Image was taken from http://bit.ly/1RyMgDjMost of metrics that used to assess customer acquisition and business success are derived from SaaS metrics. These metrics are quite simple and have clear goal: validate core business assumptions by talking to people in target market. With “reverse engineering principle”, we can also use these metrics to make early predictions of how business will run (but not addressed in this article). Two main entities of these metrics are customer and revenue. Yep, customer who subscribed to SaaS and revenue from recurring payments and subscription billing. What am I trying to say is these metrics are designed for subscription business model and used by marketing and sales program, not loyalty program.
What if we want to assess the success of loyalty program in business that use direct sales model?
For this purpose, we must “hacking” the existing metrics. First of all, we need to make basic assumptions:
Loyalty program must concern about retention rate. Due to direct sales, we can assume retention as repeated purchasing in a specific time period. Take a time period when most buyers usually made repeated purchasing.
Loyalty effect must be localised from influence of marketing/promotion activities.
So, how can we do this?
Pick targeted buyers that ever made a purchase in the previous period.
Use utm (urchin tracking module):
Campaign
Medium
Source
Term
Content
Example: http://loyalty.example.com/elitemember?utm_campaign=loyaltyq32015&utm_medium=email
For example, during Q3 2015, loyalty team launched loyalty program that spend $10,000 as total cost. This is for 1000 targeted buyers. At the end of Q3 2015, there were 700 buyers that made purchasing again (with $60,000 of revenue) and 50 new buyers (with 2,000 of revenue) who knew this loyalty program from 1000 targeted buyers.
For this case, we can develop formulas:
LAC (Loyalty Acquisition Cost)
2. LLTV (Loyalty Lifetime Value)
3. LLTV/LAC ratio
Or
Healthy indicator is 3.
4. TLLTV (Total Loyalty Lifetime Value)
5. Loyalty Rate
Maximum value is 100%.
6. Spread Rate
7. Idle Rate
8. Loyalty Traction Effectiveness
Maximum > 100%. We can also breakdown this metric into some tiers. Let’s say from 750 buyers that made purchasing, there were:
buyers with large and often purchasing (b1): 100
buyers with small and often purchasing (b2): 300
buyers with large and rarely purchasing (b3): 150
buyers with small and rarely purchasing (b4): 200
and LTE b2 = 30%, LTE b3 = 15%, and LTE b4 = 20%.
9. Loyalty Cost Effectiveness
Maximum > 100%. We can also breakdown this metric into some tiers, just like point 7 above.
And we can visualize interaction between entities:
The influence of TLLTV
2. The influence of LAC
3. The influence of LLTV
4. The influence of cohort
References:
Visualizing the Interactions Between CAC, Churn and LTV, Jason Cohen, 2014
Customer Loyalty Programme Metrics: How to Measure Success, Neil Davey, 2014
Customer Loyalty Programs: Best Practices, David Robinson, 2011
image url:
https://cdn-images-1.medium.com/max/800/1*oJZ9G2H7QW-nv7N8LTZNxw.png