Why Personalization For Large Audiences Through Anonymous Data Works

The positive effects of personalization in eCommerce businesses are more than clear to see. From higher conversions and AOV, boosted brand image and customer loyalty, if personalization isn’t being employed by your business you’re doing something wrong!

Employing personalization techniques when customer data is stored is obvious. Using readily available information can help tailor profiled customer journeys to suit their needs. The bigger question mark has been surrounding the experience of anonymous or first time customers, who don’t have any data information stored about them, and make up the largest percentage of online shoppers. This doesn’t have to be a problem anymore.

It is possible to target different shopper audiences throughout their customer journey; whether it be collections, search, autocomplete or recommendations without using any PII. Simply through a customer click profile based on current intent, data can be gathered and even anonymous users can have a shopping experience that speaks directly to them.

So how does this work?

The idea of audience segments means that large groups of shoppers are put together, and given an experience that is personalized but also relevant to more than one shopper at a time. Audiences can be set to more than statistics about the individuals such as:

• Age
• Interests
• Previous purchases
• Most viewed products

Although this is useful data to have, it is only available with customer profiles or with data privacy infringements. So instead of being tied to this outdated model, thinking outside the box allows a click profile to collect data on anonymous, even first time users. They can be categorized into audience categories such as:

• Search abandoners
• High intent shoppers
• Low intent shoppers
• Interested in X categories

As soon as any data is gathered, personalization at scale can take place to include all members of a certain category. In creating certain groups, rules can be chosen to apply to certain segments or groups that will optimize the potential for conversions. For example; low intent shoppers would see the trending or most popular products from the moment, or search abandoners would see similar products to the one they left at cart.

Time Frames

Adding time frames to the groups created can add another dimension of detail to the personalization received. For example; all cart abandoners in the past month can be grouped together, or all one time shoppers in the past year. Clearly, this applies to a huge audience segment, but by drawing their experiences together they can all be reached in a way that can send something unique to them. 

In a marketing outreach campaign, there are only a few lines of an SMS or email that have the potential to lead to customer click-throughs. A standard message to every single customer will not provide such high results as one that groups together all frequent customers over the last six months, as one time cart abandoners. This data contributes hugely to securing customer loyalty.

Then what?

Well, data leads to more data. Once these groups are set up and going, any kind of analytics system already in place will use the audiences to formulate patterns. Which personalization techniques are working will become apparent, and which campaigns need a slight tweak will be clear. Campaigns are partly predictable, but there is always room for improvement and following results can help this along, by impacting future and present groups and the effectiveness of personalization on them.

Staying Private

The cookie-free future doesn’t need to be a problem, it just leaves room for a higher scope of creativity and ingenuity- something the eCommerce market knows a lot about. Both shoppers and merchants are expecting higher privacy standards and a secure customer experience, without losing any of the unique and incredible online personalization we’ve come to expect. Personalization for large audiences is the perfect example of this.