The line that inspired this blog’s title is perhaps one of the most famous in world literature. But the entire speech of Hamlet by William Shakespeare is something that reflects the thought many involved in customer data platforms (CPDs) face daily: What is the right decision? This blog around the question is co-authored by Rene van der Laan.
We make decisions every day. Some are small, some are large, some are good, some are bad. However, often decisions are made based on a feeling, a situation or what we know now and not on information considering all variables that can guide a prediction of what the future will bring. In the world of business, decision making is increasingly being driven by this information, the data. As part of a CDP, we can lawfully take as much data into account as possible that could shape the decision into a good one for us, for the business and, most importantly, for the consumer (for the lawful part, search on “data protection” or reach out to a SAS expert).
It sounds simple to make decisions based on as much relevant information as possible, but it’s more challenging than it seems at first. We need to trust the outcomes, even when they guide us to do something out of the norm, and therefore trust the data. There are basically three questions an organisation needs to address to get to a point where making trustworthy optimal decisions is as simple as counting from one to three:
- How and what data should I collect?
- How do I make this data usable and insightful?
- Where do I apply these insights to influence the decision?
How and what data should I collect?
All your decisions start with the data. You wouldn’t build a skyscraper on foundations in quicksand, so why build a CDP on unknown data? Simply put, bad data produces bad decisions!
Organisations typically have a lot of customer-related data stored in all sorts of systems: CRM, ERP, marketing solutions, PoS systems and I’m sure you can name many more! Logically, they invested heavily in trying to centralise this data, either physically storing the data in a data warehouse or data lake or creating a defeated data model to points to all the sources directly. However, the step that differentiates organisations answering this question is the standard of data governance procedures they have in place. This ensures everyone in the organisation knows everything they need to know about their high-quality, standardised and deduplicated data.
In large organisations with lots of data, it is often hard to know what data is available. SAS provides the capability to search and identify the type of data and where it is located. From an individual’s name to medical conditions, you will know what and where your data is. Organisations can then navigate their way to customer-centric data, assessing all angles of the customer, whilst ensuring data protection regulations, such as GDPR, are met. Vitally, gaining trust with your customers as you are using their data in an ethical and mutually beneficial way can be a key differentiator in competitive markets. Organisations are then able to operate with a well-governed, protected and understood CDP, taking a single, 360-degree version of the truth across the entire enterprise for each and every customer.Vitally, gaining trust with your customers as you are using their data in an ethical and mutually beneficial way can be a key differentiator in competitive markets. Click To Tweet
‘Must haves’ vs. ‘nice to haves’
But when is there enough data? What is required to help make optimal decisions? How does data become information the organisation can act upon? That will require some homework. To make this more manageable, we often ask customers to “bucket up” their desired data into “must haves” or “nice to haves.” Any missing data item marked as “must have” requires immediate action and can then easily be incorporated into the governed CDP so all in the organisation understand what it is and how they can use it to gain insight. A plan can be also be created for “nice to have” data so users know what to expect in the future.
When “bucketing” their data, we often find organisations mark digital data as a “must have,” closely followed by real-time (contextual) data and third-party data (e.g., from DMPs) in the same bucket. Digital data is often available as aggregated data or is collected at a level where it cannot be attributed to a customer or visitor. Whereas contextual data is often available, but not actionable on it’s own, since often it is captured for operational purposes, but not used in conjunction with the customer profile for decisioning. Third-party data is often considered a requirement, but usually this data, or the segments that are created from third-party data providers, are not considered to be very accurate.
An added constraint are data privacy regulations. With appropriate grounds for storage and processing, third-party data is often out of bounds or usage in making decisions for customers. For the remainder of this post, we will not include third-party data, concentrating instead on the most “bang for your buck” data types: digital and real-time (contextual). So let’s keep the focus on the use of these within the increasingly popular CDP made popular by both software technology companies and industry analysts.
When combining digital and contextual data, the organisation can understand the entire 360-degree view of customers’ current situation: their location, personality and spending behaviours, and how and why they like to connect with the organisation. When this is part of a well-governed, protected and understood CDP, we can make trustworthy decisions for us and the customer, fostering a lifelong relationship. Coming back to our earlier analogy, we have confidence in building our skyscraper on the best foundations, with the correct alarms and the right layout for the people inside, for long-term use. At SAS, we refer to this as being able to facilitate data-driven innovation.
How do I make this data usable and insightful?
Ok, the hard bit is over. Time for the fun to start.
For an organisation to make decisions and gain insight from their data, they will need to transform it so that it is usable for all their needs. This is possible with customisable and interactive reports in SAS® Visual Analytics or advanced analytics using the power of AI in SAS Visual Data Mining and Machine Learning. The data scientist will then take these created data sources and build, tweak and tune a wide variety of advanced analytical models to create artificially intelligent insights. Initially, organisations look to predict what a customer wants and is likely to buy before they do. These predictions are often not visible with the “naked eye” and can guide marketing campaigns, product placement and recommendation engines on the organisation’s website. To take this a step further, organisations look to implement these insights in real time, taking into account the most up-to-date data to delight customers at the exact point they are ready to receive a 10% off voucher for the item they’ve been close to buying at the point they are passing by the shop.
To put it simply, SAS has made it simple and easy for all users to program in the language of their choice against the same data set. And getting trusted results.
Where do I apply these insights to influence the decision?
As humans, it is commonly expected that we are successful at making and executing decisions to a high degree of accuracy when we have adequate time to learn. Malcom Gladwell is famously quoted as saying we need 10,000 hours of deliberate practice to become world-class . Simply put, when we make decisions, we use our brains. We draw from a vast amount of information that has built up over time, both long- and short-term, which helps us to consider what has been experienced. We allow real-time information to enter the brain to give context for the decision and then try to predict different outcomes based on what we have learnt from the patterns and outcomes of the same or similar decisions in the past. However, if we must make thousands of decisions a second, using information we have never laid eyes on or understood before, we need help. Technology to the rescue!
Technology has advanced tremendously in recent years, and machine learning techniques are able to facilitate high-frequency, complicated decision making like never before, leading to a rise in the use of AI. SAS has been there since the beginning, performing advanced analytics, including machine learning and AI among a huge range of other techniques, since we were founded over 40 years ago to optimise the crops that should be planted on the fields to maximise a farmer’s revenue (we are talking 1973!).
Once we have collected, managed and modelled the necessary data for our desired outcome, the decisive step is to bring this to life for the customer. There is no value in industry-leading predictions if they don’t influence the industry! To truly connect with customers, an organisation needs to be in the moment with them and provide value when it will have the most impact, in real time. Without becoming creepy! And that’s exactly what SAS allows you to do, through a centrally managed decision engine to push decisions to where they will have most impact for the customer and you.
The subsequent elevated customer experience will drive loyalty (yes, loyalty still exists), revenue and profits for the organisation. To dig a little deeper, the decision engine needs to have access to all data from modelling, real-time data to give context to the customer’s situation and be accessible to all areas of the business and a wide range of users. That is exactly what SAS allows organisations to do. Enabling decisions to drive sales, retain current customers, discuss collections, mitigate risk or convert a customer to complete an online purchase. All whilst trusting the decisions because they’re built on a high-quality, protected and governed data foundation.
Use your brain and make (artificially) intelligent decisions!
This post discussed the three key questions an organisation should address when implementing customer data platforms to help guide customer interactions. This includes finding the right data, transforming it into a wealth of protected and understood knowledge, and applying advanced analytical techniques to ultimately form decisions to shape your customer journey. Don’t let your customers fall victim to an avoidable wrong, bad or even NO decision and negative experience with your organisation.