Fixing The Suckiness of Predictive Analytics

You have been so nice to respond to my publishing of “old” work that was never shown that I want to continue doing that.

What follows is the fist of a three part series (when you are breaking down a 3-4 page writeup into pieces three parts seems to be about ideal).

I was tasked last year to focus on how to make predictive better.  I was never a fan of how predictive analytics was implemented (I am fine with the concept, but I don’t think anyone cares about the concept – instead deteriorating it into a parrot-act of repetitiveness with no good results).

In conducting the research came upon long-forgotten concepts and ideas, and mulling through them gave me a new idea: the one you will read about in next week’s installment :-)

First, let me set the playing field.

Predictive analytics is finally changing.

An art form of sorts, revived by the recent interest in Big Data and analytics shown by global corporations in the past decade, predictive was never intended for the current uses.

The definition of predictive analytics puts it at odd with the current usage.

Predicting a behavior was not intended to be used as a harbinger of a customer’s intention to purchase but rather as a lagging indicator of an occurrence or event so that the knowledge could be used to build better analytics models.

The thought that an occurrence will repeat many times over because of past data points indicating a similar setup is criticized by many analytics experts – even when adopted by most organizations.

The difference is the narrowness of what predictive can do today. We are simply focused on one path, one way to get from point A to point B. If last time we were at point A we took a bus to get to point B, we will do the same today. The complexity of today’s world makes those “guesses” just about impossible. What if, for example, it is raining heavily and I am in a rush? Could I take a taxi instead? Or, what f I have time and it is a beautiful day? Could I walk? Or, what if I am with someone who owns a motorcycle? As you can see, the many variables that are traditionally ignored by predictive (we look for a pattern, and then try to repeat it when similar data points are recognized in the same sequence) are what make the new models far more interesting.

Keep in mind, this is not what predictive intended to be – but what became from the poor implementations along the way.

A successful bad implementation will  be repeated.  A failed good implementation never sees the light of day again.  This is how Twitter came be used for Customer Service (but I digress)

Instead of trying to predict behavior step-by-step as most predictive applications do, why not use the pattern as a loose guideline of a sought outcome, break down the steps, and consider the many options available at each step. What yonder could’ve been a monumental step in calculating and analytics is very possible today thanks to advances in data capture, storage, management, and analysis.

The “Big Data” era brought the capabilities to analyze just about any data set in real-time and add many more variables as part of the analysis yielding far more interesting insights.

And it is within this new approach that we find not predictive analytics – but anticipatory analytics: the ability to dynamically and actively generate insights at each step of the way based on previously impossible to include variables and elements: intent, decision-making by users in real time, and untold goals and objectives.

As a result, my phone may hail a taxi for me (and maybe offer me a discount) if it detects rain or nudge me towards walking on a nice day – not because I did it before, but because I am about to do it. This is where predictive transforms to become the art of the possible.

What does anticipatory analytics look like?  Come back next week to see…

ICYMM: The Modern Customer Interaction

(ICYYM: In Case You Missed Me: linking to writing done elsewhere)

Another area where I spent a long time working in 2014 is customer interactions.  I mean, I am supposed to be a customer strategist… and customer interactions are the representation of what companies and customers do together… so why not? Right?

I talked to many organizations that are embarking on initiatives to redo their customer interactions.  Unfortunately, most of them are merely focused on documenting and extending what they are already doing (usually via <gulp> Journey Mapping and Customer Experience projects… sigh) instead of focusing on transforming them into something better.

I tried to imagine what a new customer interaction would look like and talked to vendors, organizations, and even consumers in that effort to get a better idea. I read most of what was written about it and then set out to summarize this information and came up with a new model for customer interactions.

Outcome-driven Customer Interactions.

Thanks to the sponsorship and support of my friends at InsideView, I published initial thoughts and models in their blog in a three-part series.  Here are links and quotes to  start the conversation.

Part 1: The Modern Customer Interaction – Introducing the concept and delineating the four outcomes: intent, satisfaction, knowledge, resolution, and engagement.  Quoting from the post:

All of these outcomes have the same thing in common: they use insights derived from the exchange of information at the moment of interaction to co-create value for both the customers and organizations. (…)

It is about delivering complete interactions at each step that co-create value to both sides resulting in long-term engagement.

Part 2: Outcome-driven Customer Interactions – Introducing a visual representation of this model – one that extends the work I’ve done previously with customer engagement.  Quoting from the post:

These outcomes ensure that even if there is no final resolution (or engagement over the long run) significant value is created at each stage that can then be used to move to the next step – or used in the future to generate better interactions.

Part 3: Customer-centric Outcomes – Finalizing the series by giving more details on the outcomes introduced and how organizations can start planning for them.  Quoting from the post:

These outcomes, in turn, will force organizations to change the way they work, the processes they utilize, the way they leverage their systems, but more importantly how they manage the information triumvirate (data, content, and knowledge) to seek engagement over the last run with customers.

Does this resonate?  Are you planning your interactions with customers with these goals in mind? Should you? Want to?

Leave me a brief note, let me know your thoughts…would love to talk and expand this model.

Disclaimer: InsideView is a client and they have generously sponsored this research. This research was conducted as part of my sponsored research model, where kind and generous clients pay me to do research I needed to do anyways and in exchange they get to use the content and their name associated with it. If you know me, and my work, you know this research is continuation of work I’ve done in the past few years – and that no one, even sponsors, get a saying in editorial control or content. In other words, they kindly sponsored me to write what I find out there.

How SAP Missed An Opportunity

Hello, it’s me again.  Your Friendly SAP — foe?

If you follow me you know that I admire the technical prowess of SAP, but despise about twice as much their marketing “acumen” (there are no other words that are politically correct and fit nicely between quotation marks).

I wrote about it in more detail in my post talking about their Reverse Dichotomy.  They innovate in technology and throw it away in marketing.

The latest launch event, yesterday, of S4/HANA (S stands for simple, 4 is the release number, and HANA stands for — er, well… HANA #LeSigh) continued that trend, with a twist.

There were high points worth mentioning:

  • The reduced and centralized data model (finally, finally, finally — but wait, does that include SuccessFactors?  I don’t think so… we are still not there)
  • The use of metadata (I think they call it customization, but by any words the concept is to allow customers to customize services via the use of metadata – one of the very few companies to do that)
  • A complete rewrite of the platform (yay, yay, yay – 30+ years of spaghetti code base and band aids had seen its day – good riddance)
  • Performance improvements galore (yes, finally – but seriously, 8 seconds responses are not something I’d brag about – even if 4-6x better than what they offer today – and don’t even ask the SuccessFactors clients for their opinion of those “great response times”)
  • A better way to cloud (will discuss the downside of this later in the post; but there were steps forward).

All in all, a good evolution for SAP as it introduces the first major innovation in their product since version 3 (R/3).

Good – right?

Yes – but mostly no.

SAP had been late to the cloud, to platforms, and to offer what their customers demanded in the form of a platform. and Microsoft have already addressed this, Oracle is working some of their marketing magic to convince their customers they have it (although they don’t so much) but SAP had been very, very quiet.

Well, not really.

Marketing wise they have been playing marketing musical chairs for the past few years relabeling and renaming their products under different launches, names, and what-nots – but never with a centralized perspective.

This is why they were behind companies like Salesforce (who spent a good 4-5 years retooling to create the Salesforce1 platform, only to waste the opportunity with a mobile-client marketing message that was not so good… or as my iPhone auto-correct would say, ducking socks) and Microsoft (who recently launched XRM, but how long it took to build depends on who you ask).

It’s been several different incarnations and versions for CRM for cloud, HANA, on-premises, and on-demand with different names and mostly the same product or slightly repackaged for some time.  Same happened to other products in the lineup.

I was looking forward to this release as it had been touted to me as the centralized, all-in-one release that will unite all products, fix the platform, change their approach to cloud and platform, and overall drive adoption for the next generation.

They fell short.

They had an opportunity to do that (I liked HANA from the very early conversations about in-memory and improved performance, and each time they showed what it could do with analytics and data management my mouth watered at the possibilities; they had some very interesting architectural approaches for CRM and the acquisition of SuccessFactors brought top quality talent to help them move forward; and more) but they did not take advantage of that opportunity.

They let the opportunity to deliver a market-leading platform that would match their competitors languish.

They missed the opportunity for the same reason Oracle chose not to chase it (as I wrote before): the biggest worry was to move forward with their late-adopter customer base versus doing something innovative and changing the conversation – or worse, leading the market (the necessary components and thinking are widespread throughout the company, just not properly utilized or in some cases even recognized)

I get it, I am not going to chastise them for doing the safe: retaining revenue and ensuring it continues to trickle in for the foreseeable future.  Alas, they left behind the ability to both impress and capture new customers in exchange for servicing their existing ones.  A safe move.  A lost opportunity.

Some of the items that caused me to write this from their recent launch?

  • The admission that multitenancy may not all that’s cracked up to be (how i wish I’d’ve said that before…  wait, I did) but still being offered (mostly because after many years of saying it is essential you can just walk away – and because…)
  • The insistence of offering on-premises version of the solution in addition to public cloud (and won’t even stop to answer questions about private and hybrid clouds… sigh); worse was the reasoning – some verticals  cannot do it – which is antiquated and wrong, but that’s another thread/ topic/post.
  • Not building on the concept of three-tier public and open cloud in favor of retaining the “platform” in HANA with little ability to be replaced or to use supporting services from other vendors (yes, like any other vendor – they want to retain the “ownership” of the client via their platforms, old habits die hard for all of them)

Short version of the complaint? They stuck by the slow-moving, late-adopting mass of the majority of their customer base instead of using the  potential of HANA to create new and innovative.

Just like Oracle before, it sucks - but I guess it is the way they had to go.


Stop Talking About Digital Transformation


I mean, what????

Seriously?  Just last year two of my seven posts (yeah, didn’t do that well writing in my blog last year – but been working to remedy that by posting links to my other writings around the world – but I digress) were about digital transformation.

I have been talking about digital transformation for nearly four years now and began to write about the transformative power of data (what digital refers to) over 15 years ago (when I began to cover EFM at Gartner ‘member?).  Why on earth would I want to stop talking about it now – when its finally reached the peak of the hype cycle and is beginning to be adopted?

Because its too limiting.

In my (now) business transformation model data has a key place right in the middle of it (see figure below).

DT New Framework

In conversations and work I’ve done these past 6-8 months with organizations and vendors data remains the main focus.  Top  investments for 2015 are focused around data and analytics.  Talk of Big Data and related concepts are taking over the world – and my colleagues (analysts, influencers, and pundits) are all super-busy around the topic of Data.

Data has taken front-and-center positioning among organizations’ plans and strategies for 2015 – and it is too limiting.

We need to amplify the conversation.

If we are going to talk about transformation we need to talk about more than just digital.  Data is but one piece of the pie.  Data, together with content and knowledge, become part of the information layer (see figure below).


If we are to talk about a complete transformation of the business we need to also talk about content beyond marketing and about knowledge beyond service as well as we talk about data.  We need to understand that the three work together to create information and that the flow of the information, freely via public clouds, is what will transform the business.  We also need to understand how new, old, and still-unknown data is going to be used to push business forward.

Data and digital are still part of the transformation, but we cannot forget the remaining pieces — and talking about digital simply limits the conversation to a small piece of it.

Let’s stop talking about digital transformation.

Let’s talk about business transformation.

What do you think?

The Silent Rise of Chat in Customer Service Adoption

Continuing on the delivery of the early insights into the third version of the customer service adoption and usage study we are conducting with our friends at KANA, A Verint Company (the summary of early findings is here, and the findings on social can be found here, mobile here, and operationalization of customer service here) I’d like to explore the rise of chat in contact centers.

Chat has had a love-hate relationship in the contact center since its early days in the late 1990s.  Early on touted as the replacement for the telephone (with many advantages over it) due to the low-latency nature of its operations, chat has succeeded (few times) and failed along the myriad electronic channels we brought along to form a contact center.

The original idea for chat was multi-tasking agents who could handle many instances at once thus surpassing the power of the phone that mandated a single interaction at the time.  We never quite had the ability to master both the multiples and the associated tools and automation to make it work.  Since its inception we have been trying to find a workable model, but email had gained more acceptance in the meantime.

The advent of social and online communities, together with the rise of adoption of electronic channels by customers and the “long waits” of email, created a good environment to try again.  A channel that traditionally saw adoption and implementation rates in the single digits (average adoption rates for the early 2000s through 2010-2011 had been 2-4%) has been steadily growing in the past few years per the data.

Indeed, adoption of chat in 2013 was 14%, rising to 35% in 2014.  That is across the many different models we asked about (with and without automation, with and without co-browse), but each of the models has seen a similar rise.

As we did with everything else, we held interviews with some of the respondents to understand better their implementations and what we found was also very interesting.  We found four reasons why chat has been rising over the past three years:

  1. Email doesn’t always cut it. In spite of email having gained universal adoption (with adoption rates toping 98% in the past three years), the demand for resources, the latency in responses, and the overall impatience of the customers make it an effective but stale resource.
  2. Automation is working better. The early chatbots and virtual agents required too much maintenance and couldn’t integrate into existing resources well or easily. The latest generations of virtual assistants and intelligent assistants are working much better.
  3. People are more used to chatting. Today’s customers are so used to electronic channels and communicating electronically that the early resistance from them (“I want to talk to a live person”) has all but disappeared. We are seeing significantly lower rates of dissatisfaction from customers with chat as resolution times get faster over email and simplicity takes over as well.
  4. Technology has evolved to make it useful. One of the critical aspects of any electronic channel is the ability to make it easier and better for customers to get their answers.  While the original chat solutions required software implementations and lacked sufficient security and privacy resources, the latest implementations have covered all those aspects and even created platforms for multi-channel automation that show good results.

Of course, among the many interviews there was also a common cry that we know it is the reason this is working better this time around (chat enjoyed a 30%+ adoption rate in 2000-2001, but flopped after it): organizations have found the best situations to deploy it, and have tied it into the other existing resources (KM, Queuing, etc.) to ensure it works the same as other channels.

What do you think? Have you adopted chat lately? What has been your experience? Let me know in the comments down below and we can discuss the silent rise of chat in the contact center.

Disclaimer: KANA, A Verint Company, is a client and the sole sponsor of this research report.  While they get input into the topics to survey, and provide feedback on the thesis before we start, the final decisions on content, questions, and analysis remain mine.  There is no input from anyone else other than thinkJar and its employees (which, as you know, it’s just me) in making content and editorial decisions on the study, findings, and reports. All data is propriety of thinkJar and not shared or distributed.

ICYMM: Knowledge Management Questions for 2015+

ICYMM: In case you missed me.

I often write in other blogs and properties and I am sure you don’t have the same Google Alerts I do – which means you can occasionally miss my writings.  In an effort to keep you always alert to what matters (you’re welcome – its my privilege to help) I will bring the links to those posts with a brief summary here.

At the end of last year I wrote a series of blog posts on Knowledge Management.  I am very fortunate to have excellent clients with no ego problems who sponsor me to do research.  In the topic of Knowledge Management last year I was lucky enough to have IntelliResponse (since acquired by 24/7), Parature, and Transversal do that.  In exchange for their support they received different deliverables – including the series of blog posts I am including here.

Knowledge Management should be changing.  It is, unfortunately, lost in thought instead.

The world has changed, and how knowledge is created, used, and maintained has changed: communities and reachable subject matter experts make it impossible to claim ignorance or ownership.  I began to cover that two years ago when I did my then Knowledge Management series talking about new models (you can find the series I published for Stone Cobra in my downloads section) – most notably the knowledge-in use versus knowledge-in-storage concept.

This year I wanted to explore more of what’s going on and how we need to change it and I covered it in four blog posts (with relevant quotes beneath each):

Does KM Even Matter Anymore? Starting the series with the right question in most of my clients’ minds: why bother? Is there a useful purpose to Knowledge Management? Well, read to find out… but as a spoiler: yes, more than ever.

The story for the demise of Knowledge Management has been told many ways.

The Most Important Job for KM in Customer Service.  I had this conversation so many times this past 15-20 years I’d been doing and researching KM that is becoming obsolete – except that people keep asking and few are doing it.  Maintenance.  Killer stats in this post, definitely must read to get justification for your program.

Just 34% of companies have proper maintenance processes for KM.

Why Aren’t KM Budgets Sufficiently Funded in 2015? Asking the question that always has been in my mind: do we set aside special money to support KM or do we just make it happen with hope and prayer? I got tons of good data in here as well.

Thus, KM becomes a “necessary evil” for customer service. Instead of being a discipline that can alter the way customer service is done, it is a cost item that results in a technology being implemented.

Finding the Right Place for KM in the Organization. I have long maintained that KM in customer service only is a waste of time and money (and if you read the previous entries on funding and purpose I think you’d agree).  Can you place KM somewhere else and leverage it in customer service? Read on.

Outside of the necessary knowledge sharing necessary to collaborate, virtually every function inside of the organization also needs access to the right information at the right time to succeed.

There were other things I did last year about it, webinars and research reports, infographics produced, writings and more.  I will continue to post those here as time allows – but I just like this series a ton since it outlines the potential for KM as we enter a new age in business: communities.

You will read more about that here this year and going forward as communities is the catalyst for the business transformation we are experiencing.  And one of the key tenets for my forthcoming book on business transformation.

What do you think? Missed much? Would love to hear what you have to say…

disclaimer: as always, being a client means you are generous beyond need and you recognize that I am bored beyond belief.  being a client means you sponsor my work and benefit by having pieces of it to use as you see fit.  being a client means i get to keep my editorial integrity and research that which furthers my agenda.  being a client has never meant and never will mean you can tell me what to research, write or say.  and IntelliResponse, Parature, and Transversal (as well as the many others over the years) know and appreciate that.

The Operationalization of Customer Service

Continuing on the delivery of the early insights into the third version of the customer service adoption and usage study we are conducting with our friends at KANA, A Verint Company (the summary of early findings is here, and the findings on social can be found here, and mobile here) I’d like to explore a little bit more the operationalization of customer service.

From the beginning of this study we have been asking respondents to tell us who controls the customer service budget and where in the organizational chart they report.  The original intent of the questions was for cross tabbing and demographics (which we continue to do to understand all issues better), but along the way we found a very intriguing reality.  Starting last year we saw a rise in how many customer service organizations are reporting into operations (19% in 2013 and 24% in 2014).

Intrigued by this shift, and during follow-up interviews, we established a line of question to understand the change.  After all, traditionally customer service had either been an independent organization within the company or usually reporting into marketing or sales (depending on the organization’s processes and budgets).  Seldom did we see customer service report into more “internal” roles like operations or even IT.

We found three common topics among the many answers:

  1. Time for Operations. One of the most recurring answers was that during the past few years, with reduced or eliminated budgets due to the global recession, it was a great time to talk and think about strategies on how to support customers better and how to optimize customer service. Now that budgets are returning (and my inquiry load definitely supports that) we are going back to operations and implementing a lot of those strategies.
  2. End-to-End-Experiences. The trends dictating the transformation of the business (social networks, big data, customers in charge of conversations among others) are calling for organizations to deliver better infrastructure to let their customers build better end-to-end experiences – which customers are demanding and expecting. To do this, operations in customer service must match operations in the rest of the enterprise.
  3. Optimized Processes. Continuing on the trend of business transformation highlighted by the two previous insights, this is the element that was the most often cited (albeit, I may be biased due to my research in this area). As transformation takes over the business and processes are changed, operationalization of customer service to deliver on those new promises is essential.  More and more businesses moving to transform and optimize their processes are taking this operationalization of customer service as the start of the solution.

Indeed, the evolution of business matched by the availability of budget is making customer service focus on operations once more.  This is a good trend and will be explored in further detail in the detailed report to be published 2015.

Are you finding your customer service organization moving in the same direction? Are you operationalizing and optimizing customer service?

Let me know in the comments, would love to chat about it.

Disclaimer: KANA, A Verint Company, is a client and the sole sponsor of this research report.  While they get input into the topics to survey, and provide feedback on the thesis before we start, the final decisions on content, questions, and analysis remain mine.  There is no input from anyone else other than thinkJar and its employees (which, as you know, it’s just me) in making content and editorial decisions on the study, findings, and reports. All data is propriety of thinkJar and not shared or distributed.

the blog!

%d bloggers like this: