Making Business Processes more Data-Centric


In previous posts I addressed a number of core areas that I believe are necessary to effectively build a successful data-centric business, with the underlying objective of improving customer-centricity. To recap quickly, these areas included the role of the CIO, the executive team, technology and more recently, how to get employees involved. In this post we close the loop by discussing how to make business processes more data-centric.

Let us assume that all the above areas have been effectively addressed, then hypothetically speaking, the executive now leads and manages the organisation through a data-centric view of its customers, while internally it has established a data-centric culture. What now becomes critical is to harness this data-centricity and plough it back into the business. This leads me to the final key element that still needs to be addressed – how can we make business processes more data-centric?

A TDWI Checklist Report that I recently read (Best Practices for Delivering Actionable Customer Intelligence), outlines two key elements that I believe are the answer to the ‘how to’ part of this question. I’ve added a third point.

Being data-centric implies that the business has bedded down the idea that the customer is king – and it is realised in how customer data is managed and utilised. This means that the customer-centric business processes of the organisation should always be data-centric. So to my mind, a data-centric organisation is one that focuses on aligning the data affecting its decisions and actions in its business processes with its objectives. In this case, it is done in order to improve or increase customer satisfaction. Therefore, data and the analysis of that data play a key role in gaining insights into customers – and play a key role in making the business processes more data-centric.

Customer-facing insights

The TDWI Checklist Report refers to this as ‘Deploying Customer Intelligence to Orchestrate and Optimize Customer Facing Operations’. Essentially, analytics is the key element here and is used to identity how business processes could be better coordinated to achieve customer-centric goals. We know of course that analytics can assist in exposing correlations in multiple data sources and this could improve process planning or adaption, as required. However, through analysing data, analytics as a business process tool, offers the organisation a clear, data-driven view of how the business processes are operating together – all to the benefit of the customer, and then subsequently, to the business.

Making customer insights actionable

This leads into the second element, which TDWI refers to as ‘Delivering Actionable Information to Improve Customer Experiences across Channels’.

So with the business using data and analytics to make business processes more data-centric, you now have the ability to harness the intelligence you have on customers and used these data-driven insights to achieve higher quality interactions, undertake more sustained customer engagements and thereby become more profitable too. While we all know that business processes need to be more customer-centric today, in an aim to build customer loyalty and avoid customer churn – this is still a major challenge in many industries. A data-centric organisation understands this – and uses its data and advanced analytics to better understand the customer. Examples include predictive analytics to ascertain if a customer is about to leave, or cross-selling analysis to determine what actions the business can take or sentiment analysis to improve customer engagement and satisfaction.

But as the Checklist Report points out, the key lies in delivering these insights to the personnel interacting with customers. These are at the various customer touch points, which may be in the call centre, in the shop front, or at the complaints or accounts payable desk. The data must be in a format that makes sense to staff at that point of interaction. Turning the insights into actionable tasks is the real crux of making data-centricity operational. For example, showing a propensity to churn score of 0.49 may not prompt a sales person into the appropriate action to try and keep a high net worth customer, unless they had extensive training how to interpret various combinations of indicators – because you may not be concerned about a low net worth customer’s 0.49 likelihood to leave. But a red message on the point of sale screen flashing “Offer the 24-month family and friends upgrade for 50% discount” may just prompt the operator into the appropriate action! But then the various individual insights (churn potential, high net worth customer, likelihood to adapt package) had to be mapped into a set of actionable instructions.

Thus, making business processes more data-centric allows the business to personalise customer experiences in real time, ensuring that the appropriate engagement with customers happens across different channels, instantly.

Improving internal business processes

I want to add a third point, which is not necessarily related to customer-centricity, but which nonetheless directly affect it, and potentially in a great way too. Let’s refer to this as ‘Delivering Actionable Information to Improve Business Process Efficiency’. What we are talking about here is taking the operational insights about internal business processes, and also serving those insights directly to the personnel managing and performing those business processes. So now we are making the management and execution of internal (non-customer facing) processes also more data-centric. Essentially this is about how you can better measure processes within the business, and then take these insights back into those processes in order to manage them more effectively. This is done to also make these processes more data-centric and thereby more beneficial for the business.

So you may rightly ask, well how does that affect customer-centricity? Let me illustrate by an example. If a retailer has an item on special, be it on a catalogue, on-line, or in-store, any customer would become disgruntled if they cannot get hold of the item, and they get the response: ‘but the small print said “while stocks last”’. Now why wouldn’t stocks last? Obviously because the distribution centre didn’t get the stock to the store in time… Why not? Because the pickers in the DC didn’t know (on the floor) they were running behind picking the items on special for the sale. There are many similar examples that illustrate how improved data-centricity in internal non-customer facing operations can directly affect the customer experience. You made your destination, but your baggage missed the connection, anyone?

Concluding remarks

Data sits at the centre of this approach and technologies, such as big data, descriptive and predictive analytics for actionable insights are driving this business reality forward. In order to bring this to full circle require that you surface these insights in an actionable format into the business processes where they are relevant. In my view, the customer will forever be king – and so being data-centric is not a nicety – but rather a necessity for businesses to be and remain more customer-centric today.

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