To deliver superior customer experience, you need to anticipate your customer’s needs and proactively approach them with the best proposal, at the best time.
As such, we compiled a step-by-step guide on how a company fully leveraged their data to tailor personalized marketing campaigns automation.
The owner of a coffee shop has been busy during the last year. Fortunately enough, she has seen an increase in both foot traffic and online orders. She has noticed that customers are frequently visiting her, so she invested in a software that allows her to keep track of consumer behaviour in order to be able to reward loyalty. As such, for 15% of the foot traffic and 45% of her online customers, she is able to analyse how often the customers visit.
Now, she has been trying various campaigns so far: back in March she tested a 5+1 free discount campaign, she tried to encourage loyalty by launching a subscription for the employees working close to her locations, and so on. She noticed her customers have different, diverse preferences, but she did not have a way to fully integrate what she learned from consumer past behaviour and seamlessly use that in her campaigns.
The most important unanswered question she had was: are there customer segments that are not properly served? Here’s how we helped her answer this.
Step 1 – Connect, Extract, Clean and Store the Data
The first step in automating your marketing campaigns is to build a robust and efficient data infrastructure. This involves setting up a secure server, a reliable database, and implementing ETL processes.
We typically use a Linux server due to its proven stability, performance, and security features. For the database, MySQL is our go-to choice, given its robustness and versatility in handling large datasets. The ETL (Extract, Transform, Load) process is then set up to pull data from various sources, clean it, and load it into the MySQL database. This creates a centralized repository of data, which is crucial for delivering personalized, targeted marketing campaigns.
In this example, we have connected both her orders software system and her account management data source.
We firstly pulled all her customers account details data:
Step 2 – Build and Implement the Segmentation
With the data infrastructure in place, the next step is to develop a segmentation strategy. This involves categorizing your customers into distinct groups based on certain characteristics such as demographics, purchasing behavior, and customer lifecycle stage.
You can choose any other segmentation methodology, for we decided to keep it simple and go with the classic RFM analysis. If you are not familiar with it, you can find more details here.
Here’s a snippet of the underlying calculations:
And here’s the actual customer base segmentation analysis:
By combining the recency, the frequency and the monetary value of her customers, we grouped her customers into well-defined segments.
Step 3 – Define a Communication Strategy
After having a clear idea about what segment each customer belongs to, we sat down and brainstormed a strategy to each of these customer segments.
For example, we decided that for the inactive customers that used to place large, frequent orders, she will have a dedicated salesperson to call them, gather feedback, understand why they stopped ordering from her coffee shop and record all of that in a feedback form so she would implement improvements if they were necessary. Moreover, those customers will be offered a one-time discount if they will place a new order.
Since her sales force is not that large, we’ve decided that the loyal, inactive customers that used to place lower value orders, they are going to be targeted with an automated SMS campaign.
As such, we connected to her Twillio business account where she had already set up a reactivation campaign and leveraged Twillio’s API to automatically add the customers to that campaign as soon as they would become inactive. The moment it has been more than 3 months since a customer placed their last order, they would get an SMS, with 10 mins before the time they had used to make their visit:
And it worked like a charm! 80% open rate, 25% click-through rate, 10% discount code redeem rate. With this strategy in place, she has reduced her customer churn by 10% which translated in a 12% revenue increase.
This three-step process is iterative and should be continuously refined based on campaign performance and changing customer behavior.
The main point is that each of those customer segments can and should automatically be targeted. And the good part is that most of the software feature APIs that allow you and us to access the underlying data, combine it however we want and then use those same APIs to control how customers get added to specific campaigns.
Implementing automated marketing campaigns can dramatically improve your marketing effectiveness. It allows businesses to deliver personalized marketing messages, improve customer engagement, and ultimately drive better results. However, it’s a complex process that requires meticulous planning, a robust data infrastructure, and the right tools. With our experience and expertise, we can guide businesses through this process and help them to unlock the potential of automated marketing campaigns.