, we highlight the recent experience of one of our e-commerce customers to demonstrate how using A/B testing can shed light on what kinds of offers deliver the best results. 

Setting up the campaign

Whether you own a sports team, run a ski resort, or sell school supplies, you’ve likely come across some seasonality in your sales and revenue. Indeed, even seemingly noncyclical businesses, like fast food or clothing, experience some sort of seasonality.

Seasonal sales are risky because, in the most extreme cases, you have only a few days to make your sales numbers. Imagine you’re a retailer preparing for Christmas — success or failure in mid-December might mean success or failure for the entire year. With that in mind, an analytic tool that gives you an idea of what to expect can be a crucial resource during the seasonal sales cycle. Here, we present an example of how one can apply predictive analytics to forecast seasonal sales months in advance and take pre-emptive action.