Today I had the chance to present at the Direct Response Forum along with Trevor Bass and Melissa Rabasco of Litle and Andrew Lau of Never Shop Alone.
We had a great discussion together with the audience about ways organizations are using customer payment data, social data, and behavioral data together to better market to and sell to customers online.
What kinds of customer data can a company collect?
Let’s suppose an unnamed person is looking to buy a necklace for his wife, whose birthday is rapidly approaching. An online jewelry retailer might be able to collect the following kinds of customer data:
- Static Demographic Data: Balding but not-yet-middle-aged married American male.
- Business Transaction Data: Bought the following 2 things from the store over the past 3 years, spent $300 dollars.
- Behavioral Data: Visited site 8 times in last week, stayed for a total of 47 minutes, looked at items in the category necklaces, spent 15 minutes on this particular item, got to the checkout page 2 times, has item worth $150 in shopping cart
- Social Data: Asked friends on Facebook or Twitter for recommendations, shared a few pictures on Pinterest.
- Payment Data: Shopped last time with a Gold Amex Card.
How could this data be used for sales and marketing? How can you get started?
From the many examples discussed by the panelist and audience, I have amalgamated three customer stories that reflect the applications we discussed. I also suggest three specific steps you can take to combine and use customer data for marketing.
Score Customer Engagement and use it to segment customers
Consider a non-profit that scores each donor’s engagement based on events attended, amount donated, participation in online forums, and referrals. They combine this data with their knowledge of the donor’s net worth, to form four segments. Based on this segmentation, they can vary their online call to action as follows:
Practical Step #1: Score each customer’s engagement with your business. Set up a set of criteria and a scoring system to identify high and low engaged customers and use this to segment your customers with demographic data. Develop a 2×2 marketing matrix and respond differently to different segments in your online interaction with your customers.
Set up In-Context Messages
A online jewelry store tracks user behavior in their purchase funnel and over time.
If they have a customer who has gotten most of the way through the purchase funnel two times with the same product over a period of 3 days, they offer them an immediate in-context message that contains an incentive to complete the transaction.
Likewise, if they have a customer who has spent more than 5 minutes per day looking at the same item over a period of several weeks, they suggest a similar but slightly lower priced item. Their goal is not to maximize immediate revenue but the lifetime value of the customer.
Practical tip #2: Set up funnels that track your user/customers movement along a workflow or set of steps toward a goal. Then set up in-context messages that speak to the customer right in your site at each critical step.
Combine data sources for greater insight and effectiveness
Consider a subscription car rental business where users are coming back to rent cars on a repeated basis. The goal is to retain and upsell the customer. This business should begin by understanding basic business metrics – # of cars rented total and per month, amount of money spent. With this alone, they should be able to detect and respond to periods of inactivity with a proactive outreach by email to the customer.
But what if they added in something as simple as per customer visit behavior and matched that to their transactional data. They might find that a segment of customers has a high visit to book ratio. It seems it takes them many visits before they book a car or that they have many visits where they do not end up booking a car. Targeted outreach with a survey the next time they are visiting or at a later date by email/phone, could result in significant learning and upsell.
Practical Tip #3. Combine transactional data or per customer business metrics with per customer visit behavior or clickstream activity. Target surveys to customers based on their behavior to better understand and serve customers.
These examples and the discussion at the Digital Response Forum were e-commerce focused but the tips can apply to any online business with an ongoing relationship with customers.