For each customer lifecycle segment, Smart Insight calculates a set of metrics that enable you to understand and profile your segments, and monitors how these key metrics change over time.

On this page you can find a detailed explanation of these Smart Metrics, to help you interpret the Smart Insight screens more effectively.

Contents

What are Smart Metrics?

Smart Metrics are ultra-focused and condensed analytics that drive decisions to maximize customer value.

Your customers are your assets, and to maximize the value of your assets you first need to know how much they are worth, then to know how they react to your actions (or lack of actions) and finally the impact this has on your bottom line. Then you can make decisions and allocate your marketing budget and resources in the areas which will bring the most benefit to your business. Smart Metrics help you make these decisions.

In other words, Smart Metrics analyze the past activity of your contacts to help you predict their future behavior.

They provide you with key metrics around:

  • Conversion rates
  • Predictive revenue
  • Purchase patterns
  • Engagement patterns

Each of these are tailored for the optimization of your retention automation.

Smart Metrics Explained

Smart Metrics can be divided into the following groups:

  • Conversion rates – These give you an at-a-glance overview of your success with each lifecycle stage.
  • Historical revenue – Past purchase data is used to provide the up-to-date average spending figures.
  • Predictive revenue – Past purchase data is used to predict future revenue figures.
  • Purchase patterns – general – Past purchase behavior is used to provide the most likely current and predicted items to be purchased.

Conversion rates

The contribution that can be attributed to Emarsys campaigns is shown as an extra metric in these charts, in green.

Note: The actual numbers of days used in the explanation below are just examples; these are determined during setup when you establish your RFM parameters.

  • Conversion rate

smart-insight-conversion-rate

This measurement describes how many of your leads (contacts who registered for more than one day, but didn’t make a purchase yet) you manage to convert to first-time buyers before they become cold leads. This is re-calculated every day to represent your current performance as the percentage of contacts who registered in the last 180 days and have since converted to customers by making their first purchase. Please note that new customers who register and make their first purchase on the same day are treated as direct first-time buyers and will not be counted in the lead conversion figures.

  • Re-purchase rate

smart-insight-re-purchase-rate

This measurement describes how many of your first-time buyers (contacts who bought only once) you manage to convert to become active buyers (contacts who have bought more than once) before they become defecting buyers. This is re-calculated every day to represent your current performance as the percentage of contacts who converted to first-time buyers in the last 90 days and have since become active by making a second purchase.

  • Retention rate

smart-insight-retention-rate

This measurement describes how many of your active customers (contacts who bought more than once) you manage to keep as active (meaning they buy again before they become defecting buyers). This is re-calculated every day to represent your current performance as the percentage of active customers who made a purchase exactly 90 days ago and have since made another purchase.

  • Win-back rate (defecting)

smart-insight-win-back-rate-defecting

This measurement describes how many of your defecting customers (contacts who didn’t buy recently according to your RFM definition) you manage to convert to active (meaning they buy again before they become inactive buyers). This is re-calculated every day to represent your current performance as the percentage of customers who defected in the last 275 days and have since become active again by making a new purchase.

  • Win-back rate (inactive)

smart-insight-win-back-rate-inactive

This measurement describes how many of your inactive customers (contacts who didn’t buy for a long period according to your RFM definition) you manage to convert to active (meaning they buy again). This is re-calculated every day to represent your current performance as the percentage of customers who became inactive in the last 275 days and have since become active again by making a new purchase.

Historical revenue

  • Average historical spend

    For each contact we calculate how much they have spent in their entire history. This metric is then calculated as an average for all the contacts in each lifecycle stage at the current time.

    Smart Insight also looks into the real revenue figures over the past 30 days to provide figures so that you can compare the performance of the different lifecycle stages.

  • Revenue from current purchases

    This figure sums the revenue generated from all purchases made in the last 30 days by people that were in this lifecycle stage when they made the purchase. For example, for defecting customers this would be the total revenue from the win-back purchases made by defecting customers as they returned to being active customers.

  • Each 1% in conversion brought

    This figure provides a real monetary value for each percentage point of the conversion rate for this lifecycle stage. This is an aid to put these conversion numbers in to a better business context. It is calculated by simply dividing the revenue in the last 30 days by the current conversion rate.

    When the Best revenue potential indicator is shown, it means that out of all the lifecycle stages, this one gave the highest return per percentage point.

Predictive revenue

Smart Insight looks at the historical average spending figures for customers in each lifecycle stage and uses these to give an average value to each customer in each stage.

Examples

  • Leads – if 10% of leads convert within 180 days and spend an average of €30 converting, the value of each lead is calculated as:
    • (10 x 30) / 100 = €3
  • First-time buyers – if 15% of first-time buyers make a second purchase and spend an average of €40 doing it, the value of each first-time buyer is calculated as:
    • (15 x 40) / 100 = €6
  • Active buyers – if 45% of active buyers make repeat purchases within 90 days and spend an average of €50, the value of each active buyer is calculated as:
    • (45 x 50) / 100 = €22.50
  • Defecting customers – if 25% of defecting customers (no repeat purchase in 90 days) become active again and spend an average of €50 doing it, the value of defecting customers is calculated as:
    • (25 x 50) / 100 = €12.50
  • Inactive customers – if 10% of inactive customers (no repeat purchase in 275 days) become active again and spend an average of €50 doing it, the value of inactive customers is calculated as:
    • (10 x 50) / 100 = €5

Predicted revenues are then calculated by matching the likelihood of a contact to convert in each stage with the values those conversions would bring. This takes into account not only the initial conversion, but also the average revenue of all subsequent repeat purchases and conversions, giving a predicted lifetime value.

  • Predicted average future spend

    For each contact we calculate how much they are likely to spend in the future, based on their historical spend level, the average value of contacts in their lifecycle stage, the conversion rate of that stage, and how much people like them have spent when converting in the past. This is calculated for each contact individually and then averaged out.

  • Average lifetime spend

    For each contact we add up both their individual historical spend and their predicted future spend. This metric is then calculated as an average for all the contacts that are in each lifecycle stage right now.

    Smart Insight also looks into the real revenue figures over the past 30 days to provide figures that can be used to compare the performance of different lifecycle stages.

  • Predicted future revenue increase

    This is how much revenue you might expect to earn from contacts who have converted to this lifecycle stage in the past 30 days. It is calculated by summing the difference in the Predicted average future spend for each one of these contacts as they moved from one lifecycle stage to another.

  • Each 1% in conversion will bring

    This figure predicts what each percentage point of the conversion rate will bring your business in future revenue. It helps you to judge how much time and money it is worth investing to increase the conversion rate by each percentage point.

    The figure is calculated by simply dividing the future revenue increase in the last 30 days by the current conversion rate.

Purchase patterns – general

These metrics dig deeper into the purchases themselves, focusing on the context and circumstances of the purchase, and appear in all lifecycle stages.

  • Number of purchases in lifetime

    This metric shows how many purchases a customer in this stage has made in their lifetime. This metric is calculated both as a mean (average) and as a median (mid-ranking value) for all the contacts that are in each lifecycle stage right now.

  • From registration to most recent purchase

    This metric shows the number of days from registration to the most recent purchase for a contact in this lifecycle stage. It is calculated both as a mean and as a median for all the contacts that are in each lifecycle stage right now.

  • Average spend

    Located in the main row of the Dashboard, this metric shows the average value of orders made in the last 30 days by customers in each lifecycle stage. For example, in Defecting Customers it shows the average order value of defecting customers when they come back to become active customers. The value includes all the items bought in each order.

  • Mostly bought

    Located in the main row of the Dashboard, this metric shows the most common product category to be purchased in the last 30 days by customers in each lifecycle stage. For example, in Defecting Customers it shows the most popular product category purchased by defecting customers when they come back to become active customers.

Purchase patterns – per lifecycle stage

The following metrics apply only to leads.

  • From registration to first purchase

    This metric shows the number of days it takes a lead from registering to making the first purchase. This does not include Instant first-time buyers who registered and converted on the same day. This metric is calculated both as an average and as a median for all the contacts that were ever leads (except the instant first-time buyers).

The following metrics apply only to first-time buyers.

  • From first to second purchase

    This metric shows the number of days it takes a first-time buyer from making the first to the second purchase. This metric is calculated both as an average and as a median for all the contacts that were ever first-time buyers.

  • Instant first-time buyers

    This metric shows the percentage of first-time buyers that became first-time buyers on the same day that they registered. In our terms these people were never leads. This metric is calculated as the percentage of current first-time buyers who became first-time buyers on the same day as registration.

The following metrics apply only to active customers.

  • From purchase to repurchase

    This metric shows the number of days it takes an active customer to make a purchase following their previous purchase. This metric is calculated both as an mean and as a median for all the purchases that were ever made by active customers.

The following metrics apply only to defecting customers.

  • From defecting to win-back

    This metric shows the number of days that lapse between a customer defecting and then becoming active again by making another purchase. This metric is calculated both as an mean and as a median for all the win-back purchases of all the contacts that were ever defecting customers and returned to being active.

  • Win-back rate for first-time buyers

    This metric shows the win-back rate for those defecting customers that are actually defecting First-time buyers. This metric is calculated in the same way as the main win-back rate (i.e. the % of customers that became defecting in the last X days and since then became active again), but only for the defecting customers that only ever made one purchase before.

  • Win-back rate for active customers

    This metric is similar to the one above, but instead shows the win-back rate for those defecting customers that are actually defecting Active customers. This metric is calculated in the same way, but only for the defecting customers that made more than one purchase before.

  • First-time buyers

    This metric shows how many of your defecting customers right now have made only one purchase before.

  • Active customers

    This metric shows how many of your defecting customers right now have made more than one purchase before.

The following metrics apply only to inactive customers.

  • From inactive to win-back

    This metric shows the number of days that lapse between a customer becoming inactive and then becoming active again by making another purchase. This metric is calculated both as an average and as a median for all the win-back purchases of all the contacts that were ever Inactive customers and returned to being active.

  • Win-back rate for first-time buyers

    This metric shows the win-back rate for those inactive customers that are actually defecting First-time buyers. This metric is calculated in the same way as the main win-back rate (i.e. the % of customers that became inactive in the last X days and since then became active again), but only for the inactive customers that only ever made one purchase before.

  • Win-back rate for active customers

    This metric is similar to the one above, but instead shows the win-back rate for those inactive customers that are actually "inactive Active customers". This metric is calculated in the same way, but only for the inactive customers that made more than one purchase before.

  • First-time buyers

    This metric shows how many of your inactive customers right now have made only one purchase before.

  • Active customers

    This metric shows how many of your inactive customers right now have made more than one purchase before.