Transform Customer Data Platform (CDP) into Super Forecaster
The fundamental underpinning for connected customer experience is predicting the customer’s actions precisely “Best Next Action (BNA)”. Predicting the behaviour of human beings is often classified as customer or user behaviour in an organization setup. Are we confident “Best Next Action” will form the basis of CX?
· What about the outcome of the prediction?
· Can we predict outcomes based on factual evidence?
To address the diverse needs of today’s customers, we need a “Super Forecaster”. Can we transform the Customer Data Platform into Super Forecaster, which allows?
· Predicting the Best Next Action
· Predicting the outcome of the Best Next Action
Customer Data Platform as a Super Forecaster will allow the marketer to not only predict the “Best Next Action” but what the marketer will do with Best Next Action and what matters most. This will allow marketers to make predictions, as precise as possible.
Transform Customer Data Platform into a Super Forecaster with bigger and better data
The biggest asset of a successful organization is “Data”. The main bottleneck for data-driven marketing is the lack of complete data. Yes, lack of complete data, organizations have chunks of first-party data, second-party data, and third-party data. First-party data is most underutilized primarily due to a lack of completeness of data and an overreliance on small samples to infer characteristics of diverse customers.
Customer Data Platform accessibility to data beyond marketing will create empirical relationships between diverse datasets.
Customer Data Platform (Predictions + Outcome of Predictions) = Super Forecaster
Figure 1: Transform CDP into Super Forecaster
01. Construct and deconstruct the data
The completeness of data is always a question. The true potential of a Customer Data Platform is its ability to build, and deconstruct the data to generate insights around
· Coordination: one can construct and deconstruct data to create coordination from data, captured from different digital touchpoints and channels to optimize follow-up.
· Relevance: Deconstruct the “Customer 360 — profile & preferences” to understand the context to build intent for “Best Next Action”.
· Adaptive: Deconstruct the data to find how an individual is different from another customer, confusing! Identify customers as different groups or types of customers. Validate whether generic business rules applicable to all customer types.
This ability is important to your data strategy while deploying a Customer Data Platform, as it gives you a fundamental reason to ask questions about the completeness of data. So next time, when you are dealing with customer 360 platforms and discussions, see if you or your team or Customer Data Platform is capable of deconstructing the data.
02. Balance the old with the new
I emphasized bigger and better data while opening Super Forecaster for a purpose. We have the buzzword “Experimentation” to me it’s an amalgamation of old and new data. We often reach out to the customer based on available data (old) and follow up based on the customer response (new).
· Does my Customer Data Platform give me the ability to merge new data in a controlled way to the latest predictions?
· Does my Customer Data Platform give me the ability to disregard irrelevant data?
Update often in small increments, letting you leverage the best of your experimentation strategy. This gives in-road how to how new data is contributing to optimizing the customer experience. The incremental update gives you an idea of conditional dependencies between the datasets as well as the independence to make precise decisions. This will also unearth which parts of the data set you know and which ones you don’t. Finally
· You will be able to disregard irrelevant data
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· dataset determinable for a better experience
· synchronization of your findings
03. Numerate data
Taking learnings from “Bayes’ core insight” — deconstruction and balancing of data allow an organization an opportunity to gradually build precise predictions, i.e., actions matching the intention. TO numerate the data, we must deconstruct the intentions into
· The Initial driver: Fundamental factors such as a product itself or service, the need of the customer, or their preferences drive customer behaviour
· The intermediate driver: Past interactions between customer and brand, like overall customer experience, customer satisfaction, loyalty, and usage patterns can help decide the action
· The explicit drivers: The most important group of data is explicitly observed behaviour data, such as purchases, visits, browsing history, clicks, and others, which are the most important signals needed to decide actions.
04. Synthesize Predictions
“Dragonfly eye forecasting” seeing multiple pictures and synthesizing them all together into a single picture, so must a customer data platform deliver
· Not only “Best Next Action” but under what scenarios it’s likely to work better
· Ability to distinguish actions based on intentions, either as most certain, certain, likelihood, and impossible
· degree of uncertainty for the customer to act on “Best Next Action”
Synthesizing predictions requires reconciliations of data from different phases of data management, i.e.
· data collection to aggregation
· Aggregation to unification
· Unification to activation
· Activation to reception
· Reception to reconciliations
This certainly gives the organization a better view of what is going to work under what circumstances, i.e., the “outcome from Best Next Action”.
Before I close, it’s the early days of customer data platforms, with more inroads to make, explore, and laws of the land to set up. It’s important that CDPs’ shouldn’t just be seen as customer 360 but new generation “Super Forecaster” platforms with the advantage of
· Bigger and better data
· Completeness of data
· Access to the channel of engagements
· Access to mature marketing automation platforms
Finally, a customer data platform will allow us the opportunity to cultivate the data that can drive improved practices in predictions.
Excited to take your input Timm McVaigh Joe Berger Paul O'Brien Brad Haynes
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