The deluge of data from an ever-increasing number of channels and sources has opened up a wealth of new possibilities for businesses to reach out to their customers and prospects. To some, though, such volume has also proven overwhelming. Consider this: 47% of marketers of marketers claim to be “very confident” in their ability to analyze complex data, yet 64% believe that analysis should not be part of their role.
This reluctance to engage with data beyond its collection, particularly when so many marketers say they understand it, doesn’t appear to be the best approach. The same report found that over half (58%) of surveyed consumers believe they are inadequately targeted by marketing campaigns, with 83% saying they hate irrelevant ads and email marketing.
Complicating things further, while personalized messaging is clearly something consumers are interested in (61% are prepared to give up some privacy for better products and services), they also still value their privacy and are quick to voice displeasure when brands appear to have crossed the line.
My thoughts? That marketers have to get their hands dirty with analytics if they want to successfully connect with customers. However, it doesn’t have to be torture using some of these tools and tactics.
Implementing a Customer Data Platform
Robust, usable data analysis can be achieved through the use of the right tools, with Customer Data Platforms (CDPs) becoming increasingly sought after. CDPs allow for the unification, organization, and analysis of data, specifically designed to make sense of the massive amounts of information currently pouring in from all directions. They allow marketers to get a sense of their customers on an individual level through a single customer view (SCV) that ensures the targeted campaigns marketers designed are personal and potent.
Using predictive analytics
Powered by big data, predictive analytics can be used in many ways to improve the customer experience. This can include personalized product recommendations that draw on a customer’s purchase history, or tailored search results based on browsing behavior and buying patterns. Predictive analytics can also power more intelligent customer segmentation, deciding the most appropriate marketing communications through calculating a customer’s propensity towards certain actions. On a larger scale, predictive analysis can help an organization figure out what to stock, how to price, and what promotions to offer.
Using real-time strategies
Real-time marketing strategies allow marketers to be ‘always on’, delivering personalized, contextually appropriate content when and where they want it, maintaining their customer journey and sustaining engagement. These tactics can include real-time web personalization, customizing content based on visitor data, triggered messaging (like cart abandonment recovery messages) or countdown timers to spur on a purchase.
Better loyalty experiences
Customers are happy to give out some level of personal information in exchange for more relevant experiences and a great way to achieve this is through customer loyalty schemes. However, like other forms of marketing, a ‘one-size-fits-all’ is no longer as effective. Today, reward schemes must make more creative use of data, for personalized and timely offers of more benefit to individual customers.
Creative data analysis will go a long way to provide customers with shopping experiences that are personalized and unique, which in turn will encourage the sort of loyalty that’s the lofty end goal of the whole process of data collection.
A playbook for creating an engaging and personalized experience for your customers
This informative playbook looks at several areas of real-time marketing and personalization, including:
- Why you need to create a dialogue with your customers to acquire data for real-time tactics
- The benefits of getting personalization right
- How consumers feel about real-time personalization
- How you can use real-time marketing strategies, including triggered messaging, countdown timers, product recommendations and cart recovery