These days, it’s all about personalized shopping – you can achieve greater revenue and customer loyalty by offering products and a user experience that speak directly to your consumer’s preferences and need. 

And technology can drive such success. 

The best tech creates a fast and easy user interface, attractive product pages, smooth checkout processes, and more. 

Even better tech uses AI to personalize the online shopping experience. Personalized shopping involves a very individualized and non-invasive understanding of customer needs. 

AI-powered personalized shopping requires collecting personal (not private) data that feeds machine learning (ML) to develop artificial intelligence (AI) models.

The most successful online businesses know:

• What features offer a personalized shopping experience
• How to create those features without breaching a customer’s trust and privacy

Privacy and profit are not at odds with each other. On the contrary, providing machine-driven recommendations and personalized navigations that also respect privacy is a win-win proposition.

Source Salesforce

What do we mean by personalized shopping?

Here are the features that make for a personalized shopping experience:

• Personalization

Display search results and product suggestions on landing pages, product and category pages, and purchase screens – derived from user-activity analytics that help discern affinities, preferences, and future interests.

• Recommendations

Display product or category-based suggestions based on user and group profiles, derived from user-activity analytics as well as product relationships within the catalog.

• Dynamic relevance and trends

Adapt the experience to current user and industry trends based on top sellers, most viewed, and other such customer-activity signals that occur in the last 24 hours or last week.

• Outbound personalization

Reach out to your customers with personalized emails, text messages, and newsletters, with personalized information based on the customer’s recent purchases and overall preferences. 

Source Algolia

Let’s take a real example from an online sports retailer. 

Here’s what these personalized shopping features can do:

• Encourage your users to browse through related sports items; for example, guides them towards exploring related category pages (Marathon Runners or Athletic Shoes).
• Encourage them to go beyond sneakers to discover other sporting items (stopwatches, water bottles, sweatpants), or even to go beyond sports and start thinking about dress shoes, cooking utensils, or even home improvements.
• Group runners into the same profile, thus combining the actions of many runners to fine tune the recommendations. 

What personal data is the right data to collect?

There are many kinds of signals and data points used to find buying patterns. It all starts with capturing user behavior in the form of analytics data. 

The key is to:

• Capture only the right analytics
• Feed analytics into different models and algorithms that produce reasonable and probable predictions about the future

Here’s a non-exhaustive list of the right analytics to collect:

• Viewing a product’s characteristics
• Multiple clicks within product pages
• Clicks on search results and banners
• Buying or adding to a wish list
• Performing certain searches, using common keywords
• Making comments or rating products
• Capturing data, like:
  • How long a consumer interacts with a website (not just sitting on an unmoving screen, but actually scrolling and clicking on the content)
  • How long they spend viewing similar products
  • Discovering trends, which allows you to dynamically change relevance and recommendations based on buying patterns in the last 10 or 24 hours
• Automated A/B Testing

Does respecting privacy sound impossible? A false promise?

There’s nothing inherently invasive in capturing analytics events. For example, in the runner’s example above, patterns can be discovered without knowing the runner’s name, address, or any other identifying details. Personal questions, like are you married, what’s your gender, what do you do on a day off, and so forth, need not be asked. 

But a system can easily cross a line – for example, when the system uses a logged-in customer’s private information. But that’s where anonymizing the data comes in, by replacing the real-world identity (name or email) with an anonymous number. In that way, no one can use or misuse the data to find out who the user really is. 

People want personalized experiences with no strings attached. A positive privacy model increases your customer base.

People don’t want to feel like they are being watched or followed. They want to browse and shop in privacy.

Ask for consent. People don’t want to feel like they are being watched or followed. They want to browse and shop in privacy. And they want to be the ones to set the boundaries and say “stop” at any moment. 

Never push the boundaries of customer trust. Businesses that underestimate the importance of privacy will lose their customers. 

Respecting customer privacy adds value to your business

Privacy is not a constraint. Though it might seems counter-intuitive, respecting privacy by collecting only a small but smart amount of personal data is better for business. And customers want personalized experiences. 

Source Forbres

• Limited personal information ensures data quality.

You don’t need more data than is necessary. This is a basic machine learning precept – feed your system with only the most informative and reliable data. Garbage in, garbage out. Quality input brings about the most pertinent results. Big data is not the basis of personalization. You need only small amounts of smart data.

• Transparency inspires trust and loyalty.

You establish more trust by telling your users what data you are collecting, and why, than by secretly capturing private information.

• Not knowing the identity of a person can still deliver  conversion.

To personalize an experience, you only need to know a user’s behavior. Knowing behavior does not also require knowing a user’s name and address or what’s in their wardrobe. You are only interested in capturing buying and viewing habits – i.e., what they click on, view, and purchase.

• Removing user identities enables sharper profiling based on behavior not people.

Not knowing identities leads to fruitful generalizations. Grouping people with similar buying patterns helps them see an even more complete picture of what they can do as they shop. Again, you don’t need to know everyone’s name in a profile to help them make intelligent and personalized purchases. Grouping should be based on privacy-respectful data collection that combines habits and preferences not identities. 

Last word – Don’t overreach. Give people the option.

Leave space for personal choice. 

Overreaching needlessly pushes the privacy boundaries and starts making your customers feel uncomfortable. That’s when the customer drops out. 

Today’s customer is in control. Do only what is necessary to create a reasonable but not overwhelming personalized experience. 

And don’t forget – you always want to leave space for the user to explore beyond their preferences.

Whether your customers opt in or out, AI can drive their search and discovery experience. A successful online retail strategy leaves room for chance and discovery both with and without the use of collecting personal data.

Interested in knowing more about personalized shopping? Check out our full eBook. Or just dive in with a live demo or free start!