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  • admin 9:54 am on May 27, 2015 Permalink
    Tags: , Lateral, ,   

    Lateral Thinking in Data Science 

    Can market basket analysis be used to improve the performance of a website? How about path analysis on text extracted from CRM notes to identify cross sell opportunities? Many data science techniques are developed to address a specific problem within a certain industry. But often there is great benefit in re-purposing these analyses to solve problems in other contexts.

    Data Science is a broadly applicable approach to solving many different types of business problems. However, one of the challenges is to knowing which technique is best suited to solving a given problem. When an analyst only draws on standard techniques from his or her industry, solutions may not be optimised.

    To address this issue it is useful to firstly be aware of techniques and approaches from many different fields, and then to think laterally about the problem under consideration. This allows the analyst to combine and apply a plurality of techniques in novel and interesting ways.

    This has a number of benefits. It expands the range of tools and techniques which are brought to bear on specific problems and also allows the business to benefit from techniques which have been developed in different but related contexts.

    I propose a three step solution adaption cycle (Figure 1) to implement this approach.

    Step1 – Current Situation: Understand the problem you are looking to address and the context in which it is being framed. Understand the data you have available or ideally the data which needs to be collected in order to address the problem.

    Step 2 – The meta-problem: Think about the class of problem being solved and the paradigm into which it naturally falls. Is there a related technique from a another field which would apply to this situation? Would a combination of techniques be applicable?

    Step 3 – Adapt and Apply: Select a relevant technique or techniques (if they exist) from your problem-solving toolkit and adapt them to the new situation. Ensure all assumptions are met before applying. Also ensure you understand how to interpret the results in the new paradigm in which your original problem exists.

    One example of this approach is as follows. Market basket analysis was developed by retailers to understand items commonly purchased together. Items are grouped together by basket and the associations between commonly purchased items are analysed and used for store planning and recommendations.

    Thinking about this paradigm abstractly, to use this technique we need items and groupings. So, for example when analysing website usage, we could use each session as a basket and each web page visited in that session as an item. Or, if we were looking to make recommendations, we could use forms downloaded as items and then make “forms you may be looking for” type recommendations in order to reduce the customer effort score.

    This simple paradigm of items and baskets is applicable to many different situations including customer complaints, part replacement and even share trading.

    Thinking laterally about your problem and looking beyond standard analyses can vastly enrich your data science solution toolkit.

    Ross Farrelly is the Chief Data Scientist for Teradata ANZ, Ross is responsible for data mining, analytics and advanced modeling projects using the Teradata Aster platform. Previously Ross ran Datamilk, an independent bespoke data mining consultancy specialising in data mining and advanced predictive analytics. Ross is a six sigma black belt and has had many years of experience in a variety of statistical roles including Business Development Management at Minitab and as a SAS Analyst at New Frontier Publishing. Connect with Ross Farrelly on Linkedin.

    The post Lateral Thinking in Data Science appeared first on International Blog.

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  • admin 9:51 am on May 26, 2015 Permalink
    Tags: , Duckling, Swan, Ugly   

    Ugly Duckling or Black Swan? 

    BlackSwanby John Edwards

    In business, a black swan is an extreme event that flies in—seemingly from out of nowhere—to disrupt normal operations. The probability of one striking in the near term is low, yet since the threat is ever-present and can have a devastating impact, the mere knowledge of its existence makes C-suite executives nervous. Be warned: sooner or later, you will experience one.

    Since a black swan is difficult (some would argue impossible) to predict, few organizations think about preparing for the possibility. However, the risks of inaction are high and the costs can be enormous. When one does arrive, whole industries can be rattled and thrown into disarray. In extreme cases, companies close, suppliers or partners shut down, formerly profitable markets vanish and once-stable business models are shattered.

    Kathleen S. Long, CEO of Montage Analytics, a risk assessment software and management consulting company, notes that “the black swan blindsides the organization and creates a ripple effect far beyond the organization’s boundaries to customers, suppliers, shareholders and other stakeholders.”

    Global companies face a greater risk of havoc since a catastrophe, including a natural event such as a tsunami, can affect the entire supply chain as well as deliveries to customers worldwide. Even events that at first appear trivial or happen on the other side of the planet can escalate, causing unanticipated problems. In other words, if you ignore that little duckling now because you think it’s ugly but harmless, the next time you look it may very well have grown into a crisis-level black swan.

    Detect and Mitigate Events With Big Data Analytics

    Black swans can elude traditional data analytics and standard operating procedures, which means they are usually only recognized when it’s too late to head them off and after much of the damage is done. But now, big data and advanced analytics may help detect the subtle early warning signs. “If you go back to market events of a few years ago, some banks did better than others …[because] they had a much better understanding of their portfolios and better understanding of the risk,” says Dilip Krishna, a director in Deloitte & Touche’s governance, regulatory and risk strategies practice.

    For organizations to detect an impending event, they must have the ability to interpret analytic insights that point to a potential catastrophe. Most large companies have an enterprise risk management (ERM) department to identify risks. Yet ERM departments generally focus on regulations and traditional risks without addressing out-of-the-blue events.

    The knowledge, skills and capabilities needed to manage uncertainty can be challenging to find within any business, Long observes. “It’s important to distinguish between ‘risk’ and ‘uncertainty.’ Risk is measurable quantitatively and lends itself to planning. Uncertainty is the domain of the black swan and cannot be calculated or planned for in the same way,” she explains. “Global complexity compounded by the speed of change has rendered us increasingly vulnerable to apparently random and potentially catastrophic events known as black swans. In this fast-moving, complex world, if we don’t know what we don’t know, how do we prepare for an uncertain future?”

    The only way to mitigate the effects of such an event is to identify organizational and system vulnerabilities and the consequences of possible threats in order to cultivate resilience, Long points out. This process depends on understanding the cultural context and the effect of human behavior within it.

    “Strategies should be revisited locally … with enterprise updates annually or as needed,” she says. “We need better models that capture the role of human behavior in complex adaptive systems. At the core of every black swan event, you will find human behavior and interaction.” 

    Counter the Threat

    Preparing for a possible disruptive event needs to entail scrutinizing data as deeply and carefully as possible to spot clues that could indicate an impending crisis. Even if an organization is focused on what it knows are the tasks at hand, big data provides peripheral vision to see what other events could impact the business. Krishna recommends that companies ask themselves if they have the capacity to analyze events that trigger a black swan event and if they have the flexibility to ask questions that have not been asked before. In order to get the new insights the business needs, the analysis must be ongoing and agile, he adds.

    Organizations across every industry should consider the risks posed by a wide variety of potentially problematic events, and then preemptively plan for them. Such careful preparation requires the analytics team to systematically generate mitigation solutions for each major “what if?” situation.

    Solutions that address individual risks should be prioritized by the magnitude of exposure as well as the expense and ease of implementation. “Think about the worst-case scenario, what sorts of worst-case scenarios might exist and what triggering events might cause those scenarios to happen,” Krishna suggests.

    A data-driven analysis or simulation designed to determine an organization’s ability to deal with a crisis situation can help it be ready for the day a black swan lands on its front steps. Krishna notes that this type of “stress testing” can gauge the level of readiness. “[Preparation] is really all about imagining the unimaginable, understanding what’s going to blow up … [and] to be able to determine what corrective actions are needed,” he explains. “That is becoming very much an accepted approach … certainly something that, from a regulatory standpoint, is becoming mandatory for a number of financial institutions.”

    Businesses that identify a possible event early on and take evasive actions are usually in the best position to ride out the crisis. Krishna advises companies to carefully map out the exact steps they will need to take in various types of crisis situations. “Then document those actions so that if any of the expected scenarios occur, there will be no second-guessing,” he points out. “It’s simply a matter of executing what’s already been documented; executing that game plan, if you will.”

    Identify the Ugliest Ducklings

    Predictive analytics performed on integrated data can help identify potentially business-shattering events. Analytics can also provide valuable insights from past black swan encounters.

    After an attack, a careful examination of key analytics, actions and environmental factors can reveal oversights or lapses that might have caused or enabled a black swan. “Hindsight, as they say, is 20/20,” Long states. “Working backward, it’s easy to see what could have or should have been done to avert the event or soften the impact.”

    Surviving a disruptive event can be the catalyst for thinking differently about risk management. “A black swan event has the benefit of being able to focus an organization on risk overall,” Krishna notes. “Bigger risks might get headed off by an organization just getting its mind around risk management overall and thinking through risk in a more holistic manner, which could then help the organization survive.”

    Companies will never be able to completely mitigate the threat of unexpected events. They can, however, rely on analytics to give them as much warning as possible. Analytics can provide the insights to assess an organization’s vulnerability and form plans to evade different types of events or lessen their impact (or choose to accept the risk by not taking any action). The same insights can also help determine which ugly ducklings that start out as small annoyances are most likely to turn into high-impact black swans.  

    Read the full article and more in the new Q2 2015 issue of Teradata Magazine.

    John Edwards has covered the technology industry for more than two decades.



    The post Ugly Duckling or Black Swan? appeared first on Magazine Blog.

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  • admin 9:52 am on May 25, 2015 Permalink
    Tags: , , Csuite’s, , , , , , ,   

    It’s the C-suite’s turn: New studies show CEOs need to get into data-driven marketing 

    Conference RoomWhen I wrote Big Data Marketing in 2013, my goal was to get past all the hype and fear to open up the conversation about the benefits of data-driven marketing. Now, two years later, has the dialogue shifted? Are marketers becoming more data-driven?

    Results from the recent Teradata 2015 Global Data-Driven Marketing Survey indicate that marketers are eager to move beyond the status quo and use the technologies available to them to truly individualize marketing communications; however, there’s still plenty of room for improvement.  For instance:

    • 90 percent of the 1,500+ marketers we surveyed said that individualized marketing is a priority. But only 50 percent routinely apply data to individualize their messages and offers.
    • The number of companies utilizing data-driven marketing strategically has more than doubled (36 percent in 2013 vs. 78 percent today). But 44 percent reported that achieving consistency in omni-channel marketing remains a challenge.
    • 59 percent said faster decisions are a key benefit of using data, and 67 percent feel decisions involving data are more accurate. But 80 percent said silos within marketing itself prevent an omni-channel view of campaigns.

    What can companies do to keep moving forward?

    Another study Teradata conducted in partnership with The Economist Intelligence Unit found that the most important thing businesses can do is continuously evolve their data culture to become more customer-centric. That means CEOs need to remove their rose-colored glasses and start developing a shared data-driven vision, one that’s based on insights about the information and experience each customer wants.

    What’s the first step? Resolve misperceptions.

    Forty-seven percent of CEOs we surveyed believe all employees have access to the data they need, but only 27 percent of managerial respondents agreed. Similarly, CEOs were more likely than employees to think relevant data is captured and made available in real time (43 percent vs. 29 percent) and that employees extract relevant insights from data (38 percent vs. 24 percent).

    Our results showed that when those disconnects are resolved, the entire company benefits:

    • Among top performers—those from companies that “significantly” or “somewhat” outperform in profitability—63 percent said data initiatives are launched and driven by corporate leadership, and 41 percent have a centralized data/analytics group that introduces and implements data initiatives.
    • Data-driven companies are more likely to generate higher profits. They’re also twice as likely to report a culture of creativity and innovation, and they’re much more likely to reap benefits like greater collaboration and better quality and speed of execution.

    These two studies can help marketers identity the strategic pressure points for positive change. It’s time to narrow in on the C-suite and corporate data.

    This post originally appeared on the Economist Group’s Lean back blog.

    The post It’s the C-suite’s turn: New studies show CEOs need to get into data-driven marketing appeared first on Teradata Applications.

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  • admin 9:53 am on May 24, 2015 Permalink
    Tags: , , , , , , Finish, ,   

    American Cancer Society Using Data and Analytics to Finish the Fight Against Cancer 

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  • admin 9:52 am on May 24, 2015 Permalink
    Tags: , , , , Eliminate, ,   

    American Cancer Society: Employing Data and Analytics to Eliminate Cancer 

    “To be clear, our competitor is cancer, so it’s not like I have a competitive edge over another research firm or another

    Blake Sanders VP, Architecture & Data Management

    Blake Sanders
    VP, Architecture & Data Management

    nonprofit. We’re all in this together. So essentially, data and being data-driven allows us to complete our mission and drive us to a competitive edge, ” comments Blake Sanders, Vice President of Architecture & Data Management, American Cancer Society.

    With a mission to eliminate cancer as a major health problem before the end of this century, American Cancer Society’s Blake Sanders is focused on a cure  and  is using data as the primary weapon in the fight to eliminate cancer.  This is not a typical business and when our Global Customer Engagement team sat down with Blake Sanders, we were inspired by his passion for the cause and his perspective on data and analytics which touches every aspect of the organization – each area critical to the mission:

    • Helping You Stay Well;
    • Helping You Get Well;
    • Working to Find Cures;
    • Program Programs and Services;
    • Hosting Fundraising Events; and
    • Helping Pass Laws to Defeat Cancer.

    Blake Sanders compares his data to fast food-fast, fresh, and better.  Essentially giving self-service users access to fast answers with fresh data, eliminating data latency.  Better is all about reducing complexity by “reorganizing the system in such a way that asking the question of data is as easy as possible.”

    Impact on the organization has been dramatic.  Questions that used to take I-T two weeks to answer can now be answered in just minutes by the business user.

     “So that’s a culture shift that’s basically a paradigm in which you’ve now got an interactive query.  You have an ability to adjust and re-analyze and hone in on your question, start broad, go small, drill, slice and dice, things that we weren’t able to do before, we’re able to do now,” comments Blake Sanders, VP, Architecture & Data Management.

    Employees all over the organization use data in every aspect of their job function from organizing patients,  volunteers, to the nitty gritty details of their largest events as well as R&D conducted by doctors.

    Screen Shot 2015-05-20 at 9.27.42 AM“We have people that need to understand how best to organize and run the events that we put on.  One of our major events is Relay For Life.  Relay For Life is a community event.  Our internal staff helps our volunteer committees actually run the event.  Well, what information do they need?  They need to know how many teams they have, how many people are going to attend, what’s the likelihood of getting sponsors to help us out.  They don’t have to come to us at all, that’s not a question of I-T anymore, that’s literally self-service. The act of doing that enables them to ask their own question and get their own answer in such a way that they could never do that before that was something that just wasn’t even a thought process for them, ” comments Blake Sanders, VP, Architecture and Data Management

    Fundraising and Recruitment with Data

    Critical to American Cancer Society are its donors and volunteers.  When will a donor become a volunteer and vice versa? With 96% of their revenue coming from individual donors and more than three million volunteers, leveraging capacity for both groups is critical to forwarding their mission. How do they do that? Study, segment and follow up with individualized communications.

     “So we have a lot of information about our constituents and our donors and we want to let them understand how best they can help us, so that really involves a lot of study and segmentation around what type of people might be more apt to want to attend a Relay For Life event versus a Making Strides Against Breast Cancer event and who’s likely to come back? We want them to keep coming back either to participate or donate or volunteer, so that’s really an outreach from a marketing exercise and so we have an entire group that’s designed to find the right segments of people to market to and our team, in architecture and data management, feeds them as much information as possible and allows them to do analysis on our customer segment and really find the right people, ” comments Blake Sanders, VP of Architecture & Data Management.

    In the data, self-service users get more meaningful answers.  They look at more attributes than ever before and they’re able to correlate the data and be extremely confident with the answer they get.

    Eliminating Cancer

    Perhaps the most exciting facet of the mission is the goal to eliminate cancer as a major health problem by the end of the century.  Can you imagine? Conventional wisdom now says that the cure for cancer won’t be just a doctor with a microscope, but there will be a data scientist with a laptop right along side.

     “All good data science starts with a question. So when a data scientist is in front of their computer understanding and trying Screen Shot 2015-05-20 at 9.28.58 AMto make use of a good amount of data, really what they’re trying to do is find a correlation between what an actual physician or scientist, researcher is doing versus what 10 or 15 other scientists are – and researchers are doing on their own.

     So the data scientist is actually making sense of that across and is able to visualize for the other researchers how that correlation occurs. Data visualization as a practice is an art and a science both. Taking hundreds of data points and attributes and actually visualizing that in a way that makes it easy for someone to understand, that is the silver bullet, ” comments Blake Sanders.  “Data is the glue in which the American Cancer Society is able to perform its mission.” 

    Many thanks to the American Cancer Society for marching toward that mission every day, fighting the fight to eliminate cancer.

    The post American Cancer Society: Employing Data and Analytics to Eliminate Cancer appeared first on Insights and Outcomes.

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  • admin 9:56 am on May 23, 2015 Permalink
    Tags: , ,   

    Manufacturing: What Would You Do if You Knew? 

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  • admin 9:56 am on May 23, 2015 Permalink
    Tags: , , ,   


    MarketingConsumer goods marketers employ a cadre of contractors, agencies and consulting firms. That’s not going to change rapidly in the next 1-2 years, but what will change is the role these parties play with the Consumer Goods manufacturer. Something must change. According to a recent Deloitte survey of 4,047 respondents encompassing 28 product categories and more than 350 brands, brand loyalty is declining. The Deloitte study cited in this Inc. article describes price-sensitive consumers still reeling from recession as the norm, challenging any attempts to develop brand affinity. The solution? “Brand segmentation,” which requires consumer goods companies to “Rethink their product portfolio in light of the widening gap between the affluent and lower-income households. Consumer products companies may need to have distinct strategies (e.g., brands, product offering, pricing) to target affluent and lower-income consumers.”

    That sounds like a data and analytic problem. How prepared are CPG brand marketers to attack it?

    The Consumer Data Asset

    Data about your consumers should be your data – not something outsourced, scattered among contractors or agencies. This data and its insight could be an asset that drives better decision-making across the organization. If it were, it would be accounted for on your company’s balance sheet. None of this means you need to necessarily own the technology that collects analyzes and puts consumer data to use. It does imply that traditional marketing services companies purporting to offer this capability simply cannot.

    Marketing service provider business models are based on the premise that their clients care little about the underlying data and marketing technologies working behind the scenes. Instead, they emphasize servicing virtually any outsourcable marketing need. Diversity to this extent makes for thinness in certain areas – and this is becoming more and more apparent as business demands rapidly shift out of traditional marketing techniques. It is leading many to retreat from the strategy of using “one” agency for all needs and shifting back to preferring best of breed providers for specific business needs.

    Marketing services providers are largely ill-equipped to handle the realities of a data driven world – one characterized by big data, the proliferation of consumer channels and rapid technology innovation. The pace of change is so fast; only firms putting their full prowess behind adapting are capable of delivering the most differentiated capabilities.

    To fill the agency gap, CPG business leaders are investing in and partnering with new entrants in the technology space – especially in the areas of creating new social networks, mobile applications and other ways of engaging consumers. In a data driven world, the quality and effectiveness of marketing technology isn’t an inconvenience to be delegated to a services company; it’s a mission critical competency that generates insight with immense value.

    Picking the Right Partner

    In October 2014, McKinsey’s Global Co-Lead of Digital, David Edelman, posted an article to LinkedIn titled, “Time for Marketers and Agencies to Shake it Up.” His piece describes the need for agencies to change the nature of their business models to focus on higher value services. He also says:
    “Who do you need as a partner? When every different channel has its own specialist agencies claiming expertise in it, a client can get overwhelmed by a mix of as many as 14 different traditional, media, digital, social, mobile, sponsorship, etc. agencies. If each corporate business unit and geography gets to choose their own agency, the complexities are overwhelming and negate much opportunity for scale or cross-channel coordination.”

    Agencies and marketing services companies are not prepared to address these issues – it’s simply not their traditional business. Replacing redundant and wasteful database and technology development efforts across the contractor ecosystem with standards helps agencies deliver the most impactful and measurable creative. Standards also yield consistent and comparable metrics across brands and campaigns; an absolute necessity at a time when marketers are under the microscope (“The heightened focus on marketers and their related costs will spur marketers to better use analytics”).

    Bottom Line: The digital marketing business model employed by most Consumer Products companies is not flexible nor suited to enable data driven business priorities. Outsourcing is actually less efficient in a world where latency between insights and actions is not something struggling CPG brands can afford. Utilize agencies and marketing service providers for what they are “best at” (e.g. content, creative, web, mobile, programmatic, etc) and clearly define your internal data driven marketing strategy which includes who owns the data, where the consumer data should be stored and maintained, definition of insights and analytics, etc.

    The post DISRUPTING THE CPG MARKETING SERVICES ECOSYSTEM appeared first on Industry Experts.

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  • admin 10:33 am on May 22, 2015 Permalink
    Tags: ,   

    Teradata Connect 

    Learn from Marketing Visionaries | Meet the Game Changers | Ignite your MarketingDon’t miss the industry’s biggest event for insight-driven marketing! Teradata Connect is unique – you’ll not only learn about the latest Marketing trends, technologies and tactics but we’ll make sure you meet the people behind the big ideas, face to face, through our famous 1:1 meetings. Take your marketing strategy to the next level!
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  • admin 9:53 am on May 22, 2015 Permalink
    Tags: , , ,   

    Does My Asset look Big Data in This? 

    I am often asked for my point of view on trends in the global Utilities analytics market. My headline response this year is that I have seen a marked increase in interest and investment by network companies in particular looking to operate, maintain and monetise assets more effectively.

    Globally, many wires businesses are officially and unofficially asking suppliers such as Teradata “what can you offer in this space?”. Given the increased interest in such offerings, the list of suppliers in this space continues to grow. This sudden acceleration of the market has driven suppliers to approach network analytics from very different angles. It is hard to categorise the solutions on offer precisely, but most will fall into one or possibly two of the following categories:

    • Distributed network solutions implemented on the network itself which have productised analytics at the heart of what they do;
    • Virtual control and monitoring solutions that continually run and assess the state of assets based on configurable analytics algorithms; and
    • Advanced analytics and big data companies such as ourselves that can integrate data from across a utility, and any other relevant external data for analysis.

    In addition to analytics, network companies are also evaluating the pros and cons of adopting industry standards including notation on data such as the Common Information Model (CIM), Smart Grid Architecture model and others. Advice on all of the above is given by engineering consultancies, systems integrators, and others. Are wires businesses confused? You bet they are.

    The most telling statement I heard recently was from a distribution company that had just executed a short RFI around asset analytics. The most concerning thing for them was that every respondent gave a totally different response and approach to a single ‘exam question’ – so how can any supplier be ‘right’? This is a real blocker to change in the distribution space in particular, as a festering uncertainty about what value might be delivered by any solution in an immature market will continually prevent the investment required to deliver the level of benefit expected.

    Here is my headline response to this very valid concern from network sector:

    • The three types of analytics I describe are all valid, and can deliver benefits on their own.
    • However, solution selection is not a straight choice between one solution that sits in one category and one that sits in another. Forward thinking Utilities implement analytics solutions in each of these categories, often focused on the same business problem that when combined deliver exponential results.
    • Utilities asset analytics is still a young industry. Anyone who claims to have ‘out of the box’ benefits on sale and available for delivery tomorrow is probably lying! Over time you will see more uniformity in solutions, and indeed coalitions between suppliers – plus more and more reference cases as solutions are implemented.
    • Be bold – the one thing that everyone I speak to is sure of is that there is significant benefit in investing in network asset analytics. Early adopters prove this to be the case, and in addition reputable suppliers like Teradata can show you examples from many other industries far more mature in asset analytics that continue to better monetise assets using data.

    Come and talk to us about any of these things! Even though we are not a supplier in all of these areas, we are happy to talk Utilities data all day long not only around analytics but more widely as outlined above. It is in all our interests to work openly and collaboratively to enable network businesses to monetise their assets using data by pushing the boundaries – and we at Teradata are proud to be leading that charge!

    Iain Stewart is the principal utilities expert for Teradata in the EMEA region, with over 13 years of experience in utilities sector. Iain also has in depth experience of both smart metering and smart grids, including how these link to and support the wider sustainability agenda. Other areas of experience include renewable energy, and smarter cities. Connect with Iain Stewart on Linkedin.

    The post Does My Asset look Big Data in This? appeared first on International Blog.

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  • admin 9:51 am on May 21, 2015 Permalink
    Tags: , , Memorial,   

    How Will An “Early” Memorial Day Impact Retailers? 

    flagMaybe New Englanders are restless after the long winter they endured. Or maybe it’s because gas prices are low or because job prospects have improved. Whatever the reason, AAA is predicting the heaviest Memorial Day traffic in a decade – despite the fact that this year the holiday lands early, on May 25.

    Will retailers see a similar boost?  Taken as a day to ensure the sacrifices of America ’s fallen heroes are never forgotten, Memorial Day is also now widely regarded as the unofficial start of summer in the U.S. With that in mind, retailers are poised. Just a few weeks ago, NRF predicted that Americans would spend a total of $ 21.2 billion and an average of $ 172.63 on Mother’s Day, up nearly $ 10 from $ 162.94 last year and the highest amount in the survey’s 12-year history.

    Interestingly, NRF also found a slight dip in the percentage of shoppers who planned to shop online this year (25% vs. 29% in 2014). I’m not going to extrapolate from this one data point and proclaim a shift in consumer behavior. But I will give kudos to the many retailers I know who are stepping up to the plate, implementing data driven marketing and working to improve the customer experience both on- and off-line.

    For example, I’m hearing more and more about virtual dressing rooms, hi-tech interactive shelves that display brand information whenever a shopper picks up an item, and even strategies that employ augmented reality, like realistic human holograms that function as sales associates. Retailers around the world are combining technology with advanced marketing applications to enable a differentiated shopping experience, one that better suits today’s active, always-on and connected individual customer.

    That’s really what it boils down to: making connections, individualized connections. Innovative technology may be the hook that draws customers in, but you’ll need more than that to keep them coming back. You’ll need an integrated marketing approach that includes a consolidated view of your customer, real-time sales across all channels and a single view of product inventory. Once you have that in place, you’ll be able to truly empower each customer to connect, while minimizing the inconvenience and effort required to transact, communicate or enjoy your product, service and/or brand.

    Some of my friends have told me that they feel like Memorial Day is coming too early this year, that they’re not ready for the unofficial start of summer. The beauty of data-driven marketing is that it can help you find, and then retain, customers, by offering them an experience that’s individualized to their needs and preferences – no matter when on the calendar a certain holiday falls.

    What are your thoughts?  Or are you already in your car, heading off on a long Memorial Day weekend?

    The post How Will An “Early” Memorial Day Impact Retailers? appeared first on Teradata Applications.

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