Creative ways to optimize your ad spend with machine learning
How consumer marketers are using machine learning to optimize customer acquisition and advertising efforts.
Acquiring customers who are enthusiastic about your brand and will support you in the long run is vital to establishing the scalability and longevity of your business. As the first step of building out a loyal customer base, customer acquisition is a high priority for marketing teams.
This article explores four ways you can leverage data science to extend your acquisition spend further to build a loyal customer base.
Targeting predicted high-LTV customers
The fact that it costs more to acquire new customers than it does to retain existing ones is no secret, which makes finding high-LTV prospects paramount to ensuring a successful, cost-effective acquisition strategy that will fuel future revenue.
Knowing your target audience is a key piece of acquiring valuable customers from the get-go. What makes your best existing customers unique? What draws them to your brand and/or products? Defining and identifying high-LTV customers is the first step in predicting who out there will become a valuable customer.
Predictive analytics play a crucial role in identifying traits and patterns in your customer data that are indicative of high-value customers. Of course, the depth and breadth of your customer data affects how accurate these predictions will be. Introducing third-party data is a great way to gain deeper insights, fuel your predictions, and generate higher-performing lookalike audiences.
Reaching likely buyers through multiple channels
Acquisition campaigns are most effective when orchestrated across multiple channels. Reaching likely buyers with consistent messaging through paid social, direct mail, and other targeted channels helps reinforce your brand in their minds and gives them more opportunities to engage.
To reach the same consumers across online and offline channels, you can't rely on anonymized audiences. Instead, you'll need a secure way of building known-identity audiences bolstered by third-party data.
Using known-identity attribution to optimize your channel mix
A diversified marketing mix allows you to reach varying demographics across different online and offline channels. But the sheer scale of marketing across numerous platforms doesn’t guarantee a high conversion rate or substantial ROI, so knowing where your money is best spent should be a high priority.
Equip your team with the capabilities to closely monitor campaign performance across each channel so you can identify what is working and what isn’t. It’s worth noting that this can be a difficult task with cross-channel campaigns, as several digital channels provide anonymized or aggregated results.
There are several attribution models out there, but we've found that known-identity attribution can provide you with a better understanding of who you should be targeting on each channel. Acquisition spend is precious, and having the ability to — and being excited about — shifting gears to improve your strategy is crucial. Marketing strategies should not be fixed; they should evolve as your customer base grows and changes.
Delivering customized offers and discounts
Some consumers need a bigger push than others to convert — this is where promotions and discounts come in. But to protect ROI on your campaigns, it's important to predict which customers are will stay engaged after that initial purchase. Delivering a 15% discount to all consumer might help drive conversions and reduce your average cost-per-acquisition, but if they don't come back, you might find yourself in a tough spot down the road.
Similarly, not all consumers need a discount to nudge them along the path to conversion. Using predictive analytics to determine who should receive a discount and how big that discount should be can help you beat cost benchmarks and increase the overall return on your acquisition spend.
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