Retail personalization

What is retail personalization?

Retail personalization is the use of data, AI, and other digital technologies to deliver customized experiences to every customer, across physical and digital stores. It could be suggesting products, tailoring discounts and campaigns, or tweaking marketing communication,all depending on customer’s age, gender, location, and other characteristics like shopping habits, preferred channel, payment type, and more.

Why is personalization important in retail?

Personalization in retail is very important as companies are moving from having transactional relationships with customers to contextual & intelligent engagement.

It’s also important for other reasons:

1 - To improve click and conversion rates 2 - More and repeated purchases & more session time 3 - Less cart abandonment and bounce rates 4 - Make customers feel a personal connection towards the brand 5 - Stand out in a competitive retail market and carve a niche.

Is personalization and customization the same?

Personalization and customization are not same, as customization is manual and reactive and personalization is automated and proactive. There are other differences too, like personalization is driven by data and AI, whereas customization cannot happen unless there is user input.

Online vs offline personalization

Online personalization is different from offline personalization in the following aspects:

How to do retail personalization with AI?

Retail personalization starts with data collection, which is a combination of first-party purchase data and behavioural data.

Here is the list of data sources required for retail personalization.

1 - Website clickstream data 2 - POS and CRM data 3 - Customer feedback and support records 4 - App and email engagement data 5 - Loyalty program data

Post data collection, creating unified customer profiles which reflect customer data in 360 degrees. There can be retail analytics dashboards to track consolidated, real-time updates of the above data. This unified customer data is often called CDP (Customer Data Platform).

Note: Customer data platform is different from recommendation engine and the difference between them is that CDP just integrates and collects customer data, whereas personalization engine executes tailor-made connection, based on the collected data.

Once you have unified customer data that's free from duplicates, missing values, and inconsistencies, segment the data and put them into buckets and categories. If it’s basic categorization (gender, location, or age group based), manual segmentation can help. If you want segmentation based on real time behavior, then ML clustering algorithms can help.

This segmented, customer buyer intent data can be used to run the following AI-based personalization use cases.

1 - Recommendation engine for website and app. 2 - Birthday and special day offers. 3 - Search results and recommendations based on browsing history. 4 - Run automated re-targeting campaigns based on cart abandonment data, email clicks, and other behavioral data. 5 - Personalize every touchpoint by mapping customer journey from start. 6 - Personalized landing pages and campaigns.

This is how customer data can be used for retail personalization use cases.

How to personalize the in-store shopping experience?

Just like eCommerce and online channel personalization, in-store personalization is possible too. This can be very useful for retail companies who want to do omni-channel personalization.

1 - Scan to see personalized recommendations 2 - Connect loyalty cards with real-time offers 3 - Smart kiosks that suggest users personalized products, aisle suggestions, tips, and more 4 - Geolocation and context-based recommendations (like showing products available at the nearest store).

Enable sales associates and store managers track customer behaviors, patterns, and shopping preferences through personalized dashboards, so that they can help more effectively. These dashboards showing real-time updates on dwell time, customer traffic and footfall heatmaps, pick patterns, discounts and conversion rates.

AI use cases in retail personalization

AI is used in many parts of retail and eCommerce sectors and can make a huge difference in results, if used right. Here are some AI and ML applications used for retail personalization.

1 - AI & ML based recommendation engines 2 - Customer data platforms 3 - Retail analytics dashboards 4 - Marketing automation

And many more.

There are many retail personalization tools available in the market like Adobe Target, Salesforce Marketing Cloud, Dynamic Yield, etc.

Tracking it all with custom retail analytics solution

You need to check how well your personalization strategies to improve them and fix any shortcomings. This can be done with the help of retail analytics dashboards, which will reflect the following data:

1 - Engagement rates of general and personalized campaigns & comparison 2 - Any revenue impact made 3 - Customer satisfaction and improvement across stores and channels 4 - Omni-channel performance. 5 - Or any other metric and measurement your team want to regularly keep their eyes on.

Retail personalization is no longer optional—it’s expected. Whether it’s online product recommendations, in-store promotions, or personalized offers, leveraging AI personalisation retail strategies can drive real impact and help retailers stay relevant for a long time.