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.

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.

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.

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.

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.

Price optimization is the process of capturing the right pricing point. In other words, it is to maximize profit according to the purchase and payment demands of customers and competitors. It is of great importance for companies in all segments to increase their income by price optimization at every stage of their activities. Pricing issue is mainly a management decision. So, the question here is how is the price determined to gain maximum profit? The determination of the price seems pretty simple in theory. Income and expenses are examined depending on the demand curve and cost functions. Points, where revenues are equal to or greater than expenses, can be determined as prices. Many small companies still set their prices manually in this way. Even if this approach is economically meaningful, it is only this in theory. Many factors affecting the price, such as long-term customer behavior, anomalies, and the influence of competitors, are overlooked.

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.

Actual Price Optimization

Price optimization studies are carried out in many sectors such as retail, insurance, airlines, telecom, automotive spare parts by OEMs, and also finance. The factors that affect the price in real-world conditions and to consider in the price optimization process are:

• Retail or wholesale
• Customer consumption behavior in the long run
• Historical unit of sales prices of each of the product
• Promotion and campaign strategies
• Production, procurement, service, etc. costs
• Prices and campaigns of competitors
• Customer segments
• Economic variables
• Stock status
• Special days & events and season

In addition to all these conditions, there are also variations of the optimized price such as;

• Starting price
• Optimal (the best) price
• Discounted price
• Promotional price
• Campaigns

Price optimization allows companies to determine the above-mentioned prices. But these variations and the variability of factors affecting the price make it difficult to manually optimize prices. Thus, various price optimization tools and artificial intelligence & machine learning based price optimization studies have emerged.

How to Implement Dynamic Pricing Strategy

Implementing dynamic pricing can be very profitable when it’s done right. That’s why you should consider implementing dynamic pricing as an opportunity to improve your price optimization strategy and your overall margin. Implementing dynamic pricing can be tricky to begin with. So here is a five step process to help you through this journey:

1) Decide on your commercial objective:

Commercial objective is what keeps your company in the right way. It’ll help you handle any institutional changes. The commercial objective concerns more than just pricing and marketing, but it’s the main element of having a successful dynamic pricing strategy.

2) Have a pricing strategy:

Your pricing strategy uses your commercial objective to create a strategy that your team can use to sell products. For example, let’s say your commercial objective is to be known as the cheapest one on the market. Your pricing strategy would then be to make sure your products are always cheaper than the competition’s alternative.

3) Choose your pricing method(s):

Your pricing strategy shows you your future pricing goals. Your methods are how you'll achieve those goals. Your pricing methods should be more sharp and specific than your pricing strategy.

4) Set down pricing rules:

Pricing rules tell your dynamic pricing software what to do. You should set a rule for every product that the software needs to track and change.

5) Test and monitor:

Once everything’s all set up it doesn’t mean your job is done. You should keep track of the product sales, technical errors and effectiveness of the software you use. If it’s necessary you should make changes about it.

Importance of Price Optimization

Firstly, the number of decisions affecting pricing is increasing day by day in all industries. For this reason, more dynamic decisions are needed. For example; hundreds of new e-commerce sites are being established every day. The increase in competitors, consumer consumption behavior changes. Expenses will increase with the growth of the market. These are all sudden changes that affect pricing. With machine learning-based price optimization, you can be affected by these changes in the least possible way and protect your profitability.

Additionally, the best price may differ from customer to customer. This requires customization of each offer and price. For example, in holiday reservations, every service included in the package will change the price. For this reason, each customer should be given a different price according to his demand. Price optimization allows you to manage customized prices most profitably. Finally, it is important to make quick decisions, as the pricing process will be affected by more than one sector and will affect many industries. You can make the most effective decisions as soon as possible with artificial intelligence-supported price optimization tools.

Price Optimization with Automated Machine Learning (AutoML)

The price optimization process can be difficult, but it is not at all difficult to create a strong pricing strategy with the help of machine learning models. Machine learning-based pricing processes are of great importance nowadays, where customers can easily compare many prices offers with special search tools. AutoML, which has a great impact on KPIs, provides a fast and effective solution by learning the patterns given. Machine learning enables related units such as sales and marketing to develop complex strategies and optimize prices. A common example of ML price optimization is dynamic pricing. However, changes made with dynamic pricing may pose some problems. For this reason, dynamic pricing should be used together with price optimization techniques. As we mentioned before, considering the diversity of parameters that affect pricing, decision making features by reviewing many factors provided by ML is a big factor to use ML in price optimization. Machine learning algorithms allow you to reach the best decision by examining many parameters and situations. It is almost impossible to manually examine such a large number of parameters individually and accurately. Besides, with machine learning, you can analyze customer behavior in current prices, as well as predict customer buying behavior at possible prices.

The main steps of price optimization with ML are as follows:

• 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

Defining Pricing Target with B2Metric Price Optimizer

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.

Automative Spare Parts Wholesale Price Optimization

Research indicates that an average industrial or automotive company generates 10% of its revenues from spare parts sales and more than 40% of their profit. Given the profitability of the spare parts market, manufacturers have realized that this element is critical for company operations. Pricing is the key for harvesting the untapped potential of the spare parts market and is the best lever for improving profitability. Industry estimates show that a 1% increase in price can lead to an 11% increase in operating profit. By using the right pricing tactics for spare parts, manufacturers can realize significant increases in sales volumes, operating profit and customer satisfaction. Pricing of spare parts is challenging since each spare part has different competitors, consumption behavior and market potential. The most common pitfall in pricing is applying standard markup (cost-plus) pricing or competition-based pricing, both of which are attempts to elevate earnings without understanding the implications (i.e., failing to tailor the service delivery model due to lack of authority and resources to spare part managers). Instead, using a dynamic pricing software can maximize the profit.

A dynamic pricing software can identify the critical price points, analyze competitor’s pricing strategy and find out what customers would really pay for the piece. Then it can combine these with much more data and give you the optimum price. Also, it may seem like a long, big process but actually it can give you new numbers a few times a day -which is the optimum for changing prices. Everything that’s written above (and more) can be achieved with B2Metrics. To get more info you can visit products or simply contact us.

Anomaly detection is a method to notice anomalous actions or data. It predominantly focuses on the problem referring to recognize all intrusive attacks based on their anomalous activities that diverge from “normal activity profile” in a system. It also checks to see if an activity is within the predetermined acceptable condition. If it is not in this scope, it assumes that it is known as an unacceptable or bad attitude. The common way of the anomaly detection method occurs from the following components: First, a basis for all acceptable behaviors and situations is created. All activities observed while establishing this basis are taken as basis. The current situation is configured. This configuration is for a model that can be interpreted by applicable technologies. The observed activity is compared with the basis model that created, it is checked whether there is a deviation. Situations that observed as an anomaly are reported. Anomaly detection, rare observations or situations that are disparate from the remainder of the watchings, which may cause suspicion. Such "abnormal" situations typically transform into a kind of problem, such as a fault machine on a server, cyber attack, failure capsules in the cloud network, financial frauds, mobile sensor data, statistical process control (SPC) for production.
The best anomaly detection frame:
1-) Estimate main errors with up to 95% accuracy,
2-) Notice uncommon changes in system actions spontaneously,
3-) Service providers should strictly know how to fix issues. Therefore, show elementary to understand root cause analysis.

Img. reference site

Challenges in Anomaly Detection Models

Some difficulties make the task of anomaly detection difficult. Machine learning algorithms often need large amounts of data. This is because anomalies are not very likely, they are statistically small, and data sets are often unstable. Train and test data of models that developed for detect anomalies may be finite. It may also be unlabeled for testing and training. For example, there are states that normal behavior is more than abnormal behavior. This causes additional difficulties in training models that detect and predict abnormalities. Anomaly detection system ought to be as a dynamic system with fast-growing usage bases. In addition, as the underlying system develops, it has to update its behavior over time and adapt it to development.

How Anomaly Detection Approach Works?

Technically, the most distinctive criteria between normal and abnormal data point is whether there are similar data points around it in the analytical plane. In this context, the areas where similar points become very clustered are considered normal, and the areas where they become sparse are called abnormal areas. This is where the inference benefits of machine learning algorithms on analytical planes come into play. After these regions are determined by machine learning algorithms, abnormalities of data points are predicted. Unbalanced data visualization that taken from Towards DataScience



Many machine learning algorithms have been developed throughout the history of machine learning to identify these areas. So what makes machine learning algorithms different for abnormal detection processes? There are 2 major detection ways to process abnormal patterns; supervised and unsupervised anomaly detection.

Supervised Anomaly Detection:

The tagged dataset that includes both abnormal and normal sample data to create a prediction model that can classify future data points is needed for the supervised anomaly detection method. Algorithms such as Support Vector Machine Learning, Supervised Neural Networks, K-Nearest Neighbors Classifier are frequently used algorithms for this motive.

Unsupervised Anomaly Detection:

In this method, any training data does not necessary. Unsupervised anomaly detection assumes two things about data rather than training data. Only a percentage of the data is abnormal and any anomaly is completely dissimilar from normal samples. After these surmises, the data is clustered using the measure of similarity, and then data points that away from the cluster are appraised as anomalies. Large labeled data sets are needed to train these algorithms and achieve high-performance estimation results. Conversely, it is difficult to obtain such large-scale tagged data sets, and field knowledge from professional is necessary for the disclosure process.

The thriving performance of supervised learning in previous years has also led to unsupervised learning achieving very good results. Although there is a new tendency to adopt unsupervised attempts, attempts based on ML algorithms on anomaly detection generally focus on supervised models. The scarcity of tagged data is increasingly seeking to develop unsupervised learning models. B2Metric Machine Learning Studio (Register & Start Free Trial Now!) can be applied to these and many other problems, it solves these problems for you and allows you to make anomaly determinations in the most accurate way.

Real World Scenario of Where Anomaly Detection Used?

Anomaly detection affects business decisions across sectors. Sectors such of, insurance, finance, telecom, manufacturing, banking are the main sectors which anomaly detection is of great importance. Detection and prevention of abnormally high purchases-deposits, fraudulent spends, revenue fraud, abuse, service disruptions are main real case scenarios of anomaly detection.

Insurance Frauds

According to FBI reports; there is $40B loss for Insurance frauds in United States every year.

Anomaly detection in the insurance sector is one of the services that takes basic problems in different fields of insurance. For instance, identification of fraud in insurance and securities, and irregularity detection in health services' data are among the scopes of anomaly detection in insurance. In addition to these services, increasing cybersecurity calls have become a need in the insurance industry in recent years. With these developments, damage fraud detection actions have started in the insurance industry. In short, anomaly detection is a method used for insurance fraud detection. For instance, insurance companies can use anomaly detection technology to identify suspicious user behavior in the insurers' network.

Anomaly Detection for Telecommunication Industry

With the development of the telecommunications sector, the sector started to produce and collect huge amounts of data. These data are so large that it is impossible to deal with this data manually. Therefore, data mining technologies for the telecommunication sector develop. Abnormal situations such as network failures occurring in telecoms and unusual customer calls are called anomalies. Detection of these anomalies has an important place in the telecom sector.

Cyber Security Anomaly Detection

Network monitoring tools owned by cyber security systems can learn normal network behavior due to the large amount of data they have. Entries that unusual and intrusion are called anomalies. These anomalies must be detected and intervened to ensure cyber security. Denial of service (DoS) attacks is an example of anomaly. Although they don't crash or receive data, DoS attackers aim and focus on downloading a network and rejecting service to legitimate users. Starting DoS attacks is easy. DoS attacks block users from getting the right service by forcing physical resources or network connections. The attack happens the service is filled with too much traffic or data. Therefore, DoS attacks must be detected. For this, first of all, normal behaviors should be specified in the system. Then the system should alarm when the behavior deviates from normal to anomaly (DoS).

Network Faults Anomaly Detection

In order to provide high quality service in IP networks, the downtime of the service should be shortened as much as possible after the network errors occur. However, there are also network errors that cannot be detected by operators by simply monitoring device states. It is necessary to focus on anomaly detection to solve such abnormal problems.

Customer Journey Analysis is an approach to help a company see its products and services from a customer's perspective. Before the analysis, what the customer journey is and its importance should be examined. Do you know what is it and why you need it?

What is Customer Journey?

Customer journey cites to the path followed through the points of contact of your customers and potential customers before making a purchase action. Customer Journeys explains the path to successive interactions a customer has with a product, service, and company. A customer journey is an observation way about understanding your users, how they behave while they visit your website, and what you can do to improve their trip. They keep coming because of this observation.

Marketing is the main area of ​​use for customer analysis. There have been major changes in customer behavior in recent years. Customers usually do not decide to buy a product at the first interaction now years. They often examine different brands several times for a product or service before making a decision. Continuously developing mobile technologies enabled customers to interact with organizations from many different channels. On the Internet, the points of contact of potential customers for a product or service are hard can be watched from multiple channels. With the customer journey, you can better analyze, make sense of these changes in customers' behaviors, and use them to create your marketing strategies. Because customer journey proceeds at these touchpoints.

Why Do You Need Customer Journey Analytics As a Business Owner?

Customer journey analysis allows you to identify your customer touchpoints. Today, companies should think like their customers; customer journey analysis makes this easy. Starting to think like your customers and evaluating your products/services like them will increase your sales. Analyzing customer journeys reveal inconsistencies. This allows us to detect discrepancies and corrects. Analysis of journeys and acting on what is learned can reduce the effort needed of customers, rising their satisfaction and decreasing the number of abandoned journeys. Analyzing a customer's travel trends helps service providers find better ways. Analyzing journeys may push a dialog between departments to develop entire effectiveness, overcoming departmental sub-optimization. By creating and analyzing multiple customer journeys, you can provide test scenarios for a multi-channel solution. Besides, these analyzes and inferences can also be used for your other customers. Thus, your efficiency increases while your operation cost decreases.

Benefits of Customer Journey Mapping

With customer journey analysis, you can easily see the points where your customers interact with your business Customer journey analysis helps you in your sales process by providing you with an outside perspective. Customer journey analysis helps you to focus your business on customer needs It allows you to compare targeted and acquired customer experiences.

How Machine Learning Improves Customer Analysis?

In the 20th century, with the business now turning around the customer, companies move away from traditional business models and turn to customer-oriented and personalized business models. The customer experience has become the priority way of companies to set themselves apart from their competitors. Customer journey analysis is one of the biggest providers of customer-oriented personalization. Making customer journey analysis with machine learning allows you to analyze your customers and create your business plans much more easily than other methods. With machine learning and artificial intelligence algorithms, you can easily analyze your customer data and draw meaningful results from them. Such as customer churn prediction. In line with these results, you can create a business plan that makes a difference in many business areas, especially marketing and sales areas.

B2Metric Machine Learning Studio (Register & Start Free Trial Now!) uses different types of machine learning algorithms in autoML pipeline to provide the best solution for you. Each algorithm that B2Metric provides for you can be adjusted to get the most suitable model and provides you the best customer experience. You can see the customer churn segmentation screen created with B2Metric Machine Learning Studio (BMS) above.

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How Can You Analyse a Customer Journey Map?

Journey maps are infographic visualizations to understand how a customer is working towards a goal over time. There are several ways for analyze customer journey maps.

1) Identify points where customer expectations are not met. When users interact, if this interaction does not meet expectations, pain points are seen on the journey. In these cases, it is necessary to think from a user perspective. So you can understand which interactions did not meet the old expectations and experiences.

2) Identify any unnecessary touchpoints. Whether to take steps that can be eliminated to facilitate the overall experience should be examined. You should look for ways to reduce the cost of interaction.

3) Look for low points. You should see where the lowest points of the journey come from. Determine which low points you should prioritize.

4) Bestow time periods for the main stages of the journey on your travel map. Consider how long it took users to reach the sub-steps and whether these elapsed times were appropriate.

5) Look for moments of truth. Some points are based on some other points on the journey. These points are very important for the journey. If these moments go well, they can save the journey. You should analyze these points well and make sure you pay attention to them.

6) Identify the points that expectations meet. Look where the customer journeys are highest. These points are usually interactions that users are happy with. You can use similar experiences in different places. Thus, experiences will increased by you.

7) Determine high-friction channel transitions. There are many trips between the channels. The journey is disturbed when users change channels and transition should be facilitated.