How to Develop a Retail Personalization Strategy

Your tailored approach should solve business needs, create customer loyalty and stay on target with your consumers’ demands. Do this well, and you’ll reap major dividends.

Published on Nov. 13, 2020
How to Develop a Retail Personalization Strategy
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In my first column for Built In, I explored the reasons why personalization is the key to the future of the apparel industry. Missed it? You can get up to speed here.

But a question remains: How, exactly, should your company approach developing its personalization strategy?

Retailers are looking to technology to deliver the tailored experience expected from consumers. According to a survey by Accenture, “91 percent of shoppers are more likely to shop with brands who recognize, remember, and provide relevant offers and recommendations.” However, just like their business strategy, every retailer’s personalization strategy needs to be customized as well by identifying their consumers’ needs and expectations so they can develop a strategy that aligns with those individual requirements. By utilizing technology and leveraging consumer data, businesses can create deeper, more informed custom experiences.


Decide How Personalization Efforts Can Solve Your Unique Business Challenges

Understanding how fulfilling consumers’ preferences can impact your business and solve related problems will determine what technologies would benefit you most. For example, if you have a large assortment of products and want to eliminate search fatigue, an ideal place to start is to utilize AI technology that recommends products to consumers based on their shopping preferences. If you are a brand looking to gain a new audience, personalized ads and promotions can help seal the initial conversion. All in all, efforts that are tailored to meet specific consumer needs ultimately drive KPIs and customer loyalty.


Develop a Personal Connection With Your Consumers by Making Efforts to Truly Understand Them

According to a survey by Segment, 71 percent of shoppers have felt frustrated by an impersonal experience. In addition to AI, user testing and data collection can be a great way to understand the unique preferences of shoppers. With UX, for example, we tend to think less is more — but that’s not always the case. Fit Analytics ran qualitative user experience testing for Fit Finder (our apparel and footwear sizing solution), and we found that, when it comes to size, consumers prefer a longer user journey. The reason: Shoppers want to feel confident in the accuracy of their recommendation in order to proceed with a purchase. By understanding consumer needs and preferences, retailers can create personal experiences across their site to help drive increased engagement and repeat visits, which ultimately builds customer loyalty and satisfaction.


Make Sure Your Personalization Efforts Are All Working Together

There are endless personalization options available now, from sorting on the product listing page to product recommendations to targeted ads on social media. It is critical to focus on harmonizing all efforts that seek to meet consumers preferences. At the same time, its also important to stray away from personalized elements that fall short of providing consumers trust in your brand. Think: recommending out-of-stock products or providing an overly tailored experience without consumer input — like retargeting consumers on other channels where the “creep factor” sets in. This can do more harm than good. Look instead for technologies that are multifaceted and provide curated experiences on web, email and social media. At Fit Analytics, for example, we are able to provide size, fit and style recommendations in an omni-channel experience for retailers using AI learnings and API integrations.


Leverage Technology to Deliver Accurate Personalization

Once a personalization strategy is defined, AI and machine learning can then be applied to serve relevant content or products to the consumer. The system’s AI classifies all the relevant information; this could be demographic data like age, height, weight or preferences on products, styles or experiences. This classified data is then used in a series of complex machine learning models, which AI learns so that it can determine which custom experience or recommendations to serve each consumer.

By taking the time to establish a personalization strategy that actually meets the customers’ needs, retailers can expect to see improvements in KPIs such as site conversion rate, return visits, average order value and, of course, customer loyalty. Implementing technology to curate a personalization strategy that solves business needs, creates customer loyalty and is on target with consumers’ demands will pay dividends.

Intrigued? Go Deeper.Can AI Be a North Star in the New Era of Distanced Retail?

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