What if you could predict the future? And what if you could alter the future based on your predictions? Well, actually, you can.

At the Legal Marketing Association annual conference last week, several presenters discussed how predictive analytics are helping law firm marketing professionals develop data-driven marketing programs. While the practical applications of predictive analytics may seem somewhat vague, presenters shared real-life examples of how they are harnessing data when developing intelligent and efficient marketing strategies.

Subjective Observations versus Objective Data

For decades, the observations of law firm partners and marketing committees have influenced marketing programs. How many times have managing partners said, “We need to highlight our attorneys’ industry experience better on their bio pages because that’s what influences purchasing decisions”? Even client-based feedback is unreliable, since what they say they need and value can be different from what they actually need and value.

For years, law firm marketing directors have relied on non-scientific input like this from firm stakeholders and clients when developing their marketing strategies. As the speakers from “Harnessing Predictive Analytics to Drive Client Growth and Retention” explained, conventional wisdom (not analysis) leads marketing strategies resulting in most firms:

  • doing too many things …
  • in too many places …
  • for too many people …
  • based on too many assumptions …

In other words, efficiency goes out the window, which jeopardizes effectiveness.

Tips for Developing a Predictive Analytics Program

Imagine that your marketing program was developed by leveraging fact-based, objective data. Instead of throwing a bunch of darts randomly at the wall, you could hone your focus, increase efficiency and improve effectiveness while decreasing the resources and effort previously needed to run a law firm marketing program.

Here’s the overall framework for developing a predictive analytics program.

Plan a Predictive Analytics Initiative

  • Start with a small pilot project that can demonstrate results. Don't try to launch a complete overhaul of your marketing program. Rather, begin by finding the answer to one question.
  • Define the problem you want to solve. Be specific and fact-oriented. For instance, which factors contribute to client growth and retention? Which factors contribute to client attrition?

Source Datasets

  • Define datasets and source data from reliable sources. Internal historical data should come from multiple resources, including marketing, finance and human resources.

Analyze Results and Develop Predictions

  • Summarize insights and observations based on the historical data collected.
  • Analyze patterns and trends to develop predictions about future behavior. To give a simple example, if the data reveal a pattern of higher client attrition for newer clients, define the time it takes before a client is less likely to leave the firm. If data show that clients who reach the three-year mark are 75 percent less likely to leave, you can predict that attrition will be higher within the first three years of an engagement.
  • Inject business knowledge and focus on what makes sense. While instinct and subjective observations are not data-based, they still need to be part of the process.

Define Actionable Tactics to Improve Outcomes

  • Determine the easiest way to make the biggest impact. Looking back at the attrition example above, it would be smart to determine which clients are at higher risk of leaving and focus on them.
  • Outline marketing tactics that can be implemented to alter the negative predictions.
  • Identify a control group. While your tactics to improve outcomes may only apply to a small group, the remainder of the group will act as a control to measure results.

Measure Results

  • Measure the program’s success based on goals or expectations.
  • Provided your predictive analytics experiment was successful, use the results to build a case for future initiatives and broader change.
  • Use the predictive model to signal early warning signs of negative actions. For instance, if your predictive model shows that clients are more likely to leave the firm if they increase the time it takes to pay their bill, use this as an early warning sign to flag clients in jeopardy of leaving.

Obvious constraints may exist for law firms with fewer resources and lower budgets, thus barring entry to developing a predictive analytics program. However, the overarching takeaway is universal to other law firm marketing initiatives: Focus on a smaller group of clients, using tactics that achieve the greatest impact and positive outcome.

If you have insights into predictive analytics programs that have worked at your firm or would like to discuss how to achieve a more data-centered marketing program, contact me, Melanie Trudeau, at mtrudeau@jaffepr.com.