Proactive vs. reactive marketing
When developing a marketing strategy, businesses often take either a proactive approach or a reactive approach. Let’s dive into what each of these strategies are, and which one we recommend for best engaging and retaining your customers.
- Proactive marketing
Proactive marketing is when your strategy is based on informed decisions about what your customers will likely want and will likely do in the future. Anticipating your customers’ needs can help you craft more relevant messages that are likely to drive engagement and conversion. A proactive strategy can help increase brand loyalty and retention, because customers will feel like you understand and care about them at an individual level. - Reactive marketing
Reactive marketing is when you base your messaging off of what a customer has already done. While analyzing past customer actions and tweaking your strategy is important, if you are always messaging reactively, you may miss opportunities to target customers at key moments that can keep them as regular, loyal customers. By the time you’ve provided customers with what they originally wanted, they may no longer be interested in your brand.
Proactive marketing overall empowers you to send more precise and relevant messaging, which can strengthen brand loyalty and help you retain your customers for longer. In Klaviyo, you can unlock your proactive marketing strategy when you use predictive analytics.
The power of predictive analytics
Predictive analytics are AI-driven insights powered by your customer data and machine learning. In Klaviyo, you can see predictive analytics down to the individual profile level, allowing you to anticipate the actions of each of your customers. With this knowledge, you can create more refined segments, send more impactful campaigns, and even help reduce the risk of churn.
Predictive analytics are key to a proactive marketing strategy. Using predictive analytics in your messaging can also help save you time and money, while simultaneously boosting your performance.
It’s important to note that predictive analytics are predictions, so they’re not guaranteed to be exactly accurate. However, these insights can help you better anticipate likely customer actions, allowing you to develop a proactive rather than a reactive marketing strategy.
Watch the video below for an overview of what predictive analytics are, how they’re calculated, and how you can use them to drive revenue for your business.
Klaviyo gives you the ability to customize these predictions based on the metrics that matter to your business. In order to ensure Klaviyo provides you with the most relevant and accurate predictive insights, it’s important to make sure you’re tracking the appropriate metrics for your business.
Review your account-level metric mapping in Klaviyo to make sure that Klaviyo is making predictions based on the correct metrics.
Predictive analytics available in Klaviyo
Let’s dive a bit deeper into each of the predictive analytics available in Klaviyo, and how they can help you refine your approach to retention marketing:
Average time between orders
Average time between orders tells you an estimate of how frequently a customer purchases from your brand. You can identify those who are likely to buy more or less frequently by whether they have a shorter or longer predicted average time between orders.
This information can help you be strategic in your frequency of sending to certain customers, and more specific in the type of content you promote. For those who have an estimated short time between orders, you can try sending them more frequent promotional messages as well as early access to certain sales and products. For those with an estimated long time between orders, you may try sending them an exclusive discount or bundled perk to encourage them to purchase more frequently.
Expected date of next order
Expected date of next order tells you when a customer is likely to make their next purchase with your brand. This insight can help you identify customers who are likely to purchase from you very soon, not for a while, or not at all.
Expected date of next order is powerful when used in the targeting of campaigns and forms, as well as branching your flows. You can send targeted campaigns to help encourage those likely to purchase soon to place their order, and you can also save money for your business by discounting only those who are unlikely to purchase from you soon and need a little incentive.
Churn risk prediction
Churn risk prediction helps you identify customers likely to churn before they stop engaging with your brand altogether. This information is based on the number of orders they’ve placed, and how often they place orders. The less likely they are to purchase from you, the more likely they’ll churn.
This insight helps you discover which customers could benefit from re-engagement plays and extra incentivization, and who you need to focus on retaining most urgently. You can try sending your churn-risks extra incentives to return and make a purchase, or remind them of your value with social proof.
Historic CLV
Historic CLV (customer lifetime value) provides you with how much a customer has spent on your brand thus far, pulled from the total value of the customer’s historic orders.
You can use historic CLV to better contextualize each customer’s relationship with your brand to date, and identify your historic big spenders and hesitant shoppers.
Predicted CLV
Predicted CLV (customer lifetime value) provides you with an estimate of how much each customer is expected to spend with your brand in the next 365 days. This insight is beneficial for anticipating your customers’ future purchases.
Predicted CLV is a helpful metric to use for targeting campaigns and forms, and for branching your flows. You can personalize your messages to feature products customers are most likely to purchase given their predicted future spend. Promote products that have a price point in the range of what each customer is likely to spend. Consider incentivizing customers with a lower predicted CLV to raise their AOV (average order value).
Total CLV
Total CLV (customer lifetime value) is the sum of a given customer’s historic CLV and predicted CLV. With total CLV, you can see a holistic view of your customer’s past and likely future spend with your brand.
Total CLV can be used similarly to predicted CLV for targeting campaigns and forms, and for branching your flows to customize messages based on the overall total estimate of a customer’s past and future spend.
Predicted gender
Predicted gender is an estimate of each customer’s gender calculated by their first name and census data. Predicted data is helpful for further personalizing your messages with specific products that will most interest each customer.
Predicted gender is a helpful metric to use for targeting messages using product images or featured products that are mostly specific to one gender. However, since predicted gender is an estimate, it’s recommended to include some additional product details or images for products specific to all genders.