Personalization and Recommendation Engines: The Power of Data in Retail

Shoppers are willing to pay up to 16% for personalized shopping experiences –PWC.

Haven’t we all heard the buzzword ‘Personalization’ around us recently? Businesses have been implementing it with varying levels of success for around five years now! It is 2024 now, and again, the word creating a lot of stir in the business landscape is ‘personalization.’ When it comes to retail, the journey with personalization is a bit complex. Where pioneers such as Amazon and Stitch Fix have integrated retail personalization into every facet of the customer journey, several retailers have been slow in adapting this, resulting in poor online customer experiences. Even for the ones who have adopted it, many haven’t implemented it correctly. The dynamics of retail and personalization are complex and evolving. Did you know that according to Instapage, 74% of customers get frustrated when they find that website content is not personalized?

What do the customers want? They expect personalized experiences rather than being flooded with an array of products. They expect their preferred websites and retail brands to understand them just as they understand the brand. It is 2024, and customers expect their loved brands to tailor experiences for them, considering their unique style preferences. But are retailers really aware of customer personalization? In this blog, we will take you through the nitty-gritty of personalization in retail, achieving it using data-driven recommendation engines, and how to implement them to optimize retail growth.

What is retail personalization?

Retail personalization is about giving every shopper a unique shopping experience across every single channel and touchpoint. Relying on historical data and real-time shopper intent, it leverages customer and product intelligence insights. The main goal of personalization in retail is to make the shoppers feel special, unique, and connected and enhance their shopping experience. It is all about delighting your customers by going that extra mile. It can do wonders for your retail business.  However, though every retailer announces that they personalize their customer experience, few do it properly.

What is not ‘personalization’?

Many retailers confuse ‘personalization’ with ‘segmentation,’ which are very different techniques. Retail segmentation treats customers as a cohort or a group with similar tastes. Instead of focusing on them as unique individuals, segmentation categorizes, labels, and boxes them based on limited data points. Using these terms interchangeably often results in scenarios like recommending a winter jacket after buying one a week earlier. This instead results in irrelevant personalization. Real personalization requires harnessing data, investing in AI and automation to understand shoppers’ intent, and delivering valuable, real-time recommendations. According to Forrester Research, though 90% of enterprises invest in retail personalization, only 40% of customers find the brand information targeted to them relevant. The main reason for this gap between the brand efforts and actual results is segmentation instead of personalization, which creates a bad customer experience. According to a Forrester study, 68% of shoppers would not return to a website or store that did not provide a satisfactory customer experience. Thus, segmentation is not personalization. For connecting with the customers, engaging with them, and winning their loyalty, 1:1 personalization becomes imperative.

Benefits of personalization in retail

1. Enhanced customer engagement

As a customer, if you find products that precisely fit your interests, you will likely spend more time on that platform! Personalization captivates the customer’s attention and increases engagement.

2. Boosts conversion

Decision fatigue is real. Customers are daunted by a myriad of options with similar features. A tailored experience offers customers products perfectly aligned with their preferences and results in successful conversions.

3. Fosters customer loyalty

Giving a tailored experience makes customers feel a genuine connection with the brand. Personalization enables the customers to feel valued and understood. This results in them being brand advocates and loyal customers.

4. Strategic cross-selling and upselling

Personalization also can lead to cross-selling by recommending complementary products and upselling by presenting premium alternatives.

Overall, personalization is crucial in attracting and converting customers, building lasting customer relationships, and driving retail business growth.

The Power of Recommendation Engines

What is the secret sauce of providing personalized customer experiences at scale? It is a recommendation engine. They are AI-driven tools that analyze user behavior and preferences data and develop personalized product suggestions. They harness the data from all the clicks, views, and previous purchases and come up with bespoke product suggestions. Recommendation engines are genuinely transformative in retail.

Imagine stepping into a store where the salesperson knows your preferences, recalls your previous purchases, and recommends products that fit your taste! This personal approach enhances your shopping experience. Let us find out how recommendation engines make it happen digitally.

1. Understanding user behavior

Recommendation engines can track user interactions at every digital touchpoint, from views and clicks to purchases. They can gather insights into individual preferences and buying trends by harnessing that data.

2. Adapting real-time

It is not a one-time analytics project. Recommendation engines continuously learn from user actions and behavior. They update and adapt the recommendations in real time, making their performance even more accurate and relevant.

3. Cross category discovery

Recommendation engines also can suggest new categories or products to the customers, which they may not have found out on their own. It broadens the customers’ shopping horizons and encourages exploration.

4. Recovering abandoned cart

Recommendation engines can also entice shoppers back to their abandoned carts by offering products similar to the ones in the abandoned carts. This helps renew customer connections and salvage potentially lost sales.

Recommendation engines are not just algorithms when it comes to retail. They are the secret behind personalized customer journeys, redefining the user experience.

Implementing personalization and recommendation engines

1. Data collection and analysis

By efficiently collecting and analyzing user data from various digital touchpoints- browsing history, purchase behaviors, and demographics- you can analyze this data to determine customer patterns and preferences.

2. Smart segmentation

You can divide your customer base into detailed segments based on behavior, demographics, and purchase history. This segmentation approach allows further targeted and impactful personalization tailored to specific audiences.

3. Real-time personalization

Retailers can ensure relevance and customer engagement by providing tailored product recommendations and real-time content that aligns with the user’s present activity and changing preferences.

4. Experimentation

Conducting A/B testing to experiment with different recommendation algorithms and personalization strategies can help retailers identify the practical approaches from the ineffective ones for their unique audience.

5. Empowering the users with transparency and control

It is essential to build a relationship of trust with your customers. As a retailer, you can give users the option to manage their data and the level of personalization that they want. Personalization should not come at the cost of customer privacy.

6. Continuous improvement

Customer behavior keeps evolving. As a retailer, you should regularly analyze your recommendation engine’s data and performance. Subsequently, adjust the algorithms to the evolving customer behaviors, optimizing your recommendation engines.

Challenges in Recommendation Systems

Since the Recommendation System depends massively on user data, the recs engine finds it challenging to predict the user’s taste when the customer is new or there is very little data present. This leads to the Cold Start Problem, which impacts the accuracy. Furthermore, there are privacy concerns, as recommendation systems collect and harness personal data to provide personalized experiences. To build ethical personalization, retail businesses must ensure transparency and security measures and acquire user consent. They also should take measures to prevent biases and ensure fairness in the algorithms to give equal opportunities to their users. Another concern in recommendation engines is the data quality, which can affect the accuracy of the recommendations. Incomplete and inaccurate user data can lead to irrelevant and inaccurate recommendations. To overcome these pitfalls, businesses should prioritize robust security, data usage transparency, and high-quality data standards for fair and accurate results.

How can we help?

With data at the center of this personalization buzz and playing a pivotal role in recommendation engines, tracking all the metrics and insights to optimize your retail business is not child’s play. Neither is a one-size-fits-all solution. Saxon AI provides data analytics and artificial intelligence services tailored to your business needs. Are you interested in getting started and driving your retail business growth? You can get in touch with our experts with your unique use case.

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