Can 80/20 be used to improve eCommerce metrics?
I am a big fan of Perry Marshall and his application of 80/20 Pareto principle to sales and marketing. Perry Marshall opens our eyes to look the power of 80/20. In this part 1 of a multi part series, we will explore how 80/20 principles apply to eCommerce.
Why is 80/20 powerful?
We all have heard of the 80/20 or Pareto principle – 80% of the worlds’ wealth is with 20% of the people or from a business perspective, 80% of your revenue comes from 20% of your customers. But 80/20 is more powerful than that for business due to the following reasons.
- Recursive or exponential application – the 80/20 applies inside the 20% as well. So you may find that 80% of your business comes from 20% of your customers, but also that 80% of the 80% of the business comes from 20% of the 20% customers – i.e. 64% of your business may come from 4% of your customers. And if you have a large enough customer base, this can be applied inside the 4% as well, recursively.
- 80/20 can be used in a predictive forward looking manner. If you were to identify the 4% in the above example and maximize returns from them or look for more of them you will increase your business.
How can 80/20 apply in eCommerce?
- In this part we show you some insights into eCommerce, specifically finding and working with your most valuable customers
- Select a metric – revenue / profit / items sold / # of checkouts.
- Query your sales data for this metric grouping by customer over the last year or lifetime of your business
- Sort your customers by the the metric.
- Import this data into excel with the columns revenue, customer_email.
- You can now plot the data in excel on a line graph – simply select the revenue.
If you are using Magento, the following query on your database will give you
select sum(base_subtotal) as revenue, customer_email from sales_flat_order where
created_at > (curdate() - interval 1 year) group by customer_email order by revenue desc
The 80/20 power curve (ref : Perry Marshall)
We ran this on a sample real life store data but due to the long tail, we restricted to a minimum value of purchase for a customer to quality for the study. We also scaled proportionally the value number. We ran the 80/20 recursively 3 times and here are the graphs we got. Based on Perry Marshall’s power curve.
Adding more info to your study
- Add 2 new columns one that gives the cumulative metric and another that gives the % of total this cumulative metric.
- You can easily see what % of your revenue is due to your top n customers.
- You may be able to see bands or patterns of value customers.
What can be done with this data?
The top 4%
- might represent a major part of your metric. Treat these customers differently and see how you can sell more to them, but try to develop a personal relationship with them – a one on one. Create special coupons from them if they like to use coupons, analyse to see if they buy on anniversaries and connect with them, etc. If they like premium, sell them more premium, if on the other hand they like value for money, sell them more of that, individually
- Ask them for reference – they might easily turn into advocates for your site
- Is it possible for you to feature them on your site? Do you have a blog? Can you write up feature articles for your best customers?
The top 20%
- The next band needs to be treated a bit differently – the group will be larger so you cannot market to them with a personal relationship. However, consider putting them into a sales funnel. Cohort them into groups depending on categories they prefer, if they like coupons, etc. and create special newsletters for each cohort. Study the results of each newsletter and see if the sales increase