Optimize for customer lifetime value
1. Add user ID tracking to your Google Analytics tracking code in order to create a unique identifier for each website visitor.
2. Create three lists of your customers: one ordered by Recency, one ordered by Frequency and one ordered by Monetary.
Create a customer segment in Google Analytics with the condition Transactions per Session > 0. This will isolate sessions where a transaction happened. Recency: Create a custom report with Sessions and Transactions as your metrics. Use Days Since Last Session as your primary dimension. Load the report and apply your Transactions > 0 segment. Now add your User ID custom dimension as the secondary dimension. Set the date range to cover a large period of time, like the past 12 months. Export all the data to Excel (XLSX) to get your list of customers by Recency. If you have too much data and Google Analytics’s default export feature does not let you export everything you need in one go, consider using the Google Analytics Spreadsheet Add-on to bypass the limitation. Frequency: Duplicate the report to get your Recency data on the previous step. Replace Sessions with Transactions as your metric. Replace Days Since Last Session in your primary dimension with your User ID custom dimension. Load the report. Use the same date range as you did in the previous step. Export all the data to Excel (XLSX). Monetary: Duplicate the report to get your Frequency list on the previous step. Replace the metric “Transactions” with “Revenue”. Load the report. Use the same date range as you did in the previous step. Export all the data to Excel (XLSX) to get your list of customers by Monetary utility.
3. Create a user Data & RFM Scores spreadsheet and plug in the customer data to get their RFM scores.
Take the data you got in the previous step and compile it in a single spreadsheet. Your customer’s RFM score will be a sequence of three numbers, representing Recency, Frequency, and Monetary respectively, and between 1 and 3. For example, 2-2-1, 3-1-2, 1-1-3. To get the first score (R), order your customers by Recency. Now divide them into three equally sized cohorts: one of the cohorts will contain 1/3 of customers who bought most recently (top cohort). Another cohort will contain 1/3 of customers who bought least recently (bottom cohort). The remaining 1/3 will be your middle cohort. Give each cohort a Recency score between 1 and 3, where 1 is the best score given to all customers in your top cohort, 2 is the middle score given to all in the middle cohort, and 3 is the worst score given to all in the bottom cohort. To get the second score (F), order your customers by Frequency. Divide them into three equally sized cohorts as you did for Recency. Give them a score between 1 and 3. After that, do the same to find the third score: Monetary (M). After calculating all three scores each customer had on Recency, Frequency, and Monetary, concatenate the three numbers to get the RFM score of each customer.
4. Calculate the LTV of each customer.
Calculate your business’ average gross margin. Find the business’ average customer lifetime span. This is the time it takes on average for a customer to churn. Calculate your business’ average CAC (client acquisition cost). This is how much you have to spend in marketing campaigns and sales initiatives to get a customer. For each customer in your list use the following formula to calculate their LTV: (Customer’s Avg. Purchase Value * Business’ Avg. Gross Margin * Customer’s Avg. Purchase Frequency * Business’ Avg. Customer Lifetime Span) - Business’ Average CAC
5. Analyze the RFM cohorts of the top 10% LTV customers to identify general practices you can apply to your marketing and sales strategies.
Recency: Check how much time on average has passed since the top 10% of customers made their last purchase. This is your window of time from a user’s last purchase in which you can re-engage them and get them to purchase again. Frequency: Check how often the top 10% of customers buy. Use this information to assess how often you should impact customers with marketing and sales initiatives. Monetary: Check how much revenue the top 10% of customers made. Investigate the types of product that these customers most commonly purchased. Use those products to create campaigns and offers. Increase customer LTV by analyzing your customers’ buying behavior and offering promotions that match their existing behavior.
6. Order your customer list by LTV, then create a segment from the top 10% of customers in Google Analytics and analyze the most profitable customer behavior patterns.
Use the User IDs from the customers who belong in the top 10% in terms of LTV to create a segment in Google Analytics that will enable you to track the user journeys and purchasing behavior of your most profitable customers. Apply the segment. Check the following aspects: How they navigate – Behavior Flow and Goal Flow reports. The amount of time they spend on each funnel page. The products they buy. Which device they use to access. Their screen resolution.
7. Conduct an internal examination of your business goals and decide on short term vs. long term profitability.
To optimize for short term profit, focus on initiatives to improve the Recency and Frequency of customers who have scores of 2 or 3 in these parameters. To optimize for long term profit, focus on initiatives that improve the scores of parameters similar to the ones you found on the top 10% LTV customers.
8. Create test campaigns and optimize your sales funnel using RFM data.
To optimize for Recency: target customers with a Recency score of 2 or 3 by creating activation campaigns with limited timeframes, using elements of scarcity and urgency in your offers. Create landing pages with pop-ups and other micro-conversion elements that highlight the importance of purchasing now. To optimize for Frequency: offer benefits for customers who purchase often (e.g. customer loyalty programs). Push campaigns for customers with a Frequency score of 2 or 3 offering items that complement the ones bought before, accessories, small add-ons, replacement parts, “customers who bought this also bought…”. Optimization of the Monetary parameter is an indirect consequence of optimizing Recency and Frequency.