Data Engineering & Analytics Archives - Page 9 of 9

Kavika Roy
July 31, 2018
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Marketing Analytics: The Secret To Your Business Success

Monitoring websites to track the success of a business is a culture and has since become a practice. It is usually interpreted that if the graph is good, the business is on the road to success and this somehow leads to significant aspects being neglected. Now, you must be wondering why so? This is a fact though, that simply by analyzing your website, you might just never be able to reach potential leads or evaluate the conversion rate of your potential customers or maybe even understand what is actually desired of your product to fulfill the demands of the market. Marketing analytics fills in the blank here and is the answer to all the forecasting you have been doing to date based on the evidence only. Implementing marketing analytics is what is to be learned now. When it comes to analytics there are people who will behave as if it doesn’t matter. But it sure does. Smart people across the globe are investing in this science so as to improve campaigns and gain insights. Data from HBR (2015) shows various ways in which marketing analytics is being used by different companies. Given the importance of marketing analytics for business growth, let me take you through it so we can see if it actually does matter to make it a part of your everyday business operations. Customer Behaviour Prediction: The key to all questions Prediction of customer behavior has been a significant part of major e-commerce firms like e-bay and Amazon. It gives them an edge over other businesses and allows them to predict the demands and needs that may arise in the future. According to a Silicon Valley-based predictive marketing company, AgilOne, these three principal classes of predictive models are recognized: Segmentation models Algorithms for grouping customers on basis of factors like the type of products they buy or the amount of money they spend etc. Prediction models Insights like customer conversion ratio (the number of people who visited the website to the ones who got influenced by the marketing campaign and bought products from the company), the likelihood of engagement, propensity to churn, the probability to buy etc. are derived from these models. Prediction models built on customer analytics can make a significant impact on any business. Recommendation models Offering recommendations about the product that the customers may like depending upon their previous purchases. All these models collectively utilize all the data from the present as well as the past and transform it into useful insights allowing businesses to improve in lucrative terms like sales and the RoI (return on investment). Prioritize and Qualify Leads Ranking your leads based on the likelihood of the actions they might end up taking is another front marketing analytics could help you peep into. This further assists in identifying prospects with similar attributes. The leads thus generated become prospects that can be turned into permanent customers. In this scenario, it is important to consider that, to generate optimum results a large number of data sets are needed. This gives bigger firms an edge over the ones that might not own such huge data quantities, by improving their return on investment. But with the right approach and help, smaller industries may also be able to gain this insight as per their needs. Driving Better Products in Market To launch a product that will suit the present demands and patterns of the market, a deep and well-predicted insight is a must. Again, marketing analytics is the answer here, the predictive algorithms allow the companies to improve the new product based on customer data piled up while taking sales and feedbacks into consideration. All these factors integrate to form a part of the bigger marketing strategy and allow the business to enhance its earnings. Creating Better Marketing Campaigns We all are proof that marketing campaigns via emails or social media allow the firms to improve their customer base. Marketing analytics gives companies the power to monitor and optimize these campaigns so that sales and ROI can see growth. The integrated tools and techniques help rule out the lacuna that might hamper the campaigns and their implementation as decided. Also, it becomes possible to monitor the current outcome of any campaign, whether it works properly or needs to be altered for the future. Taking Wiser Decisions A company may find out that digital, print, TV and radio marketing takes up around 85% of its market spending. Now amongst these many of the activities can be directly measured but spending on digital marketing can be refined using marketing analytics tools. The company can then use the results to optimize its strategy. Using these types of approaches allows the marketers to track marketing performance on real-time marketing analytics metrics and make wiser decisions for its better implementation. How Is Data Analytics Used in Marketing? Data analytics helps the marketing teams to combine structured and unstructured data and derive real-time insights about customer behavior, purchase patterns, sales graph, market trends, and more. It helps enterprises with the following: Customer Acquisition A study said that 42% of marketers use data analytics to make decisions about customer acquisition. Customer acquisition is a technique of winning new customers and convincing them to buy the products by the brand. The process includes several stages and is represented in a funnel-shaped design. It consists of targeted promotions to reach prospective customers and then provide them with relevant information to spark interest. Attracting the audience, engaging them through promotional material, convincing them to buy, and keeping them happy with customer service can be called the customer acquisition flywheel. Data analytics helps marketers by providing actionable insights to attract customers. Customer Insight Customer insight is the process of understanding and interpreting the purchase behavior of customers. This helps in knowing which products are more popular and why. Businesses can suggest products to customers based on customer insight and increase sales. Data analytics processes the unstructured data from the World Wide Web to share reports and data visuals about customers’ preferences.

Kavika Roy
July 23, 2018
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Customer Analytics – Win Your Customers and Increase Revenue

Corporations across the globe are trying their best to look at the business from a customer-centric view. This exercise opens for them a window to peek into the interests of their clientele and create policies accordingly. But in today’s volatile business environment judgments built simply from past experience or intuition is increasingly unreliable. Customers today are more connected and empowered. Access to the internet all the time has allowed them to become more specific about their needs. They are aware of everything that is trending in the market. In such a scenario it becomes important for a business owner to predict a customer’s response with respect to his organization. The deeper businesses understand their customers’ preferences and lifestyle habits, the more they are able to attract potential buyers. However, it is not as simple as it seems. It is a big challenge for organizations to understand customer feedback, behavior and needs, well enough so as to make data-driven decisions about what customers are likely to respond to or what they are likely to purchase. Customer analytics or customer data analytics is that significant insight gained with the help of data science, that allows businesses to use customer data in order to make key business decisions. The information obtained from the process is used for designing effective marketing campaigns, site selection, customer relationship management, and secure decisions for the future. Insights pertaining to the customer’s feedbacks and responses drive the organizations to directions that help them outperform their competitors. Strategizing everything beginning from their production to their supply far before the demand arises, helps them improve their key performing metrics. Why Do We Need Customer Analysis? We already know how important customers are to any business. But knowing how to attract more customers and retaining existing ones is easier said than done. Using customer analysis helps get a better insight into what a customer wants and how we can keep them happy. Increase Customer Retention Rate Around 82% of businesses said that retaining customers is cheaper than acquiring new ones. Moreover, 65% of sales come from existing customers. Having repeat customers is good for the business. But, for this to happen, providing the customers with what they want is important. You have to understand their product preferences, their expectations from your business and find ways to prevent them from going to your competitors. Customer analysis helps answer these questions and increase the retention rate. The more you know your customers, the better you can meet their expectations. Better Customer Segmentation Segmenting customers and grouping them into different categories helps in targeted campaigning. There are different ways to segment customers- Segmenting customers into neat categories is possible only when you know enough about them and their preferences. Consumer data points are vital metrics that provide insights into customer preferences and behavior. The data points give enterprises a picture of the products preferred by customers, the frequency of purchase/ usage, and most used features/ functionalities, and so on. Customer analysis gives you the insights needed to know your customers. Develop Personalized Marketing Strategies Once you segment customers and prospective leads, you can plan a comprehensive marketing campaign for each segment. For example, sending emails to a customer who is old-fashioned and doesn’t check emails every day is not an effective marketing strategy. From choosing the marketing channel to determining the type of approach, customer analysis can help your sales and marketing teams fine-tune promotional tactics to increase market reach, sales, and returns. It also helps in understanding the market trends in relation to customer preferences. Accurately Predict Customer Behavior Customer behavior is hard to predict without using historical and real-time data. Customers decide whether or not to buy a product based on several factors. Customer behavior is broadly classified into the following- Reduce Customer Acquisition Costs As already discussed, acquiring a new customer is an expensive affair. However, knowing what the target audiences want can help reduce the acquisition costs. This is very useful for SMEs as they tend to have limited budgets for marketing and promotions. Acquiring customers means you need to spend on building a brand image that aligns with the preferences of your target audiences. Customer analysis provides you with the necessary information. For example, sustainability and eco-friendliness are being given more importance by some customers. If the target customer base is eco-conscious, emphasizing transparency in the supply chain and highlighting the use of sustainable resources will help build the brand image as an eco-friendly business. Enhance Customer Services Almost 90% of companies say that customer experience is the key to customer retention. Contented customers are more likely to stay with the business or come back even if they exhibit variety-seeking behavior. Even if your products are of good quality and match customers’ tastes, but your customer service doesn’t meet their expectations, you are at a high risk of losing your consumer base. Customer analysis allows you to streamline your customer service department and empower the agents to be more efficient at work. The insights derived from customer feedback will tell you exactly what is annoying them or what they expect for your customer service agents. You can use this information to hire more agents or train them to effectively deal with customers’ queries and complaints. Accurate Sales Forecasting When you acquaint yourself with your customers thoroughly, you can accurately predict the percentage of sales a product will generate. This helps in manufacturing, maintaining inventory levels, and calculating profits. Sales forecasting usually uses data from CRM systems, sales teams, and finance departments to get the complete picture. Moreover, enterprises can save money when making large investments in a product or service. Revamped Product Portfolio What if your customers want more features from your products? Could that be the reason they aren’t buying your products? Maybe your competitor offers more choices in terms of color, sizes, designs, etc. Customer analysis also helps in making changes to your products and revamping your product portfolio. The customer analysis reports can aid your R&D teams

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
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.