Smart Dashboards. How smart are yours?
Customer Experience (CX) has now become a top priority for many marketers, CxO’s and organisations as the next focus for their businesses.
Complacency and inertia are no longer options as marketing spend becomes more accountable, through the conversion funnel and up the loyalty ladder.
The more we understand our customers, their motivations, behaviours, and needs, the better we can engage with them, serve them, and ultimately sell more to them.
Steve Jobs famously once said at a 1997 Apple Developer's Conference, “You need to start with the customer experience (CX) and work backwards...”
Data is not a side show
This is a given. To understand our customers, we must put data at the heart of everything we do. Data is not a side show or an afterthought. Businesses who have truly digitally transformed have put data up-front and central to everything they do when it comes to understanding the customer experience.
Amazon didn’t become the dominant force that they are in e-commerce without knowing the value of every click, proposition, offer, call-to-action, content layout and funnel, that contributed to the overall customer experience of using their platform. They did this by understanding the value of data behind every customer interaction and test, that they performed.
So why is data so often deprioritised? Firstly, we believe people don’t understand what it truly is for, how it should be used effectively and how to get hold of it and at the right quality. In fact, we would be better off calling it information rather than data as no CEO would want to run a business without being informed.
But it is more often that not a challenge. Have you ever asked yourself those questions?
Have I got the right data?
Is it of the right quality?
How do I know what I am looking at? And what I am looking for?
Can I trust this data to be accurate?
Why is your data telling me something different to yours when we’re looking at the same things? How do I interpret what my data is telling me?
All of these may be familiar challenges and questions to you or your colleagues.
Well, you are not alone
The three pillars that lead to smart dashboarding
A typical model for helping us to understand all customer data, can be broken down in to three distinct layers:
1. Data provisioning (making data available), data quality and architecture
This is usually the provenance of the IT Team, CIO or Customer Data Officer, if you have one, where the need is to be able to access all customer data sources - ideally in real-time - so that they can be made available for analysis. This is now the preserve of Customer Data Platforms (CDPs).
2. Data analytics, the process of turning data into useful information
This usually sits with the Data Science and Analytics Team, who can crunch, mine, interrogate your customer data, working out how to present this to different users, stakeholder, leadership teams across the business. It’s the data analysts who can tell you that, ‘the glass is half full’.
3. Visualisation, interpretation and story-telling (The ‘So What? And how to communicate it)
It’s then up to the marketers and customer strategists to decide if the glass is indeed ‘half full’ or in fact ‘half empty’.
Visualising your customer data using tools like, Datorama, Power BI, or Tableau, so that you can see it in easier to digest formats and interpret it is key. A picture paints a thousand words, and the right visual trend line of your customer data enables you to gather insight and understanding that would otherwise be invisible to you. Good analysis only ever leads to more questions and it is this train-of-thought analysis that allows us as analysts and marketers to truly understand what our customer data is telling us.
Interpreting your customer data and CX leads us to journey orchestration – a topic for another post – which is key if we then want to anticipate what the next best action or conversation should be for us to take.
Understanding smart dashboarding and how to get there
The simple two by two matrix below allows you to work out where you are on your journey, the goal being to get from the bottom left hand quadrant to the top right-hand one where you have always-on, dynamic, smart dashboards at your disposal for monitoring customers and your business.
Figure: Moving to always-on dynamic dashboards
1. Static one-size-fits-all Reports
These types of reports are typified by those outputs we’ve all seen where large amounts of reporting data is produced. They may be churned out on regular basis, e.g. a monthly sales report, but they lack any insight and ‘so what’. For example, an emailed Monthly Sales report which summarises some activity – a moment in time – but is quickly forgotten and archived.
2. Published Reports
These are static reports capturing a moment in time that are published or archived to a website or platform, such that they are always available but the underlying data never changes and like static canned reports, they capture one moment in time and are quickly forgotten. I wonder what percentage of reports languish on a shared drive or folder and never see the light of day?! For example, a Post Campaign Analysis (PCA) report which reports on the performance of a campaign but in isolation of the bigger picture. In a smart dashboard you would be able to look across all campaigns not just individual ones.
3. Dynamic one-size-fits-all Reports
An offline or online report that consolidates an array of data (sometimes referred to as a data cube) in a presentation layer that although it may be interrogated, the underlying data never changes. For example, an example of this would be an Excel style report with drop down filters, input values or a pivot table.
4. Smart Dashboards
Smart dashboards are real-time enterprise dashboard that are linked to a live underlying dataset(s) that can be interrogated and manipulated by users, enabling data visualisation and train-of-thought analysis. With the addition of AI and custom settings alerts and notifications can be set up to flag anomalies, spikes and goals reached that are relevant to the business. For example, a Tableau smart dashboard that is set by Business Unit, based on one version of the truth from a data governance point of view, that enables interrogation of the data from a customer experience perspective, that answers the what-if scenarios marketers pose, that also alerts the user to unique changes in behaviour and outcomes.
Never stop learning or improving
So why do we need always-on, smart dashboards? I think of these as like a trading room in a stock exchange, where I have a portal in to what is happening in my marketplace and with my customers. As customers change, ergo, markets change, and we as marketers need to adapt to that change too.
The only way we can do this is if we can spot trends, react to our customer needs, their feedback, their behaviour and anticipate where they’re going next and how we can serve them better when they get there.
To do this any smart dashboard should continually evolve so that we continually learn and improve.
The future of smart dashboarding with AI and Machine Learning
With the advent of artificial intelligence (AI) and machine learning there are a number of roles for these to play in smart dashboard reporting. These include building out predictive models against an expected range of results that a data set is expected to fall within, so that any exceptions can trigger automatic dashboard updates. With this approach, the decision maker only needs to view dashboards containing unexpected data and does not have to consume vast quantities of reports that are showing only that which is expected.
Another role for AI, is often referred to as ‘human in the loop’ and refers to the practice of capturing the decisions that have previously been made and then using these to train the algorithm. In this way, decision makers will not be required to review vast quantities of data and will only need to focus on data which they need to make decisions on.
AI driven smart dashboarding is the natural evolution of business intelligence tools ensuring your business is increasing in its data maturity, whilst also increasing the value that you derive from your data, because it addresses two inherent weaknesses:
The first is human visualisation which is a poor detection tool as we as human’s reply on the observation of patterns in our data. In order to see any anomalies, we need the anomaly to be large enough that it stands out or the issue needs to occur regularly, so that we know where to look.
The second is that we are overwhelmed with too much information! Visualisation tools allow data overlays coupled with the ability to zoom in on a time series means that there are infinite potential dashboards to be viewed at any time. AI can see through this objectively and alert us to peaks and troughs in our data, anomalies and outliers as well as correlations and causative effects.
Customer behaviour is dynamic and granular, and captured at numerous touch-points often in real-time. If we are to make sense of this and the customer experience, we need the dashboards than inform us to be smarter than ever.
Steve Jobs was right
You DO need to start with the customer experience (CX) and work backwards. One of the best ways you can help to achieve this is by visualising your customers with an always-on, dynamic, smart dashboard, to better understand them and serve them better.
If you would like to find out more about how ekino can help you with smart dashboards, data visualisation, customer experience or journey orchestration, please contact us.