Check out our new guide! You can view or download it here: Data Methodology for Determining Shopper Affinity and Intent
Mapping Messaging to Shoppers
As an industry, we talk a lot about what types of messaging resonates with which types of shopper audiences.
Why? Because we’re wise! (Well, at least because it’s a smart thing to do.)
Matching “not baggy or boxy” to certain profiles, and “Drab to Fab” to others, is a game of analytics. Same goes with displaying the right recommended product accessories on a product detail page for each customer.
Ask your data analyst (or data scientist… or marketing operations manager…): there is a fair bit of work needed to dig into the trends in order to get granular about targeting and matching.
If you’re the analyst reading this – you get it.
Many modern tools can run this analysis and surface insights, which automates big pieces of this process: mapping customers to their predicted affinities and determining the purchase intent of shoppers based on their behavior (or lack thereof).
Whether it’s a team of smart analysts or it’s a software tool you’re thinking about investing in, having context for what is going on is super valuable. Not only is it educational and interesting to learn, but it helps to connect the dots in your head about how to use more data and practices to build awesome experiences for your customers.
Getting the Context You Need
How do you learn this data methodology for building out the relationships between shoppers, their preferences, and their purchase trajectory? We want to help out with that.
Our new guide steps through the system of methods and the various types of different data within your tech stack that are used in generating these models. Check it out here!
An Introductory Review of the Methods
Simple scoring of leads, customers, or any prospect tends to be built around two buckets:
- Behavioral attributes: This is what the person is doing and engaging with
- Demographic & Context attributes: This is everything else about their attributes, environment, and any static profile information.
Thorough eCommerce personalization in today’s world requires a more involved model.
A repeatable, systematic approach to predictive modeling makes a meaningful difference for eCommerce businesses. This modeling provides the necessary “intelligence” so you can connect the dots and provide better experiences for shoppers (plus, generate more efficient conversion paths for your store).
This is a simple breakdown of what our guide helps to spell out:
- Identify key data sets
- Aggregate, transform, & clean-up data
- Analyze it all to glean the top data features that correlate with the output labels you like (add to cart, purchase, etc)
- Run self-recursive statistical modeling to find more non-obvious correlating features and attributes
- Train an algorithm with historical data to map preferences back to behaviors and customer context to get better and better at predicting.
- Train and run your models on real-time site interactions (or marketing campaigns)
Above you can also find an image about the broad categories of data that are involved in this process.
Note that there is a node specifically for applying human knowledge and intuition – this is an important step, to point your algorithm in the right direction.
Stay On Top of Evolving Trends
Learning the process of how a sophisticated affinity and intent mapping engine works for optimizing your sales and marketing funnel paints a helpful picture. It contextualizes what we do as eCommerce practitioners and leaders.
Revenue generation and funnel optimization is a constantly evolving challenge. The tools at our disposal in the recent past have developed dramatically, and they will continue to grow with more and more intricacy and power.
It helps to double-click into how systems and methodologies are running to build a strong base. With a knowledgeable base, designing and executing strategy can really take off.
Check out the guide! You can view or download it here: Data Methodology for Determining Shopper Affinity and Intent