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Recent blog posts
Teaching CRM Systems to Think

There was a wonderful book that came out years ago called Teaching Elephants to Dance. It teaches managers how to drive change in any organization, large or small. But the advice seemed especially relevant and important to large, slow moving (and hard-to-change) organizations. Big organizations must also deal with growing data stores, which are increasingly being applied in many areas, including marketing. But for all the wealth of data, and growing tools to understand and process it, CRM may be left out of the loop.

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Improving Customer Experience with Cognitive Computing

Customer experience is becoming ever more critical in how consumers perceive companies and products. Brands can shine or get tarnished based on the quality of the experience that is delivered through products, services, and in customer interactions with the sales, service and support. Unfortunately, the call center can be an Achilles heel as the technology used can get in the way of optimal customer experiences. The systems used to help automate teams and scale operations all too often result in impersonal service, wasted time and customer frustration.

This is starting to change, with the advent of more intelligent CRM solutions. In particular, cognitive computing is now being used to help the front line staff better support customers while maximizing revenue opportunities. CustomerMatrix’s CEO, Guy Mounier, addressed this issue in a recent article for Call Center Times. Here is an excerpt of the article that shares Guy’s responses about the definition of cognitive computing, and its application to CRM:

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How to Make IVR Systems Smarter?

Interactive Voice Response (IVR) systems are a popular way for businesses to reduce labor costs. Most businesses today greet you with an automatic voice ‘robot’ when you give them a call, but usually this robot isn’t very approachable. Though technology firms have made progress in ‘context-aware computing’ using better algorithms and Big Data to make technology smarter, it hasn’t reached the IVR marketplace. An unintelligent IVR will interact with you via a recorded message and ask for a long identifying number to find out who you are. When you’ve called the same business over and over again, this routine of self-identification can get frustrating.

Context-aware computing can change that. With this technology, businesses can identify customers based on their cell phone numbers. Unfortunately most IVR systems are generic and make the process of case resolution quite complicated for customers. Automated IVR services were meant to be a convenient option for customers, but for most customers they are a nightmare. Some entrepreneurs have created technologies to help call centers get out of the mess created by robotic IVR systems. CustomerMatrix’s CEO, Guy Mounier, has closely looked at this problem and believes ‘cognitive computing’ can help IVR systems become more responsive.

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#1 Customer Care Accelerator: Leverage Your Data to Know Your Customer

See it in action at our breakout session on Wednesday, October 15th at 3 p.m.

Where: Dreamforce; Palace Hotel; 2 Montgomery Street; San Francisco, CA

Join us to hear from Schneider Electric, one of the largest customers, and learn how to empower your agents to respond to customer calls in half the time by enabling rock-solid, unified customer care processes. Discover how to equip agents with federated information access from internal and external systems, and powerful machine-learning recommendations that will enable them to instantly identify the customer calling in, understand their wants and needs in real-time, and quickly recommend the most accurate resolutions.

Tagged in: Cognitive Computing
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Wikipedia Definitions: What is Cognitive Computing?

Cognitive computing is perhaps the most important innovation in the IT and computing industry, despite still having some skeptics. But for cognitive computing to reach its full potential, we must first clarify the conversation.

That is why we are excited to announce two new Wikipedia definitions of Cognitive Computing and Enterprise Cognitive System. The definitions were developed by the Cognitive Computing Consortium, a group of the industry’s top thought leaders (of which we were grateful to be a part), led by Sue Feldman, the founder and CEO of Synthexis, and Hadley Reynolds of NextEra Research.

Tagged in: Cognitive Computing
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Big Data Redefines the Nature of the Conventional Search Engine

Every two weeks, Sollan finds a personality from the world of Enterprise Content Management (ECM) and asks them six questions. This week, they went to meet Guillaume Bréjaud, VP of Customer Solutions at CustomerMatrix. Read here the original interview in French


Yes. Sollan is a partner for our solutions. Sollan is an expert in content management and is particularly strong in valuing solutions for non-structured and business-oriented information. They are a strategic partner.

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Understanding Customer Context is Key to Delighting Customers

Many companies take the wrong approach when it comes to automation of various business processes within their customer engagement initiatives. They evaluate each customer interaction point and improve that point in an isolated fashion without understanding the most important thing: customer context. Unfortunately, technology limitations of yesterday prevented companies from truly understanding customer context. Accessing all relevant information, processing it in real-time, and applying it to a specific operational process is impossible to do using traditional approaches. But to be successful in automation and in human interactions, companies must know each customer and learn how to anticipate their needs, at any given moment. Google and Amazon do it well with advanced algorithms and technologies. Businesses are struggling to keep up, but isn’t there a “Google” in you?

CVS: How They Delighted Me

American businesses have a reputation for putting a lot of value on good customer service overall and are known for putting the customer first. It’s a great reputation to have.  To a consumer, when a company anticipates your needs and recommends a product or a service that is completely on-target, it is delightful. Lately, I have been shopping at CVS, and my vitamin prescription needs to be refilled every month. It is quite a hassle to go through the refill process every time, but CVS automated this processes, making it easier to re-order. Though I may sometimes get a phone call reminder a bit too early in in the morning for my taste, it’s a great reminder, and the push of a button will refill my prescription. This type of automation works well for me.

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Isolated Automated Processes are Bad for Business

In a world of automated customer-facing processes - from marketing email communication to support answering services - many companies may be hindering their ability to build real connections with customers by not taking the time to talk with and understand them. Unlike companies such as Zappos and Amazon, who understand and value consumer connection and have built their extremely successful brands around customer experiences, some companies believe that their disconnect won’t affect customer satisfaction with the overall brand. I think they are wrong. Using technology to automate an individual business process without fully understanding customer context, something that I call “isolated automation”, can be highly damaging to brands.

It is hard to measure customer lifetime value impact of this impersonal approach. It is certainly much easier to evaluate each interaction point and the immediate action to which it leads. Most of these isolated automated interactions create signals that are fairly strong, easy to track, and can be translated to business KPIs. It is easy to review time spent on the phone, optimize automated interactions, or understand whether or not a customer had their question answered. It is much harder to get a clear picture of the overall impact of the “human disconnect” caused by automated interaction between the company and their customer, and how that disconnect will affect the relationship going forward.

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Overachieving in Sales by Riding on Someone Else's Success

Yes, salespeople can drive a lot more revenue by piggybacking on their colleagues' successes. I am not talking about spending hours researching published marketing content, or going through training, or collaborating with a mentor. Not even networking on the golf course or at the bar. I am talking about using the new kind of technology that matches useful actions that worked in the past for other top performers and are relevant to the current sales context. To enable you to do what has been proven to work for others in a similar context: manage the 3 forces of situation-stakeholders-customer goal. 

But first, let me explain what triggered this blog post. It's Forrester's Scott Santucci: When Three's A Crowd: Navigating An Agreement Network Is Key To Sales Success In The Age Of The Customer (he also provides a nice graphic describing an agreement network, so take a look). An Agreement Network (a quite clever name), is a group of customer influencers that may have completely different needs and gains, on both professional and personal level, but hopefully have a similar business goal in mind. Similar, and rarely exactly the same. As we all know, each stakeholder has a different problem they may want to resolve with your product or a solution. In some cases, even hoping that the new solution will not threaten their business processes or their job. To navigate this agreement network, you have to offer a lot of value to each, appropriately, respond to all their objections, and take all the right steps to engage with them. 

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Big Data is to Sales Management What Bar Code Brought to Retail Management. Complete Operational Visibility.

I recently read with interest the results of a Sales Management survey called “In sales, Can You Manage What You’re Measuring” and conducted by Vantage Point Performance and the Sales Education Foundation. They collected thousands of data points from sales leaders in various industries regarding which sales metrics they monitor. In total, 306 metrics were discovered, ranging from top-line revenue growth down to the number of sales calls made per month. These metrics were broken down into three main categories:

  • Sales Activities (e.g. sales calls per week, account plans completed, etc.)
  • Business Results (e.g. revenue, market share, etc.)
  • Sales Objectives (e.g. winning certain customers, selling certain products, etc.)

Of these three categories, only Sales Activities can be effectively managed, in the sense that a frontline Sales Manager can actually implement a change and measure its impact. Sales Objectives are obviously incredibly important in setting a clear direction for the organization, but they cannot be directly managed. The same with Business Results, they reflect the outcome of the Sales Activities and external market conditions.

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Analytics 3.0: A Shortcut to Decision and Action

Thomas Davenport published what I consider will become a seminal article about Big Data entitled Analytics 3.0 in the December 2013 issue of Harvard Business Review. The article focused on the new capabilities required to fully capitalize on Big Data, and how a number of world-class organizations such as GE, Bosch, and UPS, are already piloting such new approaches. The following summarizes the essence of what this new era is all about “Google, Amazon, and others have prospered not by giving customers information but by giving them shortcuts to decisions and actions.”

I have summarized in the table below the 3 main eras of Analytics described by the author. The evolution is fundamentally about 1. The more widespread use of analytics to compete effectively, and 2. The speed of delivery of insights, embedded directly into core operational process to boost efficiency.

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Smarter Software is About Helping People Be Proactive

I just read an interesting blog post from David Skok Building smarter software: Proactively deliver insights. In his post, the author reflects on the state of Business Intelligence, and the mismatch between what Enterprise end-users expect and what current BI tools provide. While most solutions report on what happened, end-users expect to be notified of problems or opportunities, and even what to do about it if a useful solution can be identified as well.

From my standpoint, the end-user expectation mismatch stem from the Consumer Web revolution, and companies such as Google or Amazon or LinkedIn to name its most iconic leaders. These companies work very hard at delivering shortcuts to decisions and actions, not information per say, as Thomas Davenport puts it in his recent Analytics 3.0 article in HBR. Let’s review a few examples.

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