Fall is a busy season for trade shows and conferences. I’ve had the pleasure to be at Strata Hadoop New York, IBM World of Watson and the Chief Analytic and Data Officer Exchange within the past month and a half. The number of conversations I’ve had about “a modern approach to MDM” is striking. In a recent interview with Information Week, I discussed some of the major trends with respect to modern MDM and big data. Dozens of large organizations across many different industries are all saying the same thing – “I need a modern approach to master data management” and “I need a REAL Customer 360”.
Continue reading about customer requirements, new technology and a new approach with Customer Intelligence Management on my LinkedIn blog.
This past weekend I decided to tackle the mess in my basement. In my last , I compared the modern data lake to my “thing lake” in the basement – a random collection of stuff that may, or may not, be useful. So my big job last week was organizing my basement. I went through every item and placed a post-it note on it – the note described what it was (exercise equipment, baby clothes, etc.), who bought it, who used it. I even got different colours for different classifications of things. It took hours. But was my basement any cleaner? Was anything more useful? No. Well at least, not yet. It was a useful step towards actual organization – throwing things away, re-organizing them into sections by their classification, etc. But there was a lot of work ahead of me to make my “thing lake” really organized and useful.
Managed data lake solutions are the post-it notes of the big data world. They use meta data to classify the data within the lake. Some visualization tools do the same. But then the work is shifted back to the data scientist to join the data sets, determine what is useful, and somehow merge it into a useful larger concept, such as a customer record. Those solutions are a necessary step to organize the lake, but they are only the first step.
If you are like most companies, the number one data domain you are interested in within the lake is customer. And therefore you need solution that not only classifies raw data, but actually organizes it into a Customer 360. Customer Intelligence Management Systems do exactly that. They synthesize data into a Customer 360. They use machine learning analytics to infer intelligent attributes for the customer record. They evaluate confidence scores for all aspects of the Customer 360. They visualize customer data and present perspectives to different user audiences. And they maintain the Customer 360 for operational and analytical use.
Customer Intelligence Management Systems benefit from managed data lake or meta data tools classifying everything in the lake, as it aids in the synthesis process. But if you really want to transform and use the customer data within your lake, you’ll want to move beyond managed lake tools and towards a Customer Intelligence Management system. To see the difference and how organizations use CIM, check out this demo.
Sometimes my house gets messy. Like a lot of people, I think “I can still use that” and I don’t want to throw away a lot of items. So I put them in my basement utility room. And I add more. And more. And I think to myself “I’ve just collected a room full of potentially useful things, and one day I’ll use one of them.” And so the day comes when I need a dolly, or an easel, or even an infant mattress side guard rail, and I think to myself “I have that – it’s in the utility room (or, as I now call it, my ‘Thing Lake’)”. But I can’t always find it, and soon I begin to question whether I even have the item any longer. And the project I had in mind for it just fades away too.
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The Trials & Tribulations of the Anonymous Customer
I bought an office chair from an office retailer a few months ago. Seeing as I was buying something I wanted vs. something I needed immediately, I became a “strategic purchaser”. I went to their website and looked at different chairs and narrowed it down to a few. Although I am a loyalty member, I didn’t log in and I was browsing on my son’s iPAD. When it was convenient, I showed up at the local store to try the chairs. I tried them out for a while and mentally picked one – I’d made up my mind. I “showroomed” them – I browsed online then went into the store to try and buy. Then I went online on my phone and looked up the chair, read some reviews, and someone had posted that the chair went on sale 3 months ago for $70 off. I also looked at some competitors and found no one sold that same chair, and more or less decided I would buy it from the office retailer. Naturally I left and decided to wait for another sale. I ‘webroomed’ them. I went home, logged into the loyalty program account, and put the chair on my watch list. Two months later when their ecommerce site alerted me it was on sale, I went to the store and bought it. If they had been watching me closely, I surely would have frustrated them. But were they watching at all? I’d say probably not, as I continued to get re-targeting ads for that same chair and even emails about it well after I had already bought it. I was an Anonymous Customer.
The Anonymous Customer frustrates and delights in equal parts. Many ‘known’ customers have dozens of ‘anonymous’ interactions – browsing your site, walking through your stores, interacting via webchats, all without identifying themselves. And even the interactions where they do identify themselves, critical data is not kept or analyzed because systems can’t store it.
The anonymous customer is the customer or prospect that you don’t know exists. They are your organization’s blind spot. If you don’t know who they are, you don’t know their potential value … their needs….and how you should treat them. The goal of any customer-centric organization should be to unmask the anonymous customer – and know everything about them.
Why are customers anonymous? To be fair, organizations do have customer data and not every customer is truly anonymous. But perhaps they only know 5%, 10%, or even 20% of what they could potentially know about that customer. And that’s why they are ‘anonymous’. They browse your website without logging in and letting you know they are there. They walk through your stores without identifying themselves. They chat on your website but don’t reveal their customer ID. Those customers are anonymous, or not very well known, because organizations cannot identify them in a variety of different data sources – from warehouses to web logs to social media to PDF files. So all of their data can’t be pulled together into a real Customer 360.
Customer anonymity paralyzes organizations. Lacking a real Customer 360, organizations resort to treating all customers the same. All customer-facing experiences are poorer for it.
A Customer service rep says, “You are a valuable customer, and I’d like to offer you this cross-sell product …”
“But I don’t want that product and I told you so last month – don’t you remember?” replies the Anonymous Customer.
A marketer makes an offer, “New Customer signup discount – 25% off the rate for the first year!”
“But I’m already your customer. Do I get a discount, or are you just taunting me to sign up with your competitor?” thinks the Anonymous Customer.
A sales rep pitches, “This product line could really be useful for you.”
“I have a serious open issue with support on my existing product. Don’t sell me anything until you fix that issue” states the Anonymous Customer.
Lacking a Real Customer 360 has a serious effect on customers – it makes them feel anonymous. It is waving a red flag in front of them and daring them to leave. And many will.
A Customer 360 is a moving target. As customers evolve, their data changes and broadens. This brings tremendous challenges to IT systems to keep pace and ‘know everything about the customer’.
But customers need not be anonymous any longer. A new technology, Customer Intelligence Management (CIM), fixes this problem. CIM systems can ingest all customer data, synthesize it into a real customer 360, and reason to transform it into an intelligent customer 360. Learn more about this new technology and how it can help you unmask your Anonymous Customer – see my video interview on this topic via this link.
Sometimes it is just obvious that an organization has a high Customer IQ. I once experienced it from an electronics manufacturer. My LCD TV was starting to break down – there were horizontal lines across the screen that appeared periodically. It was only 4 years old, but well out of warranty. I called the manufacturer to ask if this was a common problem and if it could be repaired for a fee. I didn’t expect them to do anything beyond that. The CSR took my information, said they would open a case file, and someone would get back to me in two days. Initially, I was slightly disappointed – I hoped to get a simple answer on the phone and then take it somewhere for a repair. But two days later I was shocked to receive an email stating that they would replace my TV for free, with an even better unit. It was the best customer service experience I’ve ever had and it was totally unexpected. I actually wrote a letter to their CMO and head of customer service to thank them and pledge that they had now won my loyalty for life. But I also asked them ‘why did you do that for me? How did you figure out that I was worth it because I don’t think you do that for everyone.’ Unfortunately I didn’t get a response, but I now think I know how they did it. A high Customer IQ.
What is a Customer IQ? It’s the ability for an organization to make intelligent decisions on each of their customers individually. We all know what ‘intelligence’ is for human beings, but what does it really mean for an organization? It’s based on 4 key traits, all of which are powered by technology.
1 – Memory – The first part of intelligence is the ability to remember a vast quantity of information. An organization needs to have an accurate memory for its customers, which means storing all data related to the customer in one data store and one system.
2 – Synthesis – Memorization of facts isn’t enough. True intelligence is based on the ability to synthesize those facts into larger concepts or ‘the big picture’. For organizations, this involves linking together pieces of data, and inferring the relationship between data and the importance of that relationship.
3 – Reasoning – Once you’ve master the facts and understand larger concepts, the next step is reasoning. For an organization, reasoning is inferring what is truly important for each customer, and understanding what action to take next.
4 – Learning – Intelligence is built through learning – each experience and each decision we make builds our intelligence. For an organization, that means learning from each decision related to the customer – whether the data was synthesized correctly into one customer record, and whether the recommended actions where the correct ones.
In my case, the electronic manufacturer must have known
I was of a certain value to them and likely to purchase more electronics in the future (which I did – from them). But I wonder if they were able to deduce anything about my personality, specifically how brand-loyal I would be after a good customer service experience.
Customer Intelligence Management Systems are a new offering built on modern big data technology. The utilize new data management technology to store data in a more ‘natural’ way as graph relationships, machine learning to improve after each decision, and analytics to reason and anticipate what each customer needs. Customer Intelligence Management is a significant evolution in data management and analytics. Learn more about this new technology and how it can benefit your organization today.
My local telephone company has tried to win my business for television and internet services for years. Over 15 years actually. I estimate they’ve sent me 180 mailings or more – about 1 per month. They’ve offered 3 free months of service. 25% off the price for the first year. But they’ve never won my business. Why not?
Well, they’ve marketed to me on their schedule. Not on mine. Of course, there have been moments in the last 15 years when I have been unhappy with my cable company’s service (numerous times, actually). But 15 years of clockwork marketing made me think the telephone company wouldn’t be any better. They didn’t know me.
That telephone company had a low customer IQ. Sure, they had data – they had my name and address correct. And the products I already owned with them. But they lacked real intelligence, such as understanding when and why I was unhappy with the cable company and might be willing to leave. It wasn’t a mystery how I felt – check my twitter feed! They didn’t anticipate my personality and my needs – I’m a little averse to the hassle of changing things like email addresses. But if they did, they could have marketed directly to my needs, exactly when I wanted, and they would have won my business.
Organizations need to take the customer’s point of view. Does a customer think they are getting ‘acquired’? Or are they trying to acquire a product? Of course it’s the latter. So if an organization wants to improve ‘customer acquisition’, they need to become more intelligent about when, how, and who wants to acquire their services.
There are five levels of customer IQ. First, the basic level is data quality – making sure all the data you have is integrated, matched and correct. The next level is relationship intelligence – understanding networks of relationships, and degrees of influence on among customers and other parties. The third level is event intelligence – the ability to understand events occurring with each customer and future events. Fourth is location intelligence, which provides an understanding of customer locations, travel patterns, and impending travel events. And fifth is engagement intelligence. This is the critical step, as it provides deep intelligence on how to engage with each customer personally based on their sentiment, personality, customer journey, and preferences. The ability to calculate this intelligence is critical to raising your organizational IQ.
Fortunately, new technology can help. It is now possible to ingest all data and synthesize it into a realistic and complete customer likeness. And use machine learning and analytics to create all of the levels of intelligence above, automatically. Customer Intelligence Management Systems can create actionable insights and ensure that all customer-facing employees have access to intelligent customer data. Read this eBook to learn why you should raise your Customer IQ immediately.
Durham Region in Ontario, Canada has been deadlocked for years in a debate over garbage incineration. There seem to be equal amounts of proponents and detractors for this controversial plan. But Durham, like most other municipalities in Canada and the United States, doesn’t have the space for landfills, and doesn’t want them near their community. So we’ve been paying to have our garbage transported out of province for years. The proposed incinerator offers a different perspective – to take the garbage and produce something useful – electricity. Now I’m not going to wade into a political debate, but I became fascinated with this story for the simple idea of getting something from nothing. Literally turning your garbage into an asset. And I wondered whether that concept could apply to big data ….
Most organizations treat their data as if it were garbage. 88% of data is unused in an organization. If it has no use, it may as well be garbage. And just like municipalities, organizations incur significant cost to keep that ‘digital garbage’ and to dispose of it safely. The was a notion of putting it all in one place, in a Hadoop data lake, however, without some processing that lake may turn into a digital landfill.
Is there another way to get value from your “digital garbage”?
Yes, there is. If you can ingest all of that data, understand it using natural language processing, and then synthesize it into important concepts like customer records and related data. Synthesis is like a ‘spring cleaning’ process – it puts your unused data into “keep” and “throw away” piles. And once you’ve made sense of the data in the keep pile, you can then use it to generate deep intelligence for marketing campaigns and customer care initiatives.
Customer Intelligence Management is a new category of software that enables you to leverage all of your data. It ingests data you would otherwise not use – webchats, call centre transcripts, web logs, and synthesizes it with known customer data from master data systems, data warehouse, and CRM systems, to create a realistic 360 perspective of your customers.
It’s the equivalent of generating electricity from garbage.