If you’re a direct marketer, chances are that today’s environment of rising mailing costs and other economic conditions is constraining your acquisition marketing budget. So how can you continue to hit your growth, revenue, and profit goals when it’s costing more to acquire new customers? The answer lies in more profitable customer marketing.
Continuing to run well-targeted acquisition campaigns remains vital to ensuring your brand’s current and future growth. Cutting back too much on acquisition marketing is essentially borrowing against future revenue. At the same time, ensuring that your customer marketing is thriving is crucial to near-term revenue.
To take your customer marketing to the next level of profitability, you need to consider a data collaboration strategy. Just as a trusted data partner can help you reach responsive prospects, they can also help you more accurately identify the people within your file that you should—and shouldn’t—be promoting in your customer marketing campaigns.
This data-led optimizing of your segmentation can be applied to a variety of customer marketing initiatives, whether you’re focused on maximizing revenue from active customers, reactivating inactive customers, engaging with non-customers, or all of the above. It will help you reduce marketing waste, increase campaign revenue, and ensure that your marketing dollars are being spent as wisely and effectively as possible.
More Data Makes a Difference
Marketers typically possess significant first-party data based on their customers’ interactions with their brand, such as transactional recency, frequency, and monetary (RFM) information. For brands that have multiple titles, this data is often enhanced by how their customers have interacted with multiple titles—information that can inform housefile segmentation strategies.
But the fact remains that this first-party data is limited to a brand’s isolated slice of their customers’ total spending. Oftentimes, over 50% of a brand’s customers have only purchased from them once. 75% may not have purchased from the brand at all in the last 24 months, making it difficult to predict their likelihood of purchasing again. But by working with a data partner and adopting a data collaboration strategy, brands can enrich their first-party data with scores derived from a much larger analysis of their customers’ total purchasing behavior with multiple brands.
Because past spending behavior is the best predictor of future purchasing, the more spending data a data partner has, the more helpful they can be. Data partners that have high-quality, diverse, and privacy-compliant consumer data can fill in the blanks of a brand’s customer view. This 360-degree perspective broadens a brand’s understanding of who their customers are and what they care about. Such insights go far beyond a brand’s own RFM data and help it gain new insights to optimize customer marketing decisions.
Even for brands with advanced segmentation strategies led by in-house or agency analytics teams, incorporating additional high-quality data through a data partner like Wiland greatly enhances the effectiveness of predictive modeling.