Wiland Blog

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.

Strategies for Optimizing Customer Marketing

So how can data collaboration help with optimizing your customer marketing? As noted above, a qualified data partner will analyze your first-party data against the context of a much larger data set to score and segment your customer file. Such analysis can either encompass your entire file or address specific segments of it to address unique campaign goals. Here are four practical ways that you can optimize your customer data to improve the efficiency and performance of your customer marketing:

1. Rank (or Score) Your Full Customer File

Full customer file modeling and scoring is one of the most powerful, comprehensive solutions for ensuring that you maximize the value of your customer relationships. This typically involves having your data partner build custom models and analyze your full housefile. This can help you increase revenue as you identify one-time buyers and lapsed customers that can profitably be promoted. You’ll also reduce campaign costs as you discover the low-scoring customers from otherwise profitable segments who you should omit.

Here is a potential scenario for working with your data partner on full-file modeling and segmentation. Use your current segmentation strategy to select who to mail for an upcoming campaign. In parallel, have your data partner build custom models—using first-party and additional data—to use for analysis and selection. Then, have the partner identify:

1. the names that you planned to mail that the partner’s models did not select; and
2. the names that the data partner identifies as profitable and that merit mailing, but that you did not select.

Test a subset of both universes as well as the names that you both selected to evaluate the bottom-line impact that would be made to your campaign. This test allows you to determine the overall impact of enhancing your current segmentation strategy and helps set the stage for how your strategy can be improved long-term by working with the data partner.

2. Reactivate Your Best Inactive Customers

Every customer file includes people who have previously transacted with a brand but have gone inactive. However, just because these people have stopped engaging with that specific brand doesn’t mean that they are no longer spending in that category.

When you work with a data partner to gain a more comprehensive view of your inactive customers, you will discover people who are highly likely to re-engage with your brand based on several factors, such as:

1. their in-category spending with your competitors,
2. the number of companies they purchase from in a given category,
3. their recency of spend in a category,
4. their spending in correlated categories,
5. and many more.

Reaching out, perhaps with a special “Come Back” offer, can help you regain lapsed customers and increase revenue.

On the flip side of the coin, a data partner will be able to help you determine which of your inactive customers no longer have a propensity to spend in your category and whose spending behaviors do not indicate a likelihood to re-engage. When you understand both who you should and should not be mailing, you will reduce marketing waste and increase campaign ROI.

3. Identify Ideal Cross-Sell Opportunities

For brands with multiple titles, having a greater understanding of a customer’s full spending behaviors can highlight areas of interest that would make for ideal cross-sell opportunities. For instance, a customer of an apparel brand might show a propensity to spend on home goods elsewhere. If you have an additional title related to home goods, this could be an opportunity to increase that customer’s total spend and capture new market share. This data-enabled approach leads to more informed, more effective cross-selling.

4. Hone Promotions to Recent and One-Time Customers

Recent doesn’t always mean better when it comes to the customers in your file. Over 50% of a brand’s customer file could be made up of one-time buyers for whom there is very limited data to analyze. While most brands prefer to mail all of their recent customers for some set amount of time to capitalize on their timely interest, a data partner will help you fill in the blanks about those customers’ spending habits and interests. You can then determine which recent customers to mail more frequently and who is most likely to remain a one-time buyer and thus not merit as much marketing investment.

One Final Tip: Always Be Testing

A brand’s customer segmentation strategy is often its bread and butter. The best data partners will understand and respect this vital part of your business. That’s why, as you discuss optimizing your customer marketing with your data partner, they should be open and willing to test. Exploring testing opportunities and developing a plan for how testing will be implemented and measured is an integral part of working with your data partner to optimize your customer marketing. Your data partner should have the commitment to test and the analytical tools to test well. Such a partnership is well worth the effort and will result in more revenue from your customer file. A data partner will help you see more than what your own data alone can reveal, uncovering many new opportunities to grow your customers’ spending with your brand.