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They can stay hidden, testing ideas on small sets of customers, expanding successful tactics and eliminating poor ones. Customers flow through their life cycles, garnering increasingly valuable benefits and recognition if their values increase or receiving minimal attention if their value stagnates. Understanding both sides The potential for misunderstanding is obvious. Marketers trained in advertising tend to believe more in the efforts that exist for the world to see, whereas marketers trained in relationships believe in the efforts that nudge customers forward and upward, slowly and inexorably.

Programs that straddle the line can be extremely difficult for a marketing organization to manage elegantly due to these opposing points of view.

Budgets tend to be crisply separated between acquisition and retention, and project approvals evaluated with an emphasis on one side or the other. Another developed a tiered recognition program designed for invited best customers, then almost immediately rolled it out to its Website publicly and let anyone join.

In each case, the company adapted a well-thought-out program to its internal preferences, possibly reducing the eventual long-term ROI due to misalignment between the initial objectives and the eventual result. Simply define your key customer segments, and create a 2 x 2 grid for each click here for an example.

Distinguishing Correlation From Causation in Marketing

Designing new visible and hidden programs becomes much easier when you see how these programs interact before customers, with the added advantage of evaluating them against existing programs as well.

Harmonizing visible and hidden programs leads to outsize results, which may partially account for the tremendous growth in loyalty programs during the past few years. New Web technologies and improved integration will only lead to more opportunities to blur the lines between visible, published programs and hidden, segmented programs. Marketers who want to make an investment in future opportunities would be well served to start thinking in these terms today.

He can be reached at Michael LoyaltyLab.

Distinguishing Correlation From Causation in Marketing - Chief Marketer

Although this may seem extreme, companies base marketing decisions on correlations all the time. Walmart, for example, often bases marketing strategies on correlations between weather conditions and the sales of certain products. Walmart knows that, if the correlations actually are coincidences, it would eventually be reflected in sales performance, and the retailer could change tactics. While Walmart may understand the difference between causation and correlation, many brands do not.

This leads them to make suboptimal digital marketing decisions. For example, companies with limited non-branded SEO may believe they are performing well based on their traffic volume and may withhold investment, though that traffic is actually originating from other advertising and marketing activities.

Then, when the budgets for those other activities are cut and the SEO traffic dries up, these companies realize they erred by not investing more in non-branded: They had seen causation where there was only correlation. Similarly, companies often pay coupon publishers relying solely on SEO traffic high commission rates in their affiliate programs because they believe causation exists between the commissions and the revenue generated — but when they reduce commissions to test the theory, they see that revenue does not change.

Thus, no causal relationship actually exists. Mastering the Terms and Techniques Mastering causation and correlation can be challenging. Replicate events to verify results. For example, if you run an ad on the front page of ESPN.

Balancing Visible and Hidden Relationship Programs - Chief Marketer

By isolating and testing the different channels, you can confirm which generate more visits and revenue. Such elimination diets are essential for accurately measuring KPIs. Analyze the impact of correlation versus causation across channels. Companies often confuse causation and correlation when it comes to attribution, so be aware of how different channels influence outcomes. The business saw a steep decline in revenue that it attributed to its affiliate channels, as the affiliates began showing up in the middle of the conversion path.

The client perceived a correlation between affiliate activities and lower attribution rates. However, the company was also sending out special email offers to certain segments of its email lists.