Segment your Data Like a Pro

Good segmentation is what turns a superficial statistic into a business metric that drives decisions. Want to know who your most valuable customers are? What your most valuable marketing channels are? Which of your products are moving faster and why? To get to any of these answers, you have to start by segmenting your data.

In this blog post, I’m going to share some critical segments that we often recommend to our customers. I’ll also go into detail on what questions these segments can help you answer.

User Segments

User segments help you understand who your users are and how they behave.

  • Age / Birth Year: How old are your users? How old are your most active users? It usually makes sense to bucket the values into ranges for more effective analysis.
  • Gender: Do men and women engage with your website differently?
  • Address: Where do your users come from? Should you focus your marketing efforts on a particular region? Have your recent advertising campaigns performed as expected in your target regions?
  • Customer acquisition source: Do you know what marketing channel your users come from? Did they click on an ad or find you via search? Segmenting your data by user acquisition source is the first step in optimizing your new customer acquisition. Step two is to spend more money in what’s working and kill what’s not.
  • Registration device: Did users register via your mobile app or your website? iOS or Android? Is your mobile user base big enough to allocate more resources to develop your mobile product?
  • Referred by: Who are your top influencers? How many users were directly referred by others?
  • Industry: If you’re a B2B business, in which industries do your users work? Which trade organizations are worth joining?
  • Survey responses: If you perform customer surveys, use the responses as segments for a deeper level of profiling. You can ask questions that complement what you already know about your users or confirm your guesses.
  • First order amount and product category: Is there a correlation between a user’s first order and future purchasing patterns?

Orders / Events Segments

Order and event segments help with analyzing user behavior and engagement over time.

  • Billing / Shipping Address: Where do most of your orders come from? Is there a difference between billing and shipping addresses?
  • Status: How many of your orders failed to complete? What is the ratio of pending orders in the past 7 days?
  • Customer acquisition source: Beyond tracking user acquisition data at a user level, you can also track the it on an order or event level. A user that registered via one source may very well continue to access your site via other sources.
  • Device: Are the number of mobile orders increasing? How much of your revenue is currently generated via mobile purchases?
  • Fulfillment Center: Which one of your fulfillment centers is generating the most revenue? If you’re analyzing the difference between order time and shipping time, which fulfillment center is most responsive?
  • Delivery Carrier: Which is the most popular carrier? Which carrier has the least number of returned items?
  • Discount / Coupon Codes: Are your promotions actually generating extra business? How many extra items did your customers buy in addition to the item on sale? How do coupons affect your average order value? What’s your average margin on discounted vs. non-discounted items?
  • Satisfaction / Rating: How satisfied are your customers with their orders? Are your customers likely to refer business to you?

Product Segments

Product segments help you make merchandising decisions.

  • Merchant / Brand: Is one specific brand selling faster than the rest? Which brands are under-performing?
  • Type / Category: Do different user segments enjoy different types of products? Which product categories generate the most repeat business?
  • Discount / Coupon Codes: Are promotions hurting sales of non-discounted products? How do coupons affect the perceived value of your products?
  • Social activity: Is there a correlation between the buzz generated on social media and the quantity sold for a product?
  • Size / Variant: What is the ratio of inventory that you need of each variant? Which variants can be sold at discount rates?

If you’re interested in merchandising, check out a blog post where I explore how to use product segments to drive repeat business.

Establish Customer Profiles

Segmentation experts may want to move beyond one-dimensional slices and begin establishing real customer profiles. For example, people between ages of 13 and 24 that registered via a mobile device put in a group “Young & Mobile”. How does this group’s behavior compare to the rest of your user base?

This type of analysis is what marketers at Fortune 1000 companies do all day. Prior to the advent of cloud-based business intelligence platforms like RJMetrics, it was largely out of reach for the rest of us. Fortunately, that’s no longer the case.

If you’re an existing customer and want to improve your segmentation, just contact our support team. If you’re not a customer but want to learn how to slice and dice your data like an Iron Chef, we’d be happy to tell you more.

Lifetime Revenue Cohorts

There are a lot of different ways to look at your data in RJMetrics, and we know that interpretation and understanding are just as important as calculation and visualization. So, I’m writing a series of blog posts where I will do a deep dive into some of our analyses and visualizations.

The first in the series is the lifetime revenue cohort analysis.

What does lifetime revenue cohort analysis mean?

This chart shows the cumulative spending per user for a period of time after they are acquired. Cohorts of users are split up by their acquisition month.

For example, the orange line above shows the average for users who were acquired in November 2011. The first data point means that in their first month, users who were acquired in November spent an average of about $200. The second data point means that by the end of their second month, these users had spent an average of about $240. Their average spending in month two was approximately $40 (240 – 200).

The different lines represent different cohorts of users. The green represents the users that were acquired in December, and the blue is users that were acquired in October.

Why is this important?

This kind of cohort analysis can be useful for several different purposes, but the most immediate benefit is often better customer acquisition decisions.

Many companies limit their marketing spend to channels that yield profitability on a customer’s first purchase. These companies will pay to acquire customers through a given channel as long as that their average first purchase yields more gross margin than it costs to acquire them. The problem with this approach is that it often results in an underinvestment in growth. If your competitors are marketing based on a deeper understanding of buying behavior, they will outgrow you.

The lifetime revenue cohort analysis helps you to understand the consequences of expanding your customer acquisition spending, and it provides an easy way to convey this to the rest of your team. If future customers behave like existing customers, then acquiring customers for a higher CPA will result in a predictable payback period. Depending on the cash position of the business, you can define what payback period you are comfortable with, find the relevant spot on the chart, and spend accordingly.

Additionally, you can use this analysis to see if you are getting better at onboarding, engaging, and generating revenue from the users you acquire.  For example, this cohort analysis is a great way to see if a free shipping promotion for new users resulted in repeat buyers or one time purchasers that never come back.

How will this vary for different business models?

For most businesses, the lifetime revenue cohort analysis chart will show a large amount of spending in the initial period and then increase more slowly over time.

That initial spike is due to the fact that customers are more likely to make their first purchase soon after they are acquired than at any other time. In cases where the acquisition event itself is a purchase, 100% of customers make a purchase in their first period. In cases where registration can happen before purchases, this effect is less drastic. As an example, Groupon would likely have a much lower initial jump than Amazon, because many of the people who sign up for Groupon don’t make a purchase right away.

Unless there are a high number of refunds, this chart will slope up and to the right after the initial jump. The rate of growth tends to decrease over time because customers are usually most active when they first sign up. This causes the average to drop because the number of people in the cohort stays constant regardless of how many come back to buy more.

In subscription businesses, the slope will decay less aggressively than in businesses where people make one-off purchases. Occasionally, a subscription business will actually have a slope that increases over time. It is rare to see this, but it is a great signal for the business when it happens. This does not mean that there are zero churning customers, but rather that upgrades for customers that stay more than make up for the customers that leave.

How is this calculated?

There are two simple inputs to this calculation: how many members are in the cohort (which never changes), and how much revenue those members generated in the given period.

To determine the members in the cohort, we count the number of users who were acquired in the period in question. An acquisition can be a first purchase, account creation, newsletter sign up, or some other event.

The revenue calculation is a bit more complicated.  We want to sum revenue for orders that were placed by members of this cohort and took place within a fixed time period from their acquisition date (ie the first three months).

Finally, we divide the revenue by the number of members in the cohort for each time period in the chart and add this value cumulatively over time.

What are the variations of this chart?

There are many different kinds of useful cohort analyses.  The most common variation is filtering by user acquisition source. For example, you might want to look at this chart for customers who came from organic search, paid search, or an affiliate program.

This will help you understand if the customers from one acquisition source are more loyal or valuable than another. Thrillist’s subsidiary JackThreads used this analysis to understand that one of their most expensive acquisition sources was actually its most profitable. After learning this, Thrillist shifted their marketing budget to the more expensive acquisition source and accelerated their growth.

Another way to look at the data is with an incremental, rather than cumulative, data perspective.  This shows the incremental amount that an average user spends in each month after they are acquired.  This is useful for forecasting the amount of repeat purchases you will get from existing users.

We can look at this with other things besides revenue as well.  Some examples include margin as well as non financial metrics like invites, votes, or messages.

Conclusion

Lifetime spending cohorts are a powerful way of looking at at your customers’ buying behavior.  Stay tuned for more information on how to use and interpret your metrics.  

Create Contact Lists of Top Customers

For many companies, a minority of customers generate a majority of the revenue (this is sometimes referred to as the Pareto rule or the 80/20 rule). These high spenders often act as brand ambassadors, promoting your products to friends and family. Needless to say, they are an important group that you should know and understand extremely well.

In previous blog posts, we’ve discussed how to save acquisition channel data from Google Analytics (GA) and explored critical reports that you should segment by channels.

Now, we’d like to show you how to create contact lists that identify your most valuable customers in RJMetrics. You can use lists like these to send tailored communications, such as rewards and promotions, based on the attributes of these customers.

Top spenders

We can all agree that your top spenders form a very valuable customer segment. One standard RJMetrics reports is called “top spending users by LTV,” which reveals your top 1% of customers by lifetime revenue. You can easily clone or edit this report to create different top customer segments.

Here’s how this report can be customized using the RJMetrics chart wizard:

  1. Initiate the chart wizard via Charts -> Create Chart
  2. Select the “average lifetime revenue” metric. Note that you can select other metrics depending on how you’d like to sort this customer group (e.g. top customers by lifetime number of orders or average order value). Simply make sure that the metric’s time stamp corresponds to the user’s registration date.
  3. Choose a table chart by clicking the table icon on top of the preview:
          1. Untitled
  4. Now set the Time Period of analysis to your user registration period of choice, and set the Time Interval to “None”
  5. Go to the “Group by” tab at the bottom to segment by “email” and choose to limit your output data to the top X% percent or top Y categories of the dataset. Note that if you have a total of 200 customers in this registration period, the top 10% will show you the top 20 customers, whereas the top 10 categories will always show you the top 10 customers.
  6. You can also add filters to limit the customer group by demographics, acquisition channel (see next section) or any other available field under the “Filter by” tab.

Users acquired from top channels

Suppose that you discover a high-value customer group by looking at one of your marketing report, such as the “repeat orders by user sources” report shown below. You could invest more marketing dollars into one of the channels or you could pull the list of existing customers from this channel and re-activate them. After all, reminding an existing customer to buy is easier than convincing a new prospect to try.

RJMetrics makes it easy to create contact lists for different customer segments. Let’s go over an example to see how to create a contact list for customers who were acquired through a specific marketing channel.

In this example, our “Repeat orders by user sources” report (created in a previous tutorial) reveals that a large number of repeat purchases were made by users that joined in December 2012 through organic search and paid search.

Here’s how to create a contact list for these high-value customers through the RJMetrics chart wizard:

  1. Initiate the chart wizard via Charts -> Create Chart
  2. Select the “new users” metric, which counts the number of new users by registration date.
  3. Set the Time Period of analysis to the registration period of choice, and set the Time Interval to “None”. For our example, we would choose specific dates between “2011-12-01″ and “2011-12-31.”
  4. Go to the “Group by” tab to segment by “email” and choose to limit your output data to the top 100 percent of the dataset to show all users that joined during this time period.
  5. Now add filters under the “Filter by” tab to only account for repeat customers from paid search and organic search:
    • “User’s acquisition source IN paid search, organic search”; and
    • “User’s lifetime number of orders > 1″
Additional filters
In addition to the two cases we mentioned above, you can also filter your contact lists in Step 4 of the chart wizard by buying behavior with standard RJMetrics slicers such as:
  • “User’s lifetime revenue”
  • “Seconds since user was created”
  • “Seconds between first order date and user’s creation date”, which can be used to segment customers that made an order within 24 hours of registration and those that made their first order later later
Any demographic, marketing channel or calculation by which we can identify and segment a customer can be used as filter.
Reach out to your users
You can now contact this group of high-value customers by exporting the list and reach out using an email service such as Mailchimp or Hubspot.
If you need any help creating some of these reports or would like to perform even deeper analysis, simply contact us via “Help” -> “Contact Support” from your dashboard.

Driving Repeat Business

In modern e-Commerce, generating repeat business has become a key to survival. In this RJMetrics tutorial, we will teach you how to extract insights based on your customers’ first purchases. Why? Because a customer’s first interaction with you can often determine their likelihood of coming back.

The first step in analyzing these metrics is creating two new data dimensions in RJMetrics: One that identifies the first items purchased by a user (this can also be a user’s first purchase category or first event type), and a second dimension that identifies a user’s order number at the item sold level. To create new data dimensions, simply contact our support team through “Support -> Contact Support” in the dashboard sidebar: Ask our analysts to create (1) a “user’s first purchase item” dimension, and (2) join the “User’s order number” to the order item level.

Now that we have the required data dimensions, the following is a set of reports that will help you manage product offerings and boost repeat purchases. These analyses can also be reproduced for product categories.

Please note that while we are focusing on “first purchase item” for this tutorial, the same process can be applied to segment users by first order spending, quantity of items bought, coupon codes used, etc.

Top selling 1st purchase items

The “top selling 1st purchase items” chart reveals the most popular first purchase items by quantity sold.

How to create this in RJMetrics

  1. Initiate the chart wizard through Charts -> Create Chart
  2. Choose the “number of items sold” metric.
  3. Choose a table chart by clicking the table icon on top of the preview:
          1. Untitled
  4. Choose the Time Period you’d like to analyze, but DO NOT observe the metric over time by setting the Interval to “None”
  5. Go to the “Group by” tab to segment by the “product’s name” slicer.
  6. Add a filter under “Filter by” for “user’s order number = 1” to only account for 1st time orders.

Most popular 1st purchase items

The “most popular 1st purchase items” chart is another way of looking at popular first buys. Here, the number of users that purchased an item as the first purchase is revealed. This reduces the double-counting of items that were purchased in high quantities by a single user.

How to create this in RJMetrics:

  1. Create a Metric that counts the unique number of customers at the product level
    1. Go to Data -> Metrics
    2. Click to “Add New Metric” and select your items sold table (e.g. `sales_flat_order_item`, `order_item`, etc.)
    3. In the Metric Editor, give you new metric a name (e.g. Distinct buyers by products sold)
    4. Still in the Metric Editor, use the drop-down menus to perform a ‘Count Distinct Values’ on the ‘Customer ID’ column trending over the ‘Order Date’. (Note that specific field names may differ.)
    5. Under the Filter section, add the relevant filters for this Metric.
  2. Go to any dashboard and initiate the chart wizard via Charts -> Create Chart
    1. Choose the “Distinct buyers by products sold” metric that you created in (1).
    2. Choose a table chart by clicking the table icon on top of the preview:
          1. Untitled
    3. Choose the Time Period you’d like to analyze, but DO NOT observe the metric over time by setting the Interval to “None”
    4. Go to the “Group by” tab to segment by the “product name” slicer. (Note that you may also want to segment by SKU, Product Category, etc.)
    5. Add a filter under “Filter” for “User’s order number ‘Equal to’ 1″ to only account for first time orders.

LTV by users’ 1st purchase items

The “average LTV by user’s 1st purchase items” reveals the average lifetime revenue per customer by their first purchase item. In other words, it shows whether a customer generates more revenue over their lifetime based on the first purchase item.

How to create this in RJMetrics:

  1. Initiate the chart wizard via Charts -> Create Chart
  2. Choose the “average lifetime revenue” metric.
  3. Choose a table chart by clicking the table icon on top of the preview:
          1. Untitled
  4. Now set the Time Period of analysis to your user registration period of choice, and set the Time Interval to “None”
  5. Go to the “Group by” tab to segment by the “user’s 1st purchase item” slicer.
  6. Add a filter under “Filter by” for “user’s 1st purchase item IS NOT NULL” to filter out users who have not yet made a 1st purchase.

LTV cohorts where user’s 1st item is “X”

The “LTV cohorts where user’s 1st purchase item is X” chart is a cohort analysis of average lifetime revenue for customers that bought X as their first item. It allows you to analyze the LTV growth of a group of customers by their first purchase item.

How to create this using RJMetrics’s Cohort chart Editor:

  1. Initiate the chart wizard through Charts -> Create Chart
  2. Choose the “revenue” metric.
  3. Choose to “Perform Cohort Analysis”
  4. Set the Cohort Date to “User’s creation date” or “User’s first order date”. Note that this is the date used to create the cohort groups (e.g. all users that registered or made a first purchase within a time interval).
  5. Set the Interval, Time Period and Duration accordingly.
  6. Add a filter under “Filter by” for “user’s first purchase item = X” to limit the analysis to users who purchased item X as part of their first order. Note that you can also use other filter operators to limit your data (e.g. “User’s first purchase item IN pants, shirts, shoes” would include all these first purchase items in the analysis).
  7. Set the perspective to “cumulative average value per cohort member,” which will divide the metrics (i.e. cumulative revenue) by the number of people in each cohort to return average lifetime revenue per member.

* Note that you can also Save As any existing lifetime revenue charts and add other filters for acquisition source, demographics or other user variables.

AOV by customer’s top first purchase items

The “AOV by users’ 1st purchase items” chart reveals the average order revenue of customers by their first purchase item. Note that this is the AOV across all orders, not just a user’s first order.

How to create this in RJMetrics:

  1. Initiate the chart wizard through Charts -> Create Chart
  2. Choose the “average order value” metric.
  3. Choose a table chart by clicking the table icon on top of the preview:
        1. Untitled
  4. Now set the Time Period of analysis to your user registration period of choice, and set the Time Interval to “None”
  5. Go to the “Group by” tab to segment by the “user’s 1st purchase item” slicer.
  6. Add a filter under “Filter by” for “user’s 1st purchase item IS NOT NULL” to filter out users who have not yet made a 1st purchase.

User’s lifetime number of orders by first purchase items

The “average lifetime number of orders by 1st purchase item” chart reveals the average lifetime number of orders per customer segmented by their first purchase item. In other words, it shows whether a customer group makes more orders over their lifetime based on the first item they purchased.

How to create this in RJMetrics:

  1. Initiate the chart wizard through Charts -> Create Chart
  2. Choose the “average lifetime number of orders” metric.
  3. Choose a table chart by clicking the table icon on top of the preview:
      1. Untitled
  4. Now set the Time Period of analysis to your user registration period of choice, and set the Time Interval to “None”
  5. Go to the “Group by” tab to segment by the “user’s 1st purchase item” slicer, or an equivalent slicer such as product title or product type.
  6. Add a filter under “Filter by” for “user’s 1st purchase item IS NOT NULL” to filter out users who have not yet made a 1st purchase.

If you need any help creating some of these reports or would like to perform even deeper analysis, simply contact us via “Help” -> “Contact Support” from your dashboard.

Leveraging Composite Charts

In this tutorial, we will explore how RJMetrics composite charts can be used to study your your data through various ratios. RJMetrics’ composite chart feature allows you to use existing charts to create new series through calculations (e.g. “revenue” – “cost”) or overlay them on a single chart.

Please check out this quick video tutorial to get familiar with the composite chart editor.

Orders per pageview

Prerequisites:

  • a Google Analytics connection established in RJMetrics
  • “Number of orders” chart that reports the number of orders over time.
  • “Pageviews” chart from Google Analytics

This report shows the ratio of number of orders over the number of pageviews over time. The higher this number, the better your website is at converting visitors into buyers.

Here’s how to create this report:

  1. Start the composite chart editor via Add Chart -> Create Composite Chart in the dashboard menu.
  2. Using the “add inputs” drop-down menu, add the “number of orders” chart and the “pageviews” charts as sources. They will each be assigned a letter (e.g. [A] and [B]).
  3. In the “add series” section, input “[A]/[B]” as the formula, where [A] = “number of orders” and [B] = “pageviews”. Then give this series a name such as “orders over pageviews” and click “add.”
  4. Click “done creating series.”
  5. Complete step 2 of the composite chart wizard and make sure to set the “output units” as percent (%).
  6. Preview and save the chart.

Percent of first day buyers

Prerequisites:

  • “New users” chart that reports the number of new users over time

This report shows the ratio of users that made a first purchase on their registration day. The higher this number, the better your website is at converting new members into buyers on their first day.

Here’s how to create this report:

  1. Save As your existing “new users” chart and name the new copy “new users who bought in the 1st day.”
    1. Under the “Filter by” tab, add a filter for “seconds between 1st purchase and user’s creation date” <= 86400.  (Note that there are 86,400 seconds in a day.)
    2. Your new “new users who bought in the 1st day” chart now shows the number of new users that made a purchase within 24 hours of their registration.
  2. Start the composite chart editor via Add Chart -> Create Composite Chart in the dashboard menu.
    1. Using the “add inputs” drop-down menu, add the “new users” chart and the “new users who bought in the 1st day” chart as sources. They will each be assigned a letter (e.g. [A] and [B]).
    2. In the “add series” section, input “[B]/[A]” as the formula, if [A] = “new users” and [B] = “new users who bought in the 1st day”. Then give this series a name such as “percent of first day buyers” and click “add”.
    3. Click “done creating series.”
    4. Complete step 2 of the composite chart wizard and make sure to set the “output units” as percent (%).
    5. Preview and save the chart.

Number of items per order

Prerequisites:

  • “Number of items sold” chart that reports the quantity of products sold over time
  • “Number of orders” chart that reports the number of orders over time


This report shows the number of items sold per order. Retailers who are able to drive this number upward often have better cross-selling techniques and higher average order values.

Here’s how to create this report:

  1. Start the composite chart editor via Add Chart -> Create Composite Chart in the dashboard menu.
  2. Using the “add inputs” drop-down menu, add the “number of items sold” chart and the “number of orders” chart as sources. They will each be assigned a letter (e.g. [A] and [B]).
  3. In the “add series” section, input “[A]/[B]” as the formula, if [A] = “number of items sold” and [B] = “number of orders”. Then give this series a name such as “items per order” and click “add.”
  4. Click “done creating series.”
  5. Complete step 2 of the composite chart wizard.
  6. Preview and save the chart.

If you need any help creating some of these reports or would like to perform even deeper analysis, simply contact us via “Help” -> “Contact Support” from your dashboard.

Comparing Performance and Goals

Many RJMetrics users choose to enhance their dashboards by comparing actual company performance to predefined goals. For example, the report below reveals how a company’s actual revenue is comparing to their goals.

In this tutorial, we will show you how to upload information about your company’s goals and build such a dashboard.

Step 1 – Set your goals

The first step is to set your performance goals for a particular metric: e.g. revenue, repeat orders, pageviews or any other kind of event.

To do this, you can simply create a new spreadsheet document (i.e. using a spreadsheet software such as Excel or Google Doc) with at least three columns: “ID”, “date” and “goal”.

The “ID” column is simply a unique primary key that identifies each row in your data table as unique – this can simply start with one and increase with increments of one with each row. In some cases, users choose to have the “date” column serve as the unique ID for their data. As long as you only have one data point per date, this works just fine.

The “date” column should store the date of this goal value in the following format: yyyy-mm-dd. If you are creating monthly goals, then each of your date values can be the first day of a month (e.g. 2012-01-01, 2012-02-01, etc.).

The “goal” column will store the actual value of your goal. You don’t need to assign a unit to this column as this can be handled in RJMetrics. Simply input a numerical value: e.g. 100.

Now your spreadsheet should look something like this:

Note that you can add more goal type columns for each metric you’d like to assess. For example, you can have another column for “number of orders”, “number of logins”, etc. We can then simply create individual metricss based on the same data file in RJMetrics to assess each goal.

Once you’re satisfied with your goals, save your spreadsheet as a “.csv” file.

Step 2 – Upload your goals

Now’s the time to upload your goals into RJMetrics with our file uploader. Simply follow the instructions to learn how to upload your data into RJMetrics.

Step 3 – Overlay goals vs. actuals

Once you have uploaded your goals spreadsheet, all that remains is to create a metric for the goal, create a chart and finally overlay the goals with your actual data.

To create a metric, you need to use your newly uploaded data table as source table. Then simply perform a “sum” on your “goal” column and trending over the “date” column.

Once the metric is built (e.g. “revenue goals”), we can now go onto any dashboard and create a goals chart or report. Simply select your newly created goals metric in step 1 of the chart wizard and complete the chart building process. Now you should have a goals versus time chart.

The final step will be to overlay your new goal chart with your actual data. This can be done by adding the two source charts (i.e. goal chart and actual data chart) in the composite chart builder and simply adding each chart as a series in the composite chart. Note that your source charts will need to have identical settings for step 2 of their chart settings for dates.

As each of your source charts will be assigned a letter in the composite chart builder, the formula for your new series will simply be that letter: i.e. [A] as the formula for a series, [B] as the formula for the other series.

Now, you have an RJMetrics chart that maps your actual performance against your goals. You can follow these steps to create one for each goal metric you’d like to trace and soon you’ll have a full dashboard that you can monitor as you meet your milestones!

If you need any help creating some of these reports or would like to perform even deeper analysis, simply contact us via “Support” -> “Contact Support” from your dashboard.

Acting on Marketing Data

Any data-driven marketer knows that having data at your fingertips is just the start of the battle. The real value comes when you are able to use that data to influence your decisions. Here are just a few ways that our customers are acting on the data in their RJMetrics accounts.

While the following examples can be applied to many business models, we will be focusing on e-commerce analytics.

Ping your customers at the right time

One of the standard RJMetrics reports is called “average time between customers’ orders.” This reveals the average time between historical customers’ 1st, 2nd, 3rd orders, and so on.

This report can easily be broken out by the product purchased by that customer, their geographic region, their referral source and other defining characteristics.

With this information in hand, you can easily create intelligent drip campaigns to remind customers to come back for a visit when their peers have historically been most likely to come back and buy again. Using an email service such as Hubspot or Mailchimp, you can create drip email campaigns scheduled at different intervals from a customer’s 1st purchase date. You can even customize these lists based on more specific characteristics to conduct more focused targeting.

When implementing these drip campaigns, it will be very important to monitor performance and iterate to improve the message. One of the best ways to do this is to A/B test different email content and timing.

Activate your registered users

For some businesses, not all registered users have become paying customers.  ”activating” these prospects is an extremely important and valuable process.

Our “time to 1st order cohorts” report groups users that joined or registered in a particular time period and shows you the percentage of those users that have made a 1st purchase by each month after their registration.

Typically, these curves become horizontal lines after some period of time, indicating that few additional cohort members are converting organically after that point. In other words, most users that are going to make a purchase organically have already done so. At this point, these members are highly unlikely to convert and constitute dead weight in your user base. Reaching out to them with custom promotions or specifically targeted emails is an extremely low-risk way to jump-start conversion of this population.

With RJMetrics, it is easy to identify this population of users for any time period and export that list to your email service provider.  Simply follow the steps below:

  1. Initiate the chart creation process through Charts -> Create Chart.
  2. Choose the “new users” metric.
  3. Choose a table chart by clicking the table icon on top of the preview:
        1. Untitled
  4. Now set the Time Period of analysis to your user registration period of choice, and set the Time Interval to “None”
  5. Go to the “Group by” tab to segment by “email” and choose to show the top 100 percent of the dataset.
  6. Add a filter under “Filter by” for “User’s lifetime number of orders = 0” to filter for users who have not yet made a 1st purchase.

Once we have the list of unconverted users, you can directly export it from RJMetrics and upload it to a mail service like Mailchimp or Hubspot and reach out with an offer.

Re-activate lapsed customers

Once your users have made a purchase, we want to make sure that they come back for more.

Using a similar technique to the one shown above, you can generate lists of your users that have not come back to make a repeat purchase after some amount of time. Pairing this with an “average time until second purchase” chart can give you an indication of when a customer may have “lapsed.”

Reaching out to this population of users can similarly be a low-risk way of increasing conversion and repeat purchase rates. A/B testing a few different campaigns across customer groups who have lapsed can often reveal effective marketing techniques that are specifically tuned to this population.

Good luck reaching out!

If you need any help creating some of these reports or would like to perform even deeper analysis, simply contact us via “Help” -> “Contact Support” from your dashboard.

Discover your most valuable referral sources for target marketing

If you don’t already store marketing acquisition source information in your database, take a look at my colleague Chris’ blog post on how to track user acquisition source data.

With RJMetrics, it is easy to segment your revenue and users by referral source. Source can correspond to Google Analytics’s utm fields, or your custom data fields. This will allow you to find your best performing channels. We all have limited marketing budgets, so let’s invest it wisely.

In this blog, we will explore some of the reports that will help you uncover your most valuable marketing channels.

I. The “New users by sources” report shows you the number of newly registered users over time by different acquisition sources. This allows you to track the performance of referral sources in acquiring new registered users.

How to create this report in RJMetrics’ chart wizard:

  1. Initiate the chart creation process through Charts -> Create Chart.
  2. Choose the “New users” metric or an equivalent metric that counts the number of new users over time.
  3. Now set the Time Period of analysis to your user registration period of choice, and set the Interval to monthly.
  4. Go to the “Group by” tab to segment by “user’s acquisition source” and select a few sources or all sources.
  5. You can change the chart to a column chart using the icons found on top-right of the preview.

II. The “Lifetime revenue cohorts – user source” report shows the revenue over time limited to a specific user acquisition source. This allows you to see whether users acquired from a particular source spend more with you over their lifetime than another group of users.

You can replicate this report for many user referral sources and compare them side by side to discover which source generates the highest lifetime revenue fastest.

How to create this report in RJMetrics’ cohort chart wizard:

  1. Initiate the chart creation process through Charts -> Create Chart.
  2. Choose the “Revenue” metric. (Other metrics that make sense to look at from a cohort perspective include “Number of orders”, “Distinct buyers”, or “Average order value”)
  3. Choose to “Perform Cohort Analysis” under the selected Metric.
  4. Set the Cohort Date to “User’s creation date” or “User’s first order date”. Note that this is the date used to create the cohort groups (e.g. all users that registered or made a first purchase within a time interval).
  5. Set the Interval, Time Period and Duration accordingly.
  6. Add a filter under “Filter by” for “User’s acquisition source = X” to only include users from the channel “X”.
  7. Set the perspective to “cumulative average value per cohort member,” which will divide the cumulative revenue by the number of people in each cohort to return average lifetime revenue per cohort member.

(Shortcut: You may also Save As an existing lifetime revenue cohort chart and add a filter for acquisition source in the new chart)

III. The “AOV by user sources” report shows the average order value over time segmented by user sources. This allows you to track whether users acquired from a particular source spend more per order than another group.

How to create this report in RJMetrics’ chart wizard:

  1. Initiate the chart creation process through Charts -> Create Chart.
  2. Choose the “Average order value” metric
  3. Now set the Time Period of analysis to your user registration period of choice, and set the Time Interval to monthly.
  4. Go to the “Group by” tab to segment by “user’s acquisition source” and select a few sources or all sources.
  5. You can change the chart to a column chart using the icons found on top-right of the preview.

IV. The “Revenue by user registration date and sources” report shows the total lifetime revenue over user registration time, segmented by acquisition sources. This allows you to identify whether users that registered during a particular time and from a particular source generate more or less of your overall revenue.

How to create this report in RJMetrics’ chart wizard:

  1. Initiate the chart creation process through Charts -> Create Chart.
  2. Choose the “revenue by user registration date” metric. Note that you may have to create this as a new metric by replicating the “Revenue” metric’s settings and replace the “Order date” with “User’s creation date” as time stamp.
  3. Now set the Time Period of analysis to your user registration period of choice, and set the Time Interval to monthly.
  4. Go to the “Group by” tab to segment by “user’s acquisition source” and select a few sources or all sources.
  5. You can change the chart to a column chart using the icons found on top-right of the preview.

V. The “Repeat orders by user sources” report shows the number of repeat orders over time segmented by user sources. This allows you to identify whether users from a particular source make more or less repeat purchases.

How to create this report in RJMetrics’ chart wizard:

  1. Initiate the chart creation process through Charts -> Create Chart.
  2. Choose the “Number of orders” metric.
  3. Now set the Time Period of analysis to your user registration period of choice, and set the Time Interval to monthly.
  4. Under the “Filter by” tab, add a filter for “User’s order number ‘greater than’ 1″ to only account for repeat orders. The `User’s order number` dimension calculates whether an order is the user’s first, second, third,… order.
  5. Go to the “Group by” tab to segment by “user’s acquisition source” and select a few sources or all sources.
  6. You can change the chart to a column chart using the icons found on top-right of the preview.

If you need any help creating some of these reports or would like to perform even deeper analysis, simply contact us via “Support” -> “Contact Support” from your dashboard.

How to save acquisition data from Google Analytics in your database to create awesome marketing metrics

In this post we will explain how to save Google Analytics (GA) acquisition channel information into your own database – namely the sourcemediumtermcontentcampaign, and gclid parameters that were present on a user’s first visit to your website. For an explanation of these parameters, check out the Google Analytics documentation. Then, we will explore some of the powerful marketing analyses that can be performed with this information in RJMetrics.

Why?

If you’re just looking at the default Google Analytics conversion and acquisition metrics, you aren’t getting the whole picture. While seeing the number of conversions from organic search versus paid search is interesting, what can you do with that information? Should you spend more money on paid search? That depends on the value of customers coming from that channel, which is not something Google Analytics provides. [Note: Google Analytics eCommerce Tracking does mitigate this problem by storing transaction data in GA, but this solution doesn't work for non-eCommerce sites, and certain tools like cohort analysis are not easy to do in the GA interface].

What if you want to email a follow-up deal to all customers acquired from a certain e-mail campaign? Or integrate acquisition data with your CRM system? This is impossible in GA – in fact, it is against the Terms of Service for Google Analytics to store any data that identifies an individual.  But that doesn’t mean you can’t store this data yourself.

The Method

(Special Note: If you are using Magento to power your eCommerce site, we’ve already done the work for you. Check out our free acquisition source tracker extension. Not only does it track each user’s acquisition source data, it also tracks each order’s source data.)

Google Analytics stores visitor referral information in a cookie called __utmz. After this cookie is set (by the Google Analytics tracking code), its contents will be sent with every subsequent request to your domain from that user. So in PHP, for example, you could check out the contents of $_COOKIE['__utmz'] and you would see a string that looks something like this:

100000000.12345678.1.1.utmcsr=google|utmccn=(organic)|utmcmd=organic|utmctr=rj metrics

There is clearly some acquisition source data encoded into the string, and I have done some testing to confirm that this is the visitor’s first acquisition source. Now we just need to know how to extract the data. Luckily, Justin Cutroni has previously described how this encoding works, and shared some javascript code to extract the key bits of information.

We took this code and translated it into a PHP library hosted on github.   To use the library, include a reference to ReferralGrabber.php and then call

$data = ReferralGrabber::parseGoogleCookie($_COOKIE['__utmz']);

The returned $data array will be a map of the keys source, medium, term, content, campaign, gclid and their respective values.

We recommend adding a new table to your database called, for example, user_referral, with the columns like: id INT PRIMARY KEY, user_id INT NOT NULL, source VARCHAR(255), medium VARCHAR(255), term VARCHAR(255), content VARCHAR(255), campaign VARCHAR(255), gclid VARCHAR(255). Whenever a user signs up, grab the referral information and store it to this table.

How to use this data

Now that we’re saving user acquisition source, how can we use it?

Lets suppose we are using a SQL database and have a users table with the following structure:

id email join_date acq_source acq_medium
1 john@abc.com 2012-01-24 google organic
2 jim@abc.com 2012-01-24 google cpc
3 joe@def.com 2012-01-25 direct -
4 jess@ghi.com 2012-01-26 referral techcrunch.com
5 jen@ghi.net 2012-01-30 other organic

For starters, we can count the number of users coming from each referral channel by running the following query against your database:

SELECT acq_source, COUNT(id) as user_count FROM users GROUP BY acq_source;

The result will look something like this:

acq_source user_count
google 294
direct 156
referral 55
other 16

This is interesting, but of limited use. What we would really like to know is the growth rate of these numbers over time, the amount of revenue generated by each acquisition source, a cohort analysis of users coming from each source, and the probability that a user from one of these channels will return as a customer in the future. The queries required to do these analyses are complex – which is why we built RJMetrics. Armed with this information we can determine our most profitable acquisition channels and focus our marketing time and money accordingly.

My colleague Xiao has also written a blog post detailing how to generate these and other useful marketing analyses using RJMetrics for you to check out.

Magento Reports

Magento is a great platform. It’s incredibly flexible, and it is used to power everything from early stage e-commerce sites to some of the biggest sites on the internet. However, like any platform, some areas of its functionality are stronger than others. In this post, we explore an area that often leaves users unimpressed: Magento reports.

Existing Magento Reports

The default Magento reports are very basic. The main things you can do in the Magento reporting interface are:

  • See your top 5 items, search terms or customers
  • See your most recent 5 customers, orders, or search terms
  • Plot a line chart of revenue or orders for a few different time ranges
  • Pull simple lists of orders, products, customers, and search terms

These are useful for business intelligence, but they won’t allow you to build a data driven business. To take things to the next level, you need to supplement Magento’s reporting capabilities with additional analysis.

What’s missing from Magento’s Reports?

The list of things you can do with your data is only limited by the number of questions you have. While it can be tempting to run as many analyses as you have time for, it’s important to focus on actionable metrics.

Some of the analyses and metrics that savvy ecommerce companies study are:

The tools and features that help companies study and manage such metrics include:

  • Custom dashboards
  • Flexibile visualizations
  • Different views and permission levels for various stakeholders
  • Tools to incorporate data stored in other databases, Google Analytics, or spreadsheets

Augmenting Magento Reports With SQL Queries

The first thing that most e-commerce companies try when they want to answer questions with data is to ask a member of the development team run SQL queries.

This works if there’s only one data question, if you don’t need the answer immediately, and if the business user can work with the results in Excel to get what they need. However, if you want your business to use data to drive decisions on a day to day basis, this is not a sustainable solution.

I’ve spoken with hundreds of e-commerce businesses here at RJMetrics and in my previous job in venture capital. I don’t remember ever meeting someone whose IT team had plenty of time for pulling data for business users.

Manual queries can be particularly tricky because of Magento’s entity-attribute-value data model. This allows for a lot of flexibility when building out your store, but it also makes building and maintaining analytical queries much trickier.

Enhancing Magento Reports with Hosted Business Intelligence

Here at RJMetrics, we work with many companies that are on the Magento platform, and we’ve learned a lot about the platform’s strengths and weaknesses. We’ve spent even more time working with e-commerce businesses on all different kinds of platforms to understand their business challenges and how we can help use data to solve them.

We’d love to make your data understandable and actionable. Sign up for the RJMetrics free trial today.