Retailers pay a great deal of attention to metrics such as same stores sales, sales per square foot, sales per employee, gross and net profit, average transaction value and foot fall. Most of these are result metrics that measure how well a retailer performs.
However, there are certain “lead metrics” that may help predict the performance of retailer ahead of time. There are several of them in the retail industry and I plan to cover three such metrics in this blog
Refers to customers that have bought from a retailer through two or more channels in the last 12 months. Why is this metric important? In general, most of you may be aware that multi-channel customers are more valuable. But, do you know by how much?
For one of our clients, we found that the life time value of a multi-channel customer is five times more than that of a single-channel customer.
You can greatly benefit from knowing the number and value of your multi-channel customers, their relative value compared to single-channel customers, ratio of multi-channel to single-channel customers.
We see that the retailers with higher percent of multi-channel customers tend to:
(a). Be more profitable, and
(b). Have less churn
These benefits hold true across retailer types and across geographies. I encourage you to pay close attention to multi-channel customers and related metrics. Some examples are:
- Total number of multi-channel customers
- Ratio of multi-channel to the overall number of customers
- Total spend of multi-channel vs single channel customers
- Average spend of multi-channel vs single channel customers
- Total frequency of multi-channel vs single channel customers
- Average frequency/month of multi-channel vs single channel customers
- Total number of categories shopped by multi-channel vs single channel customers.
You may find that multi-channel customers are indeed more valuable. With this insight, you can develop strategies and tactics to increase the number of multi-channel customers by migrating single channel customers to become multi-channel customers.
For one of our retail clients, we identified the customers that migrated from single channel buyers to multi-channel buyers. We used this data to develop a predictive scoring model to score all single channel customers on their propensity to migrate. Using the scoring model, the retailer successfully targeted and migrated the customers with high propensity scores.
The post-study conducted three months after the migration revealed that the customers that converted to multi-channel buyers, not only increased in value, but also, churned less.
You may be tracking the total sales $ by category. But, are you tracking the number of multi- category buyers? Why is this important?
Here again, out experience with retailers show that there is a strong correlation between the numbers of categories that a customer buys to
(a). Profitability of that customer (More profitable)
(b). Sensitivity to Promotions (Less sensitive)
(c). Loyalty (more loyal)
Track the number of categories that each customer buys. Using this as a starting point, segment the customers based on number and type of categories. You can combine the demographic variables from your own card program or from 3rd party data sources to score customers based on their likelihood to migrate to
(a). Specific types of categories
(b). More number of categories
By using this approach, we were able to successfully migrate customers to more categories and increase same stores by 5%.
Web traffic by location compared to foot fall in a store at that location:
The third and final metric is a combo metric – web traffic plotted against foot fall. Basically, your ability to combine web traffic by store location to actual foot fall at that location.
Typically, I see that the foot fall is tracked and measured by the marketing or category managers and web traffic is tracked and reported by someone responsible for website. But, rarely are these two metrics combined to see the correlations, even when the same person tracks and reports these metrics.
Are you combining your online data with offline data? Are you able to see the correlation between website traffic and the store traffic (measured by transactions)? If not, start immediately. You can get online data from Google Analytics and merge with your POS data to see the patterns. For one of our clients, we used online traffic as a predictor of transactions within a store to
- Determine time and days of promotion leading to 10% lift in sales $, and
- Optimize staffing leading to 5% decrease in staff costs.
There are several other metrics that you can combine to gain deep insights into your retail business performance and use them to increase revenue. I’ll try to cover these in another blog.
So, if you are a multi-channel retailer with a chain of stores, you will greatly benefit from tracking, monitoring and using these three metrics. These metrics, when tracked and used correctly, can not only be a lead indicator of your stores’ performance, but also, help you develop proactive strategies and tactics to increase sales.