Who’s on Third: How Optimove Enriches Relationship Marketing with 3rd Party Data
Dozens of new attributes, more precise predictions, richer customer DNA and improved campaign matching: Learn how Optimove brings 3rd party data artistry to the tablePosted in Customer Retention, Data Analysis, Personalization on 2 August 2018 by:
Optimove's power as a leader in the relationship marketing realm lies in our ability to process and analyze our clients’ data and turn it into actionable insights, driving measurable growth through thoughtful customer communications.
At every turn, whether it’s the Customer Model, Campaign Analysis, or Optibot, we at Optimove leverage data to learn customer behavior and communicate with them on a personal level.
In order to successfully understand customers, we collect 4 types of 1st party data:
- Transactional Data – Data of the orders and items bought by the customer
- Customer Level Data – Customer ID, registration date, and account balance
- Real Time Data – Pages the customers visited and buttons they clicked on the site/app
- Campaign Deliverability Data – Channel metrics, for example email open/click rates
This amount of data provides an abundance of rich insights. But we still want to look at ways to take this even further to constantly improve our customer communications, engaging on a truly personal level.
With that in mind, the next logical step was working with vendors to provide additional customer data. This step allowed us and our clients to connect with consumers in a privacy-compliant way, guiding smart marketers in their effort to complete the “customer puzzle.”
With the help of 3rd party demographic data, we accessed dozens of new attributes for analysis, which gave us the ability to gain additional in-depth insights. Below, you can find the most significant demographics regarding customer survivability and future value:
- Household Income
- Marital Status
- Presence of Children
Using 3rd party data allows us to offer a more comprehensive solution:
1. The ability to create more granular and better campaigns
For instance, for a client in the fashion industry, the customers’ age turned out to be more important than we initially realized. With this new demographic data, we found that customers in their mid-40s have the highest CLTV. With this understanding, we can cater more relevant communications to this customer group (a luxurious catalog perhaps) that will match, and fulfill, their potential. For example, since we know thry have a high spending potential, we can offer them a discount on the 2nd product and increase their shopping cart value.
Furthermore, in order to enrich our campaign analysis capabilities when analyzing results, we can use 3rd party data attributes to refine the best group for each specific campaign. In the table below, we saw that a specific campaign worked best on married females.
Once those attributes are integrated into our system, our self-optimizing campaign will be able to optimize the target groups according to the data and make sure the most relevant customers receive the most relevant messages. In the example above, more married females than males or single customers will receive this campaign.
2. The ability to know the customers better
Another potential use of this data is to gain insights into the existing preferences of our customers.
In the chart below, we analyzed data from customers whose favorite department is “Natural Home.” We learned that 80% of this customer group consists of females, mostly in their 50’s and 60’s with a high household income.
This is a good illustration of how a marketer using our software can have an influence on the product itself, on the sales process, or on the different departments by identifying and pointing out trends and tendencies to the product division.
3. The ability to make more precise predictions
After understanding that applying those attributes impacted how we would segment our customers and structure our campaigns, we wanted to see how a combination of certain attributes might have an effect on our segmentation and prediction. Let’s combine Marital Status and Presence of Children and create an attribute that contains four segments that differ in their Future Value. We can see that married couples with no children generate the highest CLV while singles generate the lowest. By adding this data as a segmentation layer, we can enrich our micro-segmentation and prediction.
3rd party demographic data, as we’ve analyzed above, can be integrated into various parts of the Optimove product. Whether resulting in more accurate predictions, richer customer DNA, highly granular customer groups, or optimized campaign matching, this data can drive insights that will help smart marketing teams increase their revenue and retention by having a holistic view of their customers.