Personalisation has become a key aspect of digital marketing, with many companies now focusing on using data to create highly tailored and relevant experiences for their customers. One of the most powerful sources of data for personalisation is first-party data, which is data that is collected directly from your customers through various touch-points such as website interactions, email campaigns, and surveys.

First-party data provides a wealth of information about your customers, including their demographics, behaviour, and interests. This data can be used to create targeted and personalised marketing campaigns that are more likely to resonate with your target audience. For example, you can use first-party data to segment your audience into different groups based on their behaviour and interests, and then create targeted campaigns for each group. This can include personalised email content, website experiences, and even customised product recommendations.

Personalisation can also be enhanced by using first-party data to create lookalike audiences. Lookalike audiences are groups of individuals who share similar characteristics with your existing customers. This can be a powerful way to identify and target new potential customers who are likely to be interested in your products or services.

Another way to leverage first-party data for personalisation is by using it to create personalised product recommendations. By analysing customer behaviour and purchase history, you can identify patterns in what products or services they are most likely to be interested in. This can help you create targeted product recommendations that are more likely to be relevant to your customers.

In summary, first-party data can be a valuable asset for personalisation in digital marketing. By leveraging first-party data, companies can create targeted and personalised marketing campaigns that are more likely to resonate with their target audience, while also identifying new potential customers and creating personalised product recommendations. However, keep in mind that in order to leverage first-party data effectively, it is important to have a robust data management strategy in place, including regular data cleaning, updating and the ability to map data to real-world identities.

Einstein Recommendation Builder is Salesforce Marketing Cloud’s easy-to-use recommendation engine for products, content or banners. The way it works is quite simple: it requires an installation of snippets on the website so a cookie can track the user behaviour and return recommendations of content for the user. This way, the user will find the content he/she is looking for faster; dropping the bounce rate and thus increasing the conversion rate.

Great technology for web and email but what if this is not the only thing we can use Einstein Recommendation Builder for? What if we use it as a data collection tool for our subscribers and use that data to build highly personalised 1:1 communication!

What’s Powering Einstein Recommendations?

At heart, Einstein Recommendations in Salesforce Marketing Cloud is powered by tracking actions that a user performs on a website or email. These actions are tracked similarly to Google Analytics and then stored in Salesforce. The algorithms behind Einstein will bring this data together and create sets of data that (1) have a correlation with one another and (2) build a profile of each user. It’s this latter point that is being overlooked by Salesforce’s customers!

Below are 2 examples of an “Affinity Profile” of 2 different subscribers within one of our hospitality clients.

Based on these 2 profiles, you can clearly see that they are 2 different subscribers. Not only are they searching for hotel stays in different geographies, but one is more interested in adventurous outdoor activities while the second is more interested in fine dining.

All of this data is based on the tracking data collected by Einstein. So next to Einstein Recommendations, how can we leverage this? Below you can find a few ideas.

One Step Closer to 1-1 Email Personalisation

Emails have a limited amount of real estate to show products you’d like to promote so why not personalise this based on the recipient's affinity profile. A lot of retail brands send a single weekly newsletter containing the latest promotions or products they would like to push but what’s the point in pushing a product to the wrong gender?

We reckon that the best approach is to list the favourite categories based on the affinity profile and push products based on preferences. A good example would be that if a man purchases products for adults and boys (vs. women and girls) then a better personalised email should contain products from only those 2 categories. If a woman only looks at products for women and nothing more, then the choice is obvious as well!

Advanced Audience Segmentation

Stepping up your segmentation game is another possible benefit of using Salesforce Einstein’s Affinity Profiles. If you need to push a certain product or service, you can base yourself on existing data from Service Cloud by checking preferences and or historic purchases but you can expand this segmentation by checking the Affinity Profiles. You’ll get a wider audience based on the most recent tracking data.

Particularly useful if product stock levels remain high, to generate a boost in sales, you can target those with a high affinity rating for that product or the category it resides in.

The reverse is also an option, products that just returned in stock might have missed sales recently, what better way than to segment your most likely customers within your data set.

Bonus Tip for Einstein Email Recommendations

There are also ways to leverage data generated from Einstein to reduce costs with Salesforce Marketing Cloud. Collecting data for Salesforce Einstein is free and super messages are only charged when Einstein Email Recommendations are generated & shown. If a profile is unknown, Einstein will revert back to the wisdom of the crowd to provide recommendations; which is not necessarily a bad thing, but a simple piece of code (AMPscript) can be added to check if the subscriber is known or not in Einstein. If known, we can then display the recommendations as per normal whilst not known, you can add your own default set of products.