Real-time data. Actionable insights. 360 customer views. Personalized engagement strategies. Omnichannel campaigns.
They may be buzzwords, but they’re also critical to effective marketing and business growth. And database marketing is the key to all of them.
So what is database marketing and why is it important?
Let’s start with a basic database marketing definition.
Database marketing is a form of direct marketing. It involves collecting customer data like names, addresses, emails, phone numbers, transaction histories, customer support tickets, and so on. This information is then analyzed and used to create a personalized experience for each customer, or to attract potential customers.
Traditional direct marketing involves creating direct mail pieces like brochures and catalogs and mailing them to a list of potential or current customers in the hopes it evokes a positive response.
Database marketing takes that strategy a step further by seeking to understand how customers want to be marketed to, and then applying those insights to fulfill the customer’s need via the best channel.
Today’s marketers have access to more customer data than ever before. That’s why database marketing is once again becoming so important. It’s all about using that sea of data to create more relevant marketing messages that better resonate with customers — both current and potential.
Today’s consumers expect a personalized experience with your brand. To deliver one, marketers need a unified view of each customer across every touchpoint. Only then can they understand the customer’s journey and engage them in a meaningful way. Database marketing strategies make that easier.
Customer databases can help you:
Database marketing offers some compelling benefits — but to do it successfully, marketers need to understand the challenges as well.
Watch out for these database marketing challenges:
How should you get started creating your own database marketing strategy? Begin with the following steps.
Once you’ve built your database, you can start with some basic user segmentation. For instance, create a campaign specifically for first-time buyers or new customers, or one tailored for your loyalty program participants.
More complex segmentation methods can analyze across multiple data points to give you more detailed user segments. Recency, Frequency, Monetary (RFM) Analysis, for instance, creates customer groups based on how active they are and how much they spend, so you can quickly see and engage your champion customers, new customers, or dormant customers.
The most advanced segmentation involves sophisticated predictive analytics that can forecast a customer’s future behavior. That means predicting things like potential customer lifetime value, probability of churn, or likelihood of purchase.
These types of advanced segmentation raise conversion rates by up to 5x.
Identify High-Value Customers and Potential Upsells
An OTT app wants to understand how many of its customers are frequent binge watchers who are primed for upsell to a premium subscription plan. They use their customer database to identify their high-value, frequent watchers, and then send a combined email and push campaign offering these users a free month of premium as incentive to purchase a subscription. Using predictive analytics, they’re able to forecast how many campaign recipients will convert to balance marketing resources and prove ROI.
Provide Personalized Customer Support
A customer service representative for an ecommerce app is assigned an incoming call. They can immediately access the caller’s profile and see that they are a new customer who recently made their first purchase: a smart home device that lets them remotely lock their front door and see a video feed of visitors. With this information, the service rep can immediately provide a personalized support experience and offer a faster resolution by helping the customer quickly set up and troubleshoot their device.
Know Which Products and Services to Pitch Customers
A travel app wants to expand its product offering with a series of guided backpacking tours. Before dedicating resources to the effort, they tap into their customer database to view purchase trends and demographic patterns to first determine whether their customers would actually book guided backpacking trips, and second, to learn which customer types they should pitch these trips to. With database marketing, you can ensure that you’re offering something that your customers actually want.
Predict Who Will Buy and When
For a food delivery app, timing is critical. Understanding your window of opportunity to engage customers is key to beating the competition and boosting conversions — but spamming users at every meal time is a surefire way to lose them. By using predictive analytics with their customer database, the food delivery app team can forecast which users are most likely to buy. That way they can send notifications and promo codes to the right users at the right moment. And on the flip side, they can see who’s likely to churn so they can reach out with a tempting promo campaign and win those users back.
We’ve entered the era of relationship marketing, where brands have shifted focus from simply making the sale to developing long-term relationships with customers by consistently providing value. And database marketing is the backbone.
CleverTap’s machine learning capabilities are taking database and relationship marketing to the next level. These applications can instantly connect the dots between millions of customer data points, automatically create detailed user segments, determine the optimal time and channel to engage each user, and even predict what users will do next.
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