Law firms face increasing pressure to retain clients in a competitive legal market. Developing an effective digital marketing for law firms strategy is critical.
One emerging approach is using predictive analytics to identify client needs and prevent churn.
However, attorneys have an ethical duty to avoid even the perception that client data could enable preference-based discrimination. So how can law firms walk this tightrope and leverage analytics in a prudent way?
Using detailed client data to forecast behavior raises alarms for privacy advocates. Rightfully so – no one wants algorithmic systems making inferences about protected class membership.
Follow Three Core Principles
Law firms hoping to increase client retention through analytics should follow three core principles:
● Anonymize client data to avoid connecting predictions to individual identities
● Focus predictive factors on volatility indicators like case timelines rather than protected class attributes
● Emphasize identifying client needs as opposed to labeling clients themselves
Adhering to these guidelines allows law firms to leverage advanced analytics in an ethical way aligned with their professional duties.
The Problem of Implicit Bias
Implicit bias refers to unconsciously-held attitudes about various groups. Unfortunately, predictive models can inadvertently propagate these biases by discovering illegitimate statistical patterns in historical data.
For example, a model predicting loan default risk might utilize gender or race as input factors if the training data exhibits biases correlating these attributes with risk levels.
Such biases have no place in client retention models either. Utilizing any client attributes related to protected classes is unethical in predictive analytics.
This includes obvious factors like gender, race, religion as well as non-intuitive indicators that correlate with membership in these groups.
Anonymize Client Data
The first rule of ethical predictive analytics is rigorously anonymizing all client data entering retention models.
Specifically, firms must scrub inputs of obvious identifiers like names, addresses, or contact details.
However, protected class membership often leaves statistical fingerprints in other observational data like age, income brackets, or family structure.
These latent indicators must also be removed through aggregation or omission. For example, ages could be bumped into broad ranges rather than share exact values.
Any variable with the potential to indicate a protected class should be evaluated closely.
Anonymizing data in this manner focuses retention predictions on behavioral trends rather than individual identities. This prevents models from ever making potentially discriminatory inferences.
Emphasize Volatility Factors
Variable anonymization allows retention models to utilize client data related to case volatility without enabling bias.
Volatility refers to fluid factors that impact matter timelines and outcomes like case complexity, jurisdictional variation, counterparty disputes, or changes in legal standards.
For example, a commercial contract case stalling due to new international regulations increasing ambiguity has high volatility. As opposed to a cut-and-dry estate matter progressing through probate smoothly per the anticipated timeline.
Tracking volatility indicators allows firms to gauge client expectations and detect misalignments early.
If timelines shift for reasons beyond the firm’s control, clearly communicating with clients is essential to continued retention. Analytics simply help surface areas needing additional outreach.
Focusing retention models strictly on volatility input factors keeps analysis centered on client needs rather than ever judging or labeling clients themselves.
Prioritize Identifying Needs
While anonymity and volatility emphasis prevent models from making inappropriate inferences, retention analytics should always focus on identifying client needs rather than categorizing clients.
For example, a model may detect growing timeline uncertainty around international joint venture contracts requiring adjusted client guidance.
But applying categorical labels like “flight risk” to accounts themselves opens the door to preference-based discrimination once again.
Ensuring predictive outputs emphasize situational insights rather than grouping clients is another way law firms can wield analytics ethically.
Meetings discussing model findings should highlight addressing newly surfaced risk factors and communication opportunities instead of stigmatizing accounts themselves.
Moving Forward Responsibly
Advanced analytics provide law firms with fantastic opportunities to improve client retention in an increasingly competitive environment.
However, as models grow more complex, so too does the diligence required to wield these instruments ethically.
By anonymizing sensitive data, concentrating on volatility inputs, and emphasizing uncovering client needs, firms can progress responsibly.
Clients rightfully expect attorneys to act as trusted advisors through uncertain legal proceedings. Retention analytics that illuminate new risks allow firms to serve this duty ever better.
But ultimately, no analytic model can replace direct communication between lawyers and clients.
So while predictive insights pave the road for productive conversations, traveling that path together remains the surest way to strengthen relationships. And protecting these bonds both protects each client and safeguards every firm’s future.
