The Art and Science of Customer Empathy in Design Thinking

SAPVoice+Art+And+Science+of+Empathy+Design+Thinking+by+Kaan+TurnaliCustomer-centric solutions demand empathy. But, how we employ this principle within design thinking is as critical—if not more—as what we do in the process.

Certain slices can be easily repeated—that’s the science part. However, not everything fits neatly into a template. More than anything else, we rely on our creativity to accurately frame a problem and discover the attached opportunity. That’s the art of customer empathy within design thinking.

In my previous post, I discussed three factors that are critical turning empathy into an obsession when developing customer-centric innovations. I want to expand on this topic and elaborate on how it can also be applied to our everyday work as well.

Customer Empathy is not Inherited or Repeated—it’s Continuously Learned

The unconditional act of projecting ourselves into our customers’ (or users’) shoes has to be unreserved. Empathy works only if we open up our nerve endings and feel what it is like to be in another’s shoes. One of the key approaches is adopting a beginner’s mindset that functions as a reset button—enabling us to experience a product or service as if it’s the first time we are using it.

In human-centered design, we use a set of tools to observe and communicate with people and better understand their journey. Empathetic listening and observation are essential during the entire design process:

  • Immersion: Place ourselves in the full experience through the eyes of the user.
  • Observation: Carefully watch and examine what people are actually doing.
  • Conversation: Accurately capture conversations and personal stories.

All three approaches require focus and precision because they typically produce different insights. To learn, we must listen more than we talk. When we observe, we disappear, rather than interfere. There is no room for sharing our opinions or selling the solution. We want facts. If we can’t understand the “why” behind an experience or problem, any assumptions about the “what” and “how” become skewed or misleading.

Our Knowledge is the Source of our Bias—Sometimes

In design, what we know can be just as detrimental as what we don’t know. One of the best examples of this reality is seen in technology projects.

Senior developers cooped up in a lab can produce very sophisticated code. These teams develop customer-facing elements based on a bias that reflects their extensive knowledge of the technology while ignoring steps considered minor from their vantage point. However, these minor steps are indispensable to users who are not necessarily tech-savvy—which may make up the majority of their customer base.

By simply leaving out parts that they consider obvious corners, these teams may not observe or attempt to live the experience through the eyes of the actual user—missing out on the opportunity to create a well-rounded, customer-centric experience.

With design thinking, we always insist on seeking untested experiences so we can capture unrefined observations that frame the details of the user journey.

“Emosurances” Influence our Perception of a Product or Service

Humans tend to react to emotional assurances (emosurances). They play a crucial role in designing a human-centered user experience—especially the user interface (UI).

For example, consider the experience with a digital process or transaction:

  • How many times do you find yourself in a state of uncertainty?
  • Do you know where you are in the process or queue?
  • Will you get an alert when it’s completed?
  • Will you abandon it because you are unsure of the next step?
  • Are you given any visual feedback, such as a progress bar?
  • If there is service interruption, do you get a notification? Is the message clear enough that it does not require further translation to understand next steps?

The scenarios are endless and apply to any user experience—digital or analog, online or in person. And even though these questions appear mechanical and a matter of UI, tackling these emosurances proactively is at the core of the empathy principle.

Bottom Line

The traditional value proposition of a product or service is a promise of particular utility value. If you get X, you will receive Y as a result of Z.

The design-thinking value proposition is a promise of core values: You want to get X because you care about Y and Z matters to you.

The actual value of the empathy principle comes from understanding our customers’ 360-degree viewpoint, especially their emotional attachments. Then, we can deliver a compelling value proposition that guides us along the innovation path. This approach enables a forward-thinking mindset that fuels a cultural shift paramount for competing on design thinking.

Stay tuned for the next installment of the Design Thinking thought leadership series!

Connect with me on Twitter (@KaanTurnali), LinkedIn and

For more information on Design Thinking, contact our team at Amick Brown


What you should consider when embarking on an Advanced Analytics journey?

By Paul Pallath, PHD,  Chief Data Scientist & Director Advanced Analytics, SAP

In my previous Predictive blog, I introduced four main considerations that organizations need to keep in mind when they’re beginning that journey. Today, I’ll cover them in more detail.

1. How Do We Measure Business Value and Return on Investment?

An advanced analytics solution must make a measureable impact. If not, the solution doesn’t get noticed, never mind appreciated. This holds even more true, if the return on investment (ROI) can’t be realized as a significant opportunity to drive business growth or new market opportunities.

Take the example of a marketing campaign. The ROI is in having the intelligence to target the customers who are likely (if persuaded) to buy your product rather than finding customers who would have bought the product without any marketing required.

An advanced analytics solution will be short-lived if it creates a “wow” effect, but nothing else.  The solution must generate recurrent value, revenue, and business opportunities.

2. How Do We Use Advanced Analytics Effectively?

For your business, good questions to ask at the start of the journey are:

  • Is the enterprise truly digital?
  • Is there a single source of truth of all the data that is generated/captured by various functions of the enterprise?

These questions are important considerations. Why? Because businesses often approach advanced analytics in an ineffective manner.

Remember, advanced analytics drive value to every business function, be it marketing, finance, human resources, and so on. However, enterprise functions want often to embed advanced analytics into their business workflow and embark on advanced analytics initiatives in silos. Though there is value in doing so, the results can be underwhelming.

This is because they’re using adoptions of various technologies, methodologies, practices to address the use cases that might exists— but without an enterprise-wide vision for advanced analytics. Therefore, walls rather than bridges are built between the various functions.

The problem becomes self-perpetuating. With increasing adoption of advanced analytics solutions in various business units, the business as a whole finds it difficult to consolidate all the activities into a central initiative and have proper discipline and governance.

The solution is to create the vision and execute it across all functions— even if the pilot starts from one or two activities. The functions must agree that advanced analytics is an enterprise-wide mission. Leadership must demonstrate belief in an analytics-driven business that it is going to provide competitive advantage. In this way, advanced analytics becomes a true company asset.

3. Is Advanced Analytics Just Another Technology Project?

Advanced Analytics is not just another technology project. If considered to be a technology project, the business understands only the technical feasibility and not its business impact.

As mentioned, an advanced analytics initiative is the means by which a business gains a competitive advantage. It follows that outcomes provide the data to help make well-informed decisions.

A lesser or confined approach is a step in the wrong direction. There is no ROI associated with technology-only thinking, because no tangible results are expected as an outcome. An initiative to embrace advanced analytics must be inseparable from your business strategy.

4. Is Big Data Equal to High Quality Insight?

Big Data is not equal to high quality insight.  A traditional business approach is to think, “We’ve captured huge amounts of data, but how do we  make sense of it?” This is a wrong start.

The right approach is to start with a business question in mind. That way, you can ask if the data that you have is sufficient enough to provide the answer.

These are several pieces of the puzzle that need to be put together for one to find meaningful, actionable insights from the data. This is, after all,  the quest that we embarked on.

As we now know, advanced analytics is about business change, insight and value.

“The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data”-Sunset salvo. The American Statistician 40 (1).

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Predictive Analytics 101 – The Real Business Intelligence, part 2

by Ashith Bolar,  Director AmBr Data Labs @ Amick Brown

In my first post, “The Real Business Intelligence” ,  I emphasized on the significance of Predictive Analytics in the Business Intelligence space.  Let us take a deeper look at Predictive Analytics by way of more concrete examples.

As a refresher, Predictive Analytics is a set of tools and techniques based on statistical and mathematical techniques to analyze historical data and subsequently predict the future. The basic premise is that by analyzing historical data, determining relationships, more specifically correlations between related (and sometimes seemingly unrelated) attributes and entities, one can derive significant insights into a system. These insights can be further used to make predictions.

Let’s take a look at this process step by step.

The fundamental component in Predictive Analytics is a Predictive Model, or just model. A Predictive Model is set of data points plus a series of algorithms working on that data. It attempts to capture the relationships between the data points by means of applying mathematical or statistical computations deployed as algorithms.

The output of a model is typically a single number — called a score. The score essentially is an quantitative value for a specific prediction by the model based on historical data. Higher the score, the more likely a certain behavior is predicted. Lower the score, the more likely the opposite behaviour is predicted.

Predictive models can be built for a wide variety of problems. But the most common predictive models, especially in the context of a business application, is one that predicts people’s behaviors. Predictive models are designed to predict how people behave under new circumstances, given what we know about how they behaved in the past with other known circumstances. For instance, Netflix’s movie recommendations — based on the movies that you have seen and rated highly (known circumstances) recommendations for new movies (unknown circumstances) are generated.

You will hear terms like “Machine Learning”, “Artificial Intelligence”, “Regression Analysis”, etc. While, each one is an independent area of mathematics and computing, for a Predictive Analytics suite, these are just different algorithms (computing models) that are employed in the process of predicting.

Let’s dig deeper into the concept of Scores. Let’s take two classical examples of scores generated on customers.

  1. Based on the movies you have seen and rated in the past, Netflix tries to determine if you will like a new movie or not. Say for instance, a scoring of 0-10: 10 being a prediction of you absolutely loving the new movie, and 0 being a prediction of you absolute not caring about it. This type of a score is called a Probability Score. In essence, the score tells you the probability of you liking a movie.
  2. Another type of score is called the Quantitative Score. Here the prediction is not the probability of whether you will like the movie or not, instead to quantitatively predict the amount of something. For instance, Life Insurance companies try to predict how long a certain customer will live based on the life choices and other circumstances of the customer.

In case of the Netflix model (Probability Score), if a customer gets a 8 (out of 10) for the likelihood of liking a particular movie, it can be rephrased as “There’s a 80% chance that the customer will like this movie, and a 20% chance that they will not like it”. Such a prediction is basing its prediction on the spread of probabilities (probability distribution). Another way of looking at this score of 8 (out of 10 is) “The customer might not absolutely love this movie (which would be a 10/10), but definitely not absolutely hate the movie (0/10). Instead the customer is more likely actually liking the movie to some extent (8/10), rather than completely disinterested (5/10).

In either case, a careful examination of this score tells us that all the system is doing is categorizing people’s behaviours into a set number of ranks. Therefore, predictive models which generate probability scores are usually called classification models. On the other hand, the quantitative scoring (predicted life expectancy of an insurance customer) is really a quantitative number. Another classic example is customer spend which is how much a customer is willing to pay for a new product or service. The actual value is reached at by means of various statistical and mathematical computations. These models are typically known as regression models.

A good predictive model might not be accurate in every single case, but given a large set of data (read target customers), the model regresses to the predicted mean of the behavior.

In the following posts, we will delve deeper into predictive algorithms, and try to gain a better understanding of how they work, and more importantly why they work, and why they are important in your corporate strategy.


Are you Planning to Embark on an Advanced Analytics Journey?

Businessman Analyzing Graph

Welcome to the new world!  The manner in which data is generated and captured today has come of age. Traditionally the way of generating data for the most part from B2C/B2B2C business processes, was by having interactions captured as part of transactional systems in a highly structured format. But with changing technological landscape, much has changed in how data is generated and captured.

What data supports this view?

According to the ESG Digital Archive Market Forecast, the growth in data volumes that is driven by unstructured data amounts to more than 88% as compared to structured data. What’s more, Computer World states that unstructured information may account for more than 70% to 80% of all data in organizations.

The change has come because we in the 21st Century have redefined the way business is conducted. Significant advancement in internet technology has forced the need for digital online presence for most businesses to stay relevant. Likewise, every interaction that a customer has in the digital online ecosystem leaves behind a digital foot print containing huge amount of information.

Social media presences, for individuals and businesses, have increased the speed at which information travels. This has made it possible to share opinions as blogs or multimedia content. The result is the constant generation of large amounts of unstructured data.

All that Unstructured Data Is Good News for Data Scientists

However, this is good news for data scientists.

The previous figures imply that we have now Yottabytes [1024] of data at our disposal for deriving business value—and that amount of data is about to increase.

The Internet of Things with its emphasis on completely connected systems has resulted in the availability of high speed streaming data. This makes it possible for new innovations that use data to build technologies to enable machines talk to one another (and perhaps eventually become intelligent enough to remove humans from the loop)! Taking the trend into consideration, Brontobytes [1027] of data to work with will soon be a reality for data scientists.

So, what is the best way for a business to capture and benefit from this information? Of course, capturing the massive swathes of data available is an important part of the Big Data story. But it’s not the most important part.

The most vital activity is to generate insights that add value to your business. This takes vision, it takes change, it takes…advanced analytics.

For organizations embarking on a journey into advanced analytics, it’s vital to keep in mind these important considerations:

  • How Do We Measure Business Value and Return on Investment?
  • How Do We Use Advanced Analytics Effectively?
  • Is Advanced Analytics Just Another Technology Project?
  • Is Big Data Equal to High Quality Insight?

In future blogs, I’ll discuss each one of these considerations in more detail.

Let me know what you think. Did I leave one off the list?

Amick Brown would sincerely like to Thank Dr, Pallath for his contribution today .

Context Awareness: Online or Real Time in Digital Transformation?

By Iver van de Zand , Business Analytics Leader, SAP


Excuse me? Online instead of real time; isn’t that the same? Well, have I got news for you: It is not.

Driven by enterprise needs and technological capabilities, enterprises are massively going online. Why do they and how accurate is online? Why do I read about going online in the news sites all the time? Let me explain wearing my analytics glasses.


First, let’s get some things straight: explaining the difference between real time and online starts with a discussion on latency. Latency is a time interval between the stimulation and response, or, from a more general point of view, as a time delay between the cause and the effect of some physical change in the system being observed. Online means that there is some kind of interactivity involved, but doesn’t enforce limits in latency. Real time means that there are limits on latency. Pfff, need to re-read that a few times before it starts clearing for me.

Let me give an example: If you move your computer’s mouse, you expect the pointer to react immediately and precisely follow your actions. That’s real time. Another example is playing on a music keyboard controller and have some synthesizer program generating the sounds. Online, however, is when your actions show some response in some timely manner, but there’s no timely relationship enforced to it. For example, starting a video stream from a (remote controllable) webcam may show you the pictures with less than one second latency, or even up to several minutes, yet be online.

My phrasing “difference” should thus be adjusted—real time and online don’t differ, they relate to each other.

The Market Out There: Context Awareness and New Business Models

According to Gartner’s recent Big Data Trends for Business Intelligence, by 2017 more than 30% of enterprise access to broadly based Big Data will be via intermediary data broker services, context awareness2serving context to business decisions. These are massive amounts and it proofs that digital business require real-time situation awareness. That covers full insight into the things going on inside and outside the organization. Retailers, for example, need to know in real time how weather patterns impact the buying behavior of their customers. The inventory manager requires real-time information when his supplier is in trouble delivering his goods, so he can immediately adjust and use analytics to find alternatives, re-plan and re-adjust for example his forecast.

The issue that occurs is that more and more the enterprises corporate data is insufficient to get the necessary context awareness required to adequately respond to digital business. Think of it; to compete in digital business, a combination of non-corporate data coming from outside the organization is required all the time. This—often unstructured—data could be about social behavior, environmental influences, and government or market trends, to name a few. Some of it is even from premium data brokers preparing data from specific industries or use cases.

We could say that the ability of enterprises to adopt digital transformation and digital business for a big part is influenced by their capabilities of curating, accessing, and interpreting their data to obtain context awareness.

Context awareness is crucial for any enterprise that wants to compete in digital business. Real-time availability of insights is the logical requirement to do so. We already recognized the need forContext Awareness3 real-time insights to corporate data, however we also now recognize the need for real-time insights in contextual information:

  • Curating Insights

Digital business is about the agility to respond to market, customer and environmental influences and actors immediately when required. Digital business requires enterprises to act and respond almost real time to activities not registered in their corporate data.

  • Accessing data and insights

Digital business is about the agility to recognize and access information outside the enterprise that is necessary for curating insights immediately when it occurs.

  • Interpreting and act upon insights

Digital business is about the ability to interpret insights and act upon them instantly. This is not only about interacting with the insights, but especially about applying the closed loop portfolio of analytics: insights often generate follow-up actions that affect business planning, finance budget allocation, or require new predictive models to argument on influencing variables of the contextual information.

An interesting side effect of contextual awareness is the new business models that come with it. A new category of business-centric cloud services enters the market space that delivers data to be used as context in business decisions, whether human or automated. These information services (or data/decision brokers) will become an essential part of intelligent business operations and smart business decisions.

The Case for Online Analytics

Online analytics is primarily about cloud-based analytics. If we narrow down to business intelligence (BI), the cloud BI market will be worth $4 billion by 2017 whereas the current full BI market (software and services) is estimated at $86 billion.

But how important is the aspect of “being online” for context awareness? Well, it’s quite important:

  • Contextual information is very often residing on websites. Your company’s biggest database isn’t your transaction, CRM, ERP or other internal database. Rather it’s the Web itself and the world of exogenous data now available from syndicated and open data sources.
  • Products like SAP Cloud for Analytics connect to various cloud-based solutions like SAP S/4HANA, and others. It is obvious that online analytical tools integrate more easily with other cloud-based applications.
  • Reduced or eliminated capital costs. Because BI systems are managed on the cloud service provider’s hosted architecture, a user company has no up-front capital investments or multi-year equipment leases with depreciating value. It also stands to benefit from improved cash flow. The subscription fees charged by cloud vendors are treated as operational expenses and don’t incur additional interest charges, which can lead to better cash management and debt avoidance.
  • The simplicity of the online, cloud-based analytical applications is key when it comes to user adoption. If we realize that the people creating insights on contextual awareness are business users, you’d agree with me that simplicity makes the difference when it comes to adopting the applications and leverage there power.
  • Streamlined system design and increased elasticity. In the cloud, companies can rely on a provider to architect the BI environment, select the technologies that will power it, assemble systems and manage the hardware and software stacks. That frees them to focus their attention on running BI and analytics applications and interpreting the results.
  • Fully-integrated business analytics components into the so called closed-loop portfolio. Analytical environments hosted in the cloud comprise a complete end-to-end architecture. SAP Cloud for Analytics for example, spans the ETL, data management, analytics, planning, predictive and risk spectrums, simplifying and speeding the deployment process for users. Cloud BI systems should be ready to use out of the box, so to speak, and the standard setups can quickly be augmented with templates that vendors have developed over the course of different customer engagements.

The Case for Real-Time and Online Analytics: Context Awareness

Man, does Digital Transformation bring us interesting times! There so many aspects of it, with contextual awareness being just one of them. For me, it’s crystal clear that real-time contextual awareness is key to any enterprise that wants to be competitive in digital business. Given the flavor and behavior of the contextual information, online analytical applications can make a significant difference.

Follow me on Twitter @ivervandezand.


Five Tips for New Recruiters

By Alyanna Espina,  Business Development at Amick Brown

When my bosses told me that I was getting trained to become a Recruiter, my heart started racing and the feeling of panic started to sink in. All these questions were floating in my head, where do I begin? Is there a cheat sheet to becoming a recruiter? How do I learn about all of this technology? I have to talk to strangers? In other words, I had no idea of what I was getting myself into or what I was going to do.

Moving forward a year later, I found that recruiting is not as difficult as it seemed to be, it’s actually enjoyable. If you are willing to learn and are ready to put in the hard work, then you are on your way to becoming a successful recruiter. Yes, it is challenging at first because you are talking to complete strangers and trying to convince them about the job offer you have. But, it’s all worth it in the end knowing that you are actually making a difference in someone’s life.

In the IT industry, finding the right fit candidate is one of the biggest hurdles when recruiting in this specific field. There are tons of positions available, but not a large enough pool of candidates to choose from. This goes against the norm of our society in which finding the right kind of job is also an issue for people outside of the IT world. Regardless, getting that call from a recruiter is just exactly what you are waiting for. There are recruiters out there who truly enjoy their work and there are others who are doing it for the paycheck. Needless to say, we can all tell the difference between the two.  Of course, it is true, that the job provides a huge opportunity to earn a good amount of incentive when your candidates gets placed, but you have to work really hard to get to that point. If you have sales skills and can provide excellent customer service, then that is something you can apply to recruiting. Who knows that may even earn you a spot at the top one of these days. Perhaps, you may even own a recruiting business in the future.

Here are five pointers that will help you, if you are just starting out as a recruiter.

  1. It’s basically match making – you take the requirement that the job position has and match it with the qualifications of the candidate. If it doesn’t match, let them know that you will contact them when there is a more suitable job offer that matches their skills.
  2. Time management and organization is a must – When you are recruiting for a job, it is very important to manage everything that needs to be done within the stipulated time period. Not only that, you must have everything organized, documents, contact information, etc. You are most likely going to be working with people in different time zones. So, you must be mindful of their time when you set up a call. It helps to be prepared, that’s when organization comes in.
  3. There is no “I” in TEAM – you will have to be a team member all throughout the entire process. As a recruiter, you are not only working with your immediate co-workers, you are also working with numbers of people out there. It could be your clients’, candidates’ employers or the candidates themselves. Different people, mean different styles and personalities. So, having the ability to work in a team will make your job easier.
  4. Communication is key – I can’t emphasize enough how important communication is in the recruiting world. So many things can go wrong, if you don’t hone in on your communication skills. Heck, it may even be the reason why you lose a candidate to the next recruiter. Having the right communication skills is the key to building a relationship with others in the market. It is by far one of the most important factors in business of any kind, in this case, recruiting.
  5. Be tech savvy – there are many aspects of recruiting that requires you to use technology. Therefore, it is important that you know how to make use of computer technology. There are job boards, database systems, and networking tools that you will have to use when recruiting. So, if you aren’t tech savvy, then it will be difficult for you to keep up with the job requirements.

I’ve only listed out 5 pointers, but there are far more beyond than what I have written. Some skills are meant to be learned as you get yourself situated in your recruiting position. No matter how much research or studying we do for a position, nothing beats learning from doing the actual job. Keep in mind that staying positive and working hard has no alternatives, there’s no shortcut to get to the top.