When I meet with customers to explain that the QuickLogix qLSocial product will help them increase customer engagement and keep their fingers on the pulse of customer sentiment, they sit forward and want to know more. Why is this such a big deal for companies now? In the past decade or so, businesses have cumulatively sent consumers into a state of starvation when it comes to individual concerns and complaints. What were we doing wrong?
1. Removing customer service phone numbers from websites: Let’s face it- when you are in a bind, you want to tell someone the problem instead of typing out an email or filling out an online form. The one thing you definitely do not want to do is deal with this next thing…
2. Setting up long winded, automated phone support systems: Possibly the biggest annoyance conjured up by customer service experts in the past decade is the never ending automated response system. Even more so, the ones that won’t let you skip to a human by pressing the number 0 right at the beginning. And when you do press 0, you get in line as a valued customer for what seems like eternity. You have to setup a calendar appointment so you can figure out why there was that unexplained charge on your phone bill!
3. Providing scripts to customer service and sales reps: We have all been on the receiving end of the service representative who appears to simply not understand what you are telling them. I’ll never forget this mortgage company Sales rep who called me one afternoon. I was indeed looking to refinance my home but he would not stop badgering me about what was happening in my personal life that made me want to refinance. I tend to be a private person and I did not appreciate his probing questions and I said as much. He made me feel like I had a mental health issue because I would not confide in him. It occurred to me later that he was simply following his script- he could not give me numbers unless we walked through the questions he had to ask me. It wasn’t entirely his fault- but it’s an example of why profiling customers as a group is just not a smart idea.
4. Placing an unreasonable incentive on short customer service calls: When I worked as a software developer, we once had a manager who decided that measuring performance would be based on the number of issue tickets that people closed. It lead to the incredibly unhealthy and unproductive habit among the team wherein folks would create tickets for misspelt commented text. I think incentivizing customer service reps by the shortness of their calls can be similarly counter-productive.
But in all this time, we have been doing some things right. By moving so many business transactions online, creating customer logins and monitoring web-traffic and behavior- we have been setting the stage for recovering lost ground using big data. For the last decade or more, we have been priming consumers to yearn for a personal touch, a feeling of being understood as an individual and not part of a herd! Engaging with customers is the low hanging fruit on the big data promise tree. You want to at least start there!
I got roped into another “the time has come” debate about the Chief Data Officer. I maintain that IMHO the CDataO is pure hype. Here’s how the roles of the existing C-suite play out:
CEO: What we need to do
CMO: How can we use it or apply it to better our product/image/business
CIO/CTO: What tech strategy do we need to adopt to get there and subsequently maintain/scale
COO: How can we implement these changes while maintaining normal business operations?
There’s really no room or need for a data officer- because it is close to impossible for one individual to determine all the combinations in which data can be useful to the people in the company. Will creating a new CDO position magically fix data governance, reliability and accessibility issues let alone make it more meaningful all of a sudden? No way! It’s all about understanding that these issues exist and need to be addressed & fixed.
The reality though is that I get angry and sad to see hype like this gaining momentum- it almost makes the real promise of big data seem illegitimate. I was pleasantly encouraged when Jim Stikeleather from (CIO- Dell/Perot) concurred with this remark:
“Sharda is spot on. There should be a lead data scientist under the CInfoO, and a lead enterprise architect, and a lead security analyst, etc. The issue IMHO is that CInfraOs are neither strategic or knowledgeable enough to lead those other roles. The CMO should have the analysts (mathematical and marketing) who are supported by the data scientist. Business units / COO / etc should have their own domain experts supported by the lead enterprise architect (including enabling the orchestration and choreography among enterprise and SaaS applications). A good CInfoO understanda they are an enabler and facilitator, not an owner (or a prohibitor – which is what CInfraO tend to be).”
DO Be patient: When working with teams for whom English is a second language- it can be frustrating to have to repeat every sentence. However- put yourself in their shoes and remember you’d be struggling if you had to do business in Chinese, Hindi, Russian etc.
DO Be part of *their* team: You can get a lot from even a mediocre team (and the team I was interfacing with was definitely mediocre) if they recognize that you are part of their team. Words like “We”, “our goals”, “our progress”, “our accomplishments”, “our missed deadlines” vs you, “your ” create a team-spirit that is palpable across even a phone line.
DON’T be funny: I remember attending a seminar years ago where a senior engineer from IBM was talking to us about working with overseas teams. She said that nothing could bomb on an inter-cultural team meeting like humor. Humor does NOT transcend boundaries as much as we’d like it to. Get to know the team you are working with and over time, you can inject humor to lighten the mood.
DO be diligent: It’s very important to understand that in Asia- people really respect knowledge (and yes titles). They also assume you are stupid unless proven to be smart (if you have a good title- then you are given a little more room for errors). So be smart. Be intelligent. Don’t fake it. Do your homework. Earn their respect. Only then can you give orders and expect them to be followed.
DO know the goals of the project: Whether you chart the course from the very beginning or make adjustments along the way, have a good feel for what your working strategy is going to be. Be candid with the team about it up front. (If you expect changes to be made- tell them- so they can expect it too). Also- make sure you have a good understanding of what will define a successful project (technical, business, process wise etc). It will influence the energy and performance of the team you are working with greatly.
DO be honest: If you are unsure about something, please be honest with the team about it. If the deadline is creeping up and you are feeling the pressure- share it.
As I was browsing discussions on LinkedIn, I came across a question from Natalia Ostraverkha – COO at Mobidev. She was asking about communication between teams on both sides of an outsourcing project (here). I had to chip in! This is a close-to-my-heart topic. Read on for my comments and let me know if you agree or not!
In my past experience working with overseas teams (outsourcing partners) I found the following things to be very useful:
Working with a remote team is as much about psychology as it is about technology. Also, it was incredibly challenging when I was doing it but in retrospect, very rewarding as well.
Recently, I was involved in a discussion on LinkedIn on whether or not Big Data was enabling better customer service. I think the trend at the 2014 International Consumer Electronics Show (CES) indicates that Big Data is actually enabling better customer experience. If you are a more established provider of similar services and products, then you will be compelled to sit up, take notice and make changes to keep up with these leaner, smaller and providing service-in-real-time competitors. That is, if you haven’t already started to do so.
According to Forbes (read article here), the biggest disruptive trends include the use of MEMS (microelectromechanical sensors) in everything from your household appliances to wearable clothing to self-driving cars. Look past the coolness factors of these products and you cannot miss the incredible behemothian force of big data technology driving them.
The data points collected by the sensors can broadly be classified under the umbrella of semi-structured data. This gets stored in either a private or commercial public cloud based store. The product/service provider generally offers some out of the box apps. In addition, they offer authentication and access APIs for users and developers to consume freely to create their own apps (and most likely, contribute them back to the provider’s eco-system). This is the evolution of the B2C big data ecosystem. All this ties back to improved customer service if and only if the product or service provider makes an attempt to track consumer behavior, actual apps created by their customer base, user feedback, customer reviews, competitive trends. Extend this to measure the impact of all these intangible factors on company revenue, profitability and product planing- now you’ve just made the case for an enterprise big data analytics solution.
See http://www.quicklogix.com to learn how the qLSocial and Genie products can help your enterprise attain the goal of improved customer service and better business insights.
Prepping for the holidays? Some others are dreaming about how big data can help them power through the insanity of holiday shopping!
And check out a very entertaining blog of the #bigdatachat Tweet Chat that I participated in- it was Santa centric and a lot of fun!
Ho ho ho and a lovely holiday season to everyone!
The author prophesies that the importance and definition of a data scientist will change in the next few years- as big data technology is adopted in a more mainstream manner. One of the comments was that data scientist is a misnomer because data science is not really a science. Analysis of data does not make it a science. I cannot agree more. It isn’t data science- it is decision science. Here’s my reply to the comment
“Science has always been about setting a hypothesis and proving it right or wrong. We’re just getting started with BigData. The proper and most beneficial use of big data will be when everyone becomes a decision scientist. The data, the analytics, dashboarding and interfaces will simply be tools that will help a business/ enterprise/ organizational decision maker verify the viability of their hypothesis. That’s the real promise of bigdata. If it isn’t, it should be about being the toolkit for Decision Science.”