How to solve the cold start problem in recommender systems

Posted on 19. 01. 2024. by Andjela Culibrk

The cold start problem poses a conundrum for recommender systems when they encounter new users with no historical data or brand-new items with minimal interactions.

Without a rich history of user behavior, how can these systems provide recommendations that truly captivate and engage?

Let’s see what stands behind this cold start problem and explore some helpful strategies that can help you address it effectively.

Understanding the cold start problem

The “cold start problem” is a common challenge that occurs in recommender systems. It refers to a situation where a system or algorithm runs into difficulties when it has little or no historical data about a user or an item. Obviously, this makes it challenging to provide relevant personalized recommendations.

In the context of recommender systems, there are two main types of cold start problems — user cold start and item cold start.

User cold start: When a user first becomes part of a recommender system, the system has limited information about their preferences and behavior. This makes it difficult to provide personalized recommendations. In such cases, the system may rely on generic recommendations or ask the user to provide explicit feedback, such as ratings or preferences, to build a user profile.
Item cold start: New items, such as products or content, may not have accumulated enough user interactions or ratings to generate accurate recommendations. As a result, the system may struggle to recommend these new items effectively to the users in the system. Sometimes, collaborative filtering methods that rely on user-item interactions may not work well for item cold start problems.

Why is it important to address the cold start problem?

Bearing in mind the complexity surrounding the cold start problem, addressing it is crucial in recommender systems. Some of the reasons include:

Better user engagement,
Improved user satisfaction,
Maximizing revenue,
Reducing churn,
Discoverability,
Data collection,
Competitive advantage, and
User onboarding.

Let’s explain each reason briefly.

Better user engagement

Most recommender systems aim to provide personalized and relevant recommendations to users. However, when a system can’t make accurate or relevant recommendations for new users or items, users may become frustrated or disengaged.

By adequately addressing the cold start problem, you can engage new users from the beginning and retain their interest.

Improved user satisfaction

It’s common knowledge that accurate recommendations enhance user satisfaction.

When users receive recommendations that match their interests and preferences, they are more likely to

Use the platform,
Spend more time on it, and
Engage with its content or products.

If you constantly make sure they “have a great time” on your platform or website, you are also making sure they leave your website happy. Who doesn’t love a happy customer?

Maximizing revenue

In the context of e-commerce and content platforms, personalization can significantly impact sales and engagement. In support of this statement, McKinsey’s research revealed that personalization often results in a 10 to 15% increase in revenue.

If your goal is precisely to boost revenue, you should focus mostly on properly addressing the cold start problem. Make sure that the new items or products get the visibility they need and start seeing improvements.

Reducing churn

When a recommender system fails to engage new users effectively, it may result in churn and push customers to abandon your website.

Essentially, the higher the churn rate, the bigger the chances it affects your profits and impedes growth. So, reducing churn is paramount for your long-term success and growth, as retaining existing users is often more cost-effective than acquiring new ones.

Discoverability

Finding adequate cold start solutions helps your users discover new items or content.

In the context of retail and e-commerce, discoverability is important because it allows your customers to choose from a wide range of products instead of focusing only on a few.

When you choose to address the cold start problem, you’re allowing your products to be seen by a broader audience.

Data collection

The reason why the cold start represents a problem for you is because it makes you make decisions “in the dark”, without enough data.

In this case, the solution is easy — you focus on actively collecting user feedback and preferences. By collecting valuable data from your consumers, you can build better user profiles and improve recommendations over time.

Data collection is your ticket to better understanding your users and making sure you’re giving them what they need and want.

Competitive advantage

In today’s competitive landscape, companies and platforms that can effectively address the cold start problem and provide superior personalized recommendations have a competitive advantage.

Many studies indicate that users are more likely to choose and stay with platforms that offer better recommendations.

User onboarding

The whole point of successfully addressing the cold start problem is to make the onboarding process for your new users as seamless and engaging as possible. Especially if you rely heavily on user data and data-driven personalization.

Overall, addressing the cold start problem is crucial for both user satisfaction and the business success of recommender systems. Yet, some challenges are bound to occur along the way.

Let’s see what they can be.

Challenges associated with the cold start problem

The cold start problem in recommender systems is often associated with several challenges, both for new users and new items. Sometimes, addressing these challenges can be complex.

Here are some of the key challenges associated with the cold start problem:

Lack of data: Absence of historical user-item interaction data for new users or items hinders meaningful recommendations.
Data sparsity: Sparse or insufficient data leads to noisy or unreliable recommendations.
Cold start for new users:
- No user history: New users lack interaction data, making it harder to guess their preferences.
- Limited feedback: New users may not provide explicit feedback immediately, making it hard to learn what they prefer.
Cold start for new items:
- Lack of ratings: Few or no ratings or interactions challenge quality assessment.
- Limited feature data: Insufficient feature information hampers content-based recommendations.
Diversity and serendipity: Limited information makes generating diverse recommendations challenging.
Data imbalance: Balancing new and existing entities can lead to over-prioritization or under-prioritization.
Inaccurate models: Some algorithms perform poorly with limited data, resulting in inaccurate recommendations.
Scalability: Efficiently handling a growing number of new users and items is crucial.
Privacy and ethical concerns: Collecting extensive data raises privacy and ethical issues, requiring a balance between personalization and user privacy.

To mitigate the cold start problem and address its challenges, you can rely on a few useful strategies. Let’s see which ones.

10 Strategies for addressing the cold start problem

Addressing the cold start problem in recommender systems requires a combination of strategies and techniques to provide valuable recommendations for new users and items.

Here are some strategies you can try to implement.

Content-based recommendations

To address the cold start problem, you have to think of both new users and new items.

For new users, you can try to recommend items based on their content features (e.g., descriptions, attributes, tags). Make sure to match the user’s profile with item characteristics to make relevant recommendations.

When it comes to new items, you should try to recommend them to users who have shown interest in similar items or content, based on content similarity.

Popularity-based recommendations

Popularity-based recommendations are your safe bet for introducing items to new users. You can use them to promote or recommend items to new users based on their overall popularity or recent trends.

Collaborative filtering techniques

Go with item-based collaborative filtering for new users. How?

Identify items that are similar to the interactions they’ve had with the few items they have engaged with.

Use user-based collaborative filtering for new items by identifying users with similar preferences and recommending items that those users have interacted with in the past.

Hybrid recommendations

Another great strategy for mitigating the cold start problem is to use hybrid recommenders.

For example, combining multiple recommendation techniques, such as collaborative filtering and content-based filtering can provide you with robust recommendations for various scenarios.

Demographic and contextual data

Use demographic information about users, such as age, gender, location, or preferences, to make initial recommendations for new users.

Incorporate contextual information, such as time of day, location, or device type, to tailor recommendations based on the user’s current context.

Active learning

Encourage new users to provide explicit feedback, including:

Ratings,
Likes, and
Preferences.

Once you gather enough data and this type of feedback, use it wisely to build their user profiles and improve your recommendations over time.

Knowledge-based recommendations

Knowledge-based recommendations can be particularly useful in addressing the cold-start problem. They rely on item and user attributes, external data sources, and content analysis to make recommendations when historical interaction data is limited.

Knowledge-based recommendations can provide meaningful recommendations to both new items and new users.

If you have knowledge about the domain or domain-specific rules, use that knowledge to provide relevant recommendations for new users and items. For example, in a recipe recommender, you can use known ingredient preferences to suggest more recipes to new users.

Item metadata enrichment

By enhancing the available item metadata, recommendation systems can make more informed and relevant recommendations even when there is limited or no historical interaction data.

For new items, you can enrich their metadata or content features with additional information. This can help the system better understand the item’s characteristics and relevance.

Transfer learning

Another useful strategy for addressing the cold-start problem is to transfer knowledge from existing users or items to new ones. Techniques from transfer learning can help you make predictions for cold start scenarios by leveraging knowledge from the rest of the system.

A/B testing

Finally, A/B testing can help you evaluate the performance of different recommendation methods.

When you continuously test and iterate on different cold-start strategies to determine which ones work best in your case, you are more likely to find the one that suits your needs the best.

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You must bear in mind that the effectiveness of these strategies may vary depending on the volume of users and items, and the available data.

In practice, you might need to combine a few of these strategies to effectively address the cold-start problem in your recommender system. Here’s a preview of what this might look like in different domains and industries.

Real-world examples of the cold start problem

Although you might not be aware of it, the cold-start problem is pervasive across various domains and industries. Here are some examples of how it occurs in

E-commerce,
Streaming service,
Social media,
Job or gig platforms, and
Recommendations for IoT devices.

The continuous cold start problem in e-commerce recommender systems

When a new customer registers on an e-commerce platform, the system has no historical data about their preferences and shopping behavior.

The continuous cold start problem in e-commerce recommender systems refers to the challenge of providing personalized recommendations for new or less active users and items. This issue arises when there is insufficient historical data about these users or items to generate accurate recommendations.

For new users, the system lacks behavioral data such as purchase history, ratings, or browsing patterns, making it difficult to understand their preferences and interests. Similarly, for new items added to the platform, there might be limited or no interaction data available, making it challenging to recommend them effectively.

Addressing the continuous cold start problem involves employing various strategies. Addressing the continuous cold start problem is crucial in ensuring a positive user experience and encouraging user engagement, especially in dynamic e-commerce environments where new users and items continually emerge. Balancing accuracy, diversity, and exploration for new users and items remains a significant challenge in designing effective recommender systems.

Likewise, newly added products or items may lack user reviews and ratings, making it challenging to recommend them to other users effectively.

Tackling the cold start conundrum with new subscribers

When a new user subscribes to a streaming service, the system initially has no information about their viewing history and preferences.

Also, when new movies or TV shows are released, there may be limited viewer feedback and interaction data to base further recommendations on.

New users, new content: The cold start challenges in social media recommendation

Users who join a social media platform have no established connections or content interactions in the beginning, which makes providing personalized content recommendations difficult.

New content creators on platforms like Instagram or YouTube may face challenges in getting their content recommended to a wider audience.

Tackling cold start hurdles in job and gig platform pairings

When new job seekers join a job or gig platform, they usually have no previous job applications or interaction history, making it harder to match them with suitable job listings.

In the same way, new job postings or gig opportunities may not have received sufficient interactions, leading to challenges in matching them with potential candidates.

Addressing cold start obstacles in IoT devices

In the context of Internet of Things (IoT) devices, newly added devices may not have enough data for the system to recommend automation routines or settings effectively.

In each of these scenarios, the cold-start problem hinders the ability of recommender systems to provide personalized and relevant recommendations to new users or for new items or content.

It’s your job to be aware of these challenges and address them accordingly.

Final thoughts

The “cold-start problem” is a critical and widespread challenge that recommender systems face. It hinders their ability to provide personalized recommendations for new users and items.

However, it’s not mission impossible to address the cold-problem challenge, especially if you have proper support.

At Things Solver, we’re passionate about helping our clients overcome such challenges and provide more personalized recommendations to their customers.

Let’s work together and find the best way to tackle the cold-start problem. We can help make more informed decisions, even when there’s limited data to work with. Contact us today at ai@thingsolver.com and let’s come up with a solution that suits your needs and helps you engage with your customers more effectively.

From clicks to conversions: AI strategies that can transform eCommerce

Posted on 12. 09. 2023.06. 10. 2023. by Andjela Culibrk

In the fast-paced digital landscape, eCommerce is continually evolving. This means businesses must work hard to unlock new avenues for growth and customer engagement.

Amidst this transformative journey, Artificial Intelligence (AI) has emerged as a game-changing force, revolutionizing how online retailers operate and interact with their customers.

From personalized recommendations to real-time pricing optimization, AI strategies have become essential tools in driving conversions and boosting revenue.

In this blog post, we’ll explore some innovative AI strategies that can reshape your customers’ shopping journeys and amplify your conversions at the same time.

Join us as we uncover the power of AI and its potential to transform clicks into meaningful conversions, forging lasting connections with shoppers.

9 AI strategies that transform eCommerce

Although there are many other strategies, both AI and non-AI, that can be helpful in the context of eCommerce, in this blog post we’ll focus only on the 9 AI strategies that can have a significant impact on the way you personalize the offer for your customers in eCommerce.

To help you understand better how they fit into your customer’s shopping journey, we’ll divide them into three groups based on their unique pain points:

The first pain point is the so-called “cold start” – one of the biggest and the most expensive problems in AI.

Why?

Because it requires businesses to deal with customers they know absolutely nothing about – the ones that land on their website for the first time. Here we can rely on the following AI strategies:

Starter-pack recommender,
Session-based recommender, and
Smart search recommender.

The second pain point is the one involving conversion. Here, your main concerns can include reducing browsing time and encouraging the consumer to make a purchase, personalizing the consumer’s experience, or increasing their basket size. Useful AI strategies in this phase are:

Alternative-products recommender,
Related-products recommender, and
Cross-sell automatization.

Finally, after the customer has made a purchase, the third pain point becomes nurturing – how to behave towards the consumer who’s made a purchase, what to do with the data they left on my website, and how to encourage them to make another purchase once now that you know more about them

At this point, the AI strategies you can rely on include:

Interactions-based recommender,
Personalized recommender, and
Wheel of Fortune.

Let’s explore all of these AI strategies a bit further.

#1 Starter-pack recommender

Although not strictly an AI strategy, this is the first step to getting to know your customers better.

When a person lands on your website, they are probably lost. But you are, too. You’re unsure about what to offer to them or what types of products they might be interested in. So, your best bet is to offer them what your other customers are usually buying or looking at.

For example, you can offer them “The most viewed products” or “Top 10 selling products” – this way, you’re reducing the risk of presenting them with something completely random. If the new customer is anything like your average customer, it’s very likely that they will like the same products as your other users, too.

Moreover, these initial recommendations will help them better understand what other products they might need and what they are looking for.

What you’re actually doing here is basing your actions on pure statistics – you’re offering the products that have proven to be the most selling, attractive, or viewed previously.

Ultimately, the starter-pack recommender is a good way to start learning more about your customer in the long run.

#2 Session-based recommender

The session-based recommender is the most expensive AI model you can use in eCommerce, yet probably the most effective one. This strategy operates based on your customer’s current online behavior.

Let’s illustrate this with an example.

Let’s imagine that the customer is looking at a black T-shirt online. They find a T-shirt they like, so they click the link leading to the website. But then, they find a pair of black pants they also like, so they click another link leading to that product page, too. You get the picture.

So, what’s the role of the session-based recommender in this case?

Well, the session-based recommender is following the behavior of your customer and their session on each product page, so it can start building a “file” on them, their preferences, and perhaps a list of products they like and are willing to buy.

The main catch here is that it’s all happening at the moment of browsing – it’s like you’re behind your customer’s screen looking at what they are doing and writing everything down.

Following what the customer is doing in real-time, this recommender system quickly picks up that they have looked at three black products, so it goes on to recommend more similar products from the same website.

Depending on the current behavior and mood of the customer, this session-based recommender is trying to understand their interests and search intent better. When it learns that, it can quickly generate and recommend other products that they are very likely to buy.

What’s interesting is that the customer’s interests and intent can change from session to session – making this AI recommender system one of the most attractive and adaptable in eCommerce.

#3 Smart search recommender

Did you know that about 61% of website users expect to find what they are looking for in the first 5 seconds after landing on your website?

Thanks to smart search recommenders, you can make sure to give that to them.

Smart search is an AI strategy you can use to provide more accurate, relevant, and user-friendly search results. Its job is to understand your consumer’s intent, context, and preferences to provide them with a more effective and efficient search experience.

So, what exactly makes this system smart? Let’s find out.

What makes smart search so smart?

Smart search can truly support your eCommerce business and help you increase conversions in the long run because it:

Is multilingual: Smart search is available in 300+ languages.
Recognizes synonyms and homonyms: Smart search systems use NLP to interpret the meaning behind users’ search queries. They can recognize synonyms and homonyms, which ensures that the consumers get relevant results even if they didn’t use the exact search terms.
Supports different letters: Smart search supports both alphabetical and Cyrillic letters.
Recognizes the context: Thanks to semantic search, which focuses on understanding the meaning and context of words rather than just their literal interpretation, smart search systems can provide results that match the user’s intent even if the phrasing differs.
Tolerates minor mistakes: In this fast-paced world, fast typing is a must. Luckily for us, smart search lets us find what we need by tolerating our typos.
Supports voice search: Thanks to the growing popularity of voice assistants, users can now speak their search queries and the system processes their speech to provide accurate results.
Enables filters and facets: With smart search, consumers can use advanced filtering options and facets to narrow down search results based on price, category, brand, and more attributes.
Provides real-time updates: Smart search systems can provide real-time updates, especially in dynamic environments like e-commerce where product availability and prices usually change frequently.

Of course, this is not all there is to it but you get the picture.

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This is where the “cold start” part of your consumer’s journey ends. You’ve already gathered enough data about them so you can take your relationship to the next level.

Let’s explore your options further.

#4 Alternative products recommender

Once you move into the second phase, your main goal is to convert. To do this, you want to:

Make sure that the customer who lands on your eCommerce webshop makes a purchase,
See their basket size going up, and
Ensure they keep coming back to buy some more.

This is where the alternative products recommender comes in handy.

Alternative recommender systems are paramount for eCommerce businesses. Thanks to them, customers can explore a list of products (in the form of a product catalog) and find the thing they are looking for among an overwhelming number of options.

There are two ways in which this AI system supports your eCommerce:

First, a less complex ML-based model uses and analyzes the customer’s input to offer similar products within the same category. If they’re looking for a black shirt, this model will offer similar products that fall into the same product category.
Second, an NLP-based model takes all the details about the product that the customer inserted (color, size, price, brand, etc.) and compares it to other products in the catalog. This way, the customer can get more relevant and more precise hits, ultimately increasing the chances of conversion.

The reason why this recommender system is so interesting for the eCommerce industry is because it keeps the options open for the customer before they’ve made a decision and increases the chances of them making a purchase.

#5 Related products recommender

Similar to the previous recommender system, the related products recommender aims to increase or complete your customer’s basket.

For example, the consumer is looking at a black T-shirt, so this recommender offers other products they are most likely to buy in addition to the black T-shirt – black pants, black jacket, black socks, etc.

Considering its role, this recommender is usually found on the checkout page, but it’s not a generally applied rule.

Related products recommender can be simple and complex:

The simple one operates based on associations. It takes into consideration the products that have previously been in the same basket with the black T-shirt (i.e. a black jacket or black pants) and offers those other products to the customer, too.
The complex one considers the products in your customer’s basket and generates its own combinations of products that it thinks the customer might be interested in purchasing, too. Here, the recommender creates a list of products that are most likely to be found in the customer’s basket, together with the products that are already in it.

Overall, the related product recommender can significantly affect conversion rates and affect how your shoppers interact with your brand in the long run.

#6 Cross-sell automatization

To be able to boost your conversion rates, you simply have to learn as much as possible about your customers and their needs. You can do this by implementing a smart cross-sell strategy.

According to the definition, cross-selling means promoting or selling a different product or service to a customer who has already purchased something from you.

This technique can help you boost revenue by providing customers and clients with an additional product or service that they might find beneficial. As a result, you can increase your customer lifetime value.

Here are a few examples:

A consumer bought a pair of sneakers from you so, you offer them socks at a 20% discount.
One customer bought a very expensive dress, so you offer them a matching purse or shoes.

Although it’s common practice to offer products that are in some way related to the products the consumer already bought, this doesn’t have to always be true.

For example, if you have a product that hasn’t been selling well, you can offer it to a customer who’s already bought your best-selling product.

How does AI fit into this part?

Well, once your customer has made a purchase, AI automatically analyzes their data thoroughly so it can provide additional recommendations for that particular customer and increase the likelihood of another purchase.

For this step, you don’t wait for the customer to come back to your website but you take the next step and reach out to them via e-mail or a text message.

#7 Interactions-based recommender

Interactions-based recommender gathers and analyzes data about everything a consumer does online (not only on your website) – their clicks, their likes, their previous purchases, etc.

Based on the data it gathers about the consumer, interactions-based recommender can offer similar or related products to a consumer that goes back to your website.

Once they go back to a certain website, this recommender system already has “a file” on the consumer with details about what they bought, searched, or viewed.

Thanks to the consumer’s history, this recommender system can and will offer similar or related products, or even show them what products they viewed the last time they were here to increase the chances of another purchase.

This brings you (eCommerce) one step closer to providing your customers with a completely personalized experience.

#8 Personalized recommender

Personalized recommender systems represent the highest level of personalization.

Unlike the interactions-based recommender that operates based on the previous web activity of your consumers, a personalized recommender operates based on the previous purchases of your consumers.

Based on the data this recommender system takes into consideration, we can have three different personalized recommender systems:

Time-based: This recommender system covers all the patterns in your consumer’s previous purchases. For example, it takes into account when they buy certain products – toilet paper once a month, detergent twice a month, etc. – and based on these patterns, it knows when to recommend these products again.
Collaborative filtering: The social recommender system looks at similarities between its users and consumers so it can provide a more personalized experience to all of them. An example of this recommender is Netflix, whose recommender system finds similarities in users’ preferences. It then goes on to recommend similar or even the same series of movies to the users, especially if they haven’t seen it before.
Content-based: This recommender system usually considers a user’s history and identifies similarities between products based on their descriptions, such as product descriptions or lyrics. The similarity metric can be anything – product description, product review, or technical documentation about the product.

Essentially, personalized recommenders are a must-have for any eCommerce whose idea is to move their business forward and continuously keep providing an exceptional experience to their customers.

#9 Wheel of Fortune

Although not strictly an AI strategy – the Wheel of Fortune recommender represents the icing on your personalization cake.

This recommender is becoming more popular thanks to its ability to gamify the experience for your consumers. It allows you to provide a more diverse experience to your customers past the point of their first purchase.

Moreover, it’s a great way for you to collect valuable leads and boost your cross-sell approach.

How does it work?

The first step can be one of the following:
- The user has to register (create an account) on your website to participate in the Wheel of Fortune.
- The user wins an exclusive right to spin the Wheel of Fortune on your website after spending a certain amount of money, let’s say $100.
The next step is to spin the wheel in an attempt to win something – a discount for another product or a special/limited offer.
Usually, every other slot is empty and every other slot offers a special offer/discount, making the entire experience more random.

However, working with one of our clients, our team at Things Solver managed to develop a personalized Wheel of Fortune recommender with the help of machine learning.

This system was designed to take into account the historical data of each user and make use of data gathered by the personalized recommender to generate a completely personalized Wheel of Fortune experience.

Based on data it has on the users on the website, this Wheel of Fortune was able to offer categories of products and brands that the user has already bought before or shown interest in.

Just think about it – who doesn’t love to go back to an online shop and win a special discount on their favorite brand?

Embracing AI strategies paints a brighter future for eCommerce

In the ever-evolving landscape of modern eCommerce, the adoption of AI strategies has become a necessity.

As digital transformation continues to shape the industry, Artificial Intelligence continuously proves to be a transformative force that allows you to:

Stay ahead of the curve to grow and keep customers engaged.
Reshape the way you connect with customers and drive conversions.

By strategically addressing unique pain points in the consumer experience, the AI approaches we’ve outlined here hold the promise of bridging clicks to meaningful conversions.

These strategies, rooted in AI’s power to glean insights from user interactions and past behaviors, can elevate your personalization efforts to new heights.

Don’t hesitate to reach out to us at ai@thingsolver.com if you need help offering relevant recommendations, understanding your consumers’ intent, and enhancing your users’ engagement.

Together, we can work on nurturing your relationships and encouraging your loyal consumers to repeat purchases with suitable AI strategies.

AI in eCommerce: What is it and how does it work?

Posted on 06. 03. 2023.19. 10. 2023. by Darko Marjanovic

Here you are, reading this article – most likely because some AI-powered algorithm made it available for you based on your search and other information it might have on you.

I know this sounds a bit scary. And the truth is that, for most people, Artificial Intelligence (AI) and its closely connected sub-term machine learning (ML) still evoke some robot-like perception, fueled by movies, comics, and whatnot.

In reality, what differentiates AI and ML from other systems in business is the ability to process vast amounts of data into actionable insights for business purposes. And on top of that – learn.

So, to show you that these systems aren’t that frightening and that they can learn in a similar way a person would, we’re going to explain what AI systems are and how they work.

Moreover, we’ll do this from the perspective of eCommerce and list all the benefits of using AI in eCommerce.

Finally, we’ll discuss essential AI-based eCommerce solutions you should consider for your eCommerce business.

Understanding AI in eCommerce

Understanding AI in eCommerce is crucial for grasping the immense impact it can have on the online retail landscape.

In the eCommerce realm, the role of AI is transformative in several ways:

It affects the way businesses interact with customers, optimize operations, and drive growth.
Additionally, AI helps facilitate effective inventory management, demand forecasting, and logistics.
Finally, AI-driven chatbots and virtual assistants offer round-the-clock customer support, improving responsiveness and customer satisfaction.

As you can see, AI is quite helpful in reshaping eCommerce and empowering businesses to stay ahead in the competitive marketplace.

Yet, the helpfulness of AI in eCommerce doesn’t stop here.

The benefits of using AI in eCommerce

AI in eCommerce doesn’t only empower you to stay competitive. Its impact extends to a few other aspects, including:

Predictions: By crunching vast amounts of data, AI can provide more precise predictions about your business – be it sales, costs, stock, or customer churn. Thanks to the incredibly accurate AI-based predictions, you can greatly improve your planning and decision-making process.
Personalization: Like it should be, far above changing the name on top and remembering birthdays, AI-powered recommendation systems can offer data-based recommendations of products to customers that are a far better fit than rule-based ones
Super targeted campaigns: AI enables fully personalized campaigns, which feature products customers are more inclined to buy or be interested in, again, based on data available. In addition to that, it can learn the most appropriate channel to get a response for each customer. Less clutter for consumers, more relevance.

It’s old news that maintaining and boosting revenue are everybody’s goals, especially when competition is tough.

And since you already have so much data at your disposal, why not use it smartly?

With the help of AI- and ML-based eCommerce solutions, you can expect:

Higher conversions – based on our experience in working with different clients, conversion can increase as much as 30%, sometimes even more.
Better campaign response due to personalized offers.
Next-level UX for the customer thanks to UI adaptation and NLP-powered chatbots.
A better understanding of business segments, trends, and planning basis.

Now that you know that AI isn’t that scary after all, let’s see what AI-based solutions can be a game-changer for your eCommerce business.

4 Essential AI-based eCommerce solutions you should consider

In the dynamic world of eCommerce, delivering exceptional customer experience and staying on top of your game is vital. Moreover, embracing cutting-edge technologies and solutions is what can make or break your eCommerce existence.

Read on, as we go through the essential AI-based eCommerce solutions you should try and see how they can unlock new dimensions of growth and success for your eCommerce business.

Recommender systems

Most eCommerce sites use some kind of recommender system, but as we know from our own experience, the difference in the quality of given suggestions is huge. Having a good recommender can incredibly boost sales by using data about the customer and learning from lookalike clients.

For recommender to work best, it’s important to have options for recommendation:

From simple best sellers for new customers we know nothing about,
All the way to sophisticated cross-selling recommendations, based on the history or similar customer choices.

In general, the more data there is about the customer, the more personalized recommendations you can generate.

AI allows a deeper understanding of customers’ needs, so it can display far more relevant products to the customer and increase their basket size.

For instance, if a person is searching for a pink modern watch, and returns again, based on previous experience of color preference, the system can suggest modern pink shoes in the shoe section. Or when a person suddenly starts buying baby wipes, it might be a good time to offer them a pram, baby food, or similar baby items.

Smart segmentation

We are used to devising some sort of customer segments we follow to determine marketing tactics. And this is ok, but also limited, mostly because it is based on experience, not data.

With AI-powered segmentation, you can sift through many more variables and detect important segments based on different criteria than the ones you might customarily use.

These segments can be used to predict business trends, but also to detect the correct actions to prevent churn by addressing dormant customers and upselling to the right people based on the behavior of similar/lookalike customers.

Having such a tool enables you to address the right people with the right message. It’s also a great way to monitor how these segments change and/or new ones emerge, so you can act promptly.

Smart search

Humans that we are, we explore various websites in search of items. Yet, in return, we often get robot-like misunderstandings or irrelevant results. Miss one letter or change the word order, and you get nothing.

Now imagine how frustrating this can be when you’re looking for something specific and you know the website has it, but the search couldn’t „find“ it. Let’s not get started on how this affects the sales rates.

Luckily, AI can help here significantly!

AI-powered search engines on websites process languages similarly to humans and can provide relevant results in cases of incomplete queries, misspelled words, or even for a foreign language.

To make matters better, it recognizes the context even if the word is a synonym. This feature is called natural language processing and increases conversions on-site by up to 15%.

With smart search, you can significantly improve the experience for your customers and make sure they leave your eCommerce happy and with the product they were looking for.

Smart campaigning

To put things in perspective very vividly – how many promotional emails have you deleted this morning? We would dare to bet – a lot of them!

Now stop and think, did the ones you bothered to open contain anything of interest to you? Probably not.

For a while now, companies have been struggling to reach out to consumers. Also, consumers are overwhelmed with irrelevant offers from all over the place.

In such a context, being able to understand what your customers might really need (based on data) and having tools to offer personalized products to each of them greatly increases the chances to convert messaging into purchase.

Thanks to an AI-powered campaigning tool, you can enable high-scale personalization. This means that you can reach out to a high number of customers and still offer „to each their own“ – AI can help you determine what products are a good fit for each segment/person and select the best channel to reach them.

This might really be the beginning of a win-win marketing – the customer gets interesting offers, the seller gets high conversions, and everyone benefits.

Ready to embrace AI in eCommerce?

Everything we’ve discussed in this blog post was just scratching the surface when it comes to the benefits of AI in the eCommerce industry. However, to make the best out of AI and successfully implement it in your eCommerce business, we recommend that you start by defining your business problem. Try to answer these questions:

How do you compare to your competitors?
How is your revenue trending?
What is impacting your conversion rate currently? Is there anything specific you would like to increase – like basket size, conversion, frequency of visits, campaigning responses?
What data do you have? Are you using it? Can you extract all you need?
And your webshop – are you satisfied with search performance and the recommendation system? Can this be improved?

Once you have defined your main business problem, you can be smart and let professionals take it from there. At Things Solver, we are always happy to help businesses move forward and embrace the power of AI-based solutions. Contact us today at ai@thingssolver.com for a free consultation and we’ll make sure to help you navigate and prepare for the next level of your AI-driven business!

Mastering data-driven personalization

Posted on 28. 02. 2023.23. 10. 2023. by Darko Marjanovic

Data-driven personalization is a critical aspect of any successful retail or e-commerce strategy.

By tailoring your messages, offers, and product recommendations to each customer’s unique interests and preferences, you can create a more engaging and meaningful experience that drives loyalty and increases sales.

However, personalization is only effective if you have the right data. To help you with that part, we will explain what data-driven personalization is and walk you through the essential data for successful personalization.

What is data-driven personalization?

Do you know that, despite the economic challenges, almost 69% of business leaders are increasing their investment in personalization?

According to Segment’s State of Personalization 2023 report, they are – which paints a very bright picture about how important personalization actually is.

Let’s quickly explain what data-driven personalization is.

Data-driven personalization assumes that you already have enough data about an individual to provide tailored content precisely when they need it. Essentially, data-drivenpersonalization is all about delivering timely value. The era of indiscriminate mass messaging and generic approaches is over, isn’t it?

In our modern world, it’s imperative to be precise, individualized, and strategically oriented in your marketing endeavors.

Why is data-driven personalization important in retail and e-commerce?

The reasons behind the importance of data-driven personalization in retail and e-commerce are many. Let’s explore just a few of them:

Enhanced customer experience & engagement,
Improved customer loyalty & trust,
Increased sales and revenue,
More effective marketing & competitive advantage, and
Data-driven insights.

#1 Enhanced customer experience & engagement

The way customers are engaging with brands and businesses has significantly changed.

What they want is for businesses to understand their individual requirements and expectations. In reality, 66% of them feel they are often treated like numbers.

Who doesn’t like a personalized message from their favorite brand offering them an exclusive discount for a product they love?

At the end of the day, it’s all about how you make them feel.

This is why it’s essential to truly know who your shoppers are and use personalization to tailor the shopping experience to each and every one of them. It’s what makes them feel less like numbers and more like valued and understood members of your community.

When your customers feel valued, understood, and heard, they feel more inclined to interact with your brand or products through product reviews, genuine recommendations, and personalized content.

Engaged customers are more likely to become brand advocates in the long run.

#2 Improved customer loyalty & trust

Trust and transparency are key to gaining your customers’ loyalty.

Think about it this way – when you set out to deliver personalized recommendations and offers to your customers, they are more likely to return to your website or online shop.

Considering they are expected to leave personal information there, it’s no wonder many are concerned about the privacy of the data they leave there.

In case you weren’t aware of this problem, let us share an interesting statistic:

Only 51% of consumers feel like they can trust brands to keep their personal data secure and use it responsibly.

So, almost half of consumers think businesses don’t respect their privacy and don’t behave responsibly with the data they share on their websites.

So, the answer here is pretty simple. If you want to ensure personalization and protect the privacy of your customers, then be open and honest about the data you collect.

When they realize that their data is safe with you, they will trust you and come back in no time.

#3 Increased sales and revenue

If you’re already using data-driven personalization to boost your marketing efforts, you know that it can boost your revenue and sales.

Concrete numbers from one McKinsey Survey speak in favor of this, too. According to the survey, personalized marketing can boost revenues by 5 to 15% and increase marketing ROI by 10 to 30%.

How does personalization do that?

By suggesting relevant products, cross-selling, and upselling.

The simple math is that when your customers see products that match their interests and needs, they are more likely to make repeated purchases.

#4 More effective marketing & competitive advantage

According to Google, 90% of leading marketers think that personalization significantly contributes to business profitability.

Targeted marketing saves time and money by not sending irrelevant messages to customers. Personalized marketing sets you apart from competitors who offer a one-size-fits-all shopping experience. You know that one size doesn’t fit all!

By showing personalized product recommendations or sending reminders based on a customer’s browsing and shopping history, you can reduce those cart abandonment rates and make sure your customers come back for more.

#5 Data-driven insights

Data-driven personalization is the hardest digital strategy to implement, according to 63% of marketers.

And even though collecting and analyzing customer data might be a bit hard, it’s one of the best ways to gather valuable insights into how your consumers behave and what they prefer. But, you can use this vast data to refine your product offerings and improve marketing strategies.

The end result – enhanced personalization that provides a more tailored, engaging, and relevant shopping experience.

Overall, personalization in retail and e-commerce not only benefits customers by saving time and offering products they genuinely want but also helps you stay competitive in a rapidly evolving market.

Essential data for enhanced data-driven personalization

As we mentioned at the beginning, enhanced personalization relies on a foundation of essential data.

The bedrock of this data form:

Customer information,
Transaction history, and
Product preferences.

Let’s see how this empowers businesses to create deeply personalized experiences that foster loyalty and increase sales.

Customer data

The first step to data-driven personalization is collecting customer data. This includes crucial details about your customers like

Email addresses,
Mobile phone numbers, and
Demographic information, including age and location.

You can collect this data through sign-ups on your website, social media channels, or in-store interactions.

With the collected data in hand, you can start building comprehensive customer profiles. These profiles serve as a repository of individual preferences and characteristics, aiding in understanding and predicting customer behavior.

Finally, armed with well-defined customer profiles, you can segment your audience based on shared characteristics and interests. This enables you to create precisely targeted messages and promotions, ensuring that your engagement efforts resonate effectively with each distinct group of customers.

Transaction data

Transaction data is a goldmine of information when it comes to personalization.

By analyzing a customer’s purchase history, you can gain insights into their buying behavior, purchase preferences, and spending habits. This data can help you create targeted messages, offers, and promotions that are tailored to the specific needs and interests of your customers.

For example, if you notice that a customer regularly buys products related to a particular hobby or interest, you can create a personalized Viber campaign to send targeted messages or promotions related to that interest.

Product data

Analyzing your product data is another critical aspect of data-driven personalization.

By looking at the products that your customers are interested in or purchasing, you can gain insights into their preferences and interests. This data can help you create targeted product recommendations, promotions, and even new product lines tailored to your customers’ needs.

For example, if you notice that a significant number of your customers are interested in eco-friendly products, you can create a new product line that emphasizes sustainability and ethical manufacturing.

Leveraging personalization tools and platforms

Leveraging data-driven personalization tools and platforms

There are a variety of personalization tools and platforms available that can help you make the most of the customer, transaction, and product data you collect.

These tools can help you automate and personalize your messaging and promotions, making it easier to engage with your audience on a more individual level.

For example, you can use email marketing platforms that allow you to create personalized email campaigns that automatically pull in data from your customer profiles and transaction history.

But if you want to go beyond that, you can try Solver AI Suite, our modular platform that can drive your business from assumptions to concrete results. In fact, more than 9 in 10 companies are using AI-driven personalization to drive growth in their business

With Solver AI, you can always rely on full end-to-end personalization on multiple channels. This data-centric solution will give you valuable business insights and allow you to make more informed decisions.

Testing and optimization

Personalization can be a daunting task, but it’s important to start small and test your efforts.

You can start by segmenting your audience based on basic demographic data or interest information to create targeted messages or promotions.

After that, make sure to conduct A/B testing to compare the effectiveness of your personalization efforts and make adjustments accordingly. We can’t stress the importance of A/B enough!

Over time, you can refine your personalization efforts based on your customer and transaction data, creating a more engaging and relevant experience for your customers in the long run.

Final thoughts

Data-driven personalization is truly a critical aspect of any successful retail or e-commerce strategy. Using customer, transaction, and product data, you can easily deliver targeted messages, promotions, and product recommendations to your customers. Never stop learning about their needs and interests – that’s what data-driven personalization is all about.

By starting small, testing your efforts, and using personalization tools, you can create a more engaging and meaningful experience for your customers, driving loyalty and increasing sales.