Platform First or Effective Detection First

The other day a friend in the security industry forwarded me a blog post that really stirred me up and very much frustrated me.

The reason being that it was another example to me of a post that was asking the wrong question of our industry and making broad generalizations that, from my experience, are not true when it comes to the convergence of the big data and security ecosystems.  The question being asked was this:

What is a bigger challenge for large scale security data analysis efforts, Scalable Platform or Effective Detection Content?

Two points to consider:

1. I am getting really tired of those people that are saying that building a security data lake is a fools errand.  You can think that it is just a waste of time to do this, but the reality is that those that are doing it today, for the most part, are forward thinking organizations that have a strategic vision and plan for how they are going to use the data in the near term.  I have personally visited with 50+  companies in the last 2 years that all have a vision for how they are going to use the data they are collecting.  And even more, most of those companies that are building these lakes, are holding this data already anyway for compliance or regulatory purposes and building a lake simply makes that a cheaper proposition for them.  Seems pretty smart to me
2. The greater challenge that the larger big data ecosystem is having is the complete lack of true applications that sit on top of the stack to provide the next layer of value.  Cost savings is the first layer of value that these companies are getting from building a lake for security data.  The other is value that comes from leveraging pre built apps that can take advantage of this architecture.  Unfortunaltely, these apps are not coming as fast as the customer base deserves and needs, but they are coming.  The reality is that building the platform is not easy, in fact, it is very challenging.  But so is building the analytical apps on top of it.  It is not about just writing some python code to build models.  It is about building code that not only analyzes data, but does it at scale and can do it in a multi-tenant processing environment.  And if you think this is easy, then you really don’t know what you are talking about.

Again, I think the question asked by the blogger,  is really a naive one.  It is not about whether or not one is more challenging than the other.  It is a question of maturity of the offerings in the market and how they are delivered.  Because both building a security data lake and building the app on top that can provide analytical value are ridiculously hard/challenging.  Our job as a community is to do our best to hide this complexity and provide a software package that is easy to consume and get value from.  Not argue about which part is more challenging. 

So Tired of People Using the Word Insights!

Ok, so this is not going to be a typical blog post where I talk about some technology, focused these days in the big data or security space and how it impacts the customer experience.

Nope.... This is going to be a "Pet Peeve" blog post about something that has driven me crazy for years...

And that pet peeve is the use of the word "Insights"....

For the last few years now, every single vendor under the sun, regardless if they are an analytics company or not, have been talking about providing you "Insights" into your data or your operations or your customers etc...

JUST STOP IT!!!

No one and I really mean NO ONE is really providing customers with Insights.

What they are providing customers with is data, graphs, charts, dashboards and any other cool, wiz bang visuals that can be generated by a software program.

But can we please just agree that these are not Insights.  They are just another way of showing someone data and then forcing them to extrapolate from there what the data actually means, which will then eventually end up with a human generating some real Insights.

What I want to see more of in the industry in general is the software providing the end user with a full blown, actual Insight.  So instead of showing someone a pie chart with 8 slices in it that make up a customer segmentation, there is a short description of what the data means, why it is important, a bit of context around it and a link provided that allows the person to explore the data that generated this Insight a bit more.

I think of Insights as "Leads".  The machine doing the heavy lifting of finding where you should be focused, based on the data and then allowing the user to dig deeper if need be.

So, please vendors, stop talking about how you all provide "Insights".  And instead focus on building software that actually does provide Insights.

Big Data Technology Explained- Part 3 (Other Big Data Tech)

In the previous posts on Big Data, we talked about some of the base technology tools that are in use today by companies all over the world to drive their Big Data programs.  We talked about the Hadoop ecosystem and a few of the projects or tools that have become common in commercial use cases.  Things like Hbase, Hive, Impala, Storm, Spark etc...

But, we can't limit the big data world to just the world of Apache Hadoop.  There are dozens and dozens of other technology tools, frameworks, platforms and applications that have been developed over even the past 5 years, that drive real value for organizations.  Take a look at the chart below and you can see that there are a ton of players.  But I will only dig in to a few of them that I have seen real value be generated within the folks I work with everyday.

As I said, a TON of companies making a play in the Big Data arena.  Still lots of opportunity I think to build more useful apps, but that is for another post down the road.

Out of these many dozen companies on this graphic, I want to call out a couple:

Elastic:
Formerly known as ElasticSearch, Elastic is an open source indexing and search tool.  Similar to Apache Solr, Elastic is used by companies to take documents or chunks of data or even individual log events, index them, make them searchable and then purge when space is needed.  The beauty to me of the Elastic platform is not just the search mechanism, but also the other tools that have been built to enhance the user experience in using Elastic.  In particular, I call out Kibana as a great tool that sits on top of Elastic and makes it very easy to find data that you are looking for.

NiFi:
When it comes to the world of big data, almost nothing is more important that actually being able to easily and quickly move data from one place to another.  But not just move the data, but securely move it, at scale and the ability to recover in case something happens in transit.  This is where Apache NiFi shines.  NiFi has been picked up with incredible speed by some of the largest companies in the world to fill in the gap that they all have of more effectively moving their data around their organization.

Neo4j:
As we start to move to a world that is based more and more on relationships and networks, graph databases start to become more and more important and that is what Neo4j is all about.  Every company, whether they like it or not, over the next few years will need to start connecting the dots that exist about their customers, partners, suppliers etc... Doing this with traditional databases is almost impossible and there are not any really great tools out there within the common frameworks that make graph database a possibility, besides Spark.  So, we will begin to see real growth in this area is my view and should be an area to keep an eye on for new, more user friendly kinds of solutions.

Ok, this is the end of this part of the series on Big Data, focused on the technology.   Again, my goal was not to go toe to toe with all of the architects of the world on what big data technology really is or how it works.  My goal was to help business teams get just enough of the detail about this technology that it helps them make more informed decisions with their internal and external technology partners for their big data programs.

Big Data Technology Explained- Part 2 (The Hadoop Ecosystem)

As I talked about in the last post, Hadoop was really the engine that started to drive the application of big data 10 years ago now.  But since then, there has been incredible growth within the Hadoop ecosystem to address many of the challenges that were identified by commercial organizations as they started to adopt Hadoop.

Of the 20+ Apache projects that have come to life as a part of the ecosystem in the last many years, I will just mention a few of the larger projects and their signifigence in the grand scheme of things.  Again, this is not meant to be "developer 101", but instead, business person 101.  My goal here is to help business people know enough to ask the right kinds of questions of both their internal and external technology partners, before they embark on a big data adventure.

Hbase:

The first ecosystem component to talk about is Apache Hbase.  Hbase is what is called a NoSQL columnar database, which, to distill it all down, really just means you can read/write data really fast to or from it for time sensitive operations and not need to just use SQL queries to get the results.  One of the areas in the commercial space where Hbase is commonly used is as the "database" for lookups on websites.  So, if I am on eBay and I am searching for specific kinds of products, the search I do may take me behind the scenes to something like Hbase to do a fast look up to see if that product is available.  Or if I am on Etsy, all the clicks I may make on the site during my visit could be saved and tracked and then stored in something like Hbase for immediate access by operations teams.  So when you hear the word or term Hbase, think super fast reading and writing to a data store that sits right on top of Hadoop (HDFS).

Hive (LLAP) and Impala:

Two other major projects that were developed to help speed the adoption of Hadoop in general were Apache Hive and Impala.  Both are what is referred to as "SQL Engines"on top of HDFS.  As is the case with just about every new technology, in order to drive adoption, making it easy for users to interact in a way they are familiar with is critical.  SQL happens to be a query language that is pretty much universally accepted and used by millions of analysts around the world with relational databases.  So in order to really drive the adoption of Hadoop, it made sense to build a tool that could leverage those skill sets but still get the value, power, speed and low cost of the Hadoop backend.  That is where Hive and Impala come in.  They both allow folks with those traditional SQL skills to continue doing what they do well, but leverage the goodness of Hadoop behind the scenes.  Organizations may use Hive or Impala to do dynamic queries or large scale summarizations across multiple, very large sets of data.

Spark:

Over the last number of years, all of these projects started to solve the problems that companies had with the original Hadoop components of HDFS and MapReduce.  But one thing kept coming up over and over again as computing started to get cheaper and specifically, as memory has started to become cheaper.  How can I make my processing go faster?  This is where Spark came in and started to become the new hot tool and darling of the big data community.  Spark was developed out of Cal Berkeley in their AMP Lab and has become one of the fastest growing open source projects in history.  The big reason is that it was able to speed up processing on data by orders of magnitude for users, by storing data in memory for fast and easy access. And for many organizations using data today at the heart of their business, they needed this speed to make faster decisions.  Spark also has become popular lately because it is a tool that is much more friendly for developers and also has multiple uses for the same core engine.  It can be Spark for batch processing or Spark Streaming for real time data movement or you can use Spark for creating graph databases.  So it is a tool that is incredibly flexible for a myriad of use cases.

Storm, Flink, Apex:
With the original processing framework of MapReduce and then even the additional of other tools like Hive, Impala etc... there was always a missing piece to the puzzle.  And that was real time streaming analysis over large sets of data.  While those other tools did a great job with running batch analysis on large sets of data, they were not built to do analysis on real time, streaming data. A great example of real time streaming data would be something like Twitter.  Tons of data, coming in real time and needing to be analyzed in real time to make decisions.   This is just one of many use cases or examples where real time is being used.  There are dozens of others, in every industry, where real time streaming analytics is becoming more and more popular and valuable.   Apache Storm was the first real project that was built to address this real time streaming analytics need and was actually built by the folks at Twitter.  Apache Apex and Apache Flink are two other real time streaming projects that have also gained steam lately along with Spark Streaming.

YARN:

As many of the projects started to become a core part of any large scale big data project, there became the need to better manage the resources that were executing all of these queries or processes that people wanted to run.  So, that is where YARN comes in.  It was developed to be the layer on top of HDFS to help manage the resources effectively.  Kind of like the operating system for HDFS.  Not something that business people are really going to care about, but still good to know what YARN is and where it fits in the picture.

Ranger and Knox:

No list of tools would be complete without talking about the security of the Hadoop stack.  With all of the concern with regards to data privacy and security, over the last few years the open source community has ramped up the work on putting in place tools that make it easier to lock down the data held within Hadoop.  That is what Ranger and Knox were developed to be, the access and authorization tools to ensure that only the right people or systems, with the right kinds of privileges, are able to access data in Hadoop.  For many commercial organizations, this has been the hurdle that needed to be cleared in order to adopt Hadoop and start deriving real business value.  It just flat out needed to be more secure.

Alright, so that is a good review of some of the core technologies that make up the Hadoop ecosystem and where they fit.  As I mentioned in a previous post, Hadoop is not the only ecosystem of technologies that is used in the big data space.  There are numerous other open source and proprietary frameworks and tools that are being used to augment the use of Hadoop tools.  We will talk about a few of those other tools next time.

Big Data Technology Explained- Part 1 (Hadoop)

I am going to break this topic into a few posts, simply because it could get quite long.  But my goal here is not to go into exhaustive detail on the technology in the big data ecosystem or go toe to toe with the big data architects of the world on what tools are better or faster etc....  Instead, my goal is to help business teams get just enough of the detail about this technology that it helps them make more informed decisions with their internal and external technology partners as they begin a big data program.

After being around big data now for a number of years, I continue to be amazed at how complex the technology really is.  There IS a reason why there is such a shortage of skills in the big data space, not only in data science but also with just pure big data architects and programmers.  It is just flat out hard to get it all to work together.

But, lets just start with the basics and keep it at a high level for now.

Lets not get too deep in the weeds on when Big Data really started etc... Just a waste of time.  Lets just start with the idea that Big Data really started to get moving in the Enterprise and Consumer spaces with the advent of what is widely known as Hadoop.  

I am sure that many of you have heard this word used or have read about it somewhere in the last few years.  Hadoop started out as two simple components of a technology stack that Yahoo and Google started using many years ago to help make search faster and more relevant for users.  The two parts were HDFS and Map/Reduce.  HDFS is the Hadoop Distributed File System and Map/Reduce is a distributed processing framework or job that runs on top of this file system that executes some kind of command to then spit out a set of results that can be used.

What is unique and special about HDFS, as opposed to other file systems or databases, is that it can store mass quantities of data, in any format and any size for an incredibly small amount of money.  So, as you can imagine, when you are trying to index "all" of the worlds information, like Google or Yahoo were trying to do, it is really useful to have a data store that is incredibly flexible but also incredibly cheap.  So think of HDFS as the "Oracle" of this new world of big data.

Alongside the uniqueness of HDFS, Map/Reduce was also unique in the world of processing.  As you can imagine, with all of that data that Yahoo and Google were now storing in HDFS, they needed really fast ways of being able to process that data and make sense of it.  That is what Map/Reduce was all about.  It was this idea that you now had a framework that could distribute out processing over many many different machines to get answers to questions faster.  The concept is sometimes referred to as Massively Distributed Parallel Computing.  It just means that you can spread out the work from one computer to many, so that you can get the answers much much faster.

So, when we are beginning to talk about Big Data, these two components really were the engine that got the big data world moving in the last 10 years.  Early on, these software frameworks were donated to the Apache Software Foundation as open source tools, which allowed the technology to be used by literally anyone.  So when you hear the term Hadoop now, it typically is coupled with the world Apache (Apache Hadoop) for this reason.

Since that time, there are have been dozens of new projects that have been developed by teams of people from all over the world that layer on top of these original components of HDFS and Map/Reduce.  It is literally an entire ecosystem of technology tools that leverage or are built right on top of Hadoop that accomplish some specific task.   See below for an "architecture" picture of the different tools in the ecosystem today, 10 years later.

And as processing power grows, memory costs shrink, storage costs continue to decline and use cases for Big Data continue to evolve, companies and individuals are creating new tools everyday to deal with specific challenges that they have in their organization.

In the next post, I will break down, at a high level, some of these tools in the graphic above that have been added to the Hadoop ecosystem over the last 5-7 years and why they are important in the context of business users and their needs.

Business Value for Big Data Part 2

Ok, lets pick right up from where we left off in our last post and dig into buckets three and four from our business value use cases for big data.

Our third big bucket of value from big data projects was focused on driving real business value by more effectively predicting outcomes.  Again, the key to this bucket, like the others, is that today, companies have a much different and more effective way of bringing disparate data sources together and extracting some kind of signal from all of the noise.  I mentioned GE in my previous post as a good example of driving value in this space.  They have committed billions of dollars over the last few years to become a software company because they see the business opportunity in front of them with being able to predict when machines will fail.  Much of their marketing has gone towards talking about airplane maintenance or optimizing monsterous windmill farms in the middle of the ocean.  I think the common marketing pitch they give these days is that by better diagnosing problems with machines up front and eliminating downtime, there are trillions of dollars to be realized across the industrial machine space.  Yes, you read that right, Trillions.

But, predicting outcomes doesn't need to be focused on such a large class of assets or within a set industry to be valuable.  Using data to better predict outcomes really cuts across all industries and across all lines of business within an enterprise.  IT organizations are using predictive analytics to determine how best to optimize their hardware and software to save costs in their data centers.  Security teams are using predictive analytics to help find Advanced Persistent Threats within a network and cut off hackers before they even get started stealing data.  Sales organizations are using predictions across diverse data sources to more effectively target prospects that have a higher likelihood to buy.  Marketers have been using predictions for years to make more personalized offers to customers when they are checking out online, think "People who bought this item also bought.....".   As we move into the future, marketers are getting ever more creative with big data and using predictions to make even more personalized offers across multiple platforms.  And Customer Service leaders are using predictive analytics to more effectively match call center agents with customers that are calling in and have a particular type of personality, class of the problem or recent their activity.

Finally, big data has real value in a category I explained last time as "plumbing".  While no where near as eye catching or interesting as these other kinds of use cases that drive business value, updating the "plumbing" can be of tremendous value to many organizations.  In fact, a solid place to start a big data program can be a use case as mundane as just moving away from a traditional Data Warehouse approach to storing/operating on data.  These traditional approaches can be incredibly expensive and can be incredibly frustrating to use/maintain for generating reports across the business.  The big data alternatives seem to be able to leverage alot of the same tools that are user facing, but put in place a much more innovative back end infrastructure that allows companies to significantly reduce their costs for the data warehouse technology and help to speed up processing, or creating reports, by orders of magnitude.

I know that was a lot to ingest and consume all at once across these two posts.  But I think it is important for business people to understand that all of the hype you may hear about Big Data or Hadoop etc... has real legs and real value behind it.  The dirty little secret, well not so much a secret anymore, is that the real challenge with big data programs is less about determining outcomes to focus on and more about the complexity of the technology itself.

But we will get to that more in depth in one of the upcoming posts.

As always, please feel free to comment and share your experience with big data programs and their associate value for your company.

Business Value for Big Data Part 1

In the last post of this series, I talked about the different types of use cases that are bubbling up for using big data technologies to drive value.  I talked about four buckets that I see these use cases falling in to:

• Faster and more advanced analytics
• Customer 360
• Predictive Analytics
• Optimizing the Plumbing

One of the common blog posts seen over the last few months within the big data community and tech blogs/sites in general is focused on the lack of value that companies seem to be getting from their investments in big data programs.  It is quite common to read one analyst or another writing about the "science projects" that are going on in the market with big data and the adoption of big data technologies being no where near the forecast.  I even read a twitter post from a well known analyst the other day calling for big data companies to focus on "outcomes" versus the technology.

While I do agree with this particular analyst in focusing technology projects on outcomes, I will say that I don't think this is really rocket science or anything new.  Focusing on outcomes should be what every company is doing, whether they are doing the investing or providing the technology.  Without focused outcomes, the project will be doomed from the beginning.

So what are some of those outcomes we should be focusing on for big data projects?

Well, they all come down to the same two big buckets we have seen for many years now:

• Saving Money
• Making Money

Now, one could argue that there are sub categories to these two outcomes, but by and large, these are what business leaders are looking at when investing in projects.

So then, how do the four use cases I laid out in the previous post connect to these two outcomes?

Let's focus on bucket one and two in this post and three and four in our next post.

Let's start with number one.  When looking at the "Faster and more advanced analytics" bucket, value starts to be realized for companies by being able to find patterns that were never uncovered in the past.  As an example, a retailer who was able to optimize their truck routes in logistics more effectively because they had a more advanced way of looking at their data, thus saving huge dollars on fuel costs.  Or a Telco that was able to cross reference data from multiple silos to show broader patterns related to network outages and capacity.   Which directly impacts both customer acquisition and maintenance costs to the tune of multi millions of dollars a year. 

When we think about Customer 360, the associated business value no doubt straddles both saving and making money.  As we talked about in the last post, Customer 360 has been the panacea for marketers and customer service leaders for years.  For marketers, the Customer 360 represents the best opportunity they have at truly understanding their customers wants and needs and then being able to offer products or services that match most closely to those wants and needs.  A great example of this would be insurance companies.  They are one of the "OGs" (originals) in the big data space (along with Telcos), collecting more data in one day that some companies capture in a year on their customers.  Now, by bringing all of this data together in new ways, marketers can offer more granular tiers of car insurance, thus broadening their prospect base.  Or marketers can much more easily help identify customer life events that may trigger offers for new types of insurance to long time customers, thus driving new forms of revenue capture. 

We can not forget though, that the Customer 360 is not only a win for marketers, but also a huge win for customer service leaders.  By giving them and their teams access to the full view of the customer, they are empowered to create a set of processes and experiences for customers that ultimately drive real business value.  Whether it be through providing an authentic customer experience (soft value) or by solving problems faster (hard value) or even starting to be proactive about problems that might be coming (hard value), the Customer 360 drives real value for both customers and companies via the customer service teams.

In our next post, we will tackle bucket three and four, using big data to more effectively predict outcomes and fixing the "plumbing".

How are Companies Using Big Data

So, if we distill Big Data down to this simple concept of just getting more value out of your data, then what kinds of use cases are companies hitting first that is driving value?  Lets use this post to explore some of these use cases and start to make Big Data more tangible for everyone.

Without getting into the deep details of the genesis of the big data space, lets just say that it really all started with a few of the really big consumer focused internet companies in sillicon valley.  Yahoo, Google, Facebook etc.  Because they had so much data on users, it became increasingly more difficult to use this data without new ways of managing it and using it for their customers.  One of the first real areas that applied to big data, was using this new tech called Hadoop to more effectively help run search engines.  But from there, the technology has grown and new use cases for more traditional enterprises have become the focus.

The first broad area of focus in deploying new big data technology has been to make it easier and faster to do data discovery and advanced analytics.  For years people have been reliant on the traditional Business Intelligence tools to help understand what is happening with the data they are gathering.  These tools have been great at what they do for years, but as the amount of data explodes and the time frames for using that data shortens, the new big data ecosystem and technology has become more of the standard way to explore data and look for formerly hidden patterns that are business impacting.  As an example, many manufacturing companies are starting to put all of their production data into a big data system so they can do more advanced analytics on defect detection rates or factory yields.  Or Telcos, who are the original big data companies, are using big data technologies to help determine which areas of their networks are overloaded and how they should plan to spend capital to upgrade them, to keep customers happy.

The second area of focus for use cases has revolved around the infamous Customer 360 that we have been chasing for years.  It seems like every 5 years or so, a new technology or platform hits the market and promises to finally deliver a single view of your customers to the business.  Well, big data is that next technology.  The idea behind something like Hadoop, is that it is a distributed file storage system that lets a company store any kind of data, from any where, in any format, in the same place and bring that data together quickly to gain a single, full view of a customer.  It really becomes the single storage locker for all data being collected about or for a customer and then can be used in multiple ways to add value.  One use case is just being able to deliver a single view of the customer to contact center agents for customer interactions.  Another use case is using this centralized data to more effectively make real time decisions about next best action or offer for customers who are on a website.  Yet another might be using this single consolidated view of a customer to help automate and predict when customers will likely churn.  Picking up on those events that most likely are good predictors of a customer leaving and using them to alert the business before they go.

The third area of use cases can fall into a bucket that is more highly focused on making predictions about what is going to happen in the future.  Again, taking all of the disparate data a company may have, centralizing it into something like Hadoop and then using that centralized data to predict better future outcomes for the company or for customers.  You might have retail organizations that use the mountains of data they have to more effectively plan inventory availability in their stores to ensure customers are happy.  There are also many organizations that are jumping on board with big data to gather data from sensors to help predict outages of machines.  GE is one of the big boys in this space these days, talking about jet engines and wind turbines.  But there are many others that are closer to consumers also using this sensor data to help predict when something is about to go wrong.  Thinking about car manufacturing companies, HVAC service providers and the oil and gas market as a few of the other industries that are using machine data in a variety of ways to help ensure that the minimize down time or outages in their facilities or products.

The fourth and final area I will throw out today in this post is really focused on the plumbing layer of a company and how it can be optimized or overhauled to better serve the business or customers.  I won't go into much detail here, as it can get quite technical fast, but the idea is that there are many data management and storage systems in the market today that are getting long in the tooth.  And with that age, comes incredible expense and risk that many organizations are looking to mitigate.  One example of this would be what is called a "Data Warehouse Offload".  For years, there have been a few companies that have dominated the traditional data warehousing space and as such it has gotten ever more expensive to hold the huge amounts of data that companies are producing.  Many of these companies have begun to off load this data from these expensive, older and less flexible systems onto newer, more agile, more innovation friendly, cheaper systems like Hadoop etc...

So, we will leave it at that today.  These are some of the ways that companies are starting to use big data to bring added value to their companies and customers.  Of course, there are a number of other use cases not listed here, that are adding great value to large enterprises across all industries.  The key is finding the use cases that are going to drive the most value for you and making a plan to see it through.

Next time, we will talk about the value of some of these use cases and who in a company typically should be thinking about these things...

What Really Does Big Data Mean to People?

It seems that I go through these times in life (which I assume most people do) when life hits you up side the head and you feel like you are just a small little boat being tossed around in the middle of a hurricane.  That has what life has been for me in the last 9 months or so, but I am back writing, which is something I truly love to do.

So, lets pick right back up where we left off back in March with some discussion around big data and the value it brings to an organization.  I know some of this may be a bit elementary now, 9 months later, but it still is worth discussion.

Big data to most people is a really nebulous term that really means very little to them.  Almost everyone in the business world has heard or talked about the concepts and what it means for the last many months now, but I am unsure if business folks really understand it.  I would go as far as to say that most still do not.

The way that I look at big data is quite simplistic.  I believe the world of big data is really all about working to get more value out of data, in order to better acquire and serve customers.  Yes, you will have people writing articles talking about how it is game changing, how it is revolutionary, how it is not about big data but small data and the like.  All of this is noise in my mind.  The real focus of any business with data is, how can I get more value out it.

For most, I still think big data is a bit intimidating as it can be incredibly complex.  Within the big data "ecosystem" you have a whole bunch of new or growing concepts that fuse together to help companies derive value for their organization or customers.  There is distributed computing, dataflow, sensors, algorithms, models, machine learning, artificial intelligence etc.... And then there are the names like Hadoop, Hbase, NoSQL or Spark that get thrown around to make things even more confusing.

But lets just keep it simple.  When people talk about Big Data, it is really just a bunch of really interesting concepts and technology that are coming together to help drive value in new ways for companies.  

Again, I know that this is not a game changing post or super insightful for many, but I think keeping the world of big data simple helps many non technical folks begin to wrap their heads around where technology is enabling us to go with our customers.

In the next post, we will take this very simple way of looking at Big Data and apply it to use cases that are beginning to add value for companies.

Big Data and It's Implications For Customer Experience

Now that I have a bit of a new focus for this blog around big data and how that impacts the customer experience, I thought I would write a series of posts that, at the core, will lay out what people are doing with big data today and how it is been useful for driving customer experience in organizations.  I know there are a ton of blogs or sites that may discuss this, but I am going to try to keep it simple, short and understandable for anyone to consume.

Over the course of the next few blog posts, I am going to try to lay out:

• What big data really means to most people
• Why it is important
• What kinds of use cases are being deployed currently
• The associated business value of those use cases
• The technology behind the big data ecosystem
• The journey data takes to get from being produced to being used

By doing this, I hope to help pull back the veil of complexity that seems to trip most non developer/non technical folks up when they are learning about big data.  It surprises me how poor of a job most big data companies are doing in communicating the capabilities of big data technology and how those capabilities relate to true business value.  And this is not just a problem for executives or what most people refer to as the "business" people.  I am still seeing many people that are considered technical in their job roles who don't fully understand the technology ecosystem in big data and how it can enable business value.

So these first few posts will try to break this complexity and confusion down to small, digestable nuggets that anyone can consume and begin to use in their role.

First up, we will focus on what big data really means to most people and why it is important.

New Beginnings

This is a new beginning for me, not only with my blog but also with work changes over the last few years.

Over the last 4-5 years, I have been writing fairly regularly about technology in a space that was near and dear to my heart for many years, customer service.  This blog was all about creating authentic customer experiences, which I believed and continue to believe, are at the heart of any company that is going to survive in the ever changing business climate we live in. 

About 18 months ago now, I got a phone call from a good friend of mine from college that had some questions for me about technology.  He knew I worked at Salesforce and wanted to ask me about a company that was in the Salesforce ecosystem.  I helped answer his questions about this technology and then we wrapped up the call.  He then called me a few more times and thus started me on a unforgettable journey with him and a few other guys to start a company called Onyara.

Onyara was really the true dream startup experience that literally none of us expected.  It had the meager beginnings of starting in a basement to talk about how we were going to do this thing.  It had all of the anxiety of leaving steady jobs with steady paychecks which were providing for guys with young families.  It had the ups and downs of interpersonal relationships, raising money, finding the first customers etc…. And we were blessed to have a successful exit of the business, being sold off to a larger company.

I consider myself incredibly blessed to have been a part of this journey with Onyara and specifically with the guys that I got a chance to work with for 18 or so months.  It was a great adventure and one that I will never forget.

So now I am on to the next chapter of work life.  Again working for a much bigger company that acquired our team back in late August of 2015.  Integrating into a new company is an interesting challenge all it’s own.  But it is one that I am happy to take on and help our new parent be ridiculously successful.

Which means that I will begin to morph this blog to cover other, newer, fresh topics that are top of mind for people in the Enterprise Business space and specifically, I will focus on how technology is impacting these areas.

For now, some of the new topics will focus on data, as I believe that data really is the great equalizer.  Producing it, Moving it, Processing it, Mining it, Enriching it, Transforming it, Analyzing it, Storing it and most importantly, creating some specific and meaningful action to take because of it. This last bit about taking action being the most interesting part for me in the future.

I will then just let it flow and see where I take things.

I hope you enjoy the journey along with me as I give you my perspective and what I am seeing in the market.

Rob Sader

Not Just Designing Experiences, But Shareable Experiences

I had the pleasure of attending an event last fall that was sponsored by my current employer that was facsinating.  The speaker for the evening was a guy names Brian Solis.  Not many people in the customer service or customer experience space yet know of Brian, but those that do, are fanatic about him and his message.  Brian is an engaging and intelligent business person who is spreading the message and the concept of Designing Shareable Experiences.

The premise of what Brian is getting at in his talks and with his message is that more and more people are sharing the experiences that they have.  This in of itself is nothing revolutionary.  As we all know, there are a multitude of outlets to share your experience, either positive or negative, with the rest of the world.  But what Brian is pushing for with his clients and the Customer Experience community is that every company should be thinking about Designing these experiences so that your customers don't design or define them for you.

I had not really ever thought about Experiences from this perspective, but he is absolutely right on and I am seeing more and more of this thinking starting to make it's way into the way that service organizations design processes.

See, in the past, service organizations have thought about the customer experience in terms of designing only a process that makes is easy for customers to engage with them.  Or they have thought about how they can make the process of communicating seamless.  Or they have thought about design in terms of simplicity of interaction.  But, very few that I have worked with or heard talk about designing experiences ever really talk about designing Shareable experiences.  Again, looking at your processes not just with simplicity or ease of use in mind, but designing so that each and every touchpoint is a mechanism for someone to share their experience with your brand.

What I love about this concept for service is that, as we all know, service is the most important moment of truth in a customer journey with your brand.  This is the moment when your customer is most vulnerable.  When they need you more than ever.  The moment when you can design for an experience that they will want to share with everyone they know.  And here is where the nuance comes in.  It is not just about designing an experience that people will really like or will love.  It is about designing an experience that people will share.  Just because people had a great experience, doesn't mean they will be willing to share it.  But, if someone has an experience that they loved or even liked, but it was also creative, different, unique or jarring in a good way, then they will want to share it.  And this is what you need to design for.

So the take away for all the service execs out there is this, are you designing experiences for your customers that are something they will want to share with their network?  If not, you should be.....

Visual IVRs and the Smartphone

One of the newer trends in the last year+ that I have been keeping my eye on quite a bit for customer service technology has been the movement towards so called "Visual IVRs".  The concept is pretty simple, take the idea of an IVR, or a phone tree as some would call it, and craft it into a visual mechanism either on a mobile device or on a website.  The idea is that companies would still get the caller to where they really need to go the first time and customer would not have to endure the endless prompts and frustrations of speech recognition on their way to an agent.  Theoretically, everyone wins.

There have been a few companies in this space that have been pioneers or leaders in bringing this concept to market and have done a nice job in doing so.  First, Fonolo has been one of the companies that is on the leading edge of this trend.  They first started out as a company that focused primarily on Virtual Holding type of technology where I can grab a place in line in the queue but hang up and wait for a call back when an agent frees up.  But soon thereafter, they started rethinking that model a bit and added some visual prompts to their solution so that customers could actually do all the work that is traditionally done in a voice IVR, from a visual interface on their mobile phone or on a website.

Another player in this space today is Jacada.  About a year or so ago, they took one of their existing products called the Jacada Interaction Manager and began to rethink how it could be applied to customer facing activities on mobile devices.  They have done a nice job in taking things a step further by working to make the experience on the mobile phone or website more self service help based before then moving to the Visual IVR experience.  Think troubleshooting on a mobile device that then takes you immediately to the right place in a phone queue if you are unable to solve your problem with the help of the troubleshooting tool.  Pretty slick concept and they are having good success in the market place.

I am a big fan of this trend towards more visual experiences.  They are just flat out faster for customers, more useful for customers and when done right, they are actually more likely to deflect the transfer to the voice queue than traditional self service approaches, especially on mobile devices.

With the limited success so far of these smaller brands and companies in this space, there has been the obvious rush to the market by all of the other players in the call center market/customer service technology market.  So, soon, this technology too will be another feature that exists in everyone's bag of tricks.  Which is somewhat sad to me, as that is when innovation tends to stop and the big companies move on to the next big thing.

Take a look at the smaller guys in this space though, as they are doing some really innovative things that, in my mind, will dramatically improve your customer's experience with your brand.  Especially on mobile devices, as this is still a place that brands have not thought enough about.

Machine to Machine Service: Lets Dig in a bit

Something that I have been digging into for a while that I think is really fascinating is the whole world of machine to machine interactions.  It seems like every company is looking at their business, their products or their services and striving to find a way to connect them to the rest of the world.
You have Cisco who is pushing the Internet of Everything, Salesforce that is talking about the Internet of Customers and hundreds of other companies from Tesla to Samsung to Nest that are connecting products to each other.

So, there are number of great reasons why this makes sense to do over the coming years.  Some companies talk about the data they can collect in real time.  Some talk about over the air software updates etc... But I believe the single most important application for this machine to machine technology is in predictively finding opportunities for driving to a better customer experience.  Products and services are becoming more commoditized even faster than ever and as most of the rest of the world is finally waking up to, customer experience is becoming king.  Whether that be marketing the right service or product to me at the right time, or selling me the right thing or servicing me before I even know I need it, all of these are great simplistic examples of using machines to connect us more effectively to our customers.

Tesla is probably the best example of this right now in the market.  I am unsure if they are going to be able to really scale the model, but their current service model is game changing.  If you don't already know, Tesla is a new car manufactruer that products all electric cars that are sleek, fast and obviously environmentally friendly.  But what most people don't know, is that they are hyper connected cars.  Every little detail is being captured in the central "brain" of the car and being transmitted back to Tesla HQ for analysis.  Even to the point of knowing when the front hood of the car was opened.

But what is seperating them from the pack right now more than the big things just mentioned, is their service model.  They can tell, based on the data they are collecting, that you are about to have an issue with your car.  They then throw a red flag to a piece of software that captures the issue, sends the issue automatically to a CRM system, creates a service order, they then call the customer to let them know the issue and schedule a time when they will come to the customers house to fix the problem.  Yes, come to YOUR house, not the other way around.

Obviously, there are some really great parts of this model, but the best part for me as a car owner is the idea that they are watching and paying attention to what may happen, so that I don't have a bigger issue down the line.  Sure, they could abuse this model and try to fix every little thing and nickel and dime you to death with little charges for fixing things.  But this is not their model.  And I would love to have a company doing this for me today in a number of areas in my life.  What if your mobile phone provider actually called you when they noticed that the battery life was getting low on your phone.  And maybe even offered you a new battery for a discount.

This is the creativity and innovative type of thinking that can happen when machines start talking to machines.  But lets be clear, it is not the machines talking to machines that are the key.  It is the monitoring of the data and the automated processes in the background that kick off actions that are the key to what the customer experiences.

What is an example of a machine to machine story that changed your experience with a company?

Target Customer Experience: Empowerment

When we talk about the idea of the Customer Experience or the Customer Experience Lifecycle, there is often times a focus on the process that is involved or the touchpoints that are involved to create an expeirence that is unique or shareable.  The one thing that we often forget about though is something that really can make or break an experience for any of us with a company we are doing business with, Empowerment.
If you call into a call center because you have been charged by your service provider for something that you did not purchase, what do you hope that they will do?
If you bring back a faulty DVD player to the store because it just stopped working one week after you bought it, but already threw out the receipt, what do you hope that they will do?
If you go to a restaurant and the food that you ordered comes out cold and has a hair in it, what do you hope the restaurant will do?

I am sure that we all have our answer to each of these questions and it probably goes something like, "I hope they would understand the situation and empower the people at the front line to just make the situation right.  And treat me the way in which they would want to be treated in the same situation."

But, what happens when companies don't empower their people to make common sense decisions at the front lines?  What happens when the people that have the most potential impact on the customer experience in that exact moment are not given authority to make a decision that would make all the difference?

A quick story for you about an experience I had at Target recently.  I went in to buy a few assorted items.  (As an aside, why is it that I can never get out of Target spending less than $100?)  So I was walking through the store, buying a few groceries, some diapers etc.  Then I just happened to walk down the vaccum aisle and I saw a new vacumm that we had been wanting to get for some time.  It was a Dyson.  It was the exact one I had been looking for and it happend to be on sale for something like $150 off the regular price.  I snapped it up and went up to pay for all my stuff.  The cashier scanned all my items and then got to the Dyson.  She scanned it and the price came up with a much higher price than what was shown in the aisle.  I told her this and she called someone to go check.  Low and behold, the sign on the rack said the sale was good up until a certain date and that date had passed by two days.  But the sign was still up.  So I looked at her, fully expecting her to charge me the full price.  But something awesome happened.  She was empowered.  I am not clear if she was a manager or not, but she looked at me, said she understood what the issues was, empathized with me about how she would be bummed if that happened to her and then over rid the system to give me the price that was the sale price.

What a fantastic experience that all was enabled by a company giving their people the authority to make common sense decisions that were going to create a very positive customer experience.  She didn't think twice.  She didn't hem and haw about how she was doing me favor.  She didn't have to call anyone over and make some big show out of the issue.  She simply put herself in my shoes, applied some common sense, took ownership of the situation and made a call.  But it all would not have been possible if Target would not have empowered their front line to make judgement calls like this that will create an experience for their customers that will bring them back over and over again to spend that $100.....

The Dyson Vaccum Experience

Admittedly, I am not a shopper.  I hate shopping and all that goes along with it.  Mostly though, I just hate spending money.  I am constantly getting jabbed by my wife, sisters, father, mother, in laws etc. for having deep pockets (not a lot of money, just deep pockets) and the ol short alligator arms. 

So when we needed a new vacuum cleaner about two months ago, I was thrown into shopping mode.  My wife had been looking around for a while at different vacuums because she had sensed that are old Orek was on the way out.  She told me about some of the options on the market and as I was going out to run some other errands, she wanted me to take a look at these vacuums.

I ended up at Target that day and was looking around at some other items when I just stopped in front of the vacuum section and the Dyson brand caught my eye.  I had heard so much about the company in the past and wanted to get a closer look.  Long story short, I found the best Dyson they had, put aside my money issues and bought it.  It did happen to be on sale as well, so that helped.

The real story is when I got it home though.  Not only was my wife thrilled (brownie points) but I was just blown away by the experience of the new vacuum.  I am not talking about the actual functioning vacuum and how much better it was than the other vacuum.  But everything was thought thru by Dyson.  They thought about the design clearly.  They thought about the packaging.  They thought about the function.  They thought about the assembly.  When I tweeted about it, not less than a few minutes later they got back to me.... Everything.  I felt as if they put this product experience together with no one else in mind but me.  They put the customer at the heart of their entire process. 

So, as we bring it all back to customer experience and putting the customer in the center of everything you do as a company, I would ask you.... Do you make your customers feel like you designed the entire experience with them in mind? 

Best Buy and How They Can Be Saved

An Open Letter to Best Buy Executives:

I am writing this note to you as a native Minnesotan, who lives in Chicago but still loves the Vikings, Twins, Gophers etc.  A loyal Best Buy customer, who bought my wife a Nikon 5100 DSLR camera from the Geneva IL Best Buy Store this past year for Christmas and an Insignia TV.  A technology advocate and a business person who works with companies everyday to try to solve problems.

It saddens me everyday that I continue to hear about the demise of a great Minnesota company that employs so many great people.  I still remember going into MusicLand stores to buy music back in the day with my sisters and how much I loved that store.  I am tired of hearing about how Best Buy is just becoming Amazon’s showroom or how people are “showrooming” at Best Buy.  I can only imagine how tired you and the exec team are of hearing about this concept.

To get right to the point, I think there is a better way forward and I wanted to take a few min to layout a quick vision for your business that I think would strengthen your position going forward.  But, it takes a very different view of the customer journey/experience than the team at Best Buy may be used to.

I will do my best to keep it simple for the purposes of this open letter and if you would like me to go into more detail, maybe we can set up a time to talk over the phone or in person next time I am in Minnesota.

The fundamental problem that Best Buy has right now, in my mind,  is not price or cost or inventory or layout of stores.  The fundamental problem that you have right now is that you have no idea when your customers are actually in your stores, unless they buy something.  This one fundamental problem creates a series of subsequent issues for you as a business.  But, it also represents your greatest opportunity.  Think of the in store experience right now being similar to an e-commerce experience online in 1999 or 2000, before we had tracking tools like Omniture or Google Analytics to know when customers were shopping, what they were shopping for and what they were interested in.  That is what it is like today in a retail store.  I walk in as a customer and you have no idea that I am there, what I am looking for, what I have looked at before and even if I talk to a store rep, they have no way of noting what I was looking for and capturing it so that if I return, someone can help me again.  This needs to change.

If Best Buy could just address this one fundamental challenge, it would lead to a number of opportunities for the business and for an incredibly unique customer experience.  For example.....If I know that you are outside my store or just walking into my store, I can greet you by name to create a sense of relationship.  If I know that you are walking into my store in Geneva IL, I can offer you proactive generic deals that are available in that specific store.  (Think of the digital version of the Sunday Circular Ad)  If I know that you are in my store, I can ask you what you are looking for today so I can capture all the data I am now losing as customers walk in and walk out without buying.  If I ask you what you are looking for, I can pro-actively push you in the store to the right area to browse on your own.  If I know what you are browsing for, I can push you information, reviews and tidbits that might be useful in your shopping experience for that product.  If I know that you are looking to buy something today because of the convenience of being in the store with the product there, I can push you proactive offers for different products that might fit your need.  If I know you are looking at a certain product and I know what Amazon or Walmart are selling it for (which you do know now), I can make you an instant offer to buy the product for a similar price or even a higher price because of the convenience of being in the store and having the product now.  If you are unsure and want to talk to someone, I could page a Blue Shirt and give them all your info so they know what you are looking for and why.  If I know you want to buy now, I can push an instant coupon to your device so you don’t have to fiddle around with printing something out. If you don’t end up buying, I know who you are and what you were looking for and can re-market to you over email or text message to come back and buy from us.

You see, I actually think that you could use Amazon’s and Walmart’s perceived strengths with their transparency on price and the power of their customer reviews to create an In-Store experience that is incredibly unique for consumers.  And on the flip side, you could use your perceived weaknesses with your network of stores, your convenience, your service and your “showroom” to capture customers where they are, when they are ready to buy.  

I believe that not only would this combined concept/experience help you win more customers, but you would have a treasure trove of data that you have never been able to collect before which could lead to better inventory stocking, reduced carrying costs, better pricing power, better buying power from manufacturers and you would not have to stoop to winning on price match guarantees.  But this requires a whole different way of shaping the customer experience and journey using digital devices and intelligent software platforms that would be a strategic and transformational shift.

More than anything, I want Best Buy to succeed and to thrive.  Not only because of my allegiance to Minnesota and Minnesota companies but because of all the people that depend on Best Buy everyday to offer an alternative to where Amazon is taking consumers.  I don’t want to live in a world where Amazon is the only place I can go to buy my wife her next camera.  I want to go to the Blue Shirts and know they have my back!

2013 Trends for Customer Service Part 5: Analytics

So here we are, finally at number five on the list of Top 5 trends in 2013 for Customer Service.  It has taken me a while for this to wrap up, but this will be the final topic in the series that I will explore, Analytics.  There is a lot to say on this topic, so strap in for a few minutes while I give my view of the world and what I am seeing in my work in the field.

My deep interest in the idea of data and Analytics actually started a few years back now, actually more than 10 years ago now when I took a break from working and went back to school, in Sweden!  Back in 2002, my buddy and I were very interested in this new space that was emerging in the US and in other parts of the world called BioInformatics.  It was described at the time as Biology meets Computer Science meets Stats.  A pretty easy way to describe it back then to people, but they still just gave you a blank look and thought you were over the top smart.  If they only knew me they would know that that was far from the case.  Computer Science and Stats were never my strong suit in college, in fact the only C I got in college was in my Intro To Computer Science class.  Still burns me to this day.....

Anyway, the point is that for many years now, we have been using the idea of data or Analytics to understand more about the world around us.  And in particular, Financial Services and Life Sciences companies have been using data and Analytics tools for a number of years to pick up on business trends, spot fraud or to find the needle drug in the haystack that a pharma company would spend $500 million to develop and bring to market.

Fast forward to 2010 or so and the term Big Data entered our lexicon in a big way.  Everyone seems to have an answer for Big Data, but the challenge is that no one ever seems to be using the same definition.  And then you layer in industry jargon or department specific needs and you end up with a proliferation of companies that all say they do the same thing.  That is far from the truth and this post is meant to call out a few questions that you should be asking of yourself and companies before you get too far down the road with investing in Big Data.  These questions are not going to be the IT based questions that always come up with Big Data like speed, storage capacity, variability etc.  We will assume for the sake of this post that IT has a good handle on all of those important pieces.  In this post, we will just focus on the business side.

In the vendor world, there are a number of different types of companies that offer Analytics solutions for service organizations.  There are Business Intelligence companies, Customer Experience Analytics companies, Analytics companies, Text Analytics companies, Voice Analytics companies and a whole host of companies that have niche software products that bolt on some form of Analytics like survey companies or Voice of the Customer companies.

Thus, you see one of the biggest challenges of the new world we live in, the increasingly complicated and siloed world of data collection.  I remember sitting in front of a VP of Marketing at a Telco a year or so back and she looked at me and said, "I wish that someone would help me bring all of that data together into one spot so I could analyze it."  She is spot on and this is the same feeling that many share today across all industries.  There are just so many collection points and not one good way for business lines to aggregate all that data into a useable format.  This is the first challenge and question that I want to call out to leaders in customer service.  How are you thinking about aggregating the data you are collecting to then analyze later?

The second big challenge that many people are dealing with in service orgs today is, how do I work with IT to get the data I want, in the format I want, in a timely fashion.  For years, IT has had a strangle hold on the data in a company and has had to set up teams of people to help translate the needs of the business into searches or what are typically called queries, that gets the data that the business people need.  Many of the companies in the Analytics space today are making things easier by developing interfaces that actually allow business users/analysts to get the data they need.  But most of this is still an effort that needs to be led by a person with special training in the tool and there are not any tools that really cut across the landscape of the silos of data collection.  So you still end up needing to learn the Analytics packages from each vendor to bring together the data in a way that makes sense.  This interface proliferation and training naturally then cuts down on the numbers and types of people that have access to data to find information.  Just like anything else, if I am a busy person in my company, going from meeting to meeting all day long, I am not going to take the time to sit in a training class and learn a new interface to pull info from.  It has to be dead simple.  So simple that I don't need training.  That is when people will actually use it in droves.  So before you invest in any Analytics package, ask yourself and your team, is this only going to add another interface to our lives to pull data from?

The third biggest challenge I see all the time and to me the most important, is the idea that no matter how much data you have, it is becoming less important to just be able to run reports easier.  It is less important to have a new UI that lets business people create their own dashboards.  What is really the next wave of Analytics is the question "What am I not seeing?".  Today companies are really good at pulling up dashboards, building reports and finding information when they know they have a problem.  VPs of Marketing or Sales are really good at telling the Analytics team or their Ops teams to come up with a report or a dashboard when things are going wrong and they need to tell the CEO why.  Orgs are really good at the firedrill mentality.  But what they are not generally good at is asking questions of their data to find patterns or correlations that they never knew existed before.  This type of work is typically only done in very very large companies with incredibly advanced teams of PhDs working on regression analysis with large, complex statistical analytics packages.  Not the norm for most companies and certainly not the norm for most customer service teams.  But this is where things are going.  So the question needs to be from you the buyer to the vendor of any Analytics system, "How are you going to help me find the things I don't know?".

There are literally dozens upon dozens of new Big Data companies that are coming into the market.  It seems like a day does not go by where you don't see a new name pop up on the Start Up news sites.  But, before you go investing in point silo solutions from any new little vendor out there for a specific need.  Make sure you ask yourself and these vendors the three questions posed above.  It will save you time, money and a lot of headaches down the road!

2013 Trends In Customer Service Technology Part 4: Communities and Social Listening

Ok, so I took a bit of hiatus for a few weeks, started a new job, but am back and ready to dig into topic number four on my Top Five Trends for 2013.  We have talked about Virtual Assistants, Multi Channel, Mobile and now we are on to Communities and Social Listening.

Many and probably most would actually split these two into separate categories and tackle them independently.  But I really see them as tightly linked and therefore I believe you can't really talk about one without talking about the other.  The reason: transparency.  Both of these technologies bring transparency to the service delivery operation of any company in a way that you just don't get or see with the traditional forms of service delivery.  The very public nature of what happens in both community groups and on social media drives a "leveling of the playing field" dynamic that is both interesting and potentially disruptive if handled incorrectly.

Over the last couple of years, I am starting to see more and more leaders in the customer service landscape dig into the idea of social care and communities.  I have seen a few companies re-orientate their entire service delivery group to be social and communities first, designing an experience that is very different looking than more traditional approaches.  I even worked with one client, a large mobile telco, that designed their web experience entirely around the idea of social and communities and all other communication channels took a back seat.  Then, I have seen other companies that have looked at Social and Communities and decided that it just was not worth the investment of time and resources to try to integrate them into their service delivery model.  I think like any thing else, these two extremes are not likely the best answer for most companies and the right answer likely falls somewhere in between these two extremes.

At the end of the day, social is critically important to any brand or company and as such should somehow be baked into the service delivery arm of an org.  I don't think it is just good enough to think about social as another channel that you have to staff to or another side technology that you can play around with for a while and see how it works.  I think that social needs to be at the very core of your service delivery model.  I think all service leaders should be thinking strategically about it and how they can incorporate it deep into the processes that are a part of their delivery model.  As a first step on this journey, I would suggest and recommend that companies at least start out by investing in a social media monitoring or listening tool to understand what is being said by consumers.  I know that some of you might be shaking your head and thinking, "duh, that is so obvious Rob, you are so behind in stating this trend", but you would be surprised how many companies are still not there yet with just the basics of listening.

Some good tools to look into on this front are a number of the social media monitoring tools such as Netbase, Radian6 and the like.  But an area that I have been interested in for quite some time now that is starting to really catch on in this space are Text Analytics companies that are starting to build some really compelling and unique products to start to filter the noise.  Attensity, Clarabridge and Medallia all offer some interesting products and are worth the time to investigate.  The best of the best are using both and getting some really fantastic results.  I have one Consumer Products company in mind that is doing some really interesting things that I can share with those that might be interested in learning more.

Now what about communities?  As I said before, I think that communities should go hand in hand with social listening for two reasons.  First, I think that both social care and communities have a "crowd" like underpinning and so they tend to drive towards the same goal, which is consumers helping consumers.  The second reason I think they go together so well is that they both have a component to them that helps companies drive brand advocacy.  Because of the very public way in which communities and social care play out, transparency rules.  And as I have talked alot about on this blog before, transparency is a key foundation for developing and nurturing brand advocates and loyalty.

Most community companies or "collaboration" companies or "Enterprise Social Networking companies, as they may like to be referred to as, are working to deploy solutions that do add a lot of value.  They help to immediately cut back some of the phone calls or emails that end up heading the direction of the company by allowing people to more easily connect with a brand loyalist or advocate that already knows the answer.  They also tend to have a softer value proposition that focuses more on customer preference.  By providing choices to consumers around the service channel they want to use and when they want to use it, consumers tend to have a more fulfilling and higher quality experience.

Companies that are working this space today as point solutions and have been leading the way are some fairly well known names, Jive, Lithium, Get Satisfaction and a number of others.  Also, keep an eye out for the bigger CRM companies and how they are baking the concept of Communities into their service DNA.  Salesforce, SAP, Oracle and the like would be names to watch out for here.

As a service VP told me recently, "Rob, I get 20 calls a day from companies that think they have the best next technology that will help me cut my costs by 20% or more.  Some of them are really cool and some are just ok.  I don't doubt that they can save me money.  But my challenge is understanding how that technology fits into my broader strategy and how my customers will want to interact with me 3 years from now."

So true.  The technology today is really not the issue.  There are so many cool companies doing incredibly interesting things.  But how does it fit your plan, that is the question we all need to be asking.

2013 Technology Trends for Customer Service Part 3

So this is the third part of the five part series on technology trends that I am seeing in the customer service marketplace for 2013.  Again, for those that have not read my posts before, the information and data for these types of posts are coming straight from research that I do with leaders of customer service that I am blessed to interact with on a daily basis.  I am fortunate to hear many things and so what I try to do is to aggregate it all and semi-intelligently post what I am hearing for others to comment on.

So, part three is really all about Mobile.  Day after day we are bombarded in just about every possible medium, about how mobile devices, mobile software and everything mobile is the way of the world.  You can read articles on trade sites, expert analysts are talking about it daily and even the newpapers like USA Today are writing weekly about the explosion of Mobile and what it means for our society at large.  As always, the place where most companies are attacking the mobile experience is in the sales and marketing channel, as it is here they are trying to drive differentiation and ease of use for their mobile customers.  Much has been made of how consumers are using mobile devices to shop at all times of the day, even during their physical shopping experience in a store.  Over and over again we hear of consumers that are "showrooming" in stores by looking at a product in a physical store and then pulling open a comparison shopping site on their mobile device and finding the best price online.  Again, focused almost exclusively on the acquisition of customers and sale of products or services.

As is always the case though, no one is thinking about how this trend is going to impact the service environment.  No one is thinking about the potential unintended consequences of conditioning consumers to use their mobile devices to interact.  These consequences are pretty simple:  When you push people to interact with you to shop or purchase in the mobile channel, they are eventually and quickly going to come to you in that same channel to get their questions or issues resolved.  But, most companies are not even thinking about this, let alone planning for it in the coming year or 18 months.  Almost everyone I have talked to, that is a leader in the service space, has told me that mobile service is a second or third priority for them if it even makes the list.  The reason for this seems to be two fold, politics and money.  Imagine that.....

In terms of technology and where things are going, I think there are a number of niche players right now that are trying to define the way forward for mobile customer service.  Some are using the idea of voice to make a call to a call center from a mobile device much easier, smoother and more intelligent.  Others are looking at using smart mobile devices to use the tap interface to create what are termed "visual IVR's".  And still others are using the screen real estate issues on a mobile device to push the concept of Virtual Assistants onto mobile devices or into mobile apps.  I think all of these concepts have merit and have a place in the process of providing a high quality service experience for consumers.

The companies that are likely going to define this space over the next few years will be both the small niche companies and also some of the bigger companies that can either buy their way in or use their marketing muscle to help define the path.  The smaller companies that I would keep an eye on are companies like Fonolo, Jacada, MyCyberTwin and NextIT.  The bigger companies are the classic names in the space, Nice, Verint, Nuance, Genesys, Avaya and Interactive Intelligence.  But I would also keep one eye on the larger technology and CRM companies.  With the replacement of older CRM systems picking up pace, mobile CRM/Service, Knowledge and the like will be likely a priority investment area for SFDC, Oracle, SAP and others.

As always, I would suggest that before anyone invests in this area, that service leadership defines the optimal customer experience that aligns with optimal processes so that customers actually feel supported effectively long term.