The pandemic managed to drag many industries down—and CPG brands were NOT one of them. That is because they moved quickly to respond to changes in consumer behavior, and they did so by focusing on clear priorities: safety, product availability and a strong balance sheet.
While they sheltered in place at home, consumers stocked up on food and other essentials. These items experienced an initial bump, followed by a sustained increase compared to pre-crisis levels. Online shopping also experienced a sharp uptick, creating opportunities for CPG manufacturers to work with retailers to compound on growth efforts.
According to a report by McKinsey, e-commerce penetration is still 35 percent above pre-COVID-19 levels, and more than one-third of consumers are continuing to switch brands or retailers.1 The challenge is real, and expectations are high: nearly 80 percent of CEOs say they are looking to their marketing leaders to drive revenue growth.
But what would it take to keep the momentum going through the course of the new normal, and beyond? This requires a marketing agenda that is far more sophisticated, predictive, and customized than ever before. The name of this new glorious agenda? Data driven marketing.
Now let me make one thing clear. I have no doubt that you’re already leveraging data analytics (at least to some extent) and other technology to personalize marketing campaigns for different segments or initiatives. While doing so is great in its own right, these efforts are not sufficiently pervasive to drive sustainable growth in the long-term.
Without advanced analytics and marketing technology to recommend high-value actions, you’re probably just doing the bare minimum. People may say that if you continue doing what you’ve always done, you’ll continue getting what you’ve always gotten—but in this case the results will fizzle.
In order to really make waves in terms of impact, your CPG brand would need to capitalize on the following five components to unlock the benefits of data-driven marketing at scale.
If your brand is like most other CPG companies, you may have developed a form of reliance on third-party data to make up for the lack of first-party data. This is especially when you’d compare yourself to brands with an iron-clad fanbase and community, such as digital subscription services. But that doesn’t mean you have to be stuck in that rut forever. Take charge of your data acquisition strategy, by investing in acquiring “zero-party” data—information that consumers explicitly share with a company—and first-party data, such as purchase information.
As we stated in a previous blog post, there is so much that you can do with zero-party data! By collecting this data you will receive details about your audience that will help you better understand them and their needs at a personal level. By extension, you can use this data towards personalized campaigns that are based on their preferences.
Zero-party data will help your brand get a better grasp of the LTV of your different customers, thereby helping you make strides in acquiring additional customers that demonstrate similar LTV, especially when using a LTV predictive model. Such a model can help you acquire and target loyal subscribers who are more likely to engage with your brand, so you can increase sales while lowering churn rates. You can then leverage the results of the predictive model and the capability to send conversion signals through ad network APIs to optimize your user acquisition campaigns.
Zero-party data will also help power up AI consumer intelligence engines, for generating machine-learning outputs (such as signal optimization 😉 ) that will help your team continuously become smarter about your customers. All jokes aside though, your CPG brand will need to build and incorporate a continuously updating, AI-powered consumer-intelligence engine that ingests enough data points and signals that can not only identify demand, but also to predict it. You can think of it as tech stepping in to act as a crystal ball for growth marketing.
You will know your consumer-intelligence engine is ready for activation when it is able to use advanced analytics and marketing technologies to recommend high-value actions. Of course, these actions would need to drive the highest long term value against the three pillars of growth: acquisition, retention, and monetization.
When it comes to acquisition, it’s not just about attracting anyone and everyone to expand revenue potential. It’s important to focus on high-value audiences, as uniquely defined by your brand. You also need to be able to target them based on their expected LTV. These users can be placed into one of a few categories, which tie into their LTV:
The utilization of predictive models is especially important for DTC brands, as they consistently run loyalty focused campaigns. Predictive models can help ad networks determine which customer would have a better LTV over the other, and target accordingly to basically continue charming them over with exclusive offers. Signaling users who are more inclined to make purchases over time is where the real impact and value lies.
Then when it comes to segments that need a little extra TLC to get back into the loop—building a consumer intelligence engine that includes predictive elements can also help with data-driven smart retention. Better to work smart, than to work hard! This is because your remarketing efforts will be based on predictions. For instance, what is the best product for your team to offer to this user next?
Naturally, all this ties into the monetization element. LTV prediction-based marketing will make your growth team an unstoppable force. As I mentioned in a previous post, when growth marketers and user acquisition managers make use of LTV optimization, they can experience significant performance improvements over their go-to UA strategy. The best part? This approach is unaffected by the changes brought on by ad networks and operating systems.
The winning combination of advanced analytics and marketing technology will make it possible to activate high-value actions. It goes without saying that predictive modeling solutions are not one-size-fits-all. Adjustments would consistently need to be made, based on experimentations—and not just one or two per week. Even McKinsey states that “learnings from hundreds of tests per week need to feed back into this engine.” The results from the best performing test will help with decision making and informing adjustments to brand plans, always-on activation, and more—all in an effort to reach at-scale impact.
On that note, many CPG brands are also beginning to seek behavioral identifiers. This includes consumer engagement across media platforms, general consumer sentiment, channel preferences, sales data—basically all forms of interaction with the brand itself across all touchpoints.
We’ve all heard time and time again that data is king. Well, in data driven marketing, consistent activation is king. This is why the always-on approach needs to be taken when it comes to activation, in an effort to achieve at-scale impact at the best time, any time, and every time.
While there may arguably be many data strategies you can choose from, it’s important to note that a consumer-intelligence engine will not be built overnight. If only it were that easy!
What’s important is to get started. The first step is to make a complete inventory of existing data sources, which frequently reside in silos across the marketing organization. The data itself should be incorporated into your growth marketing tech stack. We covered everything you need to know to have the ultimate growth marketing tech stack in a previous post.
The incorporation of LTV data into prediction-based UA will prove to be the future-proofed secret to the success of your CPG brand, due to the long-term approach and lower acquisition costs.
Predictive modeling will allow your brand to use a single signal to embody a user’s LTV based on a complete set of actions and behaviors, in addition to campaign performance. This will allow you to send predictive signals for users who are most likely to make high-value purchases over time. Conversely speaking, short term optimization places focus on upper funnel events, such as registration, trial completions, tutorial engagements, and lots of one-time purchases. Those are certainly great, but fail to provide visibility into whether users will make a second purchase. The magic runs dry fast.
On the other hand, long-term LTV based optimization will allow your team to target the most loyal, pay less for one-time buyers, and tap into an untapped audience. There’s less competition there, which means lower CPA, and higher profit margins for your brand. It’s a win for all, and it’s sustainable for the long haul as we collectively navigate through the new normal!