What is a Propensity Model?

Have you ever wondered how your favorite online store seems to know exactly what you want to buy next? Or how they send you a special offer just when you were thinking about making another purchase? It’s not magic, and it’s certainly not mind-reading! It’s often thanks to something clever called a propensity model.

Imagine trying to guess what your best friend wants for their birthday. You think about what they like, what they’ve talked about, and what they already have. You’re using all that information to make your best guess, right? Well, a propensity model does something similar for businesses. It’s a smart computer tool that helps companies predict what their customers are likely to do next. This helps them offer you things you’ll truly like, making your shopping experience better and helping the business grow.

What is a Propensity Model? The Smart Guessing Game for Businesses

At its heart, a propensity model is like a super-smart fortune teller for businesses, but instead of magic, it uses data. It looks at all the information it has about customers – like what they’ve bought before, what pages they’ve visited on a website, or even how often they interact with a brand’s messages. Then, it uses this information to figure out the chances, or “propensity,” of a customer doing something specific in the future.

Think about it this way: if a store knows you love action figures and have bought several in the past, a propensity model might predict you’re very likely to buy a new action figure when it comes out. Or, if you’ve been visiting a certain clothing brand’s website every day for a week, the model might predict you’re about to make a purchase. These predictions help companies make smarter decisions about how to talk to you and what to show you.

Why Do Businesses Need to Predict What You’ll Do?

You might ask, “Why bother with all this predicting?” The answer is simple: it helps businesses serve you better and use their resources wisely. Instead of sending every customer the same message, or showing them every single product, a propensity model helps them be more specific and helpful. This means:

• You get offers and suggestions that are more relevant to you.
• Businesses can focus their efforts on customers who are most likely to respond.
• It helps companies build stronger, happier relationships with their customers.

Ultimately, a propensity model helps create a more personalized and enjoyable experience for you, the customer, while also making the business more successful. It’s a win-win!

How Do Propensity Models Work? Peeking Inside the Prediction Machine

So, how does this smart guessing game actually happen? It’s not a person sitting there making guesses. It’s all done by computers using fancy math and special programs called algorithms. But don’t worry, we’ll keep it simple!

Imagine you have a big box of LEGOs. Each LEGO piece represents a piece of information about a customer. These pieces could be:

• What they bought: Did they buy a red shirt or a blue one?
• When they bought it: Did they shop last week or last year?
• How much they spent: Did they buy one small item or a whole cart full?
• How they browse: Did they look at shoes, then hats, then leave? Or did they spend a lot of time on the jacket page?
• Their feedback: Did they leave a review for a product they bought?

The propensity model takes all these LEGO pieces for many different customers and starts looking for patterns. It’s like building different structures with the LEGOs and seeing what shapes emerge.

Finding the Patterns: The Key to Prediction

The computer looks for connections. For example:

• “Customers who bought ‘Item A’ are 80% likely to also buy ‘Item B’ within the next month.”
• “Customers who haven’t visited the website in three months are 60% likely to stop buying from us altogether.”
• “Customers who leave a 5-star review are 90% likely to buy again soon.”

These patterns aren’t just guesses; they come from observing what thousands or even millions of customers have done in the past. The model learns from history. The more data it has, the smarter its predictions become.

Once the model finds these patterns, it can then look at a new customer or a customer it hasn’t predicted for yet. It compares that customer’s “LEGO pieces” to the patterns it has learned. Based on how well their pieces match a certain pattern, it gives a score or a percentage – that’s the propensity score.

A high propensity score means the model thinks a customer is very likely to do something (like make a purchase). A low score means they’re less likely. Businesses then use these scores to decide what action to take, like sending a special offer or a friendly reminder.

This whole process helps businesses understand their customers better than ever before. It’s like having a superpower to anticipate customer needs and make their shopping journey more enjoyable.

Why Businesses Love Propensity Models: Superpowers for Growth

Propensity models aren’t just cool; they’re incredibly useful for businesses, especially those that sell online. They provide several “superpowers” that help companies make smarter decisions and grow their brand.

1. Knowing What You Want: Personalized Experiences

One of the biggest benefits is making your online experience feel more personal. Instead of seeing random products, you see things you’re actually interested in. This is called personalization.

For example, if you’ve been looking at athletic shoes on a sports website, a propensity model might predict you’re interested in new running gear. The website can then show you related items or even special deals on those shoes. This makes shopping easier and more fun because you don’t have to search as hard to find what you want.

A good example of this is how brands use Yotpo Reviews. A propensity model could identify customers who are highly likely to leave a positive review based on their past purchases and interactions. By asking the right customers for reviews at the right time, brands can gather more genuine feedback, which then helps other shoppers make informed decisions.

2. Keeping You Happy: Boosting Customer Loyalty

Businesses want you to come back again and again. This is called customer retention. Propensity models can spot customers who might be thinking about leaving or haven’t bought anything in a while. With this early warning, the business can reach out with a special offer or a friendly message to remind you why you loved them in the first place.

Imagine a scenario where a model predicts you have a low propensity to make another purchase soon. A brand could then proactively offer you a reward through their loyalty program. Yotpo Loyalty helps brands create exciting rewards programs that keep customers engaged. By using insights from propensity models, businesses can make sure these rewards go to the customers who need them most to stay active and happy, preventing them from drifting away. This creates a stronger connection and encourages customers to become loyal fans.

3. Finding New Friends: Smart Customer Acquisition

Every business wants new customers. Propensity models can help here too! They can look at information about existing loyal customers and then help find new people who share similar characteristics. It’s like saying, “We know our best customers like X, Y, and Z. Let’s find other people who also like X, Y, and Z!”

This helps businesses spend their marketing money more wisely, focusing on people who are most likely to become good, long-term customers, rather than just anyone. It’s about being efficient and effective in finding the right audience.

4. Making Smart Decisions: Marketing Efficiency

With propensity models, businesses don’t have to guess what marketing campaigns will work best. They can use the predictions to decide:

• Who to send an offer to: Only send discount codes to those who are likely to buy with one, not those who would buy anyway.
• What message to send: A customer with a high propensity to churn might get a “we miss you” message, while a loyal customer might get an exclusive preview of new products.
• When to send it: Send a reminder email about an abandoned cart only to customers with a high propensity to complete the purchase if reminded.

This targeted approach saves money, time, and ensures that customers receive messages that are truly valuable to them. It improves the conversion rate for businesses, meaning more visitors turn into buyers.

Propensity models are a powerful tool that transforms how businesses interact with their customers, turning guesses into smart predictions that benefit everyone. They help brands grow by creating more meaningful and effective interactions.

Different Flavors of Propensity Models: Predicting Specific Actions

Just like there are different types of questions you might ask your friend about their birthday, there are different kinds of propensity models, each designed to predict a specific customer action. Let’s look at some common ones:

1. Propensity to Purchase: Who Will Buy Something?

This is probably the most common type. This model tries to figure out which customers are most likely to buy a product or service. It looks at things like:

• How often you visit the website.
• Whether you’ve added items to your cart but didn’t buy.
• What products you’ve viewed or favorited.
• Your past buying history.

If the model gives you a high “propensity to purchase” score, a business might send you a personalized recommendation or a gentle reminder about items in your cart. This is a key part of the eCommerce marketing funnel, helping move customers from browsing to buying.

2. Propensity to Churn: Who Might Leave?

The word “churn” means when a customer stops doing business with a company. This model is super important for keeping customers happy. It identifies customers who are showing signs that they might leave. These signs could be:

• A sudden drop in website visits.
• No purchases for a long time, especially if they used to buy regularly.
• Less interaction with emails or other brand communications.

When a model spots these signs, the business can try to re-engage the customer with a special offer or by highlighting new products. This is vital for customer retention.

3. Propensity to Engage: Who Will Interact?

Engagement means how much you interact with a brand, beyond just buying things. This model predicts if you’ll open an email, click on a link, visit a certain page, or even leave a review. Factors might include:

• How often you’ve opened emails in the past.
• Whether you’ve clicked on specific types of content.
• If you’ve previously participated in surveys or polls.

For example, if the model predicts you have a high propensity to engage with an email about new product launches, the company will definitely make sure you get that email. Similarly, if you’re likely to leave a helpful review, the brand might send you an email asking for your feedback on a recent purchase. Yotpo Reviews helps brands collect and display this valuable user-generated content.

4. Propensity to Advocate: Who Will Tell Their Friends?

An advocate is someone who loves a brand so much they tell their friends and family about it. This is incredibly valuable for businesses (word-of-mouth marketing!). This model predicts which customers are most likely to:

• Refer a friend.
• Share positive experiences on social media.
• Leave glowing public reviews.

Customers with a high propensity to advocate are often the happiest and most loyal. Businesses might reward these customers with exclusive benefits through a loyalty program, like those built with Yotpo Loyalty, encouraging them to continue sharing their love for the brand. This can also tie into referral programs, where both the advocate and the new customer get a benefit.

By using these different types of propensity models, businesses get a much clearer picture of their customers and can plan their strategies more effectively, leading to happier customers and stronger brand growth.

Propensity Models and Your Online Shopping: A Better Experience

You might not even realize it, but propensity models are working behind the scenes to make your online shopping journey smoother and more enjoyable. They help businesses give you what you want, when you want it, making the whole experience feel more personal.

Seeing Products You’ll Love: Smart Recommendations

Have you ever been on a website and seen a section that says “Recommended for You” or “Customers Who Bought This Also Bought…”? Propensity models are often driving these suggestions. They look at your past clicks, purchases, and even what other customers like you have done, to show you products that you’re most likely to be interested in.

This means less time searching and more time discovering cool new items that fit your tastes. It’s like having a personal shopper who truly understands what you like.

Loyalty Programs That Feel Just Right

Many brands offer loyalty programs to reward their best customers. Propensity models can help make these programs even better. For instance, a model might identify customers who are highly likely to become VIPs if given a small nudge. A brand using Yotpo Loyalty could then create a special tier or offer exclusive points to those specific customers to encourage them to reach that next level of loyalty.

This personalized approach to loyalty means that rewards and benefits feel more meaningful to you. It’s about recognizing your value as a customer and giving you perks that truly matter, building a strong, lasting relationship between you and the brand.

Your Voice Matters: Asking for Reviews at the Right Time

Leaving reviews for products you’ve bought is a great way to help other shoppers and tell brands what you think. Propensity models can help businesses decide when is the best time to ask you for a review and which products to ask about.

For example, if you just bought something and the model predicts you’re very happy with it (based on your past behavior or how quickly you purchased again), a brand using Yotpo Reviews might send you an email asking for your feedback. This ensures that the brand gets valuable insights from satisfied customers, and you get a chance to share your experience when it’s fresh in your mind. This user-generated content is incredibly helpful for future customers and builds trust.

By using smart tools like propensity models with solutions like Yotpo’s Reviews and Loyalty programs, businesses can create an online shopping world that feels more tailored to you, making every interaction more enjoyable and every purchase more satisfying.

Building a Simple Propensity Model: A Peek Behind the Curtain

You might be thinking, “This sounds complicated!” But let’s break down the basic steps of how a business might build a very simple propensity model. It’s like baking a cake – you need ingredients, a recipe, and a way to check if it tastes good.

Step 1: Gather Your Ingredients (Data)

The first and most important step is to collect lots of information about your customers. This data is the “ingredients” for your model. For a simple model predicting if a customer will buy again, you might look at:

• Recent Purchases: When was their last order? What did they buy?
• Website Activity: How often do they visit the site? What pages do they look at?
• Engagement: Do they open emails? Click on links?
• Customer Reviews: Have they left a review for a past purchase? (Product reviews are excellent data!)

The more relevant data you have, the better your predictions will be.

Step 2: Spot the Patterns (Training the Model)

Once you have your data, you need to “train” your model. This means showing it historical data and telling it what happened. For example, you might show it data from 1,000 customers and say, “These 500 customers bought again, and these 500 didn’t.”

The computer then crunches all this information to find patterns. It might discover that customers who visit the website at least three times a week and opened the last five emails are much more likely to make another purchase. It’s identifying the “recipe” for someone who buys again.

This is where the algorithms do their magic, learning which factors are most important for predicting the desired outcome.

Step 3: Make Your Guess (Prediction)

After the model has learned from the past data, it’s ready to make predictions for new or existing customers. You feed it the current data for a customer (their recent website visits, email opens, etc.), and the model uses the patterns it learned to calculate a propensity score.

This score tells you the probability (a number between 0 and 1, or 0% and 100%) that the customer will perform the predicted action. For example, “Customer A has a 75% propensity to purchase within the next 7 days.”

Step 4: See How You Did (Testing and Improving)

No model is perfect on day one. After making predictions, businesses need to test them to see how accurate they were. Did the customers predicted to buy actually buy? Did those predicted to churn actually leave?

Based on these results, the model can be fine-tuned and improved. You might add more data, change the algorithm, or adjust the factors it considers important. This is an ongoing process to make the predictions even smarter over time.

Even simple propensity models can provide powerful insights, helping businesses understand their customers better and make more effective decisions. Companies use these models to improve everything from customer experience to marketing effectiveness.

Challenges with Propensity Models: Not Always a Perfect Crystal Ball

While propensity models are incredibly powerful, they’re not magical. Like any smart tool, they have their limitations and challenges. It’s important to understand that even the best models can’t predict the future with 100% certainty. Here are a few reasons why:

1. Data Quality: Garbage In, Garbage Out

The saying “garbage in, garbage out” is very true for propensity models. If the data used to train the model is incomplete, incorrect, or old, the predictions won’t be very accurate. Imagine trying to guess your friend’s favorite color if all you know is their shoe size! The model needs good, clean, and relevant data to make smart predictions.

This means businesses have to work hard to collect and maintain high-quality customer information. Reviews collected through Yotpo Reviews, for example, provide fresh, relevant insights directly from customers, which can be great data for these models.

2. Changing Customer Behavior: People Are Unpredictable!

People are not always predictable. Our tastes change, our needs evolve, and what we want today might be different tomorrow. A propensity model learns from past behavior, but what if a customer suddenly has a new interest or a change in their life?

For example, someone might suddenly start buying baby products even if they’ve never shown interest before. The model might not have enough information to predict such a big shift right away. This is why models need to be constantly updated and retrained with new, fresh data to keep up with changing trends and individual choices.

3. Too Many or Too Few Factors

Deciding which pieces of information (factors) to include in the model can be tricky. If you include too many factors, the model might become overly complex and struggle to find clear patterns. If you include too few, it might miss important clues.

Finding the right balance is key. It takes skill and experience to choose the most impactful data points that truly influence customer actions.

4. Ethical Considerations and Privacy

Using customer data always comes with a big responsibility. Businesses must ensure they are using data in a way that respects customer privacy and builds trust. The goal of a propensity model is to enhance the customer experience, not to be intrusive or creepy.

Transparency about how data is used and giving customers control over their information are super important. This helps maintain a positive relationship between you and the brands you love.

Despite these challenges, propensity models remain incredibly valuable. Businesses are constantly working to improve them, making them more accurate and useful, while also ensuring they are used responsibly and ethically to create better customer experiences.

The Future of Propensity Models in eCommerce: Smarter Than Ever

Propensity models are already making a big difference in how businesses operate and how you experience online shopping. But this is just the beginning! The future promises even smarter predictions and more personalized interactions.

More Personalized Than Ever Before

Imagine walking into your favorite physical store, and the staff knows exactly what you like, what you’ve bought before, and can instantly suggest new items you’d adore. Propensity models are bringing this level of personalization to the online world, and it will only get better.

Future models will likely combine even more types of data – from how you interact with a brand’s social media to your responses to specific marketing messages. This will allow for incredibly precise predictions, leading to recommendations and offers that feel almost tailor-made for you.

Better Understanding of Each Individual

Instead of just seeing customers in big groups, future propensity models will get even better at understanding you as an individual. They’ll be able to learn from your unique shopping habits, even if they’re different from everyone else’s. This means less generic marketing and more truly relevant communication.

For example, if you tend to buy only during sales, the model might predict your propensity to purchase is highest when a discount is offered, prompting a brand to send you a special sale notification. Or, if you’re a loyal customer who always leaves detailed reviews, the model might ensure you’re among the first to receive early access to new products as a thank you.

Building Stronger Relationships with Brands

Ultimately, the goal of these advanced models is to help businesses build stronger, more meaningful relationships with their customers. When a brand understands you better, it can provide better service, better products, and better experiences.

Companies like Yotpo help brands leverage these kinds of insights. For instance, by understanding a customer’s propensity to engage with a loyalty program, brands can tailor rewards and communication to maximize their participation using Yotpo Loyalty. Similarly, knowing a customer’s likelihood to leave a review allows brands to strategically request feedback using Yotpo Reviews, ensuring they gather high-quality user-generated content that builds trust.

Propensity models are helping shape an eCommerce world where shopping is not just about transactions, but about delightful, personalized experiences that make you feel valued and understood. They empower brands to connect with you in ways that truly matter, fostering loyalty and encouraging you to come back again and again.