Page 41 – DataToBiz

Kavika Roy
July 17, 2018
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11 Insane Machine Learning Myths Debunked for You!

The world is becoming smart, smarter than ever before. There are homes that know how to turn on the lights by judging their intensity and there are cars that can drive themselves. Isn’t it something like living in a sci-fi world? Everything that was imagined is turning into reality. Among all that we hear about the upcoming technology, machine learning (ML) is a common term being associated with almost all of them. The term has been more misinterpreted than understood and there has been a considerable measure of hype buzzing around it. With more gadgets and technologies being launched every day, customers are keen to know what is it that is making them smarter? They are curious to discern the tech running behind the smartness and understand how it can benefit them in their personal as well as business ventures. This inquisitiveness towards the “working” has lured people to read and question about the same, however, the responses have not been palatable. For instance, you may often see mobile companies using the terms artificial intelligence and machine learning interchangeably for their products, now this is how a misperception is shaped. The customers do not understand the difference between the two and start treating them as synonymous with each other. The aim here is to make you understand the similarities and differences between “machine learning” and the terms it is confused with. this write-up shall provide you with a clear insight so that you can differentiate between the hype and the reality. It is important because machine learning forms an integral part of almost all data-driven work. In the event that you intend to consolidate it into your business, you should discern what it may or may not be able to do for you. Having a clear perspective will ensure that you develop a strategy that fits into your business module and helps you accomplish the set objectives. Removing the Misconception You know how they say in school that if your basics are clear, you will understand each and every concept and if not then surely there will be trouble. This concept will hold true in your entire life and therefore if you recognize the simple notion of machine learning you’ll never be influenced by the related hysterias. The figure below describes machine learning in its most naive form. There is a lot of reality and there is a lot of hype pertaining to machine learning. But with the above-illustrated diagram, it should be clear that machine learning is, training a machine by giving it a large amount of data and then letting it perform based on that learning. Exposing the Machine Learning Myths Machine learning is currently going through a phase of inflated expectations. Along with ongoing machine learning developments around the globe , there are still a lot of organizations looking forward to conceptualizing and running ML projects without even exploring the power of basic analytics. How do you expect them to meet their goals when they do not know what ML can or cannot do? In such a scenario it becomes imperative to know the myths and truths related to the subject. #1 Machine Learning and Artificial Intelligence Are Same One of the most common misconceptions is between artificial intelligence and machine learning. Both the terms are not only different in words but are two different fields belonging to a bigger pool of data science. In order to understand the difference consider this example – You wish that the camera of your phone should recognize a dog. Now in order to do that you provide it with a huge amount of data that contains pictures of all the types of dogs present in the world. With the help of these images, the camera is able to create a pattern that resembles a dog. Now whenever you point the camera toward the dog, it matches the pattern and that is how you get a positive hit. On the other hand, pointing the camera toward a cat doesn’t identify it as anything. This is a machine-learning process where the machine is trained to accomplish a particular task. Artificial Intelligence on the other hand is a broader concept, where the machines are trained in such a way that they can make their own decisions just like the human brain. If you put a cat in front of a camera that works on an AI technology, it will use it as another input and further reuse it to train itself. This training would help the AI-enabled phone to tell that isn’t a dog but it may be something else that can be explored. #2 Hiring the Best ML Talent Is Sufficient to Resolve Business Issues Business firms are spending a lot of money in gathering the best machine learning talent which can analyze their data and offer useful insights. What they forget in the process is that machine learning is just one part of an effective strategy, the basics are to have the right type and amount of data. If there is no one who can fetch the data, what will the professionals work upon? Therefore, businesses do not need a staff good in one field but someone who knows how to work from the scratch. There are data science firms all over the globe that can help businesses develop a correct approach and provide the useful insights they have been looking for. #3 ML Implementation Requires Humongous Infrastructure Machine learning sounds scientific and complicated that many presume it is not meant for their business. After all, what will an ordinary business do with advanced technology? Not every SME hires AI experts, isn’t it? That’s where we are wrong Years before it was said that if you wish to carry our ML operations on your premises, you’ll need to invest a large amount in infrastructure. The scenarios have changed now. Since data science and data analytics has become such an integral part of the business world, there are professionals who are

Kavika Roy
July 14, 2018
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Data Science Process: Resolve Business Problems Smartly

Whether you run an empire or a small enterprise, every business faces certain common problems and hitches. The questions like, How do I improve my sales? What is my organization lacking? What to do when old customers are not purchasing my product? What ought to be done with a specific end goal to pull in more clients? And much more such, often boggle a business owner’s mind. If this is the case with you as well, then there is definitely a path that can offer you a cutting edge over the others and it doesn’t take much effort in guessing what it is. Yes, I am talking about “data science” something that is taking over the corporate world with its capability to make complex things simple. To understand the applications in a better way, you may look at the working model of big tycoons like Amazon, Uber, Netflix, Starbucks, etc. They all utilize Big data analytics to refine their marketing, manage their finances, predict frauds, evaluate the viewing habits of millions of clients, etc. This is how Uber is able to provide the easy-to-book cab service and Starbucks does not suffer losses even after having three shops at the same location. Isn’t it mesmerizing what all data science can do? The next question coming to your mind would be how to incorporate the data science process into your particular business module. Or how is this prediction even possible? Do not worry, all your queries will be answered. But before moving ahead we must understand the basic concept of data science and the fields it merges together. A data scientist combines concepts of, statistics, analytics, data processing, machine learning, predictive analysis, basic mathematics, and computer science, to bring out the desired outcome that can benefit your business. Do you run a business or work with the management closely? Have you ever thought about why sales numbers are going down? Is your operational cost going up? Why is it becoming harder day by day to retain clients? Why are customers flocking for competitors’ products? – In case you’ve ever been bothered with any of these types of questions, then you do understand the importance of data. With the technological revolution, the operational aspect of businesses has changed drastically. Data is the new gold. He who knows how to churn out insights and intel will beat all the competition. Data Science is the methodology for retrieving valuable information from the stockpile of data. What happens when you decide to incorporate the data science process into your business? Let’s decipher for you what good can come of making data science practice in your everyday business running. List out some pointers that would probably help you change your mind and walk the path with this premium service enriching your business. Increase in The Number Of Customers The transactional & customer engagement data taken at various points from the customers such as the feedback on products, services, etc. helps the data scientists to predict the return on investment for the company. Also, this data helps the company design products according to the demand pattern of the customers, hence improving the customer base. The marketing analytics designed with the use of predictive analysis is further able to attract valuable clients. Better Customer Service The customer’s demographical data is paired with the product he/she buys and is recorded for future reference. This type of data helps understand the type of customers a business must look forward to attracting. Also, when the customers are presented with other products, their interest in any particular product is recorded, so that demand can be anticipated. Recommendation engines can help in up-selling other products to increase business revenue & recommendations also works as customer delight. Improved Efficiency Apart from improving sales and customer demands, the company itself needs to function properly. It necessitates ensuring that all the equipment installed in its facilities is working efficiently. The industries that deal with perishable goods need to make sure that they do not have extra stock in their warehouses. Data Science can help business owners in inventory optimization. The idea here is to predict the problem before it actually appears so that it can be avoided by hampering the efficient working of a company. Advantages of using data science in business Data science holds the key to overhauling businesses for higher efficiency. From understanding the hidden patterns to unlocking the intel, it is the next revolution for industries. Each industry has already been touched by data science and analytics and more and more business owners are seeing the benefits of relevant information for decision-making. A few of the advantages businesses can leverage from this stream are – How can data science be used to solve business problems? Data is the collection of all the mysterious questions that companies must deal with day in and day out. From declining sales figures to rising costs to HR issues, data holds the key to all the answers. It’s just that you need to know where to look and what to look for. It solves real-time issues with the cocktail of statistics and computer science to churn out hidden insights. It goes deep into the unstructured pile of raw data and gets meaningful intel that can drive business decisions. 1. Upgrades and Improvements Innovation is the key to surviving and sustaining in the business world. But understanding the pulse of the customers is even more important to know which upgrades and improvements to the existing products/services will be accepted by them and which will be pushed aside. It’s been the biggest secret that organizations are trying to hack every day. A complete understanding of consumer behavior is nearly impossible, but data science can shed light on this matter with a great level of accuracy. The right set of improvements done as per customers’ feedback can make the product/ service widely acceptable in the market within a short span. 2. New Product or Service Development Having an idea is nothing till it

Kavika Roy
July 11, 2018
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Data Science for Small Business: Make Your Business Smarter

SMEs or small and micro enterprises form an integral part of a country’s economy. In India alone, the sector is said to have employed around 111.4 million people in the year 2014, and in 2012 it contributed 37.5% to the GDP. But, the irony is that even after being such an important part of the economy, SMEs are not able to flourish as they should have. There is a major lack of strategic business planning and innovation that hinders their growth. In order for the SMEs to remain competitive both nationally and globally, it is imperative that every SME owner investigates the lacuna and starts working on it. Developing economies such as ours often find it difficult to foster innovation in the SME sector, because there are other factors that need the government’s attention. The major problems faced by SMEs include unskilled staff, irregular finances, poor infrastructure, old marketing strategies and lack of information. An SME owner tries to tackle all the issues but neglects an important one. This factor is “information”. Even after having skilled people, a good flow of money and good infrastructure the business may cease to flourish as expected by the owner. The Real Lacuna The basic problem that SMEs face is the lack of data resources, they do not know how to collect data and put it to good use. You can also say that SMEs are unaware of the fact that “data” needs attention too. Along with this they also lack a true understanding of data science or data analytics. Even if they start to think about utilizing the small amount of data that they possess, they are not sure if that has that big data analytics dimension that can offer good results. Most of the time SMEs deal in only one domain which further restricts their view towards recent trends in the market allowing them to work traditionally. Because of this attitude, they often tend to hype data analytics as a management trend rather than a perspective opportunity. The SMEs who try to go for data analytics, give up because they are not able to afford the needed infrastructure or data analytics expertise that is needed. Another major concern faced by the SMEs that refrains them from utilizing analytics is data protection and privacy. Data Science To The Rescue With fast-changing technology, the world is becoming data-driven, and data is utilized to extract every bit of information that can take the business to the next level. The field is referred to as data science and the professionals are called data scientists. Big Data, Data Analytics, and Machine Learning are all a part of this field and are being utilized worldwide to do wonders in the cooperative world. For small and medium enterprises, data science can do things beyond imagination. When an SME owner recognizes the potential hidden in data analytics, success becomes definitive. Using data science tools the SMEs can generate knowledge from the stockpiled data and incorporate innovation into their business. Firms’ past performances and market behavior can be analyzed to uncover new insights. Data science may help SMEs to face the challenges they thought cannot be overcome. The data-driven decisions made at the micro-level may help small business owners to improve their position in the market. After conducting a data science operation a business owner may find a correlation between factors that he always neglected. The insights generated may help the business owners to deeply understand their existing potential with respect to the present competition. Data Science for Small Business Micro, Small, and Medium Enterprises (MSME) are often frowned upon by the big players when it comes to the business world, but MSME is the backbone of any country’s economy. From constituting a major part of the country’s GDP to generating employment for millions, MSME is the underrated star in the business world. According to MSME ministry data, India has approx. 63 million MSMEs and contributing to 29% of India’s total GDP. Not only this, as the government is turning more focus towards MSMEs, there has been a significant increase in MSME numbers. With MSME, the growth of analytics services has also increased significantly. Unfortunately, though MSMEs are gaining traction in the formal aspect of business, still they are yet to manage the data efficiently. Due to a lack of funds, resources, and training often they are confined to running it based on traditional methods, ignoring the immense power of data science and analytics. According to McKinsey’s study, with technology implementations, 20% of the bottom line can be ramped up and that’s all MSMEs need to survive, sustain, and grow. Data Science can be the much-needed rescue option here. With data, MSMEs can take crucial business decisions that can help them take the next steps in growth perspective and give the big competitors fierce competition. MSMEs can take the help of data science vendors to fulfill their requirements of implementation. They can guide them and provide the necessary solutions as per the budget of the business. Why do Small Businesses need Data Analytics? For any business, data-driven decisions hold the key to success. From launching a product to customer feedback to operational efficiency monitoring – if data is harnessed properly, it can do wonders for businesses. Small businesses need data analytics more than anything as one right decision at the right time is all they need to succeed and grow. The few areas where data analytics can be helpful are – Understanding the Demographics Understanding the market sentiment and demographics are the two most crucial factors for a business. Unfortunately, most businesses can’t even find the right demographics. Small businesses get affected the most due to this limitation. With data analytics in place, MSMEs can identify the right segment, apt demographics for their product or services so they can deliver quick wins for the business. Client Acquisition Cost Analysis Acquiring a new client isn’t a cakewalk and small companies also buckle to pay their savings into this. While having multiple

Kavika Roy
July 07, 2018
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How is Data Science Shaping the Manufacturing Industry?

The traditional demand-supply chain has conveniently helped the manufacturer understand the needs of the market and the consumer at the far end. The factors that drive the market are very obviously and entirely dependent upon the way the consumer or end user responds to the product delivered to him[…]

Parindsheel Dhillon
January 25, 2018
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Tips to Boost Your OTA Business Using Data Analytics

The marketing method known as “Spray & Pray” is used by many organizations. They try a whole bunch of all tactics, on a whole bunch of customers, all the time. When something works, they stick to it but when nothing works, they take up the loss and try something new until they find the magical solution. The problem is that they never pay attention to the reason why certain campaign works well and other bring losses. They do not realize that something working now, may not work tomorrow or what did not work today may work tomorrow. The essence is they don’t think analytically. OTA analytics is a game-changer for this industry. Online travel agencies (OTAs) are no exception to the “spray & pray” methodology. In fact, OTA businesses have a very complex conversion funnel as compared to any other e-commerce website, making their problem even worse. The main reason for the buying complexity is that travel booking is not part of the impulsive buy product cohort. Almost all customers do their research before booking travel tickets as travel transactions involve significant money. Every step of the sales funnel ranging from ad click to ticket booking has a significant churn rate. To understand attrition at every step of the sales funnel, online travel agencies need to have a stronghold of analytics. Understanding key performance indicators (KPI) and their impact on business are very important in any business and online travel agencies are no exception. Digital Marketing OTA Analytics Travel agencies can better utilize their marketing resources & they can strategize accordingly if they know the answers to such questions. Agencies should know customer churn rate at every sale funnel step. There are numerous marketing channels e.g TV, radio, newspaper, Facebook, Google, Bing, third-party search engines, etc. Various options in selecting marketing channels reinforce the requirement of digital marketing analytics by understanding the multi-channel marketing attribution model. Key Performance Indicators To understand digital marketing, one has to get a hold of the KPIs. Each KPI has its own business objective attached to it, KPIs monitoring makes it urgent to optimize business objectives in the first place. Starting from acquisition strategy to retention, each has its strings attached to KPIs. Here is a list of a few important KPIs which need to be monitored regularly Above mentioned KPIs are self-explanatory except the Adstock rate. Let’s understand what is adstock rate. Digital marketing does not give you immediate results. Here comes the adstock rate in the picture. You got to understand the latency effect of each channel‘s marketing campaign. Some channels have a larger delayed effect in converting the sales lead as compared to other channels. Agencies need to know the adstock rate for each marketing channel for better marketing attribution modeling. We will cover adstock rate in detail in a separate blog. The mentioned KPIs vary for each marketing channel e.g Facebook may have higher retention but the CAC of Facebook may be higher. OTA needs to understand & create its marketing strategy accordingly. In certain seasons e.g. in Nov-Dec they may see a large inflow of recurring customers as compared to other months so this type of analytics insight can help in molding marketing strategy accordingly in those months. Lead Scoring Algorithm Imagine booking agents can see the lead conversion score for every inbound lead on their screens. It is possible by making use of predictive analytics abilities. Based on historic trends of involved variables, we can predict the probability of lead to sales conversion. By predicting we can actually detach agent lead conversion skills. Pitching the right product to the right customer at right time can help in increasing the conversion rate resulting in an increase in revenue. Below mentioned data would be fetched for data warehousing to create a central database. Using Predictive propensity to buy lead score modeling, we can target the right customer at the right point in time. Right Discount selection based on lead conversion probability can also help in increasing overall profitability. Chatbots Yes, chatbots are not just fads. It can add value to your business in many ways. Considering travel leads coming to you from all across the world from different time zones, you have to employ people for 24 hours to manage demand fluctuations. Chatbots can fill that empty time gap. Chatbots can be used to filter junk leads to optimize human resources. Informative assistants can be another utility of chatbots for all travel-related inquiries and chatbots can also be used as virtual travel booking assistants. By making use of deep learning techniques in NLP, chatbots can be made really smart. Demand Forecasting Demand forecasting is predicting the future demand for travel booking. If online travel agencies knew the number of inbound leads for travel booking for the coming days, they can manage their resources efficiently. By figuring out trends, seasonality & cyclical movements in historic data, one can better predict future demand. Demand forecasting can also help to optimize manpower costs. Customer Segmentation By creating a customer persona and segmenting users based on that, can really help in conversion uplift. It is a very well-known fact that if we target a selected user set for any campaign, it gives better ROI e.g. sending direct mail detailing offers to users who have a higher probability to respond to those offers, which is better than sending direct mail to every user. Statistical clustering can be a good point to start if you need to segment your users. The mentioned techniques can help you to maximize business profit by boosting lead conversions for your online travel agency business. Do not forget to A/B test any change you are thinking to adopt. Supplier analytics Choosing the best supplier and tracking the trends and commissions is called supplier analytics. Online travel agencies survive because of the exclusive partnerships they have with their suppliers. There are a lot of factors and data that the revenue managers rely on for selecting the best suppliers and negotiating a competitive deal

Parindsheel Dhillon
November 18, 2017
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6 Innovative Ways of Using Machine Learning in E-Commerce

Machine learning is one of the most searched keyword on any search engine at this point of time. The reason is quite clear; the benefits of utilising it in any industry is beyond imagination. We are explaining how an e-commerce business can make use of machine learning for profit maximisation

Parindsheel Dhillon
November 16, 2017
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Machine Learning for Transactional Analytics: Customer Lifetime Value v/s Acquisition Cost

Understanding customer transactional behavior pays well for any business. With the tsunami of start-ups in recent times and the immense money flow in businesses, customers find lucrative offers from companies for acquisition, retention & referral strategies. Understanding the transactional behavior of a customer has become even more complex with the advent of new business houses every day. Although with the rise of powerful machines, one can easily manage to work with TBs of data, the complexity of business economics has made this behavioral analysis far more difficult. Collecting and analyzing your business data on all aspects such as acquisition cost, operational cost, base profit, revenue growth, referrals, etc can help in providing the lifecycle profit patterns from a customer. But it does not help in solving many business questions such as: What is the actual value of a new customer in Dollars worth today? How much money business can spend to acquire a new customer? Let’s take an example to understand it more intuitively. Firstly, to estimate the value of a new customer, we have to know the annual profit patterns or cash flow patterns if the cash flow pattern differs from the profit pattern of a customer. Secondly, we need to figure out how many years customers stay with your business. The figure above shows customer profits for an imaginary firm based on all factors mentioned earlier. Customer value keeps on increasing with the time for which customer stays with the company. Customer who stays 2 yrs will generate $26 of profit ($80 acquisition cost balanced in first 3 years profits $40 & $66. If the customer stays for 5 years, will generate $264 in total (-$80+$40+$66+$72+$79+$87). But the differences in customer value are very large. For the same calculations, if done for 10 years, customers will generate a net worth of $760. It would not be wise to spend $760 today for a customer who will stay with the company for 10 years as the profit generated in the future would not be equivalent to $760 today. We need to apply discount computing to take it to present value. Using a standard 15 percent discount rate will make $760 to $304. (To get the net present value of first-year profit, therefore divide $40 by 1.15, for next year divide $66 by 1.15, and so on). So for a customer who will stay with the company for 10 years, one can pay up to $304 on acquisition costs. Now we know how to calculate the value of customers based on their life expectancy of customers. Customer Acquisition Cost vs Lifetime Value Customer Acquisition Cost or CAC provides information about what losing a customer may cost your business, while Customer Lifetime Value or CLV shows how much revenue each lost customer could potentially bring to your business. With this knowledge, you can better plan your budget and strategy with your marketing team. Customer Lifetime Calculations The next question is what is the expected duration of a customer to stay with the company? To answer this, we have to find out retention rates for a customer. It is a fact that retention rates vary among customers based on age, profession, gender, acquisition source & maybe more than dozen variables. The simplest way to calculate average customer stay time is to calculate the overall defection rate and invert the fraction. First count the number of customers who defect over a period of several months, then annualize this number to get a fraction of the customer base to begin with. e.g. you lose 50 customers out of 1000 customers over three months. This works to 200 customers a year or 1/5 of all customers. Then we need to invert this number, it will become 5. So now we can say, on average, a customer stays with the company for 5 years. In percentage terms, the defection rate for customers is 20%. Lifetime Calculation Improvements To estimate customer cash flow accurately, we need to refine the above-mentioned calculations. Firstly, we have assumed defection rates are constant throughout the customer life cycles. In real life, such is never the case; defection rates are very much higher than average in the early years and much lower later on. Taking averages may lead to over or under-estimating the profit numbers. Additionally one more refinement we need to make to calculate the true value of a customer. Instead of trying to calculate the value of a single, average, static customer at a single moment, we need to think in terms of annual classes of customers at different points in their life cycles. In the real world, the company acquires new users each year. some of them defect early, others may stay for years. But the company invests money in the entire set of customers. So, to get the present value of the average customer, we must study each group separately over time. Let’s take a scenario as shown in the above image, where 100,000 new customers enter at time zero. The company invested $80 at time zero making it to a total of $80*100,000= $8 million for the whole set of customers. By end of year 1, 22% of customers defected, and only 78% left, to pay back invested 8 million. By year 5, more than half people defected. To get the present value of a customer, we will estimate the set of cash flow people generate till the time they defect. Earlier in the blog, we get the current value of the customer at $304. At a constant rate of the defection of 10%, we may be dangerously wrong in deciding the money to be invested in customer acquisition whereas the actual defection rate shown in the above image makes this number only $ 172 from $304. Imagine a company spending $200 on new customers based on earlier calculated values. It would be a completely loss-making venture. Machine Learning Scope In the above calculations, we tried to approximate the customer lifetime value & corrected ourselves initially from

Ankush Sharma
November 05, 2017
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How to Reduce the cost of Google BigQuery Data Processing?

“Mysql Server has gone away” OR “Lost Connection to Mysql Server during query”- These are some of the most common errors a developer of a DBA looks at on his/her computer screen, [….]

Parindsheel Dhillon
September 27, 2017
2 Comments

E-commerce analytics: Product Recommendation Engines

Have you ever come across a business offering you more when you have already purchased one product or service? I get offers even from my hairdresser. Saloons offer head massages or facials when you go for a haircut. Many times, offers to get converted to revenue for saloons. This is a perfect daily life example of product/service recommendations. We could see such relevant offers more when we purchase products online from Amazon, Flipkart, etc. One of the premier examples of a product recommender is a contest organized by Netflix with a prize money of $1,00,000. One can easily get an idea about the business benefit Netflix might have earned by paying a huge amount as prize money for improving their movie recommendation engine. Introduction In layman’s terms, the outcome of this technique is a simple set of product/service rules based on customer product purchasing behavior. e.g. if a customer bought milk, then will he go to buy eggs too? In this data analytics technique, what is being purchased with what is been analyzed? Is buying one specific item increases the chances of buying other items? We will explore the business grocery dataset to get such answers. Product recommendation engines are also known by a few other names such as Apriori Algorithm, Affinity Analysis, Association rules, and Market basket analysis. We will not go into technical details of how it will work in this blog. The objective is to make aware smaller & medium organizations about the topic & how it adds value to the business. Why is this technique useful? Acquiring a new customer is always more costly for any business than keeping an existing customer. By this technique, businesses can increase revenue from existing customers on the basis of customer product buying rules. Product & services up-selling and cross-selling can be one of the very intuitive use cases of basket analysis. In addition to these product combos, shop floor/website layout can also be suggested accordingly. Last but not least, products can be suggested based on real-time purchasing behavior. Technical Definitions Here are the basic technical terms useful in this analysis are as below Support: The fraction of which our itemset occurs in our dataset. Confidence: Probability that a rule is correct for a new transaction with items on the left. Lift: The ratio by which the confidence of a rule exceeds the expected confidence.Note: if the lift is 1 it indicates that the items on the left and right are independent. Do not worry if these terms go off your head. You will get over them soon! R shiny playground R shiny toy product has been used for demonstration purposes. R — an open source tool can easily be downloadable from the cran website if you want to learn more about it, but it is not required for this demo purpose. We used an R package called ‘rules’ from Michael Hahsler who has implemented this algorithm in R. There’s public data of buying records in a grocery store which will be used for this exercise using the Shiny Demo App. How to use R shiny Demo product Step 1: Open R Shiny App Step 2: Upload grocery dataset public data (If you have your own dataset, make sure to change the format as per the sample dataset) Step 3: Select input data features a) Unselect header as provided dataset does not contain a header ( if your dataset has a header, please select accordingly) b) Select space separator as sample dataset having space separation. c) Keep all default values as it is for now if you find them too technical. Step 4: Explore shiny app tabs such as top 25 item frequency, basket analysis rules, sorting rules option e.g lift, support, etc. Step 5: Find out specific product rules e.g select beer from the select product dropdown. All the product rules for the selected products will be displayed under the product combo check. This feature can be used for creating specific product combos. Step 6 (Optional): if you understand the technical terms mentioned above, try to play with them to see the effect on rules. Why are E-commerce recommendations important? For an e-commerce business, recommendation solutions are a boon. It helps them sell more to their customers as the system identifies the items the customers usually like and recommend the products to them at the right time and place. Customers end up buying items that they never had thought of buying initially. This is why recommendation systems are important. Want to implement such a system in your business? You should be connecting with renowned data analytics consulting services for the same. Conclusion & business scope Isn’t it amazing! How ecommerce analytics solutions can provide what customers might need to add to the cart in real-time. This is a very basic toy example of product recommendations based on a rules algorithm. Advanced recommender engines make use of other data points from customer behavior in addition to advanced algorithms such as factorization machines, collaborative filtering, etc. Now you can fairly co-relate how Amazon recommends different products. Any small business can make use of this technique to add value to the business in some other ways: Product combo suggestions for a marketing campaign. Website or store layout re-alignment e.g if eggs are bought with milk, re-organize accordingly Product cross-selling, real-time web/App product recommendations. We here at DataToBiz with a team of data analytics and machine learning experts can support your business to solve problems by providing an affordable machine-learning platform for your business data. Contact Us for more info.

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