Latest Digital marketing Trends

Fraud detection is one of the top priorities for major banks and leading financial institutions, which can be addressed using machine learning. In banking, fraud may include falsify checks or using stolen credit cards. Other forms of fraud may involve emphasize losses or causing an accident with the sole intent for the pay-out. With an unlimited and increasing number of ways someone can commit fraud, detection can be difficult to accomplish . Activities such as downsizing, reorganization, moving to new information systems or confront a cybersecurity breach could weaken an organization's ability to detect fraud. This means facilities such as real-time monitoring for frauds is recommended. Organizations should look for fraud in financial transactions, location, devices used, initiated sessions and authentication systems. How do you detect fraud? The basic approach to  fraud detection with an analytic model is to identify possible predictors of fraud associated with know

When we talk about intelligent retailing we talk about the technologies, today used by retailer and eCommerce seller to offer better pricing by optimizing the inventory. Intelligent retailing includes a whole ambit of technologies like Big Data, artificial intelligence, machine learning, new sensor hardware and varied solutions stemming from them. Dynamic Pricing and inventory optimization These retail trends are designed to enable dynamic pricing, markdown optimization and inventory optimization so that retailers can plan quantity and pricing of their products appropriately. Simply put, cost savings for the retailer. The predictive pricing solutions include usage of daily/weekly/monthly data and uses historical trends and current sales metrics to forecast best pricing scenarios to help the retailer in promotion planning and to determine percentage of discount per product per region. Predictive inventory management and cognitive demand forecasting solutions that were dem

Today marketers understandably tend to possess over purchases. Even if they understand an entire consumers journey leads up to a purchase, the actual sale is the tangible result of all their efforts and the thing that puts money in the exchequer. That means many ends up focusing on where that purchase gets made, either online or in-store. This is an important data point, in our research people preferred purchase certain things from online and some from store. But the truth of today shopper’s before purchase any thing they browse and research online whether they have intend to buy from store. In our research we talk some shoppers regarding their purchase journey about 60-80% people browse and research online and see the factors like features of the product, customer reviews, pricing & warranty before make any purchase. Omnichannel Channel Strategy Wins- In Some categories We talk about apparel, how you should buy something without trying it on? As in our research a

Today business using big data & machine learning to learn about customers behavior. With customer behavior analytics, business is able to make data-driven decisions because they have an individual-level view of their customers. When business know exactly what customers want, what they do and look for, and how they may act in the future, they are in a better position to create personalized customer experiences and increase customer loyalty. McKinsey  found  that 50% of companies making use of customer behavior analytics are likely to have sales significantly above their competitors, 6.5 times more likely to retain customers, and 7.4 times more likely to outperform competitors in making sales to existing customers. Overall, organizations that correctly utilize customer behavior analytics are 19 times more likely to achieve above average profitability. If you want to enhance your business performance and make improvements in your marketing campaigns, then, here are some pra

As a marketer, chances are you’ve used consumer insights to understand what your customers want and then delivered relevant experiences to them. But have you ever used intent signals to predict what your customers want? Today, marketing technology allows you to do just that. The reality is the customer journey is nothing short of dynamic. As the journey continues to shift and change shape, it’s becoming harder for marketers to make sense of all the consumer intent signals people leave behind. Because as people look for what they need, they switch between channels and devices. They may conduct a research and saw the journey of the customer from start to end. Marketers have had to navigate these twists and turns by juggling first- and third-party data, probabilistic modeling, or remarketing. But all of that manual juggling simply won’t cut it. Today’s consumers expect more. They want assistance at every step. Therefore, as marketers, we need to be one step ahead of our customers —

As a business, you know how hard it is to keep up with customers today. People have become research obsessed for most purchases and expect brands to assist them in a personally relevant way at every stage of their purchase journey.   Machine learning is one automation tool that can boost your efforts to meet people’s high expectations. Machine learning can help to create automated marketing campaigns, at scale, that place the right message in front of the right customer at the right time at the right price, delighting people and delivering the business results marketers want. We talked  with top brands decision makers who have adopted machine learning to improve their marketing, we’ve found that the top-performing brands are applying these below rules and asking themselves these questions to achieve goal with automated marketing: Optimize your campaigns for growth : Machine learning is only as good as what you ask it to optimize. Top performers optimize the c