Using a consumer’s shopping behavior to drive personalized marketing is quickly moving to the forefront of marketing technology. Driving this change is the disruptor Amazon, with other retailers scrambling to keep up. Amazon uses a customer’s actual shopping data to determine purchasing intent, and it’s clearly working with Amazon hitting $1 trillion in market value.
Personalized marketing grounded in purchasing intent covers a wide range of strategies, from abandoned cart emails to predictive analytics that forecast the next best offer across multiple channels. Across this spectrum, the output is only as good as the input — both in quality and quantity. Quality before quantity, of course, but quantity is also essential.
Read More: Decoding the B2B Purchasing Puzzle in 2018
Personalized marketing is highly dependent on the number of online shoppers identified. By “identified,” we mean linked to a persistent identifier that’s unique to an individual person, not just a bunch of browser cookies. A persistent ID enables a cumulative legacy of each customer’s interactions, connecting past and present to build a singular view that improves the customer experience through relevance and personalization.
It can get complicated, but here’s a simplified grading system designed to give you a sense of where you fall on the spectrum when it comes to using purchase intent to close the deal — and what you can do to improve:
D: You generate abandoned cart emails using an ESP or other providers that identifies a third or less of your cart abandoners (at least you’re doing something).
C: You generate a variety of triggered emails that include merchandise triggers (i.e. back in stock), but you’re identifying less than half of your website visitors.
B: You generate personalized product recommendations, determined in real-time, in your triggered email, regularly scheduled email, and on your website using lightboxes and/or perimeter bars — and you’re identifying at least half of your website visitors. (B+ if you’re also using email triggers that “fire” by a customer’s online activity outside your website.)
A: You generate personalized product recommendations and dynamic offers unique to each customer, determined in real-time, cohesively delivered across multiple channels including email, website, apps, direct mail and in-store. And you’re identifying approximately 70% of your website visitors or more.
We managed to work identification into this scale without making it too complicated but let’s not forget about data quality. As much as possible, the strategies above should be driven by first-party data (or second-party data, loosely defined as when a brand has access to another brand’s first-party data). First-party data is the data you collect about a customer directly through your own digital or offline channels (website, in-store, etc.). Avoid using third-party data, which is obtained via outside companies or platforms and often aggregated from a variety of sources that may or may not use proper capture and management procedures (and can get you into trouble with growing privacy regulations).
The best source of first-party data is your website. Which takes us back to the importance of website visitor identification. You can’t collect a customer’s first-party data if you don’t know when that customer is on your site.
It should be getting clearer that to utilize purchasing intent effectively, shopper identification is of utmost importance. Because once that shopper is identified, you’re armed to compete for his or her business with behemoths such as Amazon.
Bob Gaito is the CEO of Albany, New York-based 4Cite, the first and only full-service People-Based Identification and Insights provider with a proprietary Data Network that maximizes the identification of previously unidentifiable customers, enabling retailers to more effectively acquire, retain and reactivate customers to influence purchasing decisions, drive brand loyalty and increase revenues. For more information, visit www.4cite.com.