Why Lifecycle Marketing Isn’t Linear
To say that digital adoption and multichannel marketing have impacted the customer lifecycle would be a huge understatement. Not only is lifecycle marketing non-linear, but it’s also more of a complex web because customers are interacting with various brands via multiple channels throughout their day, every day. Likewise, the way you approach lifecycle marketing should be non-linear. Before we cover the big, impactful changes, let’s cover some lifecycle marketing basics.
Lifecycle Marketing Basics
Lifecycle marketing has always been about engaging customers, increasing revenue, and growing a brand, and that is still the case. Brands are still looking to attract customers and keep them engaged so that they will make multiple repeat purchases, and perhaps even serve as a brand advocate, sharing their positive brand experiences across social media and through word of mouth.
Different types of products have different customer lifecycles. They can vary in length and complexity. Whole Foods and CVS have much shorter lifecycles than mattress or car brands. Once you buy from an auto dealership, they will most likely focus their marketing efforts on encouraging you to be an advocate, someone who will send business their way until you are ready to buy another car years down the road.
You’ll see in the model below that the four main phases of lifecycle marketing are: reach, act, convert, and engage. At the bottom of the image, you’ll notice that the customer moves in and out of those phases over time. It is clear that customer lifecycle marketing is non-linear.
Really great lifecycle marketing is synced up with each customer as they move in and out of these phases, delivering engaging and highly personalized content and offers, providing gentle nudges for repeat purchases, and attempting to reengage lapsed customers with meaningful brand content.
Source: Smart Insights
Advanced Lifecycle Marketing
Think of all the data you are probably collecting about your customers across various channels. What content have they consumed on your website? When was the last time they made a purchase? What offers did they respond to? What products did they buy?
Successful lifecycle marketing uses customer data coupled with data science to profile and predict when customers reach critical points in their lifecycles. It takes into account the level of engagement, frequency of purchases, and predicted lifetime value.
It also taps into the predictions to inform both reactive and proactive measures. Are there tactics for preventing a customer from churning? Can you react to a first-time purchase with content or offers that encourage brand loyalty or a repeat purchase?
Advanced lifecycle marketing, supported by A.I.-enabled marketing automation does this quite handily, using predictive analytics and A.I. to deliver personalized content to each customer, at every stage when, where, and how they interact.
Let’s Not Forget About Customer Retention
Many brands today are still disproportionately focused on acquiring new customers. A lifecycle marketing approach is helpful because it also emphasizes retention. It can be much more profitable to encourage repeat purchases from current customers than it is to attract new customers.
Are you measuring your churn rate, repeat purchase rate, and customer growth rate? Examining those things will help you develop sound retention marketing strategies.
Another visual representation of the customer lifecycle is shown below. You’ll notice that the post-purchase phases of advocacy and loyalty feed right back into the awareness and consideration phases.
Source: CXBuzz
Sure, it can be stressful to think about the new ways of lifecycle marketing. It isn’t traditional or linear or simple. However, you have the opportunity to see much greater results in the way of your customer lifetime value (CLV), average order value (AOV), and repeat purchase rate. There’s no stopping you now!
ReSci is a team of marketers and data scientists on a mission to democratize AI. We make powerful recommendations and predictions accessible to brands.
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