In this article you will learn:
The differences between operational, engagement & financial metrics and how they work together
When designing and configuring an offer campaign, it is important to define your business goals as well as how you will measure success. Without these steps, it may be difficult to know where to start when evaluating your campaign’s performance. There is no single metric that can be used to measure success across every campaign, so it’s necessary to understand the different types of campaign metrics and how they interact with each other.
Basic campaign metrics fall into three key categories: Operational, Engagement, and Financial. We focus on these categories because they cover a wide spectrum of key metrics that can be used to measure the success of offer campaigns as well as provide valuable information to various arms of the business for future campaign planning. While certain teams might be more interested in one category over another based on their role in the organization, it’s important to understand all three areas and how they influence each other.
Operational metrics are used to monitor the time and resources spent on creating and launching campaigns and should be used to measure success when your goal is to be more operationally efficient. These metrics are generally easy to track and measure if you know the details of the offers you sent to each customer.
A few examples:
- Campaigns Launched - number of distinct offer campaigns launched over a given time period
- Offers Sent - total number of offers sent as a part of these campaigns
- Unique Offer Combinations - unique combinations of offer parameters (e.g. “Spend $100 get 25 points”, “Spend $100 get 30 points”, and “Spend $150 get 30 points” are three unique offer combinations)
- Time-to-Build - the time spent to configure and launch an offer
These metrics can provide valuable insight into your operational efficiency - especially when evaluated both over time and in relation to each other. For example, monitoring Offers Sent in relation to Time-to-Build gives you a sense of how much time you’re investing on a per offer basis and how that’s trending over time. When process improvements are made, you will likely see the Time-to-Build decrease even as you send more offers. Measuring Offers Sent per Unique Offer Combinations gives you insight into how many customers, on average, are receiving the same offer - when moving from static to personalized offers you may see this number decrease dramatically as more unique offer combinations are introduced.
Engagement metrics - often referred to as Funnel metrics - are used to monitor offer awareness, appeal, and difficulty. These should be used to measure success when your goal is to drive more engagement with your loyalty program or a specific product (e.g. “download the app” or “update your profile”). As long as you can track a customer’s engagement and progress through an offer, these metrics are simple to calculate and monitor.
Engagement metrics may include:
- Deliverability or Send Success Rate - for email and SMS channels, the number of offers successfully sent / number of offers created. This is particularly helpful to track if you have a high email bounce rate
- Open Rate - the number of offers viewed / number of offers successfully sent. To successfully measure this, it’s important to implement data tracking for each channel (e.g. how is an offer view defined and tracked for app vs email)
- Activation Rate - for private or gated offers, this is the number of offers activated / the number of offers viewed. It’s also referred to as “Opt-in Rate” or “Registration Rate”
- Progress Rate - the number of offers a customer has progressed on / the number of offers activated. This metric is relevant for cumulative or multi-step offers.
- Completion Rate - the number of offers completed / the number of offers activated
These metrics can provide valuable insights into offer appeal and difficulty. Top of funnel metrics like Open Rate and Activation Rate can be leveraged together to determine how effectively offers are being surfaced and how attractive they are to the customer. Metrics like Progress and Completion Rates can provide insight into how difficult an offer is.
Financial metrics are used to monitor the impact of an offer on a customer’s purchasing behavior. These should be used to measure success when your business goal is to drive more customers to purchase, increase basket size, increase purchase frequency, etc. Most of these metrics can be calculated using just your customer-level transaction data for offer recipients.
Financial metrics may include:
- Reward Costs - the dollar value of points awarded via the campaign
- Net Revenue per Customer - avg revenue, less incentive costs, generated by offer recipients
- Conversion Rate - the percent of offer recipients making a purchase during the campaign period
- Transaction Frequency - transactions per purchaser during the campaign period
- AOV - revenue / transactions for offer recipients during the campaign
- Net Revenue per Reward Cost - revenue generated by offer recipients per dollar awarded during the campaign
While Net Revenue per Customer is a good summary metric to monitor, the additional metrics can provide valuable insight into what aspects of purchasing behavior your campaign is influencing. For example, if your goal is to get more customers to purchase - perhaps via low spend offers - you may expect to see an increase in Conversion Rate but a decrease in AOV that could ultimately result in flat Net Revenue per Customer. When designing a campaign it’s important to understand your goals and define success. In that example, flat Net Revenue per Customer may be an acceptable outcome as long as Conversion Rate increases.
In order to draw more specific conclusions or to better compare to other campaigns, you may also find value in further slicing your financial metrics by offer engagement (e.g. just customers who activated their offers) or by attributable revenue (e.g. if your offer was for in-store purchases only, limit your analysis to in-store revenue only).
Interaction of Operational, Engagement and Financials
While each individual set of metrics above provides valuable information around a campaign’s performance, looking at them in conjunction with each other adds important context.
Just a few examples:
- A campaign with low spend requirements and very rich rewards might look as though it underperformed from a financial perspective, but engagement might be very high as the offer seems appealing and obtainable to a large audience.
- A campaign that shows little financial impact may have low open rates, suggesting that too few customers are aware of the offer to drive change in the metrics
- A campaign that shows no major impact from an engagement or financial perspective but has very low operational cost may still be considered a win
If You Don’t Have All the Data...
You may not have the ability to track all components of the metrics outlined above, but there is still value in measuring what you can and tweaking your calculations as needed. For example, if you cannot measure your Open Rate but can measure Activations, then you can calculate Activation Rate as Activations / Offers Sent rather than Activations / Offers Viewed and still get valuable insight into how your customers are engaging. It’s important to identify where you have gaps or inaccuracies in your data so you can understand which metrics may need extra context or special consideration.
As you can see, these three categories cover a very broad range of metrics that will enable you to evaluate success on a wide variety of business goals. However, no single metric will allow you to evaluate every campaign, and it’s likely that no one metric will tell the full story of how your campaign impacted customer behavior. While each category might be of particular interest to a single team within your organization, it’s important to work cross-functionally to define a single business goal for a campaign, determine the metrics you will use to measure success of that campaign, and understand potential impacts on other metrics.