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Marketers Will Spend More, Hire More to Turn Data into Insights in 2013
Exponential growth of customer data, and the difficulties most marketers have in keeping up with that growth, are creating a situation where marketers are “data-rich and insight-poor.” Findings of a new survey from Infogroup Targeting Solutions and Yesmail Interactive indicate that during 2013, marketers plan to increase spending and hiring to help keep pace with the explosion of customer data and turn it into actionable insight.
Almost seven in 10 (68%) of the 701 marketers attending the DMA2012 and Forrester Research conferences in October 2012 who were surveyed said they will increase data-related marketing expenditures either slightly (20%) or greatly (48%) in 2013. Another 23 percent plan to keep those expenditures level, meaning fewer than one in 10 marketers expect to decrease data-related spending this year.
Data Focus for 2013
Delving beyond simple spending, the survey also found that only 12% of marketers have no plans to improve customer data methods this year. Methods that will be popularly marked for improvement in 2013 include data analysis (38%), data cleaning (31%), data collection (28%) and data application (25%).
The study traces the “explosion” in customer data that has occurred in recent years directly to the boom in marketers’ use of digital analytics channels. Forty-nine percent of respondents said web analytics is the best channel for generating customer data, while 19 percent cited email and 12 percent mentioned social media.
This means about 8 in 10 marketers use a digital analytics channel as their primary means of generating customer data.
However, that data is not necessarily “clean.” While Infogroup and Yesmail say monthly data cleansing is the minimal frequency required, less than half of respondents cleanse customer data either monthly (23%) or weekly (24%). One-quarter (26%) can’t remember the last time they cleaned their customer data and 27 percent only cleanse it quarterly (17%) or annually (10%).
The survey indicates this shows a trend of “data hoarding,” with outdated and inaccurate data lowering the effectiveness of marketing efforts.
In one positive sign, more than half of respondents (56%) plan to hire new resources to handle or oversee data collection and/or analysis this year. Popular roles include data analyst/strategist (20%), data developer/programmer (11%), data manager and data collector (7% each), data engineer/architect (6%) and executive (5%). Survey authors advise that data analysts are the most crucial hires in this area and that only 14 percent of marketers feel they have effectively integrated data analytics across channels.
6 in 10 Marketers Apply Customization Regularly
A combined 61% of marketers use data insights such as demographics to customize messaging always (15%) or often (46%). Another 31 percent customize messaging rarely; meaning only 8 percent never customize their messaging based on data insights. And 78 percent of marketers plan to further integrate (42%) or start integrating (36%) social media data into these efforts.
Focus on Real-time Data Increases
Slightly more than half (53%) of marketers plan to make greater use of real-time data in their campaigns this year, while 11 percent plan to use it for the first time and 19% will consider using it. Survey authors expect much more focus on real-time data as a marketing tool this year with many more trials and experiments.
A Look Ahead
Survey authors predict that in 2013 there will be a “major shift toward multichannel data analysis and application, combining individual customer data across all touch points to build more complete consumer profiles.” Early adopters have already started undergoing this shift, and a majority of marketers will make at least some effort to follow them this year. The results are predicted to include “better targeting, more efficient campaigns and ultimately better customer retention and higher revenue.”
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If you’ve applied for a credit card or loan recently, you’ll be aware of the swift response you now receive. No human can crunch the numbers and make the determination that fast, right? Although big banks are now adopting Big Data, Machine Learning, and AI into their legacy processes, startups have been disrupting the sector for a few years now. As banks and credit unions scramble to keep up, Fintech innovation is bringing together machine language, analytics, and AI to help form Big Data decisions in the industry. The forward-thinking potential of these technologies has led to some real-world uses to combat fraud, offer access to alternative data sources, and suggest real-time analysis for risk. So, Robots are Determining My Credit Risk? Well, yes and no. Often, those in the financial sector are using AI to assess Credit Risk. What once required Risk Analysts to determine manually, is now done in a matter of seconds with an early warning system developed by ING, PwC, and Google. This AI-powered system helps analysts make faster and more informed decisions about potential risk. How do they do this? Using pre-set criteria, they can gauge and analyze risk based on parameters such as whether or not a client has negative media coverage or if a share price falls below a certain percentage. If the world today is based on perception, even such items as bad reviews, negative coverage, and lower than average share prices can affect determinates. In addition, having these parameters can also help determine best practices and how businesses and individuals can be given opportunities outside the scope of big bank processes. However, as data breaches continue to mar profiles of both individuals and business, Machine Learning components offer platforms the chance to stem the tide of negativity. How Machine Learning Helps Prevent Fraud This is a simple process which requires two key measures. The first is to feed the machine not just a large amount of data, but knowing the parameters set, so the machine is fed relevant information. The second is human input which gives the machine its parameters to operate by. From there, the software will take the information, gain an understanding of the data patterns, and identify any signs of fraud. If done well, the automation process will employ solutions without sacrificing quality. Machine Learning in Determining Scorecard Models Alternative data sources offer more options not only to banks and credit unions, but also to borrowers. Using Machine Learning creates a more flexible, robust model when it comes to the type of information most useful to various borrower profiles. Having profiles prepared allows for automated scorecard updates and can generate better responsiveness and intelligence of a borrower’s risk profile. This process can be empowering for both startup and big bank tech. The Matured State of Analytics Though humans must initially input parameters, the benefits of Machine Learning using a decision engine can dig deeper and reach farther than ever before. This type of platform can gather a variety of scenarios across the industry and can constantly analyze the information, helping inform the processes of setting credit limits, loan origination, and risk-based pricing. As an extension of a modern analytics platform, these processes fill in the gaps where other platforms may lack the data or programming required to run effectively. But, as these platforms mature, they are helping to drive innovation throughout the Fintech industry and shaking up the outdated, cumbersome processes of old for a much more streamlined efficient operation. Want to inform decisioning and work with data engineers to build validation frameworks? Are you looking to get in on the ground floor of a startup opportunity in the Fintech industry? If so, we may have a role for you. If you’d like to learn more, check out our current vacancies or contact one of our expert consultants to learn more. For our West Coast Team, call (415) 614 - 4999 or send an email to firstname.lastname@example.org. For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to email@example.com.
21. March 2019
What is it that makes California a mecca for the adventurous of spirit? Is it the land which sparkles gold from the goldrush years or the shiny newness of the latest in tech? From boom to bust and the promise in the dash of its life, it holds possibility in action. Or is it because, as other cities, states, and countries rally and evolve their own tech hubs, California has already settled in as the standard? The Golden State is the home of Data & Analytics. Want to see how high you can go or how to pull yourself up from a failed attempt at success? Look to the place it all began and learn how to make your insights actionable and your business decisions better. How? Begin with a platform. A Data Management Platform. You’ve Laid the Foundation, Now What? Rather than nuggets, blocks, or bars, Data gathering is cumulative. In this case, its divide, segment, and step back for the big picture; a Data Management Platform (DMP) is a unifying platform. In other words, raw Data is collated and changed into usable form. This is the core of Data-driven marketing. It is what helps businesses learn about their customers and helps to set the stage for the actionable insights that lead to happy customers. The abundance of Data can be staggering. How much of what information do you need to better manage your audience information? What do you need to know beyond the basics? How far should you drill down to shape and activate the Data you’ve been gathering and analyzing? Having the right Data reach the right customer at the right time can greatly improve a company’s bottom line. In layman’s terms, with a DMP as part of your marketing strategy, you’ll get the most bang for your buck. Making Connections Omni and multi-channel sources such as online, offline, and mobile are woven into the connections of DMPs. Unstructured Data collection is a neutral way to help marketers use their audience Data in whatever manner is best for their business. Sources come from first – and third – party sources including mobile, desktop, web analytics tools, Customer Resource Management (CRM) software, point of sale, social media, as well as the basics such as demographic and historical behavioral Data. Getting Started Organization – Determine how you want to define your Data so you can understand it when considering a DMP. How will you segment the information you’ve decided to collect?Segmenting and audience building – Once you’ve decided what information you want to gather, you can use the information to build your target audience. Imagine pinpointing a location on a map, then plotting a route to get there. Insights and audience profile reports – Here’s your chance to study the information and analyze patterns, trends, and intent. Let’s find out what exactly it is your customers want, so you can give it to them.Activation – Now, take what you’ve learned and run with it. This is the implementation phase whether it’s through advertising, messaging, even up your game and add-in the Data management platform information into your Content Management System (CMS). The possibilities are endless. Focus, Focus, Focus Here is where you’ll bring everything into focus and see just how far the possibilities can take you and your business. Below are a few ideas and things to consider: Set your audience and advertising targets – Determine the parameters for your audience’s interests and needs through the channels they most often use such as content whether audio or video.Get personal - Offer personalized experiences for web and mobile users as well as those who prefer to conduct their business offline.Game, Set, Match – When it comes to TV DMP, match your audiences on both TV devices as well as digital.Learn – Learn about your customer. Take time to get to know them online and offline through every channel available. Go deeper than point of sale information. What is it they’re looking for? What do they want? Why do they want it and how do they want to buy it? Grow – Whilst it takes a lot more time and effort to find new customers than to keep current customers happy it’s still important to use that time and effort to your advantage utilizing DMP to grow and cultivate a new audience, too. Build brand loyalty through both returning and new customers. Paid search and social – use your Data-driven audiences to target or update paid search including buys on social media. Ultimately, building a DMP will help you build a better relationship with your customers. It helps you show you have their desires at heart; and a happy customer is worth their weight in gold. Check out our current vacancies for our latest opportunities or contact one of our recruitment consultants to learn more. For our West Coast Team, call (415) 614 - 4999 or send an email to firstname.lastname@example.org. For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to email@example.com.
13. March 2019
San Francisco is both a base and a destination for tech professionals. On the edge of Silicon Valley, its uniquely small-town-big-city vibe evokes a sense of community. For better or worse. Everyone is, essentially, in the same boat. But, here’s the thing. Everyone identifies and understands what the other is going through and what they might need assistance with. Imagine being a start-up founder, CEO, or tech genius and needing to spitball, vent, or discuss projects and frustrations. Who can you turn to? Why, the bigger and more established start-ups, CEOs, and enterprising entrepreneurs, of course. The ones who have been there and done that. If you can catch them before they jet off to their next business meeting in London or Beijing. It's also home to a number of world’s best Data & Analytics events, including the World Agri-Tech Innovation Summit in March. This San Francisco summit will host over 1500 agri-food corporates, innovators, and investors in the agri-food sector. It’s theme? ‘Turning Disruptive Technology into Business Strategy through Partnership and Collaboration’ AgriTech – The Newest Frontier of Digital Transformation? It’s not that new, really. This is its fifth year. But, what it is telling is that disruptive technology is playing a large role in agriculture. Remember when scientists were trying to figure out how to make seedless watermelon? Look how far we’ve come. This summit’s focus is on sustainable agriculture and items on the menu for discussion include: Best models for successful technology commercialization. Partnerships needed to scale new technologies. How to transform the food supply chain into a more sustainable, affordable, and nutritious systems for generations (spoiler alert: sayonara high fructose corn syrup, GMOs, and additives?). Best practices and case studies of opportunities for innovation and investment. To best address the above, speakers and attendees, will consider the above within the parameters of: Automation.AI-Backed Genomics.Biological Discovery Platforms.Predictive Agriculture. Several days in the making, it seems the above is probably just the tip of the iceberg. And, from the lab to the field, greenhouses, too are transforming as Artificial Intelligence helps decrease errors in manual Data collection. Using Predictive Analytics in Agriculture In a world driven to be sustainable, and to stem the tide of overabundance generated waste, digital and analytical products in the field have moved toward these endeavors. Imagine being able to calculate how much product is needed and only growing, and cultivating that amount. Using Predictive Analytics in agriculture not only helps ensure against error, but also provides predictive modelling, Data, and Machine Learning for predicting trends in the field. Armed with this information, a more stable bottom line may be found as well as more efficient use of on-farm products. Beyond the Buzz How will analytics affect future farming and create sustainable best practices for future generations? There are quite a few predictions at the table, and the answers are helping to drive actionable insights for decisions based on Data, improving: Product decisions.Product amount.Profitability. The 3 Ps are the what. Here is the how: Mini computers in our phones grant us information from the world and, with the right applications, can tell us our stock prices, how much milk is in our fridge, and even manage the heating and cooling of our home from afar. So, what if you could check on your field before ever leaving the house? Want to get a handle on pests? How about testing your soil’s value? These are just a few of the questions being asked and answered at the summit. This is the power of predictive analytics in agriculture. The World Agri-Tech Innovation Summit is in San Francisco from March 19th and 20th. If you’re interested in Biostatistics, Bioinformatics, Computational Biology, Big Data & Analytics, we may have a role for you. We specialize in junior and senior roles. Check out our new Life Science Analytics specialism or our current vacancies for additional opportunities. Contact one of our expert consultants to learn more. For our West Coast Team, call (415) 614 - 4999 or send an email to firstname.lastname@example.org. For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to email@example.com.
28. February 2019
We are human. We are digital. We are both. The digital mindset and digital transformation, once heavily focused in marketing, advertising, finance, and retail also drives advances in Life Sciences. Computational Biology, Bioinformatics, and statistics. If you’re going to solve biological problems with data, you need Biostatistics. Just like you need a Data Engineer to create the parameters from which to build the structure of your Data, you need a Biostatistician to lay the groundwork to study the life in Life Sciences. This information can be infused in a variety of industries, not the least of which is medicine. We haven’t reached immortality yet, but we’re well on our way. Route to the Role of Biostatistician If numbers at the pixel level are your cup of tea, then this role was made for you. At its core, Biostatistics is the application of statistics to range of topics in biology. It is for the numbers geek with a creative streak, and encompasses the design of biological elements; the gathering and analyzing Data from experiments and offering solutions to problems in medicine, health, and many more. The educational component of this role is more often not at the PhD level and, as pharma works to beat the back the opioid crisis, Biostatisticians are on the rise. Not the least of which to reach out is the Food and Drug Administration (FDA), who have turned to scientists at UNC to fill knowledge gaps. Pharma may be in the news, but Biostatistics go well beyond this single focus in areas such as genetics, potential open source biological databases, and digital transformation throughout the medical fields. Want to know what else is in store for the Life Sciences? Trends to Watch The 2019 Global Life Sciences Outlook offers deeper insight into the following trends and offers a glimpse into the next wave of digital transformation with a focus on Biostatistics, Bioinformatics, and Computational Biology endeavors. Move over pharma legacy culture. There are new players in town. From tech giants diversifying into health care to small business startups controlling assets through its lifecycle, the next generation is shaking things up. The hunt for next gen meds has begun in answer to declining R&D returns making the case for strategic deal making a key innovation source for companies. Connection and integration of medical devices into existing care pathways across the Internet of Medical Things (IoMT) ecoysystem. Outsiders become insiders as increasing security risks spur companies to safeguard their data. Outsourcing expertise in AI, cognitive automation, and cloud computing for peace of mind. Cross-pollination of transformative technologies – physical, digital, and biological – to help forward thinking pharma companies evolve from pilots to determining how new technologies can best add value using:Artificial Intelligence (AI)BlockchainDIY diagnostics and virtual careInternet of Medical Things (IoMT)Software-as-a-Medical-Device (SaMD) Though only about twenty percent of organizations feel good about their place in the digital world, many remain in the experimental stage. Agile companies and the early adopters of digital technologies and platforms could benefit from deeper insights from clinical trials, better patient engagement, and faster life cycle times for products. A digital-first attitude will be a key driver of major change in the digital transformation in Life Sciences. Organizations will work toward a two-fold endeavor of divining how disruptive technologies can work together to provide value and meaningful transformation as well as putting humans back in the loop through training, retraining, or upskilling; rearranging the organization; and reconstructing how work gets done. Humans meet AI meet Machine Learning meet humans. If you’re interested in Biostatistics, Bioinformatics, Computational Biology and Big Data & Analytics, we may have a role for you. We specialize in junior and senior roles. Check out our current vacancies for additional opportunities or contact one of our recruitment consultants to learn more. For our West Coast Team, call (415) 614 - 4999 or send an email to firstname.lastname@example.org. For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to email@example.com.
21. February 2019