Great data is the basis for great marketing
Data scientists are intimately familiar with the processes data must go through before it can be used for machine learning models and analytics. Marketers, on the other hand, are usually not. The journey that customer data takes before it’s actually useful is outside of many marketers’ purview and deemed unimportant to some.
But we’re here to suggest otherwise. Having a basic understanding of what it takes for data to become highly effective and usable for customer analytics and marketing campaigns can make or break marketing efforts.
This post highlights the processes customer data goes through before it is made available to marketers for customer analytics and marketing campaigns.
Customer data is collected from multiple sources
There is a wealth of customer data available to marketers. Common customer data sources include CRM platforms, social media sites, company websites, mobile applications, and more.
Every one of these systems stores that data in different ways. Data might be structured (highly organized and thus easily processed), unstructured (no defined format, making it difficult to process), or semi-structured (somewhere in between). It may also contain different data types such as numeric, text, or categorical (data that fits into categories, like dog breeds or types of cereal). Collection is only the first step.
The data is processed
Customer data must be cleaned, normalized (adjusting data to a common framework), integrated, and deduped before it can be fed to machine learning models or used for analytics. And customer data almost always requires enrichment, where missing or out-of-date customer data fields are appended with new or updated information.
For example, a customer record that includes only a first name and email address would be appended to include company name, job title, and phone number. Data scientists are usually the ones responsible for preparing data for analysis, and it takes about 80% of their time (yes—80%), unless they have tools to automate the process.
A CDP (customer data platform) saves time by cleaning and standardizing data automatically. Like the example above, companies also want that data to be enriched with any additional required information (like augmenting a customer contact with an email address where it’s not included) and integrated into existing martech solutions.
Customer data is fed to models for segmentation
The more personalized the campaign, the better for marketers, and that all comes down to segmentation.
Once customer data has been processed, it is segmented typically using one or more clustering models. Clustering allows customers to be segmented based on demographic attributes as well as behavioral patterns. The data is segmented in hundreds of ways, providing organizations a detailed understanding of customers and providing marketers new ways to think about traditional segments and campaigns.
Segmentation is often used by organizations as the basis for lead scoring and propensity prediction (an estimate of likelihood that a deal will close). We’ve found that many organizations either rely on their marketing automation tool or build their own machine learning models for lead scoring and predictions in-house. These solutions are either not robust enough or far too expensive to create.
Great CDPs include lead scoring and propensity prediction, making it easier to find real insights within your existing data and utilize it for your marketing efforts.
The foundation for successful marketing campaigns
Data goes on quite the journey before it reaches marketers. From collection to processing to segmentation, each step is critical to effective marketing.
Machine learning is the fuel behind each of those steps, enabling martech platforms to feature capabilities such as advanced customer analytics, contextual personalization, and timely customer engagement.
Modern marketing will increasingly require advanced tools that make customer data more insightful and more usable, and ideally, that work their magic automatically, without the heavy lift from data science or research teams.
If you liked this post, check out our recent blog post: why machine learning matters to B2B companies.