We all know that acquiring new customers is more expensive than persuading existing customers to buy more. It’s a marketing paradigm, and at the end of the day, quite easy to understand:
- There are additional costs to raising brand awareness (i.e. paying for online banners, SEM)
- Customers have to be incentivized to switch from another brand
- Administrative costs of setting up new accounts can cut into profit margins
When you reach out to lapsed or churned customers you’re building on the investments you’ve already made. These customers have purchased from you before; so, you already know what they like, and you can personalize marketing for them. This means that re-engaging lapsed customers is not only cheaper but also easier and more effective. Our retention marketing expert, Ed Matibag, thoroughly explained how to reach out to lapsed customers in his 3 Steps to Creating an Effective Reactivation Strategy webinar.
Here’s a quick summary of those steps with a bonus section for campaign ideas you might want to run.
Measure. Measure. Measure.
The biggest benefits of marketing to existing customers is that you already have their data. By analyzing your existing data you not only identify who is inactive and but can also use that information to personalize campaigns.
Not all customers are the same.
Some customers may buy more frequently than other customers. Some may only buy during the holidays. Others may only buy a certain type of product. Customers with separate preferences and buying behaviors should be segmented into smaller groups. Churn rates should be calculated separately for each group. Take a look at our clustering ebook for more ideas on how to segment customers.
Find the inactive customers.
Look at customers’ past activity to understand who has become inactive. Are they seasonal buyers? Did they suddenly stop replenishing? How long have they been continuously buying from you before they stopped? This step is critical to understanding if a customer has really churned or if their buying cycle is prolonged. You don’t want to send a replenishment email to a person who just bought a bag of your product 3 days ago. Although you might want to send them a welcome email.
Calculate your Churn Rate.
This is basically the number of customers lost in a certain timeframe, divided by the number of customers at the end of the timeframe. A 24 month timeframe is usually used, however, depending on your business, this can fluctuate. You want to use a 12-14 month timeframe to include seasonal buyers, but if your business has high replenishment, for example vitamins, a 3-6 month timeframe may be more suitable.
Set a Target.
To get a certain increase in revenue, what does your churn rate need to be? Look at the past results, predict a customer’s propensity to buy to create benchmarks that could be used to measure a campaign’s performance.
Analyze & Predict.
The customer’s data will help you understand their behavior which will enable you to pick the best customer profile or segment for reactivation, and maximize ROI.
Determine patterns that cause dormancy.
Being able to understand how customers were behaving before they lapsed is critical to this process. You need to take a look at the data points below to understand customer behavior.
- Recency. When was the last order? Knowing when the customer lapsed can help you customize your campaign
- Order count. Are they on their first order or fifth?
- Promotional history. What discounts did they like using?
- Product category. What type of products did they buy on the first order? Did they buy a different product type on their second order?
- Preferred sales channel. If the last time they bought was over email, then that may be the best channel to contact them through.
- Other data points. Did they recently unsubscribe? Have they visited the website recently? There are lots of other data points that are relevant to some businesses and not to others.
Create clustering models.
If you have no time to go through your data you can easily have your customer data ‘clustered’ to discover the customers who have the highest propensity buy. Clustering refers to using algorithms to filter for patterns in customer data. It relies on machine learning to go through thousands or millions of data points & discover optimal correlations.