What is the difference between structured and unstructured data?
Structured data is highly-organized and formatted so that it’s easily searchable in relational databases.
Unstructured data has no predefined format or organization, making it much more difficult to collect, process, and analyze.
The Convergence of Data Science and Marketing
Data scientists and marketers must collaborate to create a single-point-of-control over data and interactions — one that provides a holistic customer view, connecting customer data from across sources: batch and streaming, internal and external, structured and unstructured, transactional and demographic.
Creating a single-point-of-control over data allows data scientists to work with marketers to develop a deep understanding of the customer and deploy the models and personalization strategies that have maximum impact. Then marketers can align that insight with their omnichannel strategy and deliver highly personalized experiences at speed and scale. A single-point-of-control over interactions is necessary to orchestrate those interactions in today’s omnichannel environment, ones that span multi-stage real-time and offline customer journeys.
Building this environment means that data scientists and marketers need to think more like customers: Customers have always viewed the companies they do business with as one unit, not a group of silos they have to interact with individually. Data scientists and marketers need to shift from a silo mind-set that leads to fragmented experiences to one that is centered on connecting disparate touchpoints in a way that weaves each discreet interaction into a cohesive whole.
The Importance of Data Structure for Customer Experience
This new, connected reality has two foundational elements that will enable a deep understanding of customers’ needs, expectations, and channel preferences, so marketers can then deliver consistent and contextually relevant omnichannel experiences: customer insight and customer interactions.
Customer insight to meet today’s CX expectations takes…
– Holistic view of the customer
– Real-time data access
– Single-point-of-control over data
Customer interaction to meet today’s CX expectations takes….
– Understanding where a customer is in their path-to-purchase
– Decisioning for the next best action (e.g. offer, message or content)
– Orchestrating the right actions in customers’ preferred cadence and channel(s)
Optimizing and personalizing omnichannel customer experiences to meet customer expectations requires organizational, process, and technology changes that bridge silos and center on the customer. Again, the ideal method for enabling all this is to create a single point of control over data and for real-time interactions, and phasing in process and organizational changes over time. The single-point-of-control acts as a central hub from which data scientists can provide insight and marketers can make decisions that are then feed to the channels. It comprises a single-point-of -control for aggregating and understanding customer data, as well as a single-point-of-intelligent-control over customer interactions that spans across every enterprise touchpoint.
The persistent matching of keys at data ingestion vs. customer data that is already keyed is the difference between a brand making decisions with the dynamic flexibility that aligns with today’s dynamic customer journeys that consist of multiple physical and digital channels.
Data matching at the pace of the customer adds vital context to interactions that is a cornerstone for providing an omnichannel CX, irrespective of either the volume or variety of engagement touchpoints. Done well, advanced identity resolution reconciles records across all types of data – structured, unstructured, semi-structured, batch, streaming, etc. – and all data sources.
A comprehensive customer understanding is necessary to deliver an omnichannel CX in line with customer expectations. Taking shortcuts at data ingestion by using data that is already keyed will, in the end, short-change the customer. With a personalized customer experience shown to drive revenue, the era of having to tolerate inferior data is over. Customers expect and deserve nothing less.
A customer retention strategy that rests on a Golden Record offers the accuracy, precision and flexibility needed to engage today’s digital-first customer with personalized experiences to counter individual churn signals. A Golden Record is a single customer view that aggregates customer data from every conceivable source. A Golden Record pulls together data from all sources and all types – structured, unstructured, semi-structured and from known and unknown customer records – to create a holistic view of the customer. Combined with advanced identity resolution capabilities, it provides brands and marketers with a customer identity graph that encapsulates a persistently updated view of a unique customer’s behaviors, preferences, transactions, devices and IDs. Updated in real time and together with a real-time decisioning engine, a Golden Record is the key to providing each customer with a hyper-relevant, personalized experience that is always in cadence with the customer journey.
For retention purposes, use of a Golden Record ensures a brand is never caught off guard. If notes from a call center interaction indicate a customer is dissatisfied with the contents of a shipped order, it might be worthwhile to dynamically switch out email content – sending an apology and a discount offer rather than boilerplate content asking them to rate the online purchase experience. A Golden Record makes this interaction possible because a single customer view eliminates the data siloes that – along with process, people and channel siloes – are largely responsible for introducing friction by being a step (or more) behind the customer.