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.
I’m shopping for an apple keyboard right now. I’ve visited a national retailer’s store and their website, and even engaged in on-line chat with an associate. I received an email from that same store (I’m a reward member, so they know me). Can you guess what it was? Their weekly flyer. Not personalized at all. Well, to be fair the email did say “dear David”, but there was nothing in their about what I am shopping for. Why not?
Omni-channel personalization isn’t a new concept. It’s not a new desire. So why do so many organizations still struggle with it today? Like my philosophy professor told me, when solving complex problems, sometimes it is best to go back to base principles. So what is the base, the foundation, for omni-channel? It’s DATA.
Yeah, data. That old problem. It is the foundation of any initiative to better understand customers, analyze them, and treat them as individuals. But it is often overlooked. “I want to personalize interactions to customers so let’s work on the front end systems to power this initiative. Wait, does anyone have a good source of customer data? Ok, we can take a feed from MDM or a warehouse – that’ll do.”
Only it won’t ‘do’. That’s not good enough. Those systems don’t manage unstructured data – like my webchat interaction above. 80% of data is unstructured, and a lot of the new channels generate unstructured information about customers. They don’t really deliver the full “360 degree view” that you really need to personalize interactions. At best, they only deliver 72°.
Omni-channel Personalization needs a Customer 360. A full Customer 360. Want to learn more about how to get the #missing288degrees ? This Infographic explains how to deliver omnichannel from customer 360, and how to solve the problem of the #missing288degrees in detail. AllSight will help you find the missing 288 degrees of your customer 360.
Has this ever happened to you? You want to use some information for a business purpose, only to realize that you are not permitted to use the information in that way? Sure it has. It happens all the time. It happened to one company who we will call ABC. ABC hired a Chief Data Officer, Bill. Bill’s first task was to inventory all of ABC’s data, and how and where it was being used ( learn how to inventory your data and manage governance policies by clicking here). He also had to take stock of ABC’s existing governance program, to figure out all of the existing policies and whether they were being followed.
Bill’s first few months on the job were great. By taking inventory of data, he was able to help many other people – the CFO, VP Sales, and VP Operations, by connecting them with the data they needed. Then along came “the retention project”. Mary, the Chief Marketing Officer, had commissioned many different projects – some of them with cloud-based apps and outside vendors. She was getting great results, and “the retention project” was one such example. She got that off the ground with a 3rd party vendor and cloud solution to analyze web data trends and make predictions on customer churn events. She married that data with her customer database to make decisions on who should be retained. The project was then handed over to internal IT to support, and that’s when Bill got involved.
As Bill’s team audited the ‘retention project’, they noted that customer web view data was being used without obtaining their consent. By using that data to make offers to the customer, it violated a privacy law. That was a fairly cut and dry case of a governance rule violation, so Bill moved to shut down that data feed. Mary, predictably, wasn’t too pleased. She said shutting down that data flow essentially meant shutting down the project. Bill said ‘but it’s against the law’. And then Mary produced results that showed a 30% improvement in key client retention. Believe it or not, a vigorous debate ensured about the merits of continuing the campaign even though it was in violation of the law!
After thinking it over that night, Bill came up with a different approach. He and the governance team devised a rapid out-reach program to obtain consent from their client base. The consent request was included with another “business as usual” client outreach, and the thinking was it would improve the odds of gaining consent. In the interim, the data governance team had determined that mixing the client web-log data with external data and using it to determine an “average customer retention event” could possibly work, because it wouldn’t violate privacy laws. Bill’s team worked with Mary’s marketing analysts to test the analytics – and the result was the predictions were only -5% less accurate, which was an acceptable deviation. Project ‘retention’ was kept up and running, and yielded very impressive retention results during the interim period in which client consent was gathered.
ABC learned valuable lessons through ‘project retention’. First, they now collaborate on all data-centric projects with Bill and the data governance team to determine the right way to use data while staying in line with governance rules (watch a video on how to manage proactive governance in your organization here). Second, they established a proactive customer consent mechanism to gather a broader reaching set of consents for analytics and marketing purposes. And that’s really the moral of the story. The “bad guy” in this real-life scenario was a lack of process, which unwittingly pitted two employees into a zero-sum game, when they should have been working together towards the same goal. Violating privacy laws and governance rules are significant issues, but are actually the least of your concerns when it comes to governing big data. The real potential downside of ungoverned big data is rework and lost time – which translates into lost customers, lost revenue, and missed opportunities.
For more on how to govern big data proactively, read about Building Confidence in Big Data .