Good morning, loyal readers. Today, we’ll explore the top traits that successful Customer Experience Leaders share, will discover how to separate the “must haves” from the “can live withouts” when it comes to martech tools, will talk about why data quality is even more important when machine learning is part of the process and will reveal the results of a recent consumer survey that may have an impact on the way you do personalization.
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Flexible, committed Customer Experience Leaders are most effective
Being flexible enough to make adjustments to the plan without being completely thrown off course. Showing a true commitment to stand by your employees to get your customers what they need. As Blake Morgan writes in Forbes, these are just two of the top 20 traits that are typically found in the most successful Customer Experience Leaders. If you’re looking for ways to help inspire your team to provide amazing customer experiences, get started by ensuring that you’re the most effective leader you can possibly be.
Striking the right balance with martech tools
We’ve mentioned it in the Melange before, and I suspect we’ll report on it again—marketing directors and CMOs are surrounded by a plethora of martech tools, creating new opportunities but also new strains on budgets, staff and other resources.
So how do you strike the right balance? According to Wayne St. Amand in MarTech Series, consider taking advantage of short pilot programs to assess a program’s potential compatibility with your other martech tools and overall usefulness before making a big investment. And ensure that you’re including Machine Learning and Artificial Intelligence in your long-term martech planning, as these technologies offer data insights on a scale that humans simply can’t match. Check out all of Armand’s tips here.
Data quality is imperative to effective machine learning insights
Speaking of machine learning: if you’re investing at all in any kind of machine learning technology, first ensure that your data quality is impeccable. Why? Because the old edict of “garbage in, garbage out” is even more true when it comes to working with machine learning.
As Thomas C. Redman points out in Harvard Business Review, to develop a predictive model that provides you with meaningful, actionable (and potentially profitable decision-making information) you must fill it with the right high-quality, unbiased historic data in order for it to deliver the goods. How do you make this happen? According to Redman, one way is by giving yourself enough time to perform data quality fundamentals in your larger project timeline; plan on starting six months prior to running your predictive model and allow yourself four person-months of cleaning for every person-month of model building. Read Redman’s five-step process for ensuring your data is machine learning ready here.
Survey finds consumers are forgiving…to a point
One and you’re done. In a recent survey conducted by mobile location data company Blis and reported in MarketingLand, the largest group of respondents gave brands only one chance to make a mistake—and fix it quickly—before they were switching their loyalty and taking their business elsewhere.
Other survey findings indicated that overly personalized content that indicated surveillance, as well as content that failed to recognize the consumer’s loyalty felt annoying and alienating to consumers. The takeaway? Find the fine line between the two and ride it very carefully! Check out more of the survey findings here before you put together copy for your next email marketing campaign.
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