Customer data collection is becoming more and more sophisticated and comprehensive. With the current set of tools available marketers can not only see who visited their website, for how long, where their mouse hovered, but also integrate all that information with data from brick-and-mortar, call centers, mobile, social media profiles and publicly available demographic information to really customize marketing messages.
However, collecting and using that data to hyper personalize marketing can potentially scare, annoy or even anger users. Just ask the dad whose daughter started receiving pregnancy-related marketing materials from Target. Recent breaches in data security from companies like Target and P.F.Chang’s haven’t helped ease customer wariness (especially when companies have so much data). While consumers are wary about companies collecting their data most people actually prefer that businesses use their information to customize and personalize marketing messages for them, according to a consumer survey by Accenture. 64 percent of those surveyed said that it is more important for companies to present relevant information against 34 percent who wanted companies to stop tracking their data. So how do marketers get customers to trust them and want to give away their information?
Tell Customers what you are collecting or ask them for it.
You should be very clear about what type of data you are collecting from customers. You should also tell your customers how you are going to use that data. If you are transparent about what you are going to do with that data and explain or offer some clear tangible benefits in return, customers will happily give their information. A great example of a company that has mastered the craft of the ask is StitchFix. They clearly explain why they’re asking so many questions and what they are going to do with your answers. Although they’re asking for very private information, their level of transparency let’s customers trust them. If they had used AgilOne's Data Augmentation's genderization on my name they could have recognized that I'm actually male.
Keep customers’ data anonymous.
Now that you have so much user information, it’s important to keep that data anonymous for privacy purposes. Linking behavior or profiles to personal user information is a line that must not be crossed. This is where clustering models come in to play. With clustering you let the algorithms, rather than the marketers, create customer segments. Not only are algorithms better able to segment customers based on many more variables than a human being ever could, marketers now don’t need to access that data. With cluster DNA a marketer can get very specific, deliver the best recommendations and really personalize marketing messages without being intrusive.
Explain those recommendations.
We wrote a blog post earlier about how Netflix uses customer viewing habits and behaviors to drive recommendations. A very important element in their ability to drive 75% of viewing habits with recommendations is that after they’ve collected data from the users they explain to them how they used it. “We want members to be aware of how we are adapting to their tastes.” Explained Xavier Amatriain, the Machine Learning Director at Netflix. They garner more trust from their customers by also explaining why they delivered a certain recommendation. Their openness with customers before and after the process not only promotes trust but also encourages customers to give more feedback.
Make sure your data is correct.
Nothing causes customers to lose trust faster than an incorrectly personalized marketing campaign. Imagine receiving a piece of mail that gets your name wrong, getting different types of discounts sent to different emails, or marketing messages that are not relevant to you at all. For example, Humana, a health insurance company recently shipped out a chocolate bar to a diabetic patient for poor service. The diabetic took to Reddit to shame Humana, where millions of daily users saw this: Don’t waste your valuable marketing budget, annoy customers and get bad publicity because of dirty data. Make sure your database is clean and accurate. To learn more about this topic, tune in to hear Elizabeth Canon of Fashion’s Collective and Patrick Martin of Zappos discuss other ways retail leaders are overcoming data obstacles before they impact the customer or the bottom line.