B2Metric Product Recommendation for E-Commerce

B2Metric Product Recommendation for E-Commerce
Selin Nur Barlak 25.09.2020
AutoMLB2MetricProduct RecommenderUnbalanced Data AnalysisCross-sellingEcommerce product recommendationEcommerce product recommenderMachine LearningAutomated Machine Learning

Did you know that 49% of consumers said they have purchased a product that they did not initially intend to buy after receiving a personalized recommendation? Actually, without going further with the research you can just look at your own experience. Nobody clicks a website and leaves it with just one shirt purchased anymore. And customers actively want that experience. 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 increasing product profit and chances of sale at the same time. Dynamic pricing is the fast alternative for fixed prices. COVID-19 showed us the world can change so much in a little time. So instead of having set prices for a period of time, companies can adjust the ever-changing market with updating their prices multiple times per day.

Sometimes dynamic pricing gets confused with personalized pricing. The difference is that dynamic pricing looks at the bigger picture while personalized pricing focuses on an individual. So dynamic pricing analyzes your products and their relative value in relation to the rest of the market. Personalized pricing, on the other hand, looks at individual consumer behaviors and changes the product’s value based on previous shopping experience of that particular consumer.


How to use product recommendation in E-Commerce

1- The first impression: Home page

It’s hard to make recommendations for new customers 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, best-selling section should always be updated. Remember, %20 percent of your items will provide %80 of your sales. Social proof is a big element in online shopping. People look at reviews and read comments to make sure they get what they actually 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 a recommendation during their first visit. Holidays are not magical times only for the children, but also for e-commerce. Use product recommendations to remind customers of the coming holiday season. It may lead them to quickly get holiday shopping out of the way.



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 combine. To make those happen you can use “Frequently bought together” recommendations. 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 very helpful. Because customers that clicked on recommendations are 4.5x more likely to add items to 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 type of product recommendation. If you want to give a more personalized experience you can add the customer’s name to the title of the section. Since 75% of customers are more likely to buy based on personalized recommendations it would be a nice gesture. You can add related items under the main product. Provide product recommendations when items added to the cart require accessories (fishing reels need fishing line, flashlights need batteries, 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 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 they have been largely adopted by online retailers as well. Even if you don’t know what is cross-selling you have definitely seen it at least once. For example, if you try to buy a phone from an online shop, the website will try to cross-sell you phone cases, wireless headphones, etc. It works even better for fashion retailers. They cross-sell belts, hats, accessories, 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 bigger 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 just tell your problem and they will recommend products for you. 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. Recommendations help customers realize what they may need. A customer may forget batteries while purchasing a flashlight but if you offer them batteries probably they will buy them. And if you offer them a better flashlight then they are looking for, they may realize that’s the one they actually 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 that they are important 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. If they like your services and products, they can recommend it to their friends and family. In this way, you will get more users who will make more purchases. Obviously, this leads to higher profits.

How Do Ecommerce Product Recommendation Engines Work?

  • Data collecting from various sources of product & historic price
  • Defining goals
  • Train the model & automated model selection
  • Automated feature engineering to find best features
  • Getting the optimized price for eacy products

Thanks to the AutoML technology offered by B2Metric, you can do all these steps in the easiest and fastest way, with accuracy. B2Metric automates the data preparation, feature engineering, and model selection process for you with supervised, unsupervised, semi-supervised algorithms.

Data Gathering & Cleaning with B2Metric

B2Metric automatically brings the data preparation feature. With this technology, it has improved the process of data collection & cleaning to a higher level and offered a new perspective and high-level experience to the users

Find the Best fit for each your customer 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 easily run the model with these parameters.

Automated Model Selection

B2Metric works with supervised, unsupervised, 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 to you all causes and consequences, feature relationships, micro-segments, and outputs. Thus, easy and understandable models are created for relevant non-technical teams.