As the cannabis industry matures, it will necessarily look to the best practices and lessons learned from other similar verticals in other industries. As it relates to wholesale or retail sales, the most sophisticated marketing and sales teams will want to calculate the customer acquisition cost, recency, frequency, and monetary scores, and ultimately the lifetime values of all their customers.
These formulas and calculations are used to maximize revenues and profits by focusing limited resources on acquiring the most cost-effective and valuable customers.
Customer acquisition cost (CAC) incorporates all the money spent to attract new customers
Customer acquisition cost (CAC) is the total amount of money spent on acquiring a new customer. The first step to calculating CAC is understanding how the customer purchased your item in the first place.
For instance, did the customer find and purchase your item via online ads, email, social media feed, another platform, or some combination of all these acquisition channels? Which of these platforms lead to the highest engagement and purchases?
In the cannabis industry, it can be hard to track how customers first found their way to your store, but that doesn’t mean you can’t try! For new customers, you can informally ask how they found you and mark that in your POS. If you run advertisements online or in print, assign unique coupon codes so that you can attribute sales to campaigns to define your CAC for new customers and marketing ROI for returning customers.
It is also important to account that customers use multiple platforms before making a purchase. If your customers are placing reservations or orders through your website, track their click path into and through your online ecosystem. Install Google Analytics (it’s free and so easy!) to understand if they first find your website through email and then return through social media, or if they come through a menu site like Weedmaps or Leafly.
Use attribution models to understand your most effective customer acquisition channels
To compare the effectiveness of each marketing channel (i.e. social media, online ads, referral sites), some companies employ techniques including last touch attribution, distributed attribution, and decaying attribution that give different weights to each channel based on when and how many touches that customer had with each channel. Last touch attribution accounts for only the last channel that brought the customer to your site, distributed attribution gives equal weight to each channel, and decaying attribution gives increasing weight to the channels that get closer to the time of purchase.
Inside Google Analytics, you can even create conversion events (for online purchases) and utilize the built-in attribution models to gain a holistic perspective of the value of each acquisition channel.
Another simpler way of comparing different customer acquisition channels is calculating how much each platform costs you per week, per engagement, or per purchase. For online ads, this could be your cost per mille (CPM) which is the cost for a thousand impressions, the cost per click (CPC) which is the amount you spent for every click on an ad, or the cost per purchase (CPP) which is the total amount spent on ads for each purchase from those ads. For emails or social media, you could consider the average time spent writing content and how much you are paying employees for this content.
In total, customer acquisition cost is calculated by dividing all the costs spent on the customers by the number of customers acquired in the period when the money was spent. In it’s simplest form, this formula looks like:
Customer acquisition cost (for any channel) = [Total $$ spent] / [Total customers acquired]
It’s easiest to start simple with just your direct spend, and as you get more sophisticated you can start incorporating the cost of your staff and their time as well as the opportunity cost of the discounts and deals you offer.
Recency, frequency, and monetary (RFM) scores quickly and easily quantify the value of any given customer
Outside of finding the best platform, companies should also focus on targeting the right customers instead of the largest number of potential customers. In the long run, finding a few loyal, high-spend customers is a more sustainable business model than investing tons of resources attracting one-off deal hunters.
One method that sophisticated wholesale and retail companies outside the cannabis industry use for comparing customers is with a recency, frequency, and monetary (RFM) analysis. Using RFM scorecards, customers are ranked on a 1-5 scale (5 being the highest) for each letter.
For example, a customer that purchased an item in the last week but who tends to spend below average would score high on the recency (R) but low on monetary (M).
A customer with an RFM score of 445 is in much more valuable to your company than a customer with a 323 RFM score. From here you can start segmenting along high-value and low-value customers to identify qualities or characteristics that enable you to target more high-value prospects. What types of marketing and sales campaigns drive high RFM customers to your site? What types of products are they purchasing? What can you do to inspire high RFM customers to stay loyal and keep coming back to your store or your account?
Monitoring the reviews of your most valuable customers can also give you a better idea of what they or dislike about your products or your store experience. If these customers consistently like a specific cannabis strain or value fast customer service then you should focus on stocking those items or training staff to improve in these areas.
That said, lower RFM customers are still valued customers, and their needs and desires cannot be ignored. Ultimately, the most successful companies are the ones who know how to allocate the right types of marketing and resources to maintain a healthy balance of different types of customers.
Customer lifetime value (LTV) takes into account past purchases to predict total value for all future purchases
Customer lifetime value (LTV) is a key metric for any business and measures the projected revenue that a customer will generate over their lifetime. At a basic level, LTV starts with a customer’s past purchase and adds on a prediction of all future purchases throughout their lifetime buying from your company or store. Ideally, this calculation is done with net profit numbers to understand the total contribution to the store.
First off, lifetime value allows companies to measure how long it will take to recoup the investment of acquiring that consumer. On a very basic level, if your CAC is larger than your LTV, you won’t be in business for very long because you’re bleeding money! On the other hand, if your LTV is much larger than your CAC, you can continue scaling your marketing and sales efforts and your business will simply continue to grow and prosper.
Lifetime value can be considered from multiple angles. For example, a company may be interested in an individual customer’s revenue per day or average purchases per week since their first purchase.
How to calculate lifetime value (LTV) for your customers
At its core, LTV quantifies how much money has been generated from an individual customer since they made their first purchase. Companies also need to account for the customer lifetime profit (LTP), which is the difference between the revenue from a customer’s sale and the cost of creating (or wholesale buying) the product in that customer’s basket. Below is a method for calculating customer lifetime value.
Customer LTV = [monthly $$ profit] x [# months remaining in lifecycle] – [initial CAC]
Forecasting customer lifetime value gives your business a better idea of their future buying behavior. When building a customer lifetime value model, it is important to account for the customer’s:
- typical lifecycle = how much time does a typical customer like this one purchase from your company or store before they move on?
- average purchase rate = how often does that customer purchase in a typical week / month / quarter / year (time window depends on how you build your model)
- average order value = how much does that customer spend in a typical purchase order or an average basket at your store
Further, you might consider dividing your customer base into different segments or cohorts for comparing lifetime value. These groups could be based on customers that recently made their first purchase or customers that joined through a particular ad campaign or are from a certain geographical location.
With any forecasting model, it is important to frequently re-evaluate your customer lifetime calculations and compare the predicted results against actual results. We typically recommend reviewing your data on a quarterly basis. If your calculations are consistently off then it is time to reevaluate your calculations and model.
Companies that calculate their customer’s CAC, RFM, and LTV will stay three steps ahead of their competition
The lifetime value of your customers can narrow your team’s focus and prioritize efforts across the organization from new products to marketing budgets to forecasting revenue. To build a diligent and thoughtful LTV model, you’ll likely want to incorporate other calculations such as the cost to acquire that customer as well as their RFM scorecard.
You know as well as we do that most businesses are shooting from the hip and wasting time, money and energy on advertising that doesn’t work or doesn’t drive the right customers. Why not get a leg up and use the tools and best practices from other wholesalers and retailers in other industries? You and your customers will be happy you did!
Do you have a clear idea of your current challenges and opportunities? Do you need help calculating your CAC or RFM and setting up an LTV model? We always love to hear from you on our contact page and are happy to give you an unbiased perspective on who will best serve your needs. If you’d like to chat about your specific business and needs, feel free to schedule a free data consultation.
We firmly believe that a rising tide lifts all boats, and if we can help you (and our data peers) succeed in this rapidly growing market, it is our honor and pleasure to do so.