What really is Digital Strategy?
I’m a business manager — break it down for me
A number of companies, colleagues, and friends have asked me ‘what is the hype with Digital?’
Some say Digital is a fad or is just ‘the cool thing right now’. Others say it is a rebranding of BigData with app development hacked on to it. And of course, there are many people, businesses, and consultancies that claim ‘Digital’ can’t be ignored. It must be adopted in order to compete in the current, digital economy.
So what is it?
In the broadest sense — “Digital” disrupts the traditional business and marketing model. You may recall from your old text books that the key domains in marketing are the 4 P’s — Product, Place, Promotion, and Price. Each of these domains are central to a marketing and business strategy. Other business functions support these domains such as sales, operations, risk management, cost containment, suppliers, R&D, finance, etc. Operational excellence is typically achieved through optimising these components with the use of technology, training, people, and improved work culture.
For simplicity, lets call this traditional business model the ‘operational backbone’.
The operational backbone
According to MIT (CISR), the operational backbone is one of three components. It is the foundation upon which a digital business model is built.
The operational backbone keeps your business going — albeit in a traditional way. Many businesses are successful with this approach; however, it is unclear for how long this approach will be ‘good enough’ to compete in the future.
So what is the point of Digital?
Short answer — to maximise customer engagement and deliver products, services and/or brand in unique and novel ways.
Longer answer — to acquire a much better understanding of your customer so that you can (i) fine-tune (or produce new) products and services for your customers, and/or (ii) create a compelling and relevant shopping ‘experience’ — from the time a customer enters your virtual store until the moment s/he leaves your brand.
Let’s elaborate on shopping experience as it is more ambiguous than producing new products. Think of the experience as being met by a single, virtual, multi-tasking relationship manager akin to an advisor in a store, handling anything that you throw at it — a complaint, a purchase, a refund, additional detail about a product or service; it will know your shoe size, dress size, shirt size; it will present alternatives depending on your purchase history etc. Moreover, the experience carries over to a retail store. The (human) retail advisor will be able to pick up from where you left off from the app that you were using just before you entered the store. Imagine a world in which you were looking for a pair of shoes on a mobile app, enter a store, a sales person approaches you and says “I think I have the shoes that you were looking for, and here is a cup of your favourite coffee while you try them on”. It’s a little disconcerting, but the scope is pretty huge.
Is it expensive?
That’s where Cloud computing, Data Science, and Machine learning come in.
Cloud computing enables existing and new businesses to deploy applications (often referred to as ‘webapps’) on technology that is owned, hosted, and managed by a third party such as Microsoft and Amazon. You only pay for what you use (e.g. 1 hard drive opposed to the 100 you expect to need in the future) and most of the maintenance and security is the third party’s responsibility.
Data scientists build behavioural profiles (a simpler way of saying ‘model’) that summarises data of interest that is collected from the various ways that a company engages with its customers (marketing channels, apps, social media etc).
And machine learning, after a little bit of training (yes, you essentially ‘teach’ the machine learning program what an apple or an orange looks like just as you would a child), predicts the behaviour of customers on its own without the need of a data scientist or developer having to ‘hard code’ a rule that s/he hadn’t thought of previously. A simple example of this concept is times tables; it is necessary to learn only half of the times table grid (and the squares) — you can figure out the other half from the rules you’ve previously learnt.
A quantifiable result from the data analysis would usually consume a significant number of person-hours, but may now be achieved in a mere few days. All of this has its limits. But you get the idea.
In summary, cloud computing reduces the cost of building your own data centre. Machine learning helps to refine your customer profile fairly quickly (as it does a lot of the heavy lifting on its own) so that you can maximise your revenue generation opportunities. I’m oversimplifying, but at its heart you are essentially managing the Profit = revenue — cost equation.
So your customers can’t find the buy button or their favourite coffee beans quickly. Perhaps a bank repositions its personal loan as ‘here is a car that you’ve been looking for that we can help you buy’. Or maybe one particular MRI scan reveals a feature that is inconsistent with a million other scans, and so an alert is sent to a doctor for immediate attention (opposed to having the scan reviewed in due (much later) course).
These are some very simple examples of the effects of behavioural data analysis and machine learning that apply today.
The digital strategy platform exponentially expands on this concept. MIT (CISR) refers to the combination of ‘agile’ services, operations, technology, analytics, and customer strategies as the ‘Digital services backbone’:
Conclusion
Leading brands are not questioning the purpose of ‘Digital’ but its execution.
MIT summarises this idea well:
In the pre-digital economy we designed for efficiency:
— Articulate a business strategy
— Take a divide and conquer approach to executionIn the digital economy we design for agility:
— Articulate a digital business strategy
— Empower, collaborate, synchronize, partner
The challenge today and beyond will be to achieve ‘digital operational excellence’ for competitive advantage. In other words, ‘how does one apply operational excellence to the digital services backbone and have it integrate with everything else without introducing inefficiencies, control weaknesses, and silos in other areas of my business?’
For instance, If I use machine learning to automate a lot of my data analysis, how do I make sure that the information being fed into it is good so that the information that comes out of it is good? How do I manage bias?
What does an efficient operating model look like, one that scales with my business?
How do I respond to digital market requirements quickly in order to stay ahead of the competition?
Companies are already thinking about solutions to these design challenges in order to remain competitive in an evolving digital economy — a place where the 4P’s depend on an agile business model, technology, meaningful data, and operational excellence to compete.
I hope that a person who is new to Digital strategy finds this post helpful. Concepts were simplified in order to have the big picture idea appeal to a broader audience.
