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.
I sat in the waiting chair for 40 minutes while he finished with another client.
“Hey Dave, how’ve ya been?”
“Nothing to complain about.”
I sat in the barber’s chair and without hesitation he said “Still the usual? #4 clipper, high line, longer on top and tapered towards the back?”
“You got it.”
“How’s your summer going?”
“Not bad, looking forward to a vacation in a few weeks.” I answered.
It was a full 5 minutes before either of us spoke again. I asked him how his kids were doing, and he asked about mine. The rest of the haircut was filled with the same banter – about the Euro cup, Brexit, and the new 407 highway through Brooklin, with short stints of silent work in between. Then it was over, we shook hands and said “See you in 5 weeks.”
And the next client took the chair … “Hey Brian, what’s new with you?”
The Barber of Brooklin has Customer Intelligence Management in spades. It’s all in his head. He knows all of his repeat clients. But it goes far beyond just knowing their name and saying “Hello Dave”. He has real intelligence on each individual, and treats everyone differently based on that intelligence. In other words, he personalizes the experience. How did he do it for me?
First, he knows the facts about me. That I live in Brooklin, have kids, have been coming to him for 6 years, that his kids and mine went to the same school, and loads more. He has lots of context for conversation.
Second, he knows my personality. I’m friendly, but not a non-stop talker during a haircut. So he leaves little breaks in between short conversations – just the way I like it.
Third, he knows my preferences and interests – football and that the Euro Cup would be of particular interest, even though my team, England, perennially loses!
Forth, he knows how to apply that context with current events. So as the news of Brexit played on his TV in front of the barber chair, he reasoned that I’m an England football fan, maybe my family is from there, and maybe it’s be a decent topic to bring up. “What do you think about Britain leaving the European Union – does that affect any family members?”
Finally, he can anticipate future events and make relevant offers or reminders. “Hey you mentioned that wedding coming up in August – is your son going too? Let me know and I can book an appointment for the both of you.”
Most large organizations would kill for that level of Customer Intelligence and personalization. But most have resigned themselves to think “That’s just small store service – it can’t be replicated across a large organization without seeming plastic and phony.” That used to be true. But it isn’t any longer.
Customer Intelligence Management is a new breed of software that ingests all sources of customer data, synthesizes raw data into a realistic customer likeness to get a complete context of each customer, then applies reasoning to anticipate events and enrich that customer likeness with intelligence. Customer Intelligence is shared with customer-facing employees through their existing customer care, marketing and sales force applications.
Check out the “how it works” video on AllSight’s Customer Intelligence Management System to see how a CIM System could change your organization. Empower each of your customer-facing employees to be as engaging as the Barber of Brooklin with Customer Intelligence Management.
Customer Intelligence Management (CIM) Systems have
emerged to address an unmet need. If
the customer is an important concept for your organization, then it des
erves a system to manage customer data and create deep customer intelligence. N
o more silos. No more fragmented insights. Customer experience depends upon having accurate and timely intelligence on each and every customer. Organizations have started to use Customer Intelligence Management to gain competitive advantage via exceptional customer experiences.
Existing IT systems and applications aren’t giving users what they need – actionable customer intelligence. Almost every organization wants to improve their customer-centric strategy. But their existing IT systems and business applications are an impediment. They fragment customer data into silos. They hoard insights. Data is inaccessible and hard to find.
Organizations struggle with systems designed to manage only structured data, to warehouse data, to master data … but they operate on a subset of information. Nearly 80% of data is unstructured. So even if you’re perfect at mastering and warehousing structured customer data, you only have 20% of what you need. New technologies can help manage the 80% of unstructured data. But software vendors only offer tools or ‘next-gen’ data platforms. They have the technical capabilities required, but they lack real functionality designed to manage and analyze customer data. That work is left to each organization. But no one has data scientists, data governance professionals or IT staff sitting on the bench. And that’s why Customer Intelligence is still an unmet need today.
If customer is an important concept, it deserves a system dedicated to it. A pre-built system. That’s exactly what Customer Intelligence Management Systems are designed to do.
Customer Intelligence Management Systems combine data management and analytics with a specific focus on customer data. They have pre-built capabilities to ingest any source of data and understand it – recognizing which data among an unstructured data set (e.g., email text, web chat transcripts, PDF documents, social media, etc.) is related to a customer. CIM synthesizes atomic data elements into a realistic likeness of the customer – attaching all relevant data to the appropriate customer. CIM builds a realistic customer 360 perspective, one fact at a time. It applies reasoning to that customer likeness and produces intelligence. It infers new data about the customer – their likely occupation, their relationship networks, who they influence and who influences them, their sentiment, their privacy concerns, and much more. CIM predicts what will happen with that customer – potential life events, churn events, and indicators they may purchase new products. Most important, Customer Intelligence Management Systems evolve. They are built to evolve. To ingest new data. To understand new data elements and how to synthesize them to customer records. Customer Intelligence Management systems continually learn and evolve to always ingest new data and produce deeper intelligence on each and every customer.
Most organizations don’t realize that this unmet need can be addressed and that deeper customer intelligence is possible. Now it is. Customer Intelligence Management systems can improve your Customer IQ. Learn more on CIM and how it can propel your customer-centric strategy to the next level.
I recently bought a smoker box for my BBQ from the local hardware store. I go in there all the time, often with my two children. The store owner Jeff recognized me, and asked “are you starting to smoke on a gas BBQ?”.
“Yes I’m trying it out – do they produce enough smoke?”
“Yes, they produce some and are ok. I started with one of those a few years back. I also tried a charcoal smoker – but I found it was too much work – and I’d rather be spending that time playing with my kids.”
“Yeah – that’s exactly it – I’d like a low maintenance approach.”
“Yup I hear you – starting with the smoking box is a good way to start – see if you like the flavour. After the charcoal smoker debacle, I moved up to a pellet smoker – it monitors the temperature and feeds wood chip pellets to the smoker automatically. I can set it and forget it. One time I had it going all afternoon for some ribs and didn’t have to do a thing with it.”
Wow. Jeff couldn’t have been more on-message if he lived with me for a month. That’s hyper-personalization. He’s seen me with my kids, he has some idea of the things I’ve bought before, and he could relate – and it turned into a heck of a sales pitch. Whether I buy a smoker or not, I felt like he was offering advice, not just selling a product.
But that is hyper-personalization on a micro scale. Jeff gets the advantage of seeing me in person lots of times, and dealing with a small client base. How can you mimic this on a macro-scale for an organization, but still come across as genuine and personalized (vs. most offers, which are impersonal and formulaic)?
An organization needs three things – experience, context, and reasoning – in other words, the organization needs ‘a brain’ to think in the same manner a human being would.
First, the experience. Organizations have lots of experience in interacting with their customers. Transactions. Orders. Call centre calls. Web browsing. Social media comments. There’s loads of data out there. The challenge is bringing it into one place so that it can be managed collectively. Fortunately, new big data technologies can help.
Second, the context. The problem isn’t just bringing it all together, it is linking it all back to one customer. The customer can almost be ‘anonymous’ in some of those interactions. So the organization needs the ability to synthesize all of that data into a realistic likeness of the customer.
Third, reasoning. The organization needs to take the context of the customer – their likeness, their journey, and reason about what will happen next. Just as Jeff reasoned that I was a rookie in the world of BBQ smoking but that the context of me as a father meant that time was important, organizations need the same type of intelligence on each of their customers.
Many organizations are trying to personalize customer service with varying degrees of success. Hyper-personalization is closer to reality than they may realize. The technology exists to consolidate data, understand context, and reason about the next best action. The best way to get started is to look into existing personalization efforts in marketing and customer care to see where there are gaps, and then explore whether those gaps can be met by new technology such as Customer Intelligence Management.
Most organizations think they don’t use customer data effectively. To an extent, they are right. 88% of customer data is not used in most organizations. That’s a staggering statistic. It’s also an intimidating one. Those same orgs think that they have a huge hill to clime – that they are so far away from that 88%, let alone doing advanced predictive analytics on that data. That can lead to the no-win situation – they believe that only a big-bang approach can work, and they never do it because the project is conceived to be extremely large.
But most organizations are richer than they think. The 88% of unused customer data is within their grasp. It’s inside their own organization – webchat logs and browsing history, call transcripts, unstructured notes in their applications. Social media data can be easily obtained. And making sense of all that data is now possible with pre-built systems that manage customer intelligence – ingest raw data, understand it, and synthesize it into customer profiles. And while advanced analytics should remain an objective, most orgs can reap tremendous benefit from data enrichment and analytics to generate customer intelligence – for example, understanding the customer journey across all channels (internal and external such as social media), determining relationship networks of customers and influencer scores, and understanding life events and when they will happen. These new breed of systems can augment existing systems – structured data systems such as Master Data Management and Data Warehouses, and unstructured ones such as unmanaged data lakes.
InfoTrellis’ AllSight addresses this challenge and delivers actionable customer data enrichment. A journey starts with a single step. Learn how to achieve greater customer intelligence, and how to start with understanding and enriching data on a portion of the 88% of unused customer data.