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cezarovidiu

Magic Quadrant for Sales Force Automation - 0 views

www.gartner.com/...reprints.do

crm

shared by cezarovidiu on 11 Jan 13 - No Cached
  • cezarovidiu on 11 Jan 13
     
    "Market Definition/Description Sales force automation (SFA) applications support the automation of sales activities, processes and administrative responsibilities for B2B organizations' sales professionals. Core functionalities include account, contact and opportunity management. Additional add-on capabilities focus on improving the sales effectiveness of salespeople, such as sales configuration, guided selling, proposal generation and content management, and sales performance management support, including incentive compensation, quota, sales coaching and territory management."
cezarovidiu

Why Soft Skills Matter in Data Science - 0 views

data-informed.com/oft-skills-matter-data-science

bigdata data science

shared by cezarovidiu on 24 Jan 14 - No Cached
  • You cannot accept problems as handed to you in the business environment. Never allow yourself to be the analyst to whom problems are “thrown over the fence.” Engage with the people whose challenges you’re tackling to make sure you’re solving the right problem. Learn the business’s processes and the data that’s generated and saved. Learn how folks are handling the problem now, and what metrics they use (or ignore) to gauge success.
  • Solve the correct, yet often misrepresented, problem. This is something no mathematical model will ever say to you. No mathematical model can ever say, “Hey, good job formulating this optimization model, but I think you should take a step back and change your business a little instead.” And that leads me to my next point: Learn how to communicate.
  • In today’s business environment, it is often unacceptable to be skilled at only one thing. Data scientists are expected to be polyglots who understand math, code, and the plain-speak (or sports analogy-ridden speak . . . ugh) of business. And the only way to get good at speaking to other folks, just like the only way to get good at math, is through practice.
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  • Beware the Three-Headed Geek-Monster: Tools, Performance, and Mathematical Perfection Many things can sabotage the use of analytics within the workplace. Politics and infighting perhaps; a bad experience from a previous “enterprise, business intelligence, cloud dashboard” project; or peers who don’t want their “dark art” optimized or automated for fear that their jobs will become redundant.
  • Not all hurdles are within your control as an analytics professional. But some are. There are three primary ways I see analytics folks sabotage their own work: overly complex modeling, tool obsession, and fixation on performance.
  • In other words, work with the rest of your organization to do better business, not to do data science for its own sake.
  • Data Smart: Using Data Science to Transform Information into Insight by John W. Foreman. Copyright © 2013.
cezarovidiu

Choose Your Own Adventure: Map Buyer Personas in a Content Marketing Program | ClickZ - 0 views

www.clickz.com/...in-a-content-marketing-program

marketing

shared by cezarovidiu on 07 Nov 13 - No Cached
  • learn more about the brand or to be entertained or educated in some way.
  • Mapping out the ifs-ands-or-buts pathways for each persona can become complicated, so automation solutions such as Infusionsoft that offer a drag-and-drop interface can make the mapping of the funnel stages manageable.
cezarovidiu

Magic Quadrant for CRM Lead Management - 0 views

www.gartner.com/...reprints.do

CRM Automation Management

shared by cezarovidiu on 26 Mar 14 - No Cached
  • Web registration pages and campaigns, direct mail campaigns, email marketing, multichannel campaigns, database marketing and third-party leased lists, social CRM and social networking sites, and tradeshows.
  • SugarCRM
  • Chatter and Data.com
cezarovidiu

Analyzing Human Data: Take a Dive to Find Out What Your Customers Really Feel - Content... - 0 views

contentmarketinginstitute.com/...man-data-customers-really-feel

machine learning blog sentiment analysis customers marketing

shared by cezarovidiu on 30 Oct 14 - No Cached
  • What really interests me, and what I think should interest marketers, is what I’ll call signals – one of which is intent. Intent is critical because it can predict action. For example, “Is this person shopping to buy a product like my product?” “Is this person unhappy and needing some form of attention?” “Is this person about to return the product for a reason that is addressable?”
  • Sentiment is one ingredient of intent. If someone is happy, sad, angry … that can be determined via sentiment analysis technologies.
  • Many tools struggle with context.
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  • An example I hear over and over again is “thin” – good when you’re talking about electronics, but bad if you’re talking about hotel walls or the feel of hotel sheets. To do sentiment analysis correctly, you need refinement. You need customization for particular industries and business functions.
  • The market, unfortunately, is polluted with tools that claim to have sentiment abilities, but are too crude to be usable. Even with refinement (e.g., the ability to handle negators and contextual sentiment), approaches that deliver only positive and negative ratings don’t take you very far.
  • There are definitely easy, inexpensive entry points that can meet basic, just-getting-started needs: tools for social listening, survey analysis, customer service (handling contact-center notes, for instance), customer experience (via analysis of online reviews and forums), automated email processing, and other needs. These technologies are user friendly, available on demand, as a service.
  • Text mining:
  • Digital Reasoning, Luminoso and AlchemyAPI.
  • Image recognition and analysis: Image analysis now automatically identifies brand labels in pictures.
  • VisualGraph (now owned by Pinterest), Curalate, Piqora (nee Pinfluencer), and gazeMetrix.
  • Emotional analysis in images, audio, and video: These companies promote analysis of speech and facial expression primarily for structured studies
  • • Affectiva conducts webcam emotional analysis for media and ad research, including development tools to integrate emotional study in mobile apps. • Emotient performs emotional analyses in retail environments, evaluating signage, displays, and customer service. • EmoVu by Eyeris tests the engagement level of both short- and long-form video content. • Beyond Verbal studies emotion based on a person’s voice in real time.
cezarovidiu

Difference between CRM lead and an opportunity - Pipeliner CRM Blog - 0 views

blog.pipelinersales.com/...en-crm-lead-and-an-opportunity

CRM documentation blog

shared by cezarovidiu on 31 Mar 15 - No Cached
  • Any individual fish or pod of fish in your sea represents one lead.
  • Your Nemo will not be the first or the second fish that you catch. At the beginning, you will have very little information about the Nemo you would like to catch. You will start to examine your fish and create some criteria as to how Nemo should look like. In other words, you are qualifying your fish.
  • Lead = Any Fish in The Sea. Opportunity = Nemo
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  • The process of examination and adding the criteria represents your sales pipeline strategy. It’s always true that: “Without a commitment to pursue working together (something that results in this company potentially buying from you) there is no opportunity.” - Anthony Iannarino
  • At the end of your examination ie. of your sales process, you will either let the fish swim back into your sea (lost opportunity) or you will put Nemo into your aquarium (won opportunity). Won Opportunity = You have found Nemo Lost Opportunity = You have not found Nemo
  • A Lead – is a contact or an account with very little information. It could be just a person who you might have met at a conference. You will need to retrieve more information regarding this lead in order to create (qualify) an opportunity in your sales pipeline.
  • A old sales rule says: “If you have never contacted your contact, it’s a lead.”
  • An Opportunity - is a contact or an account which has been qualified. This person has entered into your buying cycle and is committed to working with you. You have already contacted, called or met him and know their needs or requirements. The old sales rule says: “The opportunity is a deal that you have the possibility to close!”
  • “Think about the difference between a lead and an opportunity as an evolving process i.e. each lead needs to be qualified to an opportunity. There will always be plenty of leads in your sales territory, but only few of them will qualify to become real sales opportunity.”
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