Three ways the value of data is destroyed within your CDP

There are more definitions of what a CDP is than there are people who will read this blog post. And no two vendors will agree on what the most important functionality is for a CDP.

At heart though, a CDP is the latest attempt to reach the nirvana of a Single Customer View where All Business Problems Will Be Solved. What is genuinely good about the latest focus on this task is the inclusion of several new types of functionality that help create a dynamic real-time customer profile, including good identity matching, real-time and streaming data analytics, and sophisticated machine learning.

However, the value of the data in your CDP is only as good as the way you make use of that data. Getting the most value from customer data requires ditching old-fashioned ideas about data processing that stubbornly linger in organisations.

So, what are common ways data value is destroyed?

Segmentation of customers into groups with similar important characteristics is an artefact of a time when we had little data on our customers and rarely had the computing power to treat customers as individuals. With modern big data stores and cloud environments, that is not true. There is no fundamental reason why you cannot assess your customers one-by-one as individuals with a rich, vast and dynamic data profile around them, and construct messages and actions for that customer that are unique to that individual.

Indeed, that is why you have invested in a CDP, is it not? You want to make the best possible decisions about each customer?

So, why do you take your customers and put them in big buckets — segments — and treat all the members of that segment as if they are the same? Your optimised strategy for that segment is only optimal for the average customer in that segment. You will be missing opportunities and value with all the customers that are not like the average one, as you are not resolving that customer with the resolution that can yield significant differential insight.

Segmentation smooths out all the insight you might glean from your customer profile. If you put a customer in more than one segment then you will have difficulty in understanding for that customer which is the most important segment or journey they are on, and which is the best action to take for that customer. You are prioritising segments, and the associated campaigns, rather than individual and personalised actions for customers.

Using your customer data to focus on one customer at a time, and applying Next Best Action approaches, will ensure you maximise the value of your data to pick the optimum set of actions to take with that customer.

Real-time CDPs now exist that allow you to apply data analytics to inflight real-time data. Your data is at its most valuable the moment you capture it. Every second that then elapses will result in the decay of value in that data. The rate at which the value decays will depend on the type of data.

The value of capturing a year of birth will decay very little over time. However, the value of understanding what page on your web site a customer is looking at may well decay in seconds or minutes. Maybe your customer is stuck on filling in a form, or is looking at the contract cancelation terms. Every second you delay means you have a smaller window of opportunity to act. Delay long enough and the window shuts.

It is the fast decaying data that can be the rocket fuel for decision making. A date of birth gives little insight into a customer’s intent. What transaction they last made, or web page they visited, probably does.

Delay by seconds or minutes and you can no longer influence the current web session to nudge the customer towards a valuable goal. Delay by hours and your customer will be researching competitors. Delay by days and the customer will have bought elsewhere or cancelled their contract.

Understanding your current customer context (where they are, what transactions they have just made, what channel they have been on, what service issue they have etc) will always lead to insight into their intent and will allow you to predict what the best action to take is. This will require the execution of many rules and predictive models to optimise your actions. If you wait for a batch run to compute much of this, the value of the data will decay.

Any latency in your CDP and decision making will make your actions less relevant to the current customer context. Your rule evaluations may be no longer valid. Your predictive model may be using out-of-date predictor values.

Data value can be maximised by ensuring the analysis you do with your data is done the moment you capture a new significant piece of data or see a customer state change. Being able to act on that data change in the moment ensures you can act within all the windows of opportunity you have available to you.

A core capability of a CDP is to make available its data and insight to applications and channels that can make use of it. A weakness here is that each channel and application may use that data in inconsistent and incoherent ways. For example, a web channel may use the insight that a customer is near the end of contract to put up a banner for a 10% discount renewal offer. In contrast, the email campaign tool may decide a free gift offer is the best approach for this customer.

The inconsistency comes from different decision making strategies existing in each activated channel. Rules may be different. Definitions of offers and campaigns may differ. Treatment selection strategies may be different. Despite important insight being gained on the CDP, how that insight is used may vary wildly and incoherently.

A centralised decision authority mitigates this problem. This may be an integral part of the CDP in that complex decision strategies may be run at the time of data capture to decide on the best action to take, what time to take the action, what channel(s) to use/activate, what calls to action to make, what visual templates and content to use.

If you make the decision centrally, and then use channel applications for delivery only and not decision making, you can ensure the customer experience is always coherent and coordinated. You can orchestrate channels seamlessly in guiding customers along their own personalised journeys. You can ensure your contact history is consistent and being used to nudge customers towards business goals maximally. You can decide what channels are most likely to succeed and only go for the expense of paid channels when that adds sufficient incremental lift and a positive business case for doing so.

It may be strange to have to say this, but using a CDP be should always be about the customer, their experience and the decisions you want to make about them with their data to optimise their experience. The use cases you want to enable with a CDP will drive the types of decisions you need to make. And the decisions you need to make will drive what data is valuable in that decision. This insight allows us to see that not all customer data is equally valuable and that the CDP, if it is to add value, should be ensuring valuable data is being exploited and not just creating a single customer view per se. Focussing just on data will ensure you collect a lot of data that has little value. Focussing on decisions puts the spotlight on how that data drives business value.

And that business value is maximised when we make decisions about one customer at a time, understanding their current real-time context, intents and needs, and can ensure that this complex decision is consistent across any point of use and channel.

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