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GUEST COMMENT Business intelligence is dead: long live decision intelligence!

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The internet has dramatically increased consumer choice over what where and how they buy – meaning that every retailer or brand owner needs to be in control of every aspect of their business, every day, in order to maximise sales and avoid lost opportunities. Here, Michael Ross, chief scientist at eCommera, discusses the how a decision-intelligent approach to commerce can help retailers to optimise their online operations to improve both the customer experience and their overall profitability.

With new technologies and new service models being introduced virtually every day, the expectations of consumers are continually being reset upwards, demanding even higher standards of personalisation, service and delivery on promise. Coupled with diminishing levels of brand loyalty, consumers can and will go to a competitor if there is the slightest failure to deliver to expectations.

Added to this is the increasingly complex nature of retail. The customer path to purchase is no longer just defined by the footfall through the store. It is highly complex. Customers can be attracted to the brand through a multiplicity of traditional and digital channels. They can review their options using multiple devices. They can opt for delivery at a variety of locations. Their reason for purchase or otherwise can be governed by anything from a poorly-designed page to a lack of price competitiveness.

The upside of this new paradigm is that all these new paths to purchase throw off a vast digital exhaust that contains the clues on what is working, and what is not, in any online retail business. Indeed, online retail is the data. The data required to make almost every decision is available with very high quality, in near real-time. Consequently, the way forward for those wanting to grasp the opportunities of eCommerce is to organise themselves around understanding the data and using it to determine their actions.

However, the volume and complexity of online data is a very real challenge for all retailers. How to make sense of the vast mountain of information now available, how to organise it as a cross-functional asset rather than as stove-piped reports, and how to use it to drive the most effective decisions?

From business intelligence to decision intelligence

It is this issue of driving the most effective decisions that is key. Retailers are not only challenged by the complex environment, but by a shortage of skilled and experienced resources. In such circumstances any business will always seek to ensure that their team is focused on the most effective and profitable actions in a cohesive way. The question is – what are the right actions?

Until recently, the answer to the conundrum was business intelligence (BI). The language of data warehouses, ‘slice and dice’ and graphing options has been the staple of management reporting across all industries for years. However such tools are horizontal in nature, designed to allow organisations in any industry to create their own specific reporting environments. They are designed to provide the answer to a question you have already defined. They require that users interrogate the data themselves to surface insight. The users need to make their own conclusions as to what that might mean – and those conclusions will almost certainly be fashioned by the functional context in which they work.

In the world of online retail, where the requirement is to manage and optimise each and every customer journey, from end-to-end, this reports-driven approach is not enough. In the world of big data it is simply not realistic to expect users to be able to surface all the required insight and make the optimal cross-functional decisions without assistance.

Step forward… decision intelligence

Decision intelligence makes the logical step forward from merely telling the user what has happened. It tells the team what actions to take next, specifically, automatically and prioritised by the impact on profit.

The premise of decision intelligence is determining all the relationships between the inputs and outputs of the industry sector, in this case online retail. By defining these relationships it ensures that a true map of cross-functional cause and effect can be determined. By overlaying the costs and revenues associated with the inputs and outputs a model of the financial impact of the cause and effect can be drawn. From the financial model can be deduced the relative impact on profit of any action. The result: a scientific approach, based on the operational data, to making systematic and prioritised cross-functional decisions.

Capitalising on decision intelligence

The heart of decision intelligence is undoubtedly the technology. However, as with all technology, the promise can only be delivered when married with the people and the process. To create a decision intelligent commerce operation requires that retailers and brand owners respond to three imperatives: establishing the data at the heart of cross-functional decision making; aligning the organisational model around the delivery of an end-to-end customer experience; and establishing an agile technical infrastructure which can surface all the required data and which can dynamically execute on the team decisions.

One brand which has employed this approach to great success is TM Lewin, the high quality men’s and women’s tailoring retailer. Before, the fashion brand was focused on getting customers to its site and if conversion rate looked good, stopped its analytics there. However, with decision intelligence, TM Lewin engages with visitors to its site right through to product delivery. This has led to a joined up approach across all business functions so that for example email marketing campaigns are continually evaluated. Similarly, high visit pages that don’t see high conversion rates can be addressed through changes to product availability or sort order adjustment.

While there are hundreds of possible causes for fluctuation in online trading, there are hundreds of possible actions. Actions should be prioritised according to their ability to impact profit. Decision intelligence is the one approach which will helps brands and retailers understand the ever-evolving behaviour of their customers.

Michael Ross is chief scientist at eCommera

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