Hello Sense and Respond, Bye Bye Command and Control

Shalini raghavan | August 31, 2016

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It was a few years ago, my colleague Matt Beck, General Manager for FICO’s marketing solution, and I were discussing the challenges of doing business in an omnichannel world. Our ability to interact with customers across multiple communication channels is just one factor contributing to the fact that there is now more data than ever before.

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OTHER ARTICLES

A BRAND NEW CHIP DESIGN WILL DRIVE AI DEVELOPMENT

Article | February 20, 2020

The world is now heading into the Fourth Industrial Revolution, as Professor Klaus Schwab, Founder and Executive Chairman of the World Economic Forum, described it in 2016. Artificial Intelligence (AI) is a key driver in this revolution and with it, machine learning is critical. But critical to the whole process is the need to process a tremendous amount of data which in turns boosts the demand for computing power exponentially.A study by OpenAI suggested that the computing power required for AI training surged by more than 300,000 times between 2012 and 2018. This represents a doubling of computing power every three months and two weeks; a number that is significantly quicker than Moore’s Law which has traditionally measured the time it takes to double computing power. Conventional methodology is no longer enough for such significant leaps, and we desperately need a different computing architecture to stay ahead in the game.

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Time Machine Big Data of the Past for the Future of Europe

Article | February 24, 2020

Emerging technology has the power to transform history and cultural heritage into a living resource. The Time Machine project will digitise archives from museums and libraries, using Artificial Intelligence and Big Data mining, to offer richer interpretations of our past. An inclusive European identity benefits from a deep engagement with the region’s past. The Time Machine project set out to offer this by exploiting already freely accessible Big Data sources. EU support for a preparatory action enabled the development of a decade-long roadmap for the large-scale digitisation of kilometres of archives, from large museum and library collections, into a distributed information system. Artificial Intelligence (AI) will play a key role at each step, from digitisation planning to document interpretation and fact-checking. Once embedded, this infrastructure could create new business and employment opportunities across a range of sectors including ICT, the creative industries and tourism.

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Here’s How Analytics are Transforming the Marketing Industry

Article | July 13, 2021

When it comes to marketing today, big data analytics has become a powerful being. The raw material marketers need to make sense of the information they are presented with so they can do their jobs with accuracy and excellence. Big data is what empowers marketers to understand their customers based on any online action they take. Thanks to the boom of big data, marketers have learned more about new marketing trends and preferences, and behaviors of the consumer. For example, marketers know what their customers are streaming to what groceries they are ordering, thanks to big data. Data is readily available in abundance due to digital technology. Data is created through mobile phones, social media, digital ads, weblogs, electronic devices, and sensors attached through the internet of things (IoT). Data analytics helps organizations discover newer markets, learn how new customers interact with online ads, and draw conclusions and effects of new strategies. Newer sophisticated marketing analytics software and analytics tools are now being used to determine consumers’ buying patterns and key influencers in decision-making and validate data marketing approaches that yield the best results. With the integration of product management with data science, real-time data capture, and analytics, big data analytics is helping companies increase sales and improve the customer experience. In this article, we will examine how big data analytics are transforming the marketing industry. Personalized Marketing Personalized Marketing has taken an essential place in direct marketing to the consumers. Greeting consumers with their first name whenever they visit the website, sending them promotional emails of their favorite products, or notifying them with personalized recipes based on their grocery shopping are some of the examples of data-driven marketing. When marketers collect critical data marketing pieces about customers at different marketing touchpoints such as their interests, their name, what they like to listen to, what they order most, what they’d like to hear about, and who they want to hear from, this enables marketers to plan their campaigns strategically. Marketers aim for churn prevention and onboarding new customers. With customer’s marketing touchpoints, these insights can be used to improve acquisition rates, drive brand loyalty, increase revenue per customer, and improve the effectiveness of products and services. With these data marketing touchpoints, marketers can build an ideal customer profile. Furthermore, these customer profiles can help them strategize and execute personalized campaigns accordingly. Predictive Analytics Customer behavior can be traced by historical data, which is the best way to predict how customers would behave in the future. It allows companies to correctly predict which customers are interested in their products at the right time and place. Predictive analytics applies data mining, statistical techniques, machine learning, and artificial intelligence for data analysis and predict the customer’s future behavior and activities. Take an example of an online grocery store. If a customer tends to buy healthy and sugar-free snacks from the store now, they will keep buying it in the future too. This predictable behavior from the customer makes it easy for brands to capitalize on that and has been made easy by analytics tools. They can automate their sales and target the said customer. What they would be doing gives the customer chances to make “repeat purchases” based on their predictive behavior. Marketers can also suggest customers purchase products related to those repeat purchases to get them on board with new products. Customer Segmentation Customer segmentation means dividing your customers into strata to identify a specific pattern. For example, customers from a particular city may buy your products more than others, or customers from a certain age demographic prefer some products more than other age demographics. Specific marketing analytics software can help you segment your audience. For example, you can gather data like specific interests, how many times they have visited a place, unique preferences, and demographics such as age, gender, work, and home location. These insights are a golden opportunity for marketers to create bold campaigns optimizing their return on investment. They can cluster customers into specific groups and target these segments with highly relevant data marketing campaigns. The main goal of customer segmentation is to identify any interesting information that can help them increase revenue and meet their goals. Effective customer segmentation can help marketers with: • Identifying most profitable and least profitable customers • Building loyal relationships • Predicting customer patterns • Pricing products accordingly • Developing products based on their interests Businesses continue to invest in collecting high-quality data for perfect customer segmentation, which results in successful efforts. Optimized Ad Campaigns Customers’ social media data like Facebook, LinkedIn, and Twitter makes it easier for marketers to create customized ad campaigns on a larger scale. This means that they can create specific ad campaigns for particular groups and successfully execute an ad campaign. Big data also makes it easier for marketers to run ‘remarketing’ campaigns. Remarketing campaigns ads follow your customers online, wherever they browse, once they have visited your website. Execution of an online ad campaign makes all the difference in its success. Chasing customers with paid ads can work as an effective strategy if executed well. According to the rule 7, prospective customers need to be exposed to an ad minimum of seven times before they make any move on it. When creating online ad campaigns, do keep one thing in mind. Your customers should not feel as if they are being stalked when you make any remarketing campaigns. Space out your ads and their exposure, so they appear naturally rather than coming on as pushy. Consumer Impact Advancements in data science have vastly impacted consumers. Every move they make online is saved and measured. In addition, websites now use cookies to store consumer data, so whenever these consumers visit these websites, product lists based on their shopping habits pop up on the site. Search engines and social media data enhance this. This data can be used to analyze their behavior patterns and market to them accordingly. The information gained from search engines and social media can be used to influence consumers into staying loyal and help their businesses benefit from the same. These implications can be frightening, like seeing personalized ads crop up on their Facebook page or search engine. However, when consumer data is so openly available to marketers, they need to use it wisely and safeguard it from falling into the wrong hands. Fortunately, businesses are taking note and making sure that this information remains secure. Conclusion The future of marketing because of big data and analytics seems bright and optimistic. Businesses are collecting high-quality data in real-time and analyzing it with the help of machine learning and AI; the marketing world seems to be up for massive changes. Analytics are transforming marketing industry to a different level. And with sophisticated marketers behind the wheel, the sky is the only limit. Frequently Asked Questions Why is marketing analytics so important these days? Marketing analytics helps us see how everything plays off each other, and decide how we might want to invest moving forward. Re-prioritizing how you spend your time, how you build out your team, and the resources you invest in channels and efforts are critical steps to achieving marketing team success. What is the use of marketing analytics? Marketing analytics is used to measure how well your marketing efforts are performing and to determine what can be done differently to get better results across marketing channels. Which companies use marketing analytics? Marketing analytics enables you to improve your overall marketing program performance by identifying channel deficiencies, adjusting strategies and tactics as needed, optimizing processes, etc. Companies like Netflix, Sephora, EasyJet, and Spotify use marketing analytics to improve their markeitng performance as well. { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "Why is marketing analytics so important these days?", "acceptedAnswer": { "@type": "Answer", "text": "Marketing analytics helps us see how everything plays off each other, and decide how we might want to invest moving forward. Re-prioritizing how you spend your time, how you build out your team and the resources you invest in channels and efforts are critical steps to achieving marketing team success" } },{ "@type": "Question", "name": "What is the use of marketing analytics?", "acceptedAnswer": { "@type": "Answer", "text": "Marketing analytics is used to measure how well your marketing efforts are performing and to determine what can be done differently to get better results across marketing channels." } },{ "@type": "Question", "name": "Which companies use marketing analytics?", "acceptedAnswer": { "@type": "Answer", "text": "Marketing analytics enables you to improve your overall marketing program performance by identifying channel deficiencies, adjusting strategies and tactics as needed, optimizing processes, etc. Companies like Netflix, Sephora, EasyJet, and Spotify use marketing analytics to improve their markeitng performance as well." } }] }

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What Is The Value Of A Big Data Project

Article | April 7, 2020

According to software vendors executing the big data projects, the answer is clear: More data means more options. Then add a bit of machine learning (ML) for good measure to get told what to do, and the revenue will thrive.This is not really feasible. Therefore, before starting a big data project, a checklist might come in handy.Make sure that the insights gained through machine learning are actionable. Gaining insights is always good, but it is even better if you can act on this new knowledge.A shopping basket analysis shows which products are sold together. What to do with that information?Companies could place the two products in opposite corners of the shop, so customers walk through all areas and will find other products to buy in addition. Or they could place both products next to each other so each boosts the sales of the other. Or how about discounting one product to gain more customers?As all actions have unknown side effects, companies have to decide for themselves which action makes sense to take in their case.

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Spotlight

SAS

SAS is the leader in business analytics software and services, and the largest independent vendor in the business intelligence market. Through innovative solutions, SAS helps customers at more than 70,000 sites improve performance and deliver value by making better decisions faster. Since 1976 SAS has been giving customers around the world THE POWER TO KNOW®.

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