Although there are many drivers of overall campaign success, four factors with significant untapped ROI potential are outlined based on strategic customer relations management practice assessments conducted by Data Square and informal polls administered during a series of Direct Marketing Association database marketing seminars I taught.
The key factors include: de-duplication, predictive analytics, enterprise campaign testing strategy and inclusion of referral metrics.
1. De-duplication: In a number of strategic CRM practice assessments, the presence of duplicate customer records was found to be a key issue plaguing both mid-market and large-sized companies. Duplicates within marketing databases ranged from 5 percent to 15 percent, often stemming from an inability to standardize certain types of addresses. Multiple mailings to the same customer, communications with inappropriate messages (e.g., cross-sell, reactivation) and inability to identify high-value customers because their purchase history is spread across multiple records are just a few of the ramifications. Consolidating duplicates will typically pay for itself—not just for heavy direct mail organizations, but also for those that use e-mail as their primary communication channel.
2. Predictive analytics: A second tactic that can have considerable impact on campaign effectiveness is the use of predictive models. However, this is typically going to hold true for expensive or high-volume campaigns.
- Tier 1 analytics represent list selection methods and are by far the most frequently used category. In Tier 1 Data Square analytic implementations, we have routinely seen returns of 300 percent to 1,500 percent within B-to-B and B-to-C sectors across a range of verticals including retail, consumer packaged goods and technology. Tier 1 predictive models that were being used most frequently and/or associated with the highest ROI include: a) response models that identify customers who are most likely to respond to a marketing communication; b) best customer models that rank customers on their predicted profit in the next year/quarter; c) cross-sell models that rank customers on their likelihood to purchase specific products; and d) acquisition models used to select prospects from a larger base.
- Tier 2 analytics include methods designed to optimize offer, pricing and messaging at the customer level. Research shows that a significant percent of coupons are not redeemed and cost organizations millions of dollars annually. Analytic opportunity areas include: a) offer optimization analytics that consider interactions between customer value segments and offers; b) messaging analytics that consider interactions between customer need and preference segments with messaging strategies; and c) pricing analytics that consider a combination of needs, preferences and customer value.
- Tier 3 analytics represent ways to optimize campaign timing and relevancy and include lifecycle, life stage, purchase cycle and behavioral triggers. Examples include the identification of leading indicators for positive behaviors (such as cross-sell, upsell) and negative behaviors (such as bad debt, churn, spend reduction, fraud). Although Tier 3 analytics are least common, several organizations have reported successes based on these tactics. For example, Staples and Fidelity report doubling their performance via a timing focus. Schwab and Wells Fargo show significant returns by focusing on relevancy driven by trigger-based marketing and lifecycle messaging.
- Tier 4 analytics refer to contact and channel/marketing mix optimization. Analytics in this category are the most complex and designed to optimize the frequency, timing and sequence of contacts across channels for each customer and campaign. For one large B-to-B marketer, contact cadence analysis helped identify the optimal number of marketing communications, resulting in the redirection of millions of dollars previously spent on wasteful touches.
For direct mail campaigns, Tier 1 is the first place to start. One should commence with Tier 2 offer optimization at the customer-level for e-mail and mobile marketing campaigns. For multichannel campaigns, Tier 4 analytics become extremely relevant—as they can help identify the optimal mix of channels, touch frequency and sequence.