In the evolving world of digital analytics, customer data platforms (CDPs) have emerged as the next great tool for harnessing customer information. CDPs help businesses consolidate, organize, and utilize customer data from a multitude of sources, thereby enabling them to craft a more holistic understanding of each of their customers. Businesses can use this newly unified data to deploy dynamic marketing campaigns and deliver personalized content.
CDPs aggregate data thanks to their matching algorithms, which are responsible for identifying key data points and associating them with individual customer profiles. While it’s true that each CDP on the market today uses its own proprietary matching algorithm, all CDP matching algorithms are based on a combination of machine learning modeling and statistical inference. These proprietary algorithms fall into one of two prominent categories: deterministic and probabilistic.
Deterministic matching algorithms only assign different data sources to the same individual when there is absolute certainty that it corresponds to the same person. For example, if a site visitor signs into a website using the same credentials across multiple devices, a deterministic algorithm would match the data from each session to a single individual.
In comparison, probabilistic matching algorithms assign different data sources to the same individual when the CDP calculates that it's overwhelmingly likely that the sources correspond to the same person. It does this by taking a variety of data points into account and, through robust statistical analysis, generates a series of probabilities that bits of data from separate sources may belong to a common user. If these probabilities exceed a certain threshold, the algorithm estimates that they originated from the same profile.
Omni-channel personalization is no longer a dream-state feature; it has become the norm. To remain competitive in the rapidly changing digital landscape, your business needs to activate your customer data, and a CDP is a strong tool for achieving this. However, given the significant investment required to effectively implement a CDP, several factors need to be considered before choosing a single one. The choice between a deterministic and probabilistic CDP will have a profound impact on your analysis, so it is not a decision to be made lightly. This blog serves as a crash course on the differences between deterministic and probabilistic CDPs and, ideally, will help you decide which type of CDP is right for your marketing practice.
Deterministic matching algorithms function on the principle of certainty. They rely on explicitly defined identifiers or attributes, such as email addresses, phone numbers, or account numbers, to link customer data across disparate sources. By using unique identifiers, deterministic matching algorithms leave no room for ambiguity, offering a precise and reliable method for data linkage. Consequently, these algorithms are adept at providing accurate, individual-level data aggregation. This enables businesses to create comprehensive customer profiles with a high degree of confidence. However, deterministic matching algorithms face limitations in scenarios where specific identifiers are absent, leading to potential data silos and incomplete customer profiles.
The clearest advantage that deterministic matching algorithms hold over their probabilistic counterparts is that they ensure precise and reliable linking when exact identifiers are available. A deterministic CDP won’t create a match unless the identifier is present and as a result, will identify fewer matches on average than a probabilistic one. For businesses where data accuracy is the highest priority, a deterministic CDP would be preferable.
Probabilistic matching algorithms operate on the premise of likelihood and statistical inference. They utilize a combination of various data points, including behavioral patterns, demographic information, and contextual data, to establish potential connections between disparate data sets. Leveraging machine learning and data analysis techniques, probabilistic matching algorithms can generate probabilistic matches even when explicit identifiers are unavailable. This flexibility allows businesses to link data across multiple touchpoints and platforms, providing a more holistic view of customer interactions. However, this approach introduces an element of uncertainty, potentially leading to false positives or data inaccuracies that may compromise the reliability of customer insights. But in situations where data is incomplete or where there are inconsistencies in data collection, probabilistic CDPs deliver deep insights and tangible value.
Probabilistic algorithms will generate more profile matches compared to deterministic ones. When aiming for broad audience coverage rather than accuracy, probabilistic algorithms have an advantage. For this reason, probabilistic matching lends itself well to audience targeting rather than content personalization. These algorithms are designed primarily to create holistic customer personas rather than individual profiles, making them better suited for analyzing aggregate consumer audiences. As a disclaimer, probabilistic matching algorithms are certainly capable of creating individual user profiles and personalizing content, it is just that deterministic algorithms generally excel in these areas.
A probabilistic CDP can assist businesses in expanding their customer bases by identifying potential customers through behavior similarities with existing ones. This allows for tailored marketing messages to prospective audiences, boosting the likelihood of content resonance. Marketers can then feel empowered to make comprehensive business decisions, backed by data that justifies their initiatives.
It would be disingenuous to declare either algorithm type is superior to the other, as they each have their own strengths and weaknesses under specific circumstances. In practice, many CDPs use a combination of both probabilistic and deterministic matching algorithms, so the choice between the two is a bit of a false dichotomy. Deterministic matching is often applied when high-confidence identifiers are available, while probabilistic matching is used to capture relationships in cases where identifiers are incomplete, inconsistent, or lacking. By using a hybrid approach, CDPs aim to maximize accuracy and coverage, creating more comprehensive and accurate customer profiles.
1. What are the data governance standards of your industry?
If your business is part of an industry with strict data privacy compliance requirements, it might be wiser to choose a CDP that utilizes a deterministic matching algorithm. The increased audience scope and reach afforded by a probabilistic CDP may be enticing, but the increased number of false matches returned could potentially expose your business to unwanted legal liability and other unforeseen risks.
2. How complete is the data?
If your data is consistently clean and complete, a deterministic CDP could be a suitable choice for your business. Deterministic CDPs rely on a series of key identifiers, so if these identifiers are not consistently captured or available, it may not be the optimal option. However, having complete data alone doesn't necessarily mandate choosing a deterministic CDP over a probabilistic one. The critical factor is the availability and consistency of key identifiers; lacking this completeness is a clear reason to avoid a deterministic CDP.
3. Personalization or targeting?
The strategic goals of your business should influence your decision as well. If you’re looking to retain existing customers and provide more personalized experiences for them, opt for a deterministic CDP. Content personalization depends on being able to identify individuals precisely and accurately, something better suited for a deterministic CDP. Conversely, if your focus is on broadening your customer base by targeting larger segments, a probabilistic CDP would be more suitable.
4. Precision or coverage?
Similar to the previous point, you should consider the scope of business questions you need to answer to make informed decisions. Precision broadly measures the correctness of predictions, while coverage indicates the proportion of instances covered by the data. Depending on the application, you might prioritize one level of granularity over the other. For instance, in healthcare, high precision is critical for diagnostic models, while coverage is crucial in public health models. In this hypothetical scenario, a deterministic CDP, with its higher accuracy, would be ideal for diagnostic modeling. On the other hand, a probabilistic CDP would better capture general trends in a large data set, as needed for public health modeling.
5) What percentage of your site visitors log in?
If most of your site visitors authenticate or consistently identify themselves, a deterministic CDP might be the better choice. However, if large portions of your website are accessible without authentication, it's worth considering a probabilistic CDP instead.
Curious about the right CDP for your needs or need assistance with data engineering? Whether you’re just getting started, or have already implemented a CDP, Concord’s Data and Analytics team can provide full support from evaluating platforms to implementation and more. Take a look at our CDP Evaluation and Accelerator here.
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