Data collection and analytics are tightly coupled. The mistake we see made over and over again is that companies tend to focus their customer data collection efforts with a single objective (or a single program) in mind. This treats the data collected as a short-term objective, not as a long-term asset. Over time, this results in data islands that eventually “go dark” given that no one is managing customer data as part of an explicit long-term effort.
Have A Long-Term Data Strategy
When it comes to customer data, a long-term data collection strategy almost always proves critical for any advanced analytical work that leads to meaningful business outcomes that can optimize (i.e., simulation management, condition-based maintenance, predictive maintenance and digital twins). Trending analysis, predicting behavior and customer profiling all benefit from long-term data collection strategies. Companies that understand customers’ buying patterns over longer time frames stand to win key insights versus their competitors.
Customer data deserves a data-access-centric strategy to ensure that the data is treated as a reusable asset. This implies that the data should be available to the right people in the company when they need to repurpose it or mine it months or years later. If the data is not findable, threadable (tied to other data sets) or readily accessible, then it’s effectively dark, and its chances of being repurposed are low.
If you are storing your customer data like you store everything else, chances are much of the data you’ve collected from customers has already gone dark. The tendency is to focus on analytical outcomes without preparing the precondition required for the analytics to occur over a longer period of time. If a data strategy for customer information isn’t well-executed, then customer data will reflect the problem you already have in your data center — lots and lots of data sets that represent difficult-to-access data islands.
Thread Your Data
Sophisticated analytical efforts require advanced techniques such as data threading. Threading data across many silos of data is a challenging undertaking. Techniques deployed to achieve threading include (re)ingestion of data, aggregation, parsing, meta data enrichment and indexing. Data is often so extremely siloed that the most efficient first step is simply discovering data islands and recollecting them into an architecture that allows for advanced analytics. The good news is that data capture and storage technologies are relatively cheap, but finding data and then curating it properly does require significant investment.
Customer data needs to be curated and managed as an asset. As more data is collected, it needs to be aggregated with customer data collected during the previous year (or the last campaign, the last payables cycle, etc.).
For example, if a financial institution wants to understand if a customer is approaching a life-changing event such as marriage, having children or purchasing a home, then threading becomes important because it lets you piece together various customer data collection efforts into a single threaded digital dossier. The threaded customer digital dossier allows for different customer data (collected at different points in time) to be accessed for future analytics. It treats customer data as valuable, evergreen and interconnected. A data architecture that allows you to thread and incrementally expand the customer data set is an essential component to making more with your customer data. Advanced analytics, in turn, will allow you make better use of customer data that is properly curated through threading or other data access techniques.
Separating the customer data collection process from the data curation process from data analytics is not a recipe for success. Unfortunately, most companies treat these three activities independent of each other. As a result, customer data is underutilized, undervalued and is not curated as a long-term asset.
The best customer analytics happen when you intersect people who understand the customer data being collected with people who understand how to use and access the data over time. This means that customer data collection efforts need to be discussed in one room with data architecture folks, analytical/data science teams and traditional marketing/customer success teams, ensuring that all have an active voice at the table.