.. who are my customers? really.

There was a time, believe it or not, when the person behind the counter at the corner milk bar knew me by name, and recognised that I was visiting for my regular milk and sugar. And, she knew Mum and Dad along with some of our friends as well. She knew when I was in a rush, when I was just plain tired, and what I wanted; all the time.

Today, it's a little creepy instead - when I purchase online, because some websites are either suggests items I really just do not need, or prompting me to supply more details repeatedly, or just plain nosey. Not only an nuisance, but rather suspicious because of a seemingly irrelevant need for my details.

Truly knowing your customers is about more than identity. Yes, its often about preferences, needs and the contextual knowledge of time and place but customers expect to be known as individuals. It should be a given, not a please tell me, and tell me more, and then tell me again.

Most folk I talk to suggest this begins (and finishes) with lots of data. Perhaps its not data per se, rather the knowledge, insights or understanding that is needed. Many suggest a small fraction (perhaps 5%) is attributable to the data collected and stored, but the significant proportion (the other 95%) is the organisation's ability to model behavior of your customer that delivers knowledge. Therefore, I often suggest, lets not spend a disproportionate amount of effort on getting every little morsel of data. There is a lot of 'information' that is simply ... useless.

Today, analytics is sufficiently mature to deliver understanding of not only anticipated behaviour but suggest factors to influence future customer reactions to prompts. Why? Because understanding every customer allows you to interact with your customers (the people who contribute to revenue, by the way) across all touch points and channels imaginable.

Supermarkets like Coles & Woolworths meticulously build product range, price and communications strategies to suit their best customers. Telstra and Optus work at reducing customer churn with incentives and plans focused at their most profitable customer segments. The most profitable airlines constantly tweek flight schedules, prices and both inflight and on the ground experience to be one better than the competition. The most profitable banks are constantly busily at work on personalisation to match optimum lending and operational profiles. The best restaurants are adjusting menus to suit guest mission and preferences. Websites like seek.com suggest available jobs that not only suit, but are suitable (both employer and employee).

If you can’t add your name to this list of companies, it’s time to ask why. Also, how you need that added understanding of your customer to do more than your competition. When you treat customers differently, and better, its little wonder why more are attracted to you. And then stay with you. Its being simply, human.

And then more recently I have had folk I work with use the same insights to prevent identity theft, financial fraud, money laundering and more mundane human error. When considered from this perspective, knowing your customer moves beyond marketing to become a broader corporate initiative. Understanding the customer is far more than just matching the customer with her favourite product but to support a safe environment for both customers and employees knowing their data is secure and their investments are protected.

Without analytics and appropriate governance (to identify, store, provision and process information), data can potentially be a significant liability.

Yes, a liability rather than that invaluable asset most refer to. And it is little wonder how this is linked directly to what you do with the data that can determine which category of the balance sheet your data resides.

It all starts with the ability to work with analytics. Analytics, once again, is not analysis. Analytics when used correctly delivers additional knowledge from the discovery, interpretation, and communication of meaningful patterns in data and applying those patterns towards effective decision making. Analytics, by way of analogy, is that essential connection between data and effective decision making especially as it delivers through real evidence. It allows for description of the current in responding to why?, and prediction of the future in responding to so what (will happen next)?

And how? Well ...

it's todays application of some of the most extensive use of computer skills, mathematics and statistics; in combination with actions motivated and rooted in business context.

It's certainly not accomplished some nerd in the back office, but a combination of the cream of the organisation's 'A-Team' empowered through evidence, led by the Chief Executive herself.

Analytics is also not driven by individual analyses or analysis steps, but with the business strategy and competitive tactics. I prefer the term advanced analytics because the adjective emphasises the technical aspects of analytics, especially in the emerging fields such as the use of machine learning techniques like neural networks, Decision Tree, Logistic Regression, linear to multiple regression analysis, Classification to do predictive modeling. It also includes Unsupervised Machine learning techniques like cluster analysis, Principal Component Analysis, segmentation profile analysis and association analysis. All this are basic and fundamental tools to deliver improved understanding of your customer. And necessary because we no longer work in that milk bar, rather need to interact with the millions who (we wish) knowing everyone not only by name, but what they want, when they will want it and for how much; accurately, all the time.