The analytics space is getting more crowded by the day, with new startups, products, and tools being announced regularly. If you’re a business executive or manager and are hoping to implement an analytics strategy, the choices can be overwhelming. Many tools purport to be easy to use, to have no integration requirements, and to give you answers to every question you have about your customers and the segments they’re in. While this might be the case, consider the following five rarely-considered points when choosing a tool — not only will they save you grief, but ideally, they’ll help you implement a best-in-class strategy.

1. Ensure that customer support is available daily. Your business doesn’t have time to stall while waiting on a customer service request. Ensure that the company you are buying from is available constantly or has a “maximum response time” guarantee. When you do not understand a chart or recommendation, it is crucial you get your conundrums addressed.
2. Clarify integration responsibilities. Companies often require you to pay additional fees for custom integrations, or refuse to integrate your data unless you use their supported platforms. Before making any decisions about a platform, ensure it supports your existing databases or commerce tools. Some companies (like ours!) will do the integration at no additional cost.
3. Make sure it’s end-to-end. Most companies are looking to buy an analytics tool to solve a specific need — be it discovering cross-sell opportunities, clarifying costs, or improving e-mail conversions. Whatever your goal, strive to find a tool that actually helps you achieve the goal rather than simply providing the analysis.
4. Check for a scripting language. As you become well-versed in analytics, your questions will become significantly more complex and oftentimes will be beyond the purview of the out-of-the-box dashboards most tools provide. Ensure there is a scripting language powering the tool you buy, and ideally, a customer support team that can write queries for you.
5. Be comfortable! Some sales teams will specifically try to confuse you or intimidate you with complex and fancy statistics. While such tools can be powerful, any tool that seems complex or intimidating is likely to remain unused once you actually purchase it. If anything, take the time to learn it before you buy; the sales team should understand and support you in this.

Have other tips for those buying tools? Let us know!

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