Epoq + Censhare: Turning Personalization Insights into Omnichannel Growth

In the PIM, an outdoor shoe is classified as a “hiking shoe.” However, analysis of click-through and purchase behavior shows that many customers tend to find and buy it through categories such as “Travel,” “Everyday,” “City Outdoor,” or “waterproof sneakers.”

Personalization only:
This allows us to better recommend these products in the store or display them in specific categories.

PIM only:
Reflects the official category, but not necessarily actual customer behavior.

Together:
Click and purchase behavior indicate that customers perceive the product differently than it is classified internally. PIM can use this information to create additional categories, tags, or channel-specific classifications.

Added Value:
Products are not only sorted correctly internally, but also in the way customers actually search for and purchase them.

Click-through and purchasing behavior reveal which products are frequently bought together. For example, when it comes to cameras, it turns out that customers do not choose the officially recommended memory card, but usually opt for a different model with more storage.

Personalization only:
I often recommend this memory card in the store.

PIM only:
May have a well-maintained accessory relationship, but this is based more on manufacturer logic than on actual purchasing behavior.

Together:
The actual combination is identified based on click and purchase behavior. The PIM system records this relationship as a verified product relationship—for example, as an accessory, bundle, cross-sell, replacement product, or recommendation.

Added Value:
Purchasing behavior is transformed into official product relationships that can be used across all channels.

Analysis of click and purchase behavior shows that certain product recommendations are particularly effective. For example, people who buy running shoes click on certain running socks and a care product more often than average.

Personalization only:
Display this recommendation in the store.

PIM only:
It can manage bundles, but it doesn't automatically know which combinations actually perform well.

Together:
Click-through and purchase behavior are used to identify which combinations work. The PIM then turns this into an official bundle or a robust product relationship.

Added Value:
Personalized recommendations become scalable commercial product packages.

An email tests various messages, such as “sustainably produced,” “especially lightweight,” or “ideal for long distances.” The click data shows which message actually works best.

Personalization only:
Can better personalize future emails.

PIM Only:
Manages product descriptions and attributes, but doesn't automatically know which selling point is truly effective.

Together:
Click-through and purchase behavior clearly indicate which product message is effective. The PIM can incorporate this information into channel-specific product descriptions, priorities, or attributes.

Added Value:
Performance data from emails improves product communication across all channels.

In addition to clicks, purchases and shopping cart patterns also become visible through store personalization. This makes it possible to see that customers are particularly likely to buy sustainable products when they cost less than 100 €.

Personalization only:
I can highly recommend more affordable sustainable products.

PIM only:
Knows price, sustainability attributes, and product data, but does not automatically recognize this combination of demand.

Together:
Personalization reveals which combinations of attributes are actually relevant to purchasing decisions. The PIM can use this information to develop product assortment logic, for example, for tags, landing pages, filters, campaign products, or internal product assortment decisions.

Added Value:
Behavior translates into robust product line knowledge.

Some products are viewed frequently but rarely purchased. At the same time, customers search for terms such as “size chart,” “material,” “washable,” or “goes well with.”

Personalization only:
Identifies the problem and can recommend alternative products.

PIM only:
Can maintain product information, but does not automatically know what information is missing.

Together:
By analyzing click and purchase behavior, we can identify where users are leaving the site and what information appears to be missing. The PIM can use this information to generate specific tasks for product data maintenance.

Added Value:
Conversion issues become specific product data tasks.