Digital Personalization

With the rise of Big Data, Digital Personalization and the technology to deliver personalized experiences is within the reach of all types of businesses, large and small.
This data is leverage at many different levels to identify patterns across users coming from specific channels, the behavior those user exhibit on digital properties as well as the Click Paths taken and the demographic data of the users. Employing some level of Digital Personalization in a Digital Strategy is extremely important.

Profile based personalization
The commonly known profile based digital personalization is where recommendations are based on past purchases. This type of personalization can make educated guesses for future purchases, but one purchase that significantly deviates from the overall purchase history can skew recommendations.

Recommendations made on past purchases often do not reflect what a customer is looking for and businesses using this type of digital personalization does not reflect customer intent, which is very powerful. This type of personalization is less effective for product or service recommendations and is better suited for Retargeting, advertising to consumers based on their previous actions or behavior.

Profile based personalization requires data from purchases or subscription histories and other personally identifiable information, such as email addresses or other account information in order to make recommendations. While profile based personalization can provide solid recommendations for a single user, trying to make predictions based solely in this manner can have a businesses making off-base recommendations.

Rule driven personalization
This personalization type builds rules for personas based on what is already known about a consumer, this includes things like age, gender, income, location, etc. Users are grouped together depending on geography and/or social graph and presented with a specified range of recommendations. This is not a true personalized approach because the recommendations are broad and not unique to the user.

To accomplish this type of personalization, businesses are hiring digital strategy companies like us who employ the skills of data strategists and statisticians to sort through seas of data to discover patterns and new ways to target consumers. As an example we can assign each customer an ID and tie it to their credit card, email address, other personal identification, tracking pixels, etc. and store a history of everything that user has bought or interacted with. This data is used to create user groups and market specific products to them.

Intent based personalization
Intent based personalization leverages the consumers real time actions, this includes search terms, clicks, visitation lengths, device type, channel and more to figure out what the user wants at that moment and shape the experience as they continue to engage. The idea behind intent based personalization is that the consumers gets what they need when they need it.

Of the three types of personalization, this is the most highly individualized, user centric approach where making the sale is a function of what the customer is specifically searching for. Showing the consumer exactly what they want rather than trying to convince them to buy something. Very Powerful!

Intent based personalization is completely anonymous. No sensitive profile or personal data is used and recommendations are based almost entirely on user behavior from that specific interaction. Even when a user profile is tied to these interactions, the data is kept anonymous and only the algorithms access the needed data.