Personalization is the digital equivalent of walking into a store, being greeted by name, and offered a perfectly curated selection of products. But what seems warm and friendly face-to-face can seem obtrusive and overreaching in the online space.
And after years of analyzing the behavior, needs, and preferences of our customers and serving up customized, targeted content and advertising, marketers are now being asked some hard questions.
- Are our personalization initiatives hitting the mark?
- Are we prioritizing the needs of our companies or our customers?
- How can we remain compliant with regulations including the European Union’s (EU) sweeping new legal framework for data protection?
No More Obsessive Tracking
The EU’s General Data Protection Regulation (GDPR) went into effect May 25. It ushers in meaningful changes in the ways businesses operate and more clearly defines how marketers interact with consumer data. In short, the GDPR requires marketers to stop obsessively tracking every click and interaction of customers and potential customers.
In today’s more privacy-conscious environment, marketers must temper their voyeuristic tendencies.
Instead, we have to be clear, unequivocal and earn the right to collect data from each and every person who interacts with our digital channels. We need to treat our customers as we would like to be treated and make sure we use both our data and technology to support that outcome.
Recommendation Engines: Value for Data
Recommendations are a great way to provide value in exchange for data. Michael Schrage, a research fellow at MIT Sloan School’s Center for Digital Business, said recommendation engines are mutually beneficially, helping organizations gain greater value from their data while creating greater value for their customers.
“Recommendation engines aren’t just about recommendations; they’re platforms enabling all manner of digital informational interaction. Appropriately designed, these interactions create value for your customer and for you. Virtuous cycles can become more virtuous and valuable,” he noted in an article in Harvard Business Review.
Recommendations Drive Revenues, Sales
Last year Salesforce looked at the impact of product recommendations across critical metrics like revenue, conversion rate, average order value (AOV), and time spent on-site. It’s Personalization in Shopping report found:
Product recommendations drive revenue. Visits where the shopper clicked a recommendation comprise just 7 percent of all visits, but 24 percent of orders and 26 percent of revenue.
Shopper spend soars with personalization. Purchases resulting from the click on a recommendation saw a 10 percent higher average order value, and the per-visit spend of a shopper who clicks a recommendation is five times higher.
Recommendations are linked to longer shopping visits. Shoppers that clicked a product recommendation spent an average of 12.9 minutes on-site vs. 2.9 minutes for those that didn’t click recommendations.
Recommendations lead to sales. Nearly one in four products bought by recommendation-clickers came from recommended items.
Optimizing Recommendation Engines
To design the best recommendation engines, consider investment in three areas:
Voice of the Customer (VoC)
Data shows you what your customers have been doing. But voice of the customer (VoC) analysis provides insight into individual and segment motivations; that is, it helps illuminate why your customers are behaving the way they do.
It’s easy to sit in an office and make a lot of assumptions based on isolated data sets. But whatever your level of sophistication as an organization, nothing beats talking to your customers. Consider direct feedback (surveys, complaints, market research or panels); indirect feedback (social listening, review sites, and text analytics); and inferred feedback (website clickstream data, purchase history or contact center data).
Integration of customer systems
Better integration of your marketing systems will give you a more holistic and complete view of your customers across channels and touchpoints.
Business Intelligence (BI) tools
Consider using a business intelligence tool such as Tableau to analyze key data, create visualizations and reports, and share insights throughout the enterprise. Once you align various propensities to the audience segments, you can begin to make recommendations.
I love the story of how a fashion startup called StitchFix lured away a key data scientist from Netflix. It certainly wasn’t because he was interested in culottes as a fashion trend. He was excited to work for a company whose entire model was based only on recommendations.
Recommendations — of product, of content, of adoption practices, relevant information — are the best ways to let your customers know that yes, you’ve been watching them. But you’ve also been listening and, you are here to help them be successful.
For More Information
- Understanding Customer Behavior in a Buyer’s Economy
- What ‘Love Languages’ Do You Use With Your Customers?
- How to Deliver Value to Your Customers Post-GDPR
- How You Can Provide Effortless Customer Experience
Want to learn more about deepening relationships with your customers? Email me for more information.
Atlanta-based Arke develops strategies and implements digital technologies for better brand experience for your customers.