In the first part of this blog, we read how using market basket analytics in a better way, small and medium retailers, too, can stay ahead of the competition by upselling or cross-selling.Today, we will look at just what goes on under the hood. One of the ways of using predictive analytics in basket analysis is by relying on “Association Rule Mining”.It means finding a relation between different objects in a set or similar patterns in a transaction database, or any such similar repository.Using Association Rule Mining applications of clustering and classification can tell you what items your customers buy frequently together, by generating a set of rules called Association Rules.So it’s kind of, if this, then that technique. Eg: If Pete has bought eggs, he will also buy margarine.----------------------------- Increase Your ROI with Intelligent Product Recommendations <a href="https://www.expressanalytics.com/contact-us" rel="noopener noreferrer" target="_blank" style="background-color: transparent;" data-faitracker-click-bind="true">&gt;&gt;&gt; Schedule a call&gt;&gt;&gt;&gt; </a>----------------------------- <h2 class="ql-align-center">Retailers have used this for:</h2><ol><li>Changing the store layout</li><li>Analyzing customer behavior</li><li>Designing catalogs</li><li>Cross marketing</li></ol>The extracted patterns from the database are used to build data mining models, which, in turn, are used to predict performance or behavior.The model works by making a prediction about values of data, which uses known results found from different datasets.The predictive data-mining model predicts the future outcome based on history. But today, with easy access to the Internet and mobile computing devices, things have gone way beyond the scope of the older models of <a href="https://www.expressanalytics.com/blog/predictive-analytics-use-case/" rel="noopener noreferrer" target="_blank" style="background-color: transparent; color: rgb(17, 85, 204);" data-faitracker-click-bind="true">predictive analytics</a>.With the ever-increasing erosion of margins due to tight competition, it’s a major challenge to create and enhance the precision of predictive models. It may lie in the discovery of new features, inputs, or predictors.Predictive models are improved by enhancing the rules generated from Market Basket Analysis.With shopping being over in minutes and not in days as before, the huge amount of big data, structured and unstructured, collected daily from various customer touchpoints leads to the creation of extremely large data sets that need to be quickly analyzed for application, sometimes in real-time.So, only if all that data is properly integrated, analyzed, and interpreted can the analysis offer crucial insights to cater to the customer’s needs. When Pete buys eggs, almost immediately, discount offers by margarine brands need to be put up before him, before he exits the e-commerce site.That’s where artificial intelligence comes in. <a href="https://www.expressanalytics.com/blog/machine-learning-marketing/" rel="noopener noreferrer" target="_blank" style="background-color: transparent; color: rgb(17, 85, 204);" data-faitracker-click-bind="true">Machine learning</a>, for example, can go deep into a retailer’s data, look at the patterns and come up with an even more precise model for the recommendation engine.Why someone buys something at that precise point in time can never be a simple mathematical exercise.Pete, our fictitious shopper, always buys eggs and margarine together, right? But there comes a day when he decides to buy unsalted butter with his eggs, stumping his retailer. Now, why did he do that? Therein lies the challenge.Our fictitious customer perhaps got bored of eating margarine and decided to switch to unsalted butter. Or, he went on a diet and was advised to do so by his dietician.Or, well, on this particular shopping trip, the fancy label of a new butter product caught his eye, and he thought to himself, “What the heck? Let me try it out.” Perhaps it was raining and Pete thought his eggs could do with a dash of butter in them.Couldn’t have to throw up a 25% off on the margarine offer to Pete this time, right?Shopping is a constant, yet variable, too. People’s preferences change based on their age, diet, income, those they live or work with, and even the season or the weather.If nothing else, shopping is affected by a consumer’s state of mind, a gloom-doom mindset, and the man is bound to something different on that day while shopping.The complexity of shopping and shoppers is the reason why those like Amazon invested in data scientists and IT to work on their <a href="https://www.expressanalytics.com/solutions/recommendation-engine" rel="noopener noreferrer" target="_blank" style="background-color: transparent; color: rgb(17, 85, 204);" data-faitracker-click-bind="true">recommendation engines</a>.The war for the retail customer is coming down to that – a battle between recommendation engines.They are getting more and more sophisticated, even intelligent if that’s the right word.Here’s an example: If you still spend some time searching for what to watch even after your OTT provider’s ‘You May Enjoy’ movie recommendations, it’s not really working, is it?Remember, recommendation engines are a filtering tool to ensure that consumers are given the data that “best fits” their taste, style, and preferences, ensuring he spends minimum time searching for the right product or service.The algorithms have supposedly “learned” from past data about the products preferred or bought by the customer.So how can one recommendation engine best the other?Your data, of course. Imagine the big data of Amazon versus that of your neighborhood store. It’s a no contest. Data is followed by the sophisticatedness of the algorithm. Simple association rules will no longer result in customer satisfaction.----------------------------- Increase Your ROI with Intelligent Product Recommendations <a href="https://www.expressanalytics.com/contact-us" rel="noopener noreferrer" target="_blank" style="background-color: transparent;" data-faitracker-click-bind="true">&gt;&gt;&gt; Schedule a call&gt;&gt;&gt;&gt; </a>----------------------------- So, the major retailers have upped the ante. Like using <a href="https://www.expressanalytics.com/blog/neural-networks-prediction/" rel="noopener noreferrer" target="_blank" style="background-color: transparent; color: rgb(17, 85, 204);" data-faitracker-click-bind="true">neural networks</a> to increase the power of their algorithms.A neural network is a computer program that mimics the human brain cells in the process of learning.It learns on its own without human intervention how to recognize patterns, and basically decides like a human being.Although neural networks are nowhere near the duplication of a human brain, it’s a work in progress.As of now, it may not be possible for an e-commerce site’s recommendation engine to “understand” Pete’s sudden “desire” one day to buy unsalted butter instead of margarine because of his diet plan, but that day is coming.

The use of Association Rule Mining applications in recommendation engines can tell you what items your customers buy frequently together, by generating a set of rules.


How Recommendation Engines are Getting More Sophisticated

Why are Recommendation Engines Becoming Popular?

<h1 class="ql-align-center">Using Artificial intelligence to Sense Buyer Intent – Customer Intent AI</h1>Some months ago, booking.com joined the ginger group of brands to combine artificial intelligence with mobile to get a heads-up in anticipating a customer’s purchase intent.Booking.com app users now not only receive instant booking access to a destination with a single QR code but also get personalized offers based on their earlier travel experiences, preferences, and interactions. That’s how it tackled the issue of buyer intent.Over the last year or so, the process of anticipating a buyer’s intent has got even more scientific.We have seen brands like booking.com actively deploy the “cold and emotionless” instrument of AI in a field that is almost always centered around human emotions – intent.Instead of spending solely on advertising that could target perhaps the wrong demographics, or address audiences who may have no contextual relevance at all, brands are increasingly utilizing their funds to invest in AI-driven tech to “understand” a buyer’s intent.----------------------------- Looking to Scale Your Retail Business Leveraging the Use of AI? <a href="https://www.expressanalytics.com/contact-us" rel="noopener noreferrer" target="_blank" style="background-color: transparent;" data-faitracker-click-bind="true">&gt;&gt;&gt; Let's connect&gt;&gt;&gt;&gt; </a>-----------------------------Ironical, isn’t it, that cold algorithms are being used to predict, accurately one must say, a human act which is a chain of events driven by a person’s knowledge and experience.Purchaser intent is akin to a guessing game but AI is taking out some of the guesswork.Targeting buyer intent calls for using a combination of active and passive data to accurately deduce, with some degree of probability, whether a customer is in the market right now to buy or not.Data sources capture such “intent” signals that consumers emit and point the brand in the right direction.AI, riding on a blazing fast computational power, lets brands quickly understand what the consumers are thinking to identify patterns, and also predict how quickly they will respond to advertising that touches an emotion.<a href="https://www.expressanalytics.com/blog/machine-learning-predicting-consumer-behavior/" rel="noopener noreferrer" target="_blank" style="background-color: transparent; color: rgb(17, 85, 204);" data-faitracker-click-bind="true">AI and machine learning</a> are linking the marketer with the “connected” individual. More and more marketers are relying on intent targeting to execute outbound demand generation campaigns.It’s no longer only about grouping together like-minded customers and trying to second-guess what they want.The customer journey just got more sophisticated and granular with the deployment of AI in understanding what an individual buyer intends to do next in his journey.It’s like a race today; the first brand of the blocks who gets this also gets the customer as the prize.There are a few brands in the hospitality business like bookings.com that have stepped up to the plate and integrated AI in their operations, giving consumers a plethora of <a href="https://www.expressanalytics.com/blog/the-role-of-artificial-intelligence-ai-in-customer-experience/" rel="noopener noreferrer" target="_blank" style="background-color: transparent; color: rgb(17, 85, 204);" data-faitracker-click-bind="true">AI-driven experiences</a>.Other brands are looking at using AI tech to do an even more effective predictive lead scoring, fine-tuning their marketing efforts, and predicting a purchaser’s intent near-automatically.<h3>Purchase Intent and AI</h3>AI tools have the capability to evaluate thousands of website paths and hundreds of digital footprints, including page sequencing, referral URLs, time spent on pages, and page visits.Later, these results can be compared against millions of past customer interactions and profiles.Finally, you can see an increase in success rate from 5% to over 90%, and that’s possible because of <a href="https://www.expressanalytics.com/blog/innovations-in-predictive-analytics-ml-and-generative-ai/" rel="noopener noreferrer" target="_blank" style="background-color: transparent; color: rgb(17, 85, 204);" data-faitracker-click-bind="true">AI and ML</a>.<h2 class="ql-align-center">Purchase Intent AI Vs. Predictive Analytics</h2>Monitoring users’ behavior and later applying rules to customize their journey is similar to predictive analytics.Predictive analytics uses rule engines and hypothetical projections according to the small amount of data.The hypothesis is the user, especially a marketer’s concept of what pathway a potential user will take.Now, what’s different regarding purchase intent AI? ML. Machine learning enables you to analyze thousands of data points, even if you are not sure if they will be useful or related to each user’s behavior.<h2 class="ql-align-center">How AI Anticipates Customer Buyer Intent?</h2>AI is a suitable tool to identify patterns in big data, which can then be used to develop product promotion and marketing approaches.Tools including <a href="https://www.expressanalytics.com/blog/current-state-of-natural-language-processing/" rel="noopener noreferrer" target="_blank" style="background-color: transparent; color: rgb(17, 85, 204);" data-faitracker-click-bind="true">Natural Language Processing</a> (NLP), AI, computer vision, and ML enable you to go through several thousands of pieces of data online to know the behavior of all individuals.AI algorithms are trained in such a way that they can predict purchasing intent in real-time for millions of data points based on billions of familiar results.Below mentioned are some ways that elaborate the process of buyer-intent prediction using AI:<h5>Identifying the problems that customers face</h5>Specific features of a product, numerous marketing strategies, or various factors that you need to modify according to the needs of customers can be understood by determining the problems of your customers.By identifying problems before they arise further, you have to analyze the concept of buyer intent for particular products and figure out how to improve them further.&nbsp;&nbsp;You can do market research and analyze feedback received from customers to evaluate problems that can be improved further using AI.&nbsp;Clients Data can be collected from surveys, <a href="https://www.expressanalytics.com/blog/7-ways-to-boost-retail-revenue-using-voice-of-customer-data/" rel="noopener noreferrer" target="_blank" style="background-color: transparent; color: rgb(17, 85, 204);" data-faitracker-click-bind="true">voice of customer analysis</a>, social media and purchase ratings.You can combine this data with CRM systems using customer feedback analysis tools.&nbsp;Buyer-intent AI can predict customers’ problems and is really helpful in situations where you have client feedback and market research data.&nbsp;<h5>Intent prediction using interest signs&nbsp;</h5>If direct data is unavailable, AI algorithms can gather data from a variety of sources.You can find many 3rd-party service providers to gather data regarding client interaction to detect the factors that influence user buying intent.AI can use only a few signals for buyer-intent prediction for products based on such data. These signs include:<ol><li>Number of customers who visited your website</li><li>Duration of your webpage</li><li>Scroll speed</li><li>Products that were viewed and&nbsp;</li><li>The amount of time spent to view every item</li></ol>Predicting consumers buying-intentions is completely guesswork and a complicated task.The use of AI in marketing can minimize this guesswork, ensure the whole process is more data-based, and make the predictions more accurate.<h2 class="ql-align-center">Popular Use Cases of AI in Understanding Buyer’s Intent</h2>To predict why someone may want to contact your organization even before they do.Send proactive notifications by identifying customer patterns and trends and contact them even before they call or contact your company.<h3>Sources of data:</h3>Earlier in this post, we spoke of sources of intent. Here are a few:SearchThis is the most obvious touchpoint, besides, of course, social channels. A fav source of the intent signal is what a customer has searched for online.So, if someone has just searched for red shoes, there’s a strong chance she is looking to buy them.Editorial contentWhat a customer is currently reading is also a pointer. What’s more, articles read online can also indicate at what stage a buyer is.If he is simply browsing and looking up articles about smartwatches, he is still at the research stage. When he starts reading about specific watch brands, he is almost ready to buy.Advertiser contentAgain, the very type of ads that people are clicking on can tell you what they are looking out for.All of this monitoring and deduction can now be entrusted to AI-driven programs, to collect and collate, to take proactive action. All of which will elevate the <a href="https://www.expressanalytics.com/blog/customer-journey-map/" rel="noopener noreferrer" target="_blank" style="background-color: transparent; color: rgb(17, 85, 204);" data-faitracker-click-bind="true">customer’s experience and improves business results</a>.

Buyer intent is akin to a guessing game, but AI is taking out some of the guesswork. Targeting buyer intent calls for using a combination of active and passive data to accurately deduce, with some degree of probability, whether a customer is in the market right now to buy or not.


Artificial Intelligence for Sensing Purchase Intent

Using Artificial Intelligence to Sense Buyer Intent

Predictive Analytics is increasingly becoming the “weapon of choice” of insurance firms and industries.The reasons are many, ease-of-use is one, and the other is because of advances in Artificial Intelligence (AI) and Machine Learning (ML), deployment is easier and faster.Data analytics overall has started to play a big role in almost all areas of the insurance sector such as risk management for companies, maximizing their returns on investments, and improving customer service, including client retention; while Predictive Analytics specifically has become the number one defense for insurance firms against fraud.According to the Coalition Against Insurance Fraud, fraud is one of America’s largest white-collar crimes and leads to, hold your breath, about US $80 billion in fraudulent losses each year.With Predictive Analytics, insurance companies can better detect and “flag” potential fraudulent claims.<h2 class="ql-align-center">How is Predictive Analytics Used in Insurance?</h2>Any operation in the insurance industry relies on two elements: relevant information, and accurate risk assessment. But, a large quantity of data can interrupt the flow of a working process, resulting in inaccurate results.Hence, there arises a need of using analytics tools as they can change simple information and convert it into business intelligence as per the requirement.In the insurance industry, Predictive Analytics is used to find out the risk level of various policies, which helps arrive at the price of insurance premiums.Why is analytics rapidly gaining ground with insurance firms now, after all these years?It’s not as if data analysis is something that’s brand new to this sector. What has happened of late, though, is the rapid advancements in IT and data science technology, that has brought analytics within the easy grasp of players in this sector.Ease of use and understanding, reduction in cost barriers, and the availability of the Cloud, along with some other factors have all combined to help it along in the data-heavy world of Insurance.-----------------------------Interested to know how to implement predictive analytics in your business? <a href="https://www.expressanalytics.com/contact-us" rel="noopener noreferrer" target="_blank" style="background-color: transparent;" data-faitracker-click-bind="true">&gt;&gt;&gt; Connect with us&gt;&gt;&gt;&gt;</a> ----------------------------- Specifically, where Predictive Analytics is concerned, with easier dashboards and even DIY kits, insurance companies have finally started to understand <a href="https://www.expressanalytics.com/blog/data-analytics/" rel="noopener noreferrer" target="_blank" style="background-color: transparent; color: rgb(17, 85, 204);" data-faitracker-click-bind="true">how to operationalize</a> the output from this form of analytics.<h2 class="ql-align-center">Top 3 Use Cases of Predictive Analytics In Insurance</h2>Below-mentioned is some of the use cases of Predictive Analytics for insurance:Fraud Prevention: Predictive analytics in the insurance industry analyzes different social media channels to monitor online activities to find out red flags.After referring to various external sources, predictive analytics software can study the behavior of the individual user, and record their visits, credit score, and total reputation.Customer Support: Customer support plays a key role in retaining customers.Predictive analytics tools provide you valuable insights to track the initial complaints, and first signs of customer dissatisfaction by scanning the habitual patterns.Apart from customer retention, and support, predictive analytics increases customer loyalty ratio by predicting their needs and personalizing the entire experience.Risk management: This advanced analytics can help insurance companies identify multiple customers who require special attention.It sends insurers warning signs and saves precious time that could be used to find out a potential solution.That being said, Predictive Analytics is also used by insurance firms for other activities, one of them being marketing.The aim is to <a href="https://www.expressanalytics.com/blog/customer-acquisition-vs-customer-retention/" rel="noopener noreferrer" target="_blank" style="background-color: transparent; color: rgb(17, 85, 204);" data-faitracker-click-bind="true">acquire customers</a> and then, of course, to retain them. Predictive Analytics helps target the right customers and to predict customer churn.Another area where <a href="https://www.expressanalytics.com/blog/predictive-analytics-guide/" rel="noopener noreferrer" target="_blank" style="background-color: transparent; color: rgb(17, 85, 204);" data-faitracker-click-bind="true">Predictive Analytics</a> is deployed is underwriting. In the world of insurance, underwriters play a vital role because it’s their job to determine whether an insurance agency should undertake the risk of insuring a client or not.They determine the risk and exposure of clients, and how much insurance should be accorded to a client, among other things.Most of the underwriting was done by humans all these decades but now analytics propelled by ML and AI has started taking over, making the task not only so much easier since they help “predict” risk better than humans, but even far more accuratePredictive Analytics modeling leads to better pricing of policies, eventually ending in better profitability for insurers.Clearly, advances in AI and <a href="https://www.expressanalytics.com/blog/machine-learning-techniques-guide/" rel="noopener noreferrer" target="_blank" style="background-color: transparent; color: rgb(17, 85, 204);" data-faitracker-click-bind="true">ML</a> have accelerated the ability of insurers to predict risk. Algorithms can find trends and patterns that help forecast the probability of a risk situation recurring.<h2 class="ql-align-center">5 Predictive Analytics Procedures</h2>The below-mentioned stages of Predictive Analytics are required to improve the outcomes of predictive modeling:Identification: This step involves the identification of goals, objectives, and the expected results of the project for customizing the prediction models.Collection of information: After gathering relevant information from various resources, automatically transfer or submit the information from different software, and store it into the preferred predictive analytics model.Cleaning, analysis, visualization, and modeling: In this step, data must be free from unrelated bits, analyzed, and visualized. Later, data sets are made into assessing models via statistical analysis.Testing and deployment: This step involves testing the predictive model for accuracy, and deploying it into current business processes.Monitoring: This step defines monitoring the model’s efficiency, and accuracy in different scenarios.<h2 class="ql-align-center">How Predictive Analytics Beneficial for Life Insurance Companies?</h2>Predictive analytics techniques are useful for life insurance companies in the following ways:<ol><li>Reduction in underwriting expenses</li><li>The rapid increase in sales</li><li>Significant improvement in profits</li><li>Expense management</li><li>Analyzing trends</li></ol>Yet, this is just the start of the insurance sector’s journey with <a href="https://www.expressanalytics.com/blog/small-business-data-analytics/" rel="noopener noreferrer" target="_blank" style="background-color: transparent; color: rgb(17, 85, 204);" data-faitracker-click-bind="true">data analytics</a>. For now, data sources are being explored in administrative systems, claims, medical records, and credit scores, to name a few departments.But the day is not too far off when analytics will be used to transform their business model, expand customer relationships, and improve internal performance management.

Predictive Analytics in the insurance industry is becoming the “weapon of choice” of insurance firms. It is because of advances in AI & ML deployment.

How Predictive Analytics is Changing the World of the Insurance Industry

How Predictive Analytics is Reshaping the Insurance Industry

What is a recommender system? A recommender system is a compelling information filtering system running on machine learning (ML) algorithms that can predict a customer’s ratings or preferences for a product.A recommendation engine helps to address the challenge of information overload in the e-commerce space. What is Recommender System? How to build a Recommender System a Primer.Modern recommender systems were created first by e-commerce giants like Amazon and then popularized by OTT platforms like Netflix.But now you might encounter recommendations in almost every space of the digital world, be it Facebook Posts, Insta Stories, YouTube videos to food delivery services, and e-commerce business.With the introduction of cost-effective and easily available software, more and more enterprises can now develop and deploy their recommender systems.<h2 class="ql-align-center">Building A Recommender System</h2>Building recommendation engines: One software that <a href="https://www.expressanalytics.com/" rel="noopener noreferrer" target="_blank" style="background-color: transparent;">Express Analytics </a>uses in developing recommenders engine for clients is the Neo4j software. This is a graph database management system, unlike traditional RDBMS.The&nbsp; Neo4j foundation is on “Nodes”, “Relationship”, and “Properties”. A “Node” is a data or record in a graph database while a “Relationship” is an element using which two nodes of a graph can be connected.This relationship creates a pattern that associates two chunks of information together and defines a flow by assigning a direction to it. We can add pieces of information to our nodes and relationships by assigning properties to them. But more on the Neo4j later.Just to make it easy to understand it better, for this blog post, we will help explain how we design a recommendation engine for an e-commerce client.-----------------------------Increase Your ROI with Intelligent Product Recommendations <a href="https://www.expressanalytics.com/contact-us" rel="noopener noreferrer" target="_blank" style="background-color: transparent;" data-faitracker-click-bind="true">&gt;&gt;&gt; Talk to Us&gt;&gt;&gt;&gt; </a>-----------------------------But before we dive deep into building a recommendation engine let’s add context to the analogy of using one with this example. Let’s say there are 27,000 restaurants in New York City.One evening, you plan to dine out. Being new to the city you do not know which restaurant to choose.Clearly, the selection depends on a lot of “factors” such as the distance from your location, your food preference; maybe even your country of birth. These factors are in a way “certain or fixed” and can be translated into numbers.But there are some “uncertain” factors too, to be taken into account like your mood or your curiosity to try something you haven’t before. Does that mean for an analyst it is impossible to predict what kind of cuisine you want to try out tonight?It is rational for an <a href="https://www.expressanalytics.com/services/customer-analytics" rel="noopener noreferrer" target="_blank" style="background-color: transparent;" data-faitracker-click-bind="true">analytics </a>model to have uncertainty in the mix as several factors are involved to derive the results.But those uncertainties can be removed by building more algorithms or putting more context to the filtering, or generating more recommendations per algorithm to increase diversity.The whole idea is to use the customer’s preferences which are reflected by his browsing pattern and generate recommendations around it.<h2 class="ql-align-center">3 Types of Recommender Systems</h2>Before going ahead with the explainer on how to build a <a href="https://www.expressanalytics.com/solutions/recommendation-engine" rel="noopener noreferrer" target="_blank" style="background-color: transparent; color: rgb(17, 85, 204);" data-faitracker-click-bind="true">recommendation engine</a>, let us learn some of the various types:Recommendation systems can be classified into three categories:<ol><li>Content-based filtering</li><li>Collaborative Filtering</li><li>Hybrid</li></ol>Below flow chart can make the classification and sub-classifications of recommender systems a bit clearer:Recommendations can be made based on the customer’s recent purchase history, demographics, and gender.<h2 class="ql-align-center">What is Content based Recommendation System?</h2>Recommendations through content-based filtering techniques are influenced by what the user has browsed earlier, or what he is currently browsing.It is mostly dependent on keyword searching techniques and tries to follow the user’s own browsing patterns by filtering items based on keywords to describe the items and make recommendations around them.<h2 class="ql-align-center">What is Collaborative Filtering Recommender Systems?</h2>Unlike content-based filtering which only takes into account user-specific item interactions, the collaborative filtering technique follows a more mature approach and finds out similar users based on user-item interactions.An example: consider two user browsing patterns. We can increase the confidence level of the two users being similar by comparing the number of common products they have browsed.<h2 class="ql-align-center">What is Hybrid Recommendation System?</h2>Once you mix concepts of content-based filtering and collaborative filtering to generate recommendations you have developed a hybrid system.So, let’s commence building a recommendation engine for a hypothetical e-commerce firm.Consider the above graph data structure. Here, we have four nodes, i.e. State, Customers, Category, and Products, and three relationships with directions connecting the four nodes. Their respective properties are in the assigned dialogue type box.We want to recommend products to the customers although we can recommend them anything, making rich recommendations will increase the probability of them buying one. For this purpose, we will develop some algorithms to make the recommendations.Consider the following Node-Relationship structure of the database. We have data for customers stored in ‘Customer’ nodes with properties “Customer_Id” and “Customer_Name”. Separately, we have information on all the distinct products under the “Product” nodes and their respective categories under the “Category” node. Each customer individually interacts with a set of products and this interaction is captured in the ‘Interacts With’ type relationship. The relationship captures data on the date of interaction, the type of interaction i.e. ‘Purchase”, “Product View”, ‘Cart Addition”, ‘Wishlist” “Review” and the “Quantity” sold, if any.Data alone does not drive your business. Decisions do. <a href="https://www.expressanalytics.com/contact-us/" rel="noopener noreferrer" target="_blank" style="background-color: transparent; color: rgb(17, 85, 204);" data-faitracker-click-bind="true">Speak to Our Experts</a> to get a lowdown on how a recommender system can help your business.So, can we come up with a recommendation system based on this information only?Let’s say for each customer that has interacted with at least one distinct product we identify the categories of those products. To identify the most recent categories we arrange the categories in descending order of the transaction date and say consider at most the top 5 categories from that if present.Till now, we have at best 5 recent categories for each customer who has interacted with at least one product. For each category in consideration, let us collect 5 products from each to have at most 25 products for recommendation to the customers. Simple, isn’t it?----------------------------- Increase Your ROI with Intelligent Product Recommendations <a href="https://www.expressanalytics.com/contact-us" rel="noopener noreferrer" target="_blank" style="background-color: transparent;" data-faitracker-click-bind="true">&gt;&gt;&gt; Talk to Us&gt;&gt;&gt;&gt; </a>-----------------------------<h2 class="ql-align-center">Making Recommendations More Dynamic</h2>Now let us make some more adjustments to the query to make our recommendations more dynamic.Recommending a set of products from a category won’t spice the recommendation for obvious reasons so rather than recommending fixed products from each category we can recommend random products from the category each time.Still not quite good, is it? Okay, what if we find the most popular products from each category based on the number of interactions and recommend them as an add-on, we can find the state of the customer too and find the most popular products in the same region. Hmm… now this looks good.The recommendation of the above type comes under the “Content-based filtering technique” where we have used filtering on the customer’s browsing pattern to find the recommendations.As a note, filtering is the key to generating rich recommendations. Start with a basic idea and build around it by including filters to generate recommendations the customer is most likely to buy.<h2 class="ql-align-center">Recommendation based on the Product A Customer is Searching for in Real-Time</h2>Another content-based recommendation algorithm that we can develop is based on finding out products similar to the one being browsed at the moment.Suppose one of our customers is browsing X product. That product belongs to a category as is mentioned in our database structure. To start, we can note the category and grab random products from that category to make our recommendations.To make the recommendations more rich and relatable let us dig a little further. Like in the earlier example, filtering is the key here too.As an example, look at the details we have about the browsed product. We know its category and its price.To add more details to the recommendations, instead of grabbing random products we can grab products that belong in a certain price range. The price range can be around the price of the browsed product.At last. We have our recommendations for products that belong to the same category and have a price around the price of the browsed product.<h2 class="ql-align-center">User-user Collaborative Filter-based Recommendations</h2>The earlier two recommendations are running on content-based filtering which makes use of inputs from customers and builds recommendations around it. Now, let us build a collaborative filtering-based recommendation system.Here the idea is to use the customer’s profile and make recommendations purely based on his/her preference and liking.User-user collaborative filtering aims at finding customers who are similar to you based on the products you both are browsing, or by comparing the ratings you both give to a particular product.Both the approaches are valid and adopt a different similarity measure to find similar customers.This is where the Neo4j software proves to be superior to others as it provides some different similarity algorithms. To name a few — “Jaccard Similarity” and “Cosine Similarity” are mostly used in this type of collaborative filtering.Suppose we have the data of ratings that each customer has given to a product. For this particular case let’s use the cosine similarity to calculate the similarity score whose value range between -1 and 1, where -1 is perfectly dissimilar and 1 is perfectly similar.To compare the ratings firstly we need to have common products that the two customers have browsed. Let’s make two separate lists of the ratings for the common products in order by each customer. Let us call the two lists A and B. The cosine similarity is defined as:In our case, A and B are the lists of ratings of the common products browsed by the two customers. Imagine doing this for all the customer pairs in your database which have customer count in thousands. This makes the similarity-based algorithms computationally costly.Suppose we have generated the similarity scores for each customer pair. The question is: how can we exploit this information and make use of it to generate recommendations?Things are pretty logical from here onwards. Say you want to generate recommendations for a customer. Just grab the topmost similar customers based on the similarity score.Now, make use of the logic we developed earlier during the content-based filtering examples. To start, we can recommend products that similar customers have purchased recently.Or to add more context we can grab products only from the category of interest. We can even exploit the data if we have similar customers from the same state and can recommend the products that the customers from the same state have purchased.In short, we can make use of any content-based filtering technique to generate recommendations once we have the data of similar users. With this, in a way, we are reducing the sample space to find our recommendations.Earlier, we were looking at the whole database but here we are only considering customers who are most similar to other customers under consideration.Let’s take a breather here. In the 2nd part of this post, we will show you how to use the Neo4j platform for building a recommender system, a comparison between the Neo4j, the MongoDB, and MySQL, and various use cases of recommendation engines.

A recommender system is a compelling information filtering system running on machine learning (ML) algorithms that can predict a customer's ratings or preferences for a product. Here is a step-by-step Tutorial

What Is a Recommender System? How To Build One

What is A Recommender System? A Primer

Artificial Neural networks are an extremely complicated piece of research/technology being developed for decades. They use man-made computing power to try and replicate the way the human brain calculates, and hence, may be a little dense to grasp at the first go.In this two-part post, we will understand the definition of neural networks, just what is the “artificial” component, and their involvement/use in data science.&nbsp;Let’s begin with a neural network. A copycat version of the human brain, scientists have been at it since the 1940s – trying to replicate the mathematics of the brain in a lab.A simplistic explanation would be to call it a group of algorithms designed to recognize patterns in data, just like the brain.<h2 class="ql-align-center">To understand Artificial Neural networks, one must know how the brain works</h2>To understand<a href="https://www.expressanalytics.com/blog/neural-networks-prediction/" rel="noopener noreferrer" target="_blank" style="background-color: transparent; color: rgb(0, 0, 0);"> </a><a href="https://www.expressanalytics.com/blog/neural-networks-prediction/" rel="noopener noreferrer" target="_blank" style="background-color: transparent; color: rgb(17, 85, 204);">neural networks</a>, one must also understand the basic structure of the human brain. The brain is a collection of interlinking neurons. Each neuron analyses the outputs of the other neurons, and then either “fires” (sends out an output) or does not.&nbsp;&nbsp;So, like in the human brain, “artificial” (man-made) neural networks also consist of linked “artificial” neurons which try and perform similar mathematical calculations. A neural network consists of layers: input, hidden, and output. Each has nodes, loosely modeled on the lines of the neurons in the brain.&nbsp;&nbsp;Data alone does not drive your business. Decisions do.<a href="https://www.expressanalytics.com/contact-us/" rel="noopener noreferrer" target="_blank" style="background-color: transparent; color: rgb(0, 0, 0);"> </a><a href="https://www.expressanalytics.com/contact-us/" rel="noopener noreferrer" target="_blank" style="background-color: transparent; color: rgb(17, 85, 204);">Speak to Our Experts</a> to get a lowdown on how artificial neural networks are important.Each neuron is accountable for classifying a single feature. It relies on the other neuron to do its job correctly to make an accurate decision eventually.With the advent of machine learning, they are used largely as a deep-learning tool, a sub-field of data science, to solve problems like handwriting recognition and face detection by extracting “hidden patterns” within the data. This is especially useful for extracting patterns from images, videos, or speech.It is an artificial technique used for the classification/prediction of any variable, just like the human brain tries to predict values.&nbsp;&nbsp;Running on machine learning techniques largely used for predictive analytics, neural networks are a part of larger machine-learning applications involving algorithms for reinforcement learning, classification, and regression.&nbsp;For mainstream<a href="https://www.expressanalytics.com/blog/machine-learning/" rel="noopener noreferrer" target="_blank" style="background-color: transparent; color: rgb(0, 0, 0);"> </a><a href="https://www.expressanalytics.com/blog/machine-learning/" rel="noopener noreferrer" target="_blank" style="background-color: transparent; color: rgb(17, 85, 204);">data scientists</a>, it is one of many ways to solve a business problem that involves copious amounts of data.&nbsp;One of the better explanations of artificial neural networks is given by Luke Dormehl in<a href="https://www.digitaltrends.com/cool-tech/what-is-an-artificial-neural-network/" rel="noopener noreferrer" target="_blank" style="background-color: transparent; color: rgb(0, 0, 0);"> </a><a href="https://www.digitaltrends.com/cool-tech/what-is-an-artificial-neural-network/" rel="noopener noreferrer" target="_blank" style="background-color: transparent; color: rgb(17, 85, 204);">Digital Trends</a>. He writes:While neural networks (also called perceptrons) ….it is only in the last few decades where they have become a major part of artificial intelligence. This is due to the arrival of a technique called “backpropagation,” which allows networks to adjust their hidden layers of neurons in situations where the outcome doesn’t match what the creator is hoping for — like a network designed to recognize dogs, which misidentifies a cat, for example.Another important advance has been the arrival of deep learning neural networks, in which different layers of a multilayer network extract different features until it can recognize what it is looking for.&nbsp;&nbsp;So the reason why artificial neural networks may soon go mainstream is the arrival of deep learning neural networks, in which different layers of a multilayer network extract different features until it can recognize what it is looking for.Transform your business using Express Analytics’ machine learning solutions<a href="https://www.expressanalytics.com/contact-us" rel="noopener noreferrer" target="_blank" style="background-color: transparent;">&gt;&gt;&gt;&gt; Connect with us &gt;&gt;&gt;&gt;</a><h2 class="ql-align-center">How does an Artificial Neural Network make Predictions?</h2>Artificial neural networks decode sensory data through machine learning techniques. It clusters raw input to produce output with labels.&nbsp;Here’s an even deeper explanation: There’s a basic building block of one at the heart of a neural network. It’s called a “perceptron”, not to be confused with a neuron. A perceptron is necessary since it decodes complex inputs into smaller portions of information.&nbsp;&nbsp;An example would be taking the image of a person’s face and breaking it down to, just as a human would, facial features like eyes, nose, etc. Each one of these then becomes a perceptron in a single layer. These features can be broken up into smaller features in the next layer.For example, a left and right eye, upper and lower lip, and so on. By drilling down, the machine soon has the building blocks of a face. A neuron is more a “generalization” of a perceptron.&nbsp;&nbsp;You can think of neural networks as a clustering and classification layer on top of your database. How? From inputs, this technique helps to group unlabeled data according to similarities. It also clubs together similar groups of data when it has a labeled dataset to work from.&nbsp;&nbsp;Artificial neural networks are trained to find correlations. According to this formula by pathmind.com<a href="https://pathmind.com/wiki/neural-network" rel="noopener noreferrer" target="_blank" style="background-color: transparent; color: rgb(0, 0, 0);"> </a><a href="https://pathmind.com/wiki/neural-network" rel="noopener noreferrer" target="_blank" style="background-color: transparent; color: rgb(17, 85, 204);">deep learning maps</a> inputs to outputs.&nbsp;It is known as a “universal approximator”, because it can learn to approximate an unknown function f(x) = y between any input x and any output y, assuming they are related at all (by correlation or causation, for example). In the process of learning, a neural network finds the right f, or the correct manner of transforming x into y, whether that be f(x) = 3x + 12 or f(x) = 9x – 0.1.&nbsp;&nbsp;<h4>In part 2 of this blog post, we shall delve deeper into how this technique actually works, and the use of artificial neural networks in predictive analytics.&nbsp;</h4>References:<a href="https://pathmind.com/wiki/neural-network" rel="noopener noreferrer" target="_blank" style="background-color: transparent; color: rgb(17, 85, 204);">A Beginner’s Guide to Neural Networks and Deep Learning</a><a href="https://data-flair.training/blogs/artificial-neural-networks-for-machine-learning/" rel="noopener noreferrer" target="_blank" style="background-color: transparent; color: rgb(17, 85, 204);">Artificial Neural Networks for Machine Learning – Every aspect you need to know about</a><a href="https://www.oreilly.com/library/view/data-science-from/9781491901410/ch18.html" rel="noopener noreferrer" target="_blank" style="background-color: transparent; color: rgb(17, 85, 204);">Chapter 18. Neural Networks</a>

Artificial neural networks use machine learning algorithms that can make adjustments as soon as they accept new input. They are simple mathematical models to improve existing data analysis techniques.


What is Artificial Neural Networks and their use in data science

Why Are Artificial Neural Networks Important

The customer data platform (CDP), along with the predictive model, provides valuable insights to combine your data and provide a single customer view of your customer journey. Know how to get actionable insights from customer data platform development.

Getting Actionable Insights From Customer Data Platform

How to Get Insights from Customer Data Platform (CDP)

<h1 class="ql-align-center">Even A B2B Business Needs A B2B Customer Data Platform</h1>Unlike a B2C company, a B2B company has companies and businesses, not individuals as customers. Not only that, a B2B enterprise has to deal with a lengthy and often intricate buying cycle involving multiple departments. Sales is never a straightforward, one-on-one process here.Hence, if you are a B2B company, you have to be forgiven for not having contemplated adding a customer data platform (CDP) to your MarTech stack, in the mistaken belief that it was only for B2C businesses. You couldn’t be more wrong.In fact, studies exist that B2B businesses, as buyers of a CDP, will multiply between 2020 and 2025.A CDP can play a major role in account-based marketing in a B2B business. It will help evolve a single source of truth, and with it, help identify buyers, create buyer segments, deliver targeted content; in short, deliver a successful one-on-one marketing campaign.----------------------------- How Oyster CDP can help in Predictive modeling <a href="https://www.expressanalytics.com/contact-us" rel="noopener noreferrer" target="_blank" style="background-color: transparent;">&gt;&gt;&gt; Speak to Us&gt;&gt;&gt;&gt; </a>-----------------------------<h2 class="ql-align-center">Are B2C And B2B Customer Data Platforms Very Different From Each Other?</h2>At a macro level, the answer is “no.” The underlying principle of both CDPs is the same: to provide a <a href="https://www.expressanalytics.com/blog/identity-resolution-shaping-modern-marketing/" rel="noopener noreferrer" target="_blank" style="background-color: transparent; color: rgb(17, 85, 204);">unified view of customers</a> based on the data available to make marketing and customer service highly effective.Where the difference comes in is in the strategy that both forms of CDPs help assist with. For example, in a B2B company, a CDP is at the very heart of its account-based marketing strategy. The platform is used to gain insights from their prospects’ data, say, for example, targeting a strategic prospect with a special offer based on a certain action by the latter. On the other hand, in the B2C world, CDPs are used to gather intelligence and to activate insights related to existing customers.<a href="https://www.expressanalytics.com/blog/how-to-choose-cdp/" rel="noopener noreferrer" target="_blank" style="background-color: transparent; color: rgb(17, 85, 204);">Using a CDP</a>, a B2B business can compile data from all its marketing and sales stacks, and third-party sources. This eventually helps B2B companies deploy smarter campaigns to grab enterprise accounts. Some B2B companies have reportedly increased as much as 70% higher engagement with fellow companies when using a CDP.<h3>What Capabilities Must A B2B Customer Data Platform Have?</h3>First and foremost, what any B2B CDP worth its price tag must be able to deliver is the wherewithal to map leads to accounts. Because as a B2B business, you’re selling to individuals in buying centers and accounts that live in hierarchies.<h4>Here are some of the must-have features of a B2B CDP:</h4>Integration with Sales and 3rd party sources: This is of primary importance because Sales must be able to effectively work on the leads passed on it by Marketing. Things must go beyond the simple sharing of data.Also, the CDP must be able to ingest and integrate data from 3rd party sources into unified profiles.‍Flexible: A B2B CDP should be able to connect with outside systems and data from any source. Never go in for CDPs that cannot do so. It is also vital that a B2B vendor is not linked to one specific platform but they need to be functional across your technologies.Scalable: There are cases where initially a B2B company may have to work low volumes of data, but a good B2B CDP must be scalable, i.e. must be able to enlarge in scope and size once the customer base increases.<h4>Oyster B2B CDP</h4>The Express Analytics Oyster CDP has all of the above qualities and more. It allows marketers to map user &amp; account details to help them run account-based marketing campaigns.So, for example, Oyster collects user IDs when multiple apps such as HubSpot, LinkedIn, etc. are connected to develop one single master profile. No more confusion about the identity of a contact. What’s more, you can even create the same profile for logged-in and non-logged-in users. All of which help eliminate duplicate contacts.Oyster b2b CDP also gives Sales &amp; Marketing the data to truly understand their customers to drive personalized engagement. It allows <a href="https://www.expressanalytics.com/blog/what-is-stp-in-marketing/" rel="noopener noreferrer" target="_blank" style="background-color: transparent; color: rgb(17, 85, 204);">customer segmentation</a> and audience management, be it lists of people or a list of accounts, at a drilled-down level, something not seen in other B2B CDPs.<h4> </h4>

A B2B Customer Data Platform help identify buyers, create buyer, deliver targeted content, create buyer segments, in short, deliver a successful one-on-one marketing campaign. It compile data from all its marketing and sales stacks, and third-party sources. This eventually helps B2B companies deploy smarter campaigns to grab enterprise accounts.

Even A B2B Business Needs A Customer Data Platform

Even A B2B Business Needs A B2B Customer Data Platform

A customer data platform (CDP) is a customer data unifying software. You can manage your customer experience across every touchpoint by adding it to your MarTech stack. This software brings together data from all sources – batch, streaming, structured or unstructured, transactional or demographic – and makes it available 24×7 to all enterprise users.Today, CDPs have become a “must-have” tool for marketers as it helps collect customer data from all sources to create a 360 degree profile of every client. The idea is to deliver the “ultimate customer experience”. In order to do that, certain best practices have to be put in place vis-à-vis the use of the CDP, and this article looks at some of the important ones.<h2 class="ql-align-center">Top 10 CDP Best Practices to Adopt</h2>What are the 10 CDP Best Practices to Enhance Customer Experiences? Let’s first give a glance at the ten best practices for using a CDP itself for better customer experience:<ol><li>Identify the key business objectives for using a <a href="https://www.expressanalytics.com/blog/how-to-get-insights-from-customer-data-platform-cdp" rel="noopener noreferrer" target="_blank" style="background-color: transparent;" data-faitracker-click-bind="true">CDP</a></li><li>It’s important to have clear goals for implementing a CDP, such as increasing sales or reducing costs.</li><li>Establish a comprehensive data governance framework</li><li>A comprehensive data governance framework will ensure that all data is handled responsibly and that data accuracy and completeness is maintained.</li><li>Select the right CDP technology</li><li>There are a number of different CDP technologies available, so it’s essential to choose one that meets the specific needs of your business.</li><li>Train employees on how to use the CDP</li><li>Employees need to be trained on how to use the CDP and how to manage and protect data.</li><li>Implement a data retention policy</li><li>It’s essential to determine how long business data will be retained in a CDP and to have a data retention policy in place.</li><li>Use data analysis tools to improve business operations</li><li>Data analysis tools can help businesses improve their operations by identifying patterns and trends in data.</li><li>Use CDPs to engage with customers on a more personal level</li><li>Plan plain and clean data feeds</li><li>Optimize for Real-time result</li><li>Measure the Success of your CDP Implementation</li></ol>-----------------------------How does a CDP help Marketers Strengthen Customer Relationships? <a href="https://www.expressanalytics.com/contact-us" rel="noopener noreferrer" target="_blank" style="background-color: transparent;" data-faitracker-click-bind="true">&gt;&gt;&gt; Schedule a call &gt;&gt;&gt;&gt; </a>-----------------------------<h2 class="ql-align-center">Mistakes to Avoid when Implementing A CDP</h2>There are some common mistakes that companies make while deploying a CDP.<ol><li>One is failing to integrate CDP data with other data sources, such as marketing automation platforms and CRMs. This can lead to data silos and make it difficult to get a holistic view of the customer.</li><li>Another mistake is not having a clear plan for how the CDP will be used. Without a clear use case, it can be difficult to justify the investment in a CDP.</li><li>Finally, some companies make the mistake of trying to do too much with their CDP, instead of focusing on a few key use cases. By doing this, they risk overwhelming their teams and not getting the most out of their CDP.</li></ol>With that out of the way, let’s move on to this post’s main topic – how to better use a CDP to enhance your customer’s experience. A CDP can be used to personalize the customer experience and to provide additional services.<h2 class="ql-align-center">How You can Use a CDP to Enhance Customer Experiences?</h2>Now that we’ve seen what a CDP is, and some of the best practices for using it, let’s take a look at how you can use a CDP best practices to enhance customer experiences.<ol><li>Use a CDP to enhance customer experiences across all touchpoints: One of the key benefits of using a CDP is the ability to improve customer experiences across all touchpoints.</li><li>Collect customer data from all sources: It’s important to collect <a href="https://www.expressanalytics.com/blog/customer-journey-map" rel="noopener noreferrer" target="_blank" style="background-color: transparent;" data-faitracker-click-bind="true">customer data</a> from all sources in order to create a 360 degree profile of every client.</li><li>Use data analysis tools to identify patterns and trends in data: Data analysis tools can help businesses improve their operations by identifying patterns and trends in data.</li><li>Use CDPs to personalize the customer experience.</li><li>A CDP can be used to personalize the customer experience and to provide additional services.</li><li>Focus primarily on top revenue generating clients (if financial resources are limited): If financial resources are limited, it’s critical to focus primarily on top revenue generating clients.</li><li>Use CDPs to improve customer experience across all touchpoints: It’s important to use a CDP to improve customer experiences across all touchpoints in order to create a truly omnichannel customer experience.</li></ol>To begin with, the first step would be to lay down a roadmap for enhancing the customer experience. <h2 class="ql-align-center">Laying Out A Roadmap</h2>Your business’ overall target are tied in directly with your marketing goals, which, in turn, are directly or indirectly tied in to your customer experience. For all of this, you need to work to a plan, which has to be put down on paper. This has to be shared with all stakeholders involved in the decision-making.The roadmap must ask basic questions like: What do customers want from your business? How do you meet those needs? The plan must not only have questions, but answers, too.There should be measurable key performance indicators (KPIs), which may include customer satisfaction (CSAT) scores and net promoter scores (NPS).Whenever you make a commercial or marketing decision, you can use your CDP best practices roadmap as a compass – whether you’re customizing your CDP capabilities or identifying which data sources you’ll need.<h2 class="ql-align-center">Ensuring Your Business Goals Sync with Customer Experience Plan</h2><a href="https://www.expressanalytics.com/services/marketing-analytics" rel="noopener noreferrer" target="_blank" style="background-color: transparent;">Customer experience </a>could become the driving force in your enterprise, taking it towards its commercial goals. You will, however, need to ensure that your business goals and customer experience are in alignment. It’s the only way that you can make sure that you reach the target market and meet their needs in a way that improves your profits.There are a few ways that you can do this. You need to make sure that you understand your customer’s needs. What motivates them? What makes them happy? Do you have a strategy that actually anticipates a customer’s needs even before they are expressed? Once you know these things, you can start to tailor your products and services to meet those needs.<h2 class="ql-align-center">Optimizing Marketing Campaigns With A CDP</h2>By combining various customer data points, such as email addresses, phone numbers, first-party cookies and purchase data, the platform creates a single customer profile. In fact, once your business acquires a CDP, the aim should be to unify all disparate sources of data. Once done, you need to send out personalized content to consumers.CDPs give marketers complete control over customer related data collection and its segmentation. They offer integration of data both identifiable and anonymous customer data with any external source such as a CRM, point of sale (POS) machine, smart computing device, website and so on.A CDP takes the confusion out of your marketing campaigns. Data management has become increasingly complex in recent years, with the rise of big data and the proliferation of devices and sensors. This has made it difficult for marketers to keep track of customer data and use it effectively. A CDP helps marketers overcome these challenges by collecting, organizing, and analyzing customer data from multiple sources.But how does one know which content to send out? This is where the use of customer data points come into play. By understanding what actions consumers actions took on your site or app, you can better predict what they will want to see next.For instance, if a consumer makes a purchase on your ecommerce site, you can email them thanking them for their purchase and suggesting similar items that they might be interested in. Customers are extremely valuable to businesses, and with the help of a CDP best practices, you can optimize your marketing campaigns in order to create a more engaged customer base.-----------------------------How do You Choose a Customer Data Platform? <a href="https://www.expressanalytics.com/contact-us" rel="noopener noreferrer" target="_blank" style="background-color: transparent;" data-faitracker-click-bind="true">&gt;&gt;&gt; Schedule a call &gt;&gt;&gt;&gt; </a>-----------------------------<h2 class="ql-align-center">Using The CDP As An Enabler</h2>That’s right. Your CDP is the sole central source of truth. Gone are the days when simply tracking a customer’s journey would suffice. Today, it’s all customer experience with a brand. A customer or prospect uses multiple channels and multiple devices to shop.They expect their shopping journey to be seamless, and get frustrated when it is not. That’s where a CDP plays a big role. This critical piece of software helps not only properly collect data but also activate it for personalization, thus driving sales.<h2 class="ql-align-center">How Can You Ensure Successful Adoption Of A CDP?</h2>Before signing off, here are a few CDP best practices to keep in mind when implementing a CDP.<ol><li>It’s important to have a clear understanding of your organization’s needs and goals.</li><li>You’ll need to select the right CDP for your organization. There are a lot of options on the market, so it’s important to do your research. Once you’ve selected a CDP, it’s essential to get buy-in from all stakeholders and ensure that everyone is on board with the implementation process.</li><li>You’ll need to establish clear metrics to measure the success of your CDP. By following these CDP best practices, you can set your organization up for success with a CDP.</li></ol>

The key answer to this question is purely dependent on the stage your business is in. If yours is a brand-new business, improving brand awareness through customer acquisition is your highest priority.

Top 10 CDP Best Practices To Enhance Customer Experiences

CDP Best Practices To Enhance Customer Experiences

Discover the key differences between customer acquisition and customer retention, and learn which strategy drives better ROI. Explore insights on customer loyalty, lifetime value, and growth-focused marketing.

Customer Acquisition vs Retention: Which One is More Vital?

Customer Acquisition vs Customer Retention: Which is More Important?

One of the first things any organization has to do to stop dirty data from getting into the system is to set up a data-friendly company culture.Whether yours is a big, medium, or small-sized Enterprise, it is very important to have a culture to encourage the proper use of data quality and for its analytics.But the term ‘culture’ can be vague and intangible. So here’s a quick definition – the Enterprise head honchos need to constantly talk to employees at all levels about the use of data analytics and its benefits.This should also include getting across the importance of accurate data and the <a href="https://www.expressanalytics.com/services/data-cleaning-services" rel="noopener noreferrer" target="_blank" style="background-color: transparent; color: rgb(17, 85, 204);">impact of dirty data</a>.What’s more, roles and tasks have to be assigned to designated members of the team; more so to those who will be responsible for ensuring the consistent accuracy of all incoming data.Data quality and its importance, and its link to data management cannot be stressed enough.Managers need to constantly emphasize to all team members that to make the right data-driven decisions, the first and foremost task is to get the correct data in.----------------------------- Grow your business operations using our data cleaning services &gt;&gt;&gt; <a href="https://www.expressanalytics.com/contact-us" rel="noopener noreferrer" target="_blank" style="background-color: transparent;">Let's connect </a>----------------------------- That’s also because the data gets connected from the master database across a company’s CRM, DRM, and other such services so data management requires a certain consistency across all. Wrong or poor data can even become dangerous for an Enterprise’s survival.By now, you would have also understood that monitoring incoming data for inconsistencies or other errors is not a one-off thing. It is always a priority.Once you have done all of this and are confident that the message relating to dirty data and its avoidance has gone down the file and rank, you can go ahead and make your investment in creating the processes, including the software and assigning the people to man them.Yet, do not forget, after this too, you will need to continuously monitor and better the quality of incoming data.<h2 class="ql-align-center">Here are Some Common Roles to Ensure Master Data Management</h2>There are 3 ways this can be bifurcated: You can have a 3-tiered structure comprising a data owner, a data steward, and a data manager. At first, these may sound like overlapping or similar roles, but they are not.<h3>For example, here’s what the data owner does:</h3><ol><li>Play a pivotal role in data domains</li><li>Defines data requirements</li><li>Preserves data quality and accessibility</li><li>Decides who in the team gets what kind of access rights and</li><li>Permits data stewards to manage data</li></ol>So the data owner operates on a macro level in the ecosystem.On the other hand, the data steward is actually the one who lays down the rules and the plans and then coordinates data delivery. He is the operations guy.Last in this change is the data manager. This guy operates at a micro-level in the Enterprise. He normally coordinates between the data owner and the technical part of implementing the plans.Now that you have the systems, procedures, and manpower in place, what next? Remember, a fruitful data quality and management project require a holistic approach.This is where the ‘How’ part comes in – how to go about ensuring consistent good data?To determine the quality of data, here are some aspects to look out for:Accuracy, completeness, adherence to standards, and duplication. A combination of IT software, hardware, and human resources will take care of this.Your designation team with the given infrastructure first needs to identify all the problem areas from where bad data is likely to come in. Remember, all this effort is towards establishing a single source of truth.Thereon, your Enterprise will then have to develop a data quality program, and with the help of a data steward, apply the business processes that ensure all future data collection and use meet regulatory frameworks and eventually adds value to the business at hand.The correct method of matching high data quality with technology is to integrate the different stages of the data quality cycle into operation procedures and tie them in with the individual roles.----------------------------- Grow your business operations using our data cleaning services &gt;&gt;&gt; <a href="https://www.expressanalytics.com/services/data-cleaning-services" rel="noopener noreferrer" target="_blank" style="background-color: transparent;">Let's connect </a>----------------------------- <h3>Use of AI in manipulating large data sets</h3>In an earlier post, we had written how with the entry of AI, data stewards could now use <a href="https://www.expressanalytics.com/blog/ai-data-cleaning/" rel="noopener noreferrer" target="_blank" style="background-color: transparent; color: rgb(17, 85, 204);">data cleansing</a> and augmentation solutions based on machine learning (ML).ML and deep learning allow the analysis of the collected data, making estimates, to learn and change as per the precision of the estimates. As more information is analyzed, so also the estimated progress.While identifying where your data is lacking or erroneous, large data sets always present a problem. How do humans track say a million data points? And say, in real-time? But with ML getting into the mix, that hurdle, too, can be surmounted.AI can be used to detect anomalies in data sets by being “trained” to continuously track and evaluate data, even as the data is being processed.What is even more important is that an ML solution can detect and deal with data integrity issues at the very start of data processing, and quickly convert such vast volumes of data into dependable information.<h4>In conclusion:</h4>Tracking, analyzing, and correcting/updating incoming data will eventually help an enterprise in making well-informed business decisions, providing a single source of truth, and eventually increased productivity.Handling dirty data in analytics isn’t simply a technical fix—it’s a strategic need.Understanding how to clean dirty data not only improves your analytics—it lets your team act with confidence and saves time.References:<a href="https://orangematter.solarwinds.com/2019/01/24/how-to-clean-dirty-data-the-life-of-a-data-janitor/" rel="noopener noreferrer" target="_blank" style="color: rgb(17, 85, 204); background-color: transparent;">How to Clean Dirty Data – The Life of a Data Janitor</a><a href="https://www.dnb.com/perspectives/master-data/6-key-responsibilities-of-data-stewards.html" rel="noopener noreferrer" target="_blank" style="color: rgb(17, 85, 204); background-color: transparent;">6 Key Responsibilities of the Invaluable Data Steward</a><a href="https://searchdatamanagement.techtarget.com/definition/data-quality" rel="noopener noreferrer" target="_blank" style="color: rgb(17, 85, 204); background-color: transparent;">Data Quality</a>

How to prevent dirty data? To determine the quality of data, here are some aspects to look out for: Accuracy, completeness, adherence to standards, and duplication. A combination of IT software, hardware, and human resources will take care of this. Your designation team with the given infrastructure first needs to identify all the problem areas from where bad data is likely to come in.

How Data Quality can be Preserved? How can AI be used to do it?

Here’s How to Tackle Your Organization’s Dirty Data

<h1 class="ql-align-center">Analytics Dashboard and its importance</h1>When a year comes to an end, there’s always a tendency to look back at the days gone by to analyze what certain developments mean for a certain industry or sector.So, as the curtains are set to come down in 2020, we at Express Analytics, too, plan to bring you a roundup of all the prominent developments of the year in the world of data analytics. But that will be in another blog post.Today, though, we will look at a prediction made by none other than well-known research firm Gartner in late October and the intense debate it generated in the data analytics industry.In “<a href="https://www.gartner.com/smarterwithgartner/gartner-top-10-trends-in-data-and-analytics-for-2020/" rel="noopener noreferrer" target="_blank" style="background-color: transparent; color: rgb(17, 85, 204);" data-faitracker-click-bind="true">The Top 10 Trends in Data and Analytics for 2020</a>” report, Gartner laid out the top technology trends that data and analytics leaders need to focus on as they look to make essential investments to prepare for a reset.In it, it has virtually forecasted the end of “pre-defined analytics dashboards”, to be replaced with “dynamic data stories”.Said the report: Dynamic data stories with more automated and consumerized experiences will replace visual, point-and-click authoring and exploration. As a result, the number of time users spend using predefined analytics dashboards will decline.The shift to in-context data stories means that the most relevant insights will stream to each user based on their context, role, or use.The Gartner team added that such dynamic insights would leverage technologies such as <a href="https://www.expressanalytics.com/blog/augmented-analytics-the-future-of-business-intelligence/" rel="noopener noreferrer" target="_blank" style="background-color: transparent; color: rgb(17, 85, 204);" data-faitracker-click-bind="true">augmented analytics</a>, NLP, streaming anomaly detection, and collaboration.The report was followed up with a series of articles on Medium and on other blogs, much in the same vein – “Analytics Dashboards are dying”. Not that this lot was saying something entirely new.For some time now, there have been similar predictions, on and off, claiming pre-defined analytics dashboards were “just not up to the mark”, and their end was near.Ranged against such naysayers are those who continue to insist (a) analytics dashboards are here to stay (b) dashboards do not really have a suitable replacement for now (c) notebooks are the best alternates for dashboards (for now, at least).When they first came into play, <a href="https://www.expressanalytics.com/services/business-intelligence-analytics-services" rel="noopener noreferrer" target="_blank" style="background-color: transparent; color: rgb(17, 85, 204);" data-faitracker-click-bind="true">data analytics dashboards</a> represented a leap forward from the staid (and cumbersome) spreadsheet. Suddenly, visualizing and reporting complex data using interactive and, dare we say, colorful dashboards was so much better than the average, one-dimension spreadsheet.<h2 class="ql-align-center">Why the Problem with Analytics Dashboards Now?</h2>The way we see it here at Express Analytics, the foremost problem with analytics dashboards is context, or rather, the lack of it. The other is that “one dashboard does not fit all.”Let us not forget that with the increase in data democracy, dashboards today are used by two distinct categories of users – the data scientists/analysts, and then the “non-technical” users like the Sales and Marketing teams.But context is the paramount reason for the negative outlook on analytics dashboards today. Often, perspective is missing when dashboards are designed. Without it, a dashboard’s as good or as bad as your spreadsheet.Here’s an example: If you have a dashboard that visualizes the number of clicks and likes got on your company’s social media channels, you need to put it in perspective in the same dashboard.That perspective could be: how are the numbers in comparison to the previous month’s, or the same time last year, or how are they in comparison to your rival’s?A poorly designed analytics dashboard seems to be the main reason for detractors predicting its death. Ease of use maybe the other.<h2 class="ql-align-center">Pre-defined or DIY Analytics Dashboards?</h2>This brings us to the question – do DIY Analytics dashboards offer a better alternative to pre-defined ones? There’s no clear answer here, but we can offer some tips: everything depends on the requirements of the data project, the budget, and the profile of the end-users.Out-of-the-box boards often come with some expensive license fees, but if you have a special requirement there are always boards you can design using open-source code like Python, for example.But what’s even more critical is that the time has also come to move on from static&nbsp; analytics dashboards to those that provide actionable insights. Actionable insights cause action to happen rather than merely answer a question. Which means instead of the user spending valuable time to make sense of the visualization, actionable analytics dashboards can directly tell him about an insight.At Express Analytics, for example, as part of our customer data platform Oyster, we are currently building a sentiment analysis and voice of customer analytics service.Here, one of our aims is to develop an analytics dashboard that can directly tell the client that 40% of the negative reviews are for Product X because it’s over-priced. This, then, will serve as direct insight to the user which they can promptly rectify.For interactive <a href="https://www.expressanalytics.com/services/data-visualization" rel="noopener noreferrer" target="_blank" style="background-color: transparent; color: rgb(17, 85, 204);" data-faitracker-click-bind="true">analytics dashboards, unlike say Tableau or PowerBI</a>, users can create data visualizations on the web using d3.js JavaScript libraries which give developers a more granular control of the visualizations.D3 stands for Data-Driven Documents. d3.js can thus become your preferred tool for data visualization.<h3>Notebooks: Are They the Ideal Replacement for Analytics Dashboards?</h3>For a couple of years now, data notebooks have been climbing the popularity charts, and some users have even started touting them as a replacement for the dashboard.Editor of Memory Leak <a href="https://medium.com/memory-leak/data-science-notebooks-a-primer-4af256c8f5c6" rel="noopener noreferrer" target="_blank" style="background-color: transparent; color: rgb(17, 85, 204);" data-faitracker-click-bind="true">Astasia Myers</a> describes them as: Notebooks are a form of interactive computing, in which users write and execute code, visualize the results, and share insights.Notebooks are used by analysts and even non-technical users for exploration tasks. The versatility of a notebook has led to users demanding more by way of visualizations.But data enthusiast Axel Christ while writing in Medium says notebooks still face some challenges that need sorting. “I believe that Analytics dashboards have their place in a business context and are here to stay. At the same time, I would love it if the foundation they were built on would be a notebook-style technology”, he concludes in the article.<h4>In Conclusion</h4>As with any other technology, data analytics, too, is evolving. Analytics dashboards are certainly not the most optimal interface in visualization and reporting, hence, they cannot be the last word, even as data analytics gets more common-place and used by non-tech teams.There’s a case then perhaps for two types of analytics dashboards – one for the analysts and the other for those who are not. Market evidence certainly supports this view.In the meanwhile, if notebooks or any other form of data visualization tool come online that helps democratize access to data, even more, they could evolve and replace dashboards eventually. But for now, dashboards are not going anywhere. Perhaps insisting on adding context to the dashboards and making them easier to use could lead to more adaptability.

Analytics dashboards are certainly not the most optimal interface in visualization and reporting; insisting on adding context to the dashboards and making them easier to use could lead to more adaptability.

Analytics Dashboards: Pre-defined Or DIY Analytics Dashboards

Analytics Dashboards: Some Say The End Is Near, We Say Not So Fast

Data wrangling is the practice of converting and then plotting data from one “raw” form into another. The aim is to make it ready for downstream analytics. Here are the steps in data wrangling: 1. Data Discovery 2. Data Structuring 3. Data Cleaning 4. Data Enriching 5. Data Validating 6.Data Publishing

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