THE POWER OF PERSONALIZATION: B2METRIC'S PRODUCT RECOMMENDATIONS FOR E-COMMERCE
Table of Content
- How to use product Recommendation in E-Commerce
- The Core of Product Recommendation: Cross-Selling
- Benefits of Product Recommendation
Did you know that 49% of consumers said they had purchased a product they did not intend to buy after receiving a personalized recommendation? Actually, you can look at your own experience without going further with the research. Nobody clicks a website and leaves it with just one shirt purchased anymore. And customers actively want that experience. For example, 52% of consumers say they would share personal data in exchange for product recommendations.
- How Do Ecommerce Product Recommendation Engines Work?
Dynamic pricing is adjusting products’ prices throughout the day. In a way, it’s making sure to have optimum prices while variables are changing. The main reason to use this method is to increase product profit and the chances of a sale simultaneously. In addition, dynamic pricing is a fast alternative to fixed prices. COVID-19 showed us that the world can change so much in some time. So instead of setting prices for a period, companies can adjust to the ever-changing market by updating their prices multiple times per day.
Sometimes dynamic pricing needs to be clarified with personalized pricing. This is because dynamic pricing looks at the bigger picture, while personalized pricing focuses on an individual. So dynamic pricing analyzes your products and their relative value concerning the rest of the market. Personalized pricing, on the other hand, looks at individual consumer behaviors and changes the product’s value based on the previous shopping experience of that particular consumer.
- How to use product recommendation in E-Commerce
1- The first impression: Home page
Making recommendations for new customers is hard, so your home page should be more generic and leading. Featuring best-selling items makes customers feel more secure. It shows them there are people already trusting you. Recommending best-selling products on the homepage has shown to be a highly-effective tactic for hooking your users’ attention as soon as they reach your site.
Also, the best-selling section should continuously be updated. Remember, %20 percent of your items will provide %80 of your sales.
Social proof is a significant element in online shopping. People look at reviews and read comments to ensure they get what they want. Showing the highest-rated items will guarantee the quality of your products. 37% of shoppers that clicked a recommendation during their first visit returned, compared to just 19% of shoppers that didn’t click an offer during their first visit. Holidays are not magical times only for children but also for e-commerce. Use product recommendations to remind customers of the coming holiday season. It may lead them to get holiday shopping out of the way quickly.
2- Where everything happens: Product page
Product pages are the best places for upselling. Use product recommendations for moving the buyer up to a more fully-featured version of the one currently being browsed. If there isn't a better version of the same thing, you can always try something slightly different but more expensive.
Sometimes the search for a new book can lead us to buy a set of 12 books. Or a T-shirt may become a T-shirt skirt combined. You can use "Frequently bought together" recommendations to make those happen. It differs from the shopping cart recommendations by the price. At the shopping cart, you offer side pieces like socks, but here you can offer more expensive jeans for a sweater. "Customers who bought [this item] also bought [that item]" recommendations provide social proof and peer-generated recommendations of relevant products the user may be interested in. For people, it's like seeing their best friend wearing a new pair of sunglasses.
3- Sealing the deal: Shopping cart page
It's your last chance to persuade customers to buy items. Luckily, shopping cart page recommendations can be beneficial because customers that click on offers are 4.5x more likely to add items to their cart and complete their purchase. Displaying a list of suggested products based on the customer's browsing history ("Recommended for you") is an often-used and effective product recommendation. If you want a more personalized experience, add the customer's name to the section's title.
Since 75% of customers are likelier to buy based on personalized recommendations, it would be a nice gesture. In addition, you can add related items under the main product. Provide product recommendations when items added to the cart require accessories (fishing reels need fishing lines, flashlights need batteries, and shoes often require socks). In addition to that, you can create groups of related items. Generating product groups (items frequently purchased together) and giving a discounted price for them could be another way of accessorizing.
4- Customer disappointment: Out of stock page
No one wants to see an apology when they can't get their dog's favorite food. But it doesn't have to be the end of the road. You can turn it into an opportunity. Instead of just saying sorry, you can add a section of similar products, related products, and even the old items they added to their cart and didn't buy. One of them will catch the customer's attention for sure.
- The Core of Product Recommendation: Cross-Selling
Cross-selling is a marketing strategy that persuades prospective customers to purchase add-on products. Add-on products are very popular with health care and insurance providers, but online retailers have broadly adopted them. Even if you have yet to learn to cross-sell, you have seen it at least once. For example, if you buy a phone from an online shop, the website will attempt to cross-sell your phone cases, wireless headphones, etc. It works even better for fashion retailers. They cross-sell belts, hats, accessories, and even t-shirts with just a pair of jeans.
- The difference between cross-selling and upselling
Upselling is a marketing strategy that persuades prospective customers to purchase higher-value products or upgrade a product or a service. If we go with jeans example, upselling means offering better quality jeans. Or instead of phone cases, it means selling the next model of the same phone. Cross-selling means fries with your burger; upselling means getting a giant burger.
- Benefits of Product Recommendation
One of the best things about shops is there are people to help you. Even without knowing what you need, you can tell them your problem, and they will recommend products. It’s the whole point of product recommendation in e-commerce. You need happy and satisfied customers. Seeing belt options while looking for jeans is the experience customers are looking for. No one wants to wander around the shop to find items; they want them gathered together for them.
Product recommendations improve not only sales but also customer experience. Happy customers mean more customers and increased sales. Suggestions help customers realize what they may need. Customers may forget batteries while purchasing a flashlight, but if you offer them batteries, they will probably buy them. And if you provide them with a better flashlight than they are looking for, they may realize that’s the one they need.
According to research, personalized product recommendations are estimated to account for more than 35% of purchases on Amazon.
Upselling and cross-selling are a huge part of revenue anymore. Let’s say a customer purchased everything they needed and liked recommendations. What happens next? Happy customers, knowing they are essential to you, are much more likely to stay loyal to your service. This applies both to your already existing customers and newcomers. And happy customers can help you to increase customer retention. They can recommend your services and products to their friends and family if they like your services and products. In this way, you will get more users to make more purchases. This leads to higher profits.
- How Do E-commerce Product Recommendation Engines Work?
- Data collected from various sources of product & historic price
- Defining goals
- Train the model & automated model selection
- Automated feature engineering to find the best features
- Getting the optimized cost for each product
Thanks to the AutoML technology offered by B2Metric, you can do all these steps the easiest and fastest way, with accuracy. B2Metric automates your data preparation, feature engineering, and model selection process with supervised, unsupervised, and semi-supervised algorithms.
-Data Gathering & Cleaning with B2Metric
B2Metric automatically brings the data preparation feature. This technology has improved the data collection & cleaning process to a higher level and offered users a new perspective and high-level experience.
-Find the Best fit for each of your customers with B2Metric Product Recommender
While creating a model with the B2Metric Machine Learning Studio product, you can easily select the target value and input from your ready data and run the model with these parameters.
-Automated Model Selection
B2Metric works with supervised, unsupervised, and semi-supervised algorithms and automates the processes of champion model selection for data teams with AutoML.
-Modeling & Deployment
All models that run with B2Metric ML Studio are prepared and interpreted for you. B2Metric interpretable ML models explain all causes, consequences, feature relationships, micro-segments, and outputs. Thus, accessible and understandable models are created for relevant non-technical teams.
Register and try B2Metric Machine Learning Studio.