Deloitte Customer UK

One of the keys to success in any area, whether it is on the sports field or the business arena is knowledge, and knowledge has always been acquired from insights based on empirical information. In other words: contextualised data. Hence, so much of what we do is about enabling our clients to acquire a deeper understanding of their business and customers from data, and the ability to act upon this understanding. 

In most situations there is a particular goal or output we wish to achieve. However, knowing which controlling factors, or variables, to change and how to change them can be difficult in even a simple system or process comprised of a limited number of variables. Understanding the dependencies between variables and outputs becomes increasingly important in today’s world of ‘Big Data’. Data is being generated on such a vast scale, at such a rate and in such an unstructured way that the full potential of it can no longer be realised, or even accessed, through the traditional techniques predominantly used until now. Hence, we are turning to a greater extent to the power of computing, specifically data mining (a subset of machine learning), as well as more advanced machine learning techniques, to extract insights from this sea of data.

We may all be tired of hearing about big data but the human dimension to gathering, storing and analysing consumer data has been thrust back into the spotlight by two major news events in the last week.

Firstly, the midata initiative is set to switch from being a voluntary to compulsory code: companies will have no choice but to make the data they hold on an individual available to that individual. Suddenly the big data opportunity is looking more like a big problem!

Secondly, the OFT has initiated a call for information, often the prelude to an inquiry, into loyalty schemes and personalised online promotions. The regulators and legislators seem keen to redress the balance between business and consumers in terms of the information that one holds on the other and the power to wield it. At the very least they want to ensure that the former behaves ethically when dealing with the latter.

The last few weeks has seen corporate banks hit the headlines as they cut 10,000’s of jobs and announce restructuring plans to enable a focus on key markets.

On the face of it the segmentation they are using focuses on profitability of markets and risk exposure. Very simple but also very sensible. The differentiating factor is that they are changing how they work based on these insights. What other businesses are changing how they work based on changes happening in their market segments or customer segments?

As we went into recession we saw changes in the consumer market we’d not seen in previous recessions, as interest rates fell mortgage payments changed, with some households being better-off and others worse off. For the worse off we saw that spending reduced but items seen as luxuries in the ‘90’s recession where now necessities, like mobile phones, Sky TV and holidays. Many companies predicted and observed these market splits and changes but how many acted on them?

Deloitte frequently publishes articles on the power of customer analytics for deriving insight into customer behaviour and motivations. Particularly interesting news in this field last week was that Tesco is enhancing its already market-leading analytics capability by using comments customers have made on social networking sites, as well as card and mobile data, to influence its eCommerce display sequencing (i.e. if you have an inclination for higher value goods, you will see more higher value goods up front on a page customised to you).

While the era of mass customisation is not new, the example of Orbitz – an online travel portal – displaying higher value rooms to Mac users on the (empirically accurate) assumption that they are willing to pay more begs the question: how long will it be before organisations begin simply charging higher prices for identical products to those with the ability and willingness to pay, by using information from external sources?

The amount of information being collected by business is increasing exponentially and the ability to unlock the value of this will be a key determinant of success over the next decade.

A few years back I remember reading an amazing story about how the American store chain Target, keen to corner the ‘new mother and baby’ market, had analysed its data to discover which products pregnant women bought – with the aim of winning customer loyalty before the birth rather than targeting after the birth as its competitors did. It discovered there were 25 products that expectant mothers typically bought and by combining the timing and volume of these purchases they could accurately predict when the birth would be and send out vouchers and catalogues accordingly. Its approach was so successful that it actually resulted in one teenager in Minneapolis receiving a catalogue of baby products before she had got round to telling her father she was pregnant!

Many of our clients are talking about Real Time Decisioning (RTD) and Customer Next Best Action (CNBA) but what does this mean? Real Time Decisioning is not a strategy but an enabler to implement a data-driven customer interaction strategy, helping organisations to use their analysis and customer insight to deliver the right message to the right customer at the right time. Customer data and insight is combined with real time contextual data, and inputted into the decision engine to determine the most relevant next best action for a particular customer during a specific interaction.

For example, take two customers logging onto a website to look at their bills. Churn models indicate that customer one is a high risk customer meaning they are likely to terminate their service soon, customer two has a lower churn risk. Both customers look through their bills on the website and when they get to their third month bill a next best action is presented to the customer. Customer one is offered a retention offer to encourage them not to cancel their service, customer two is offered an educative message about their usage of the service. It is the Real Time Decisioning engine which determines which next best action is most relevant for the customer.