partner talk #2: modern data strategy: how leading firms

Partner Talk #2: Modern Data Strategy: How Leading Firms Turn

Analytics into an Advantage

Mark Masson

Partner and Head of

Professional Services Practice

@TRIExecutives #C2Platinum

© 2021 Axiom Consulting Partners all rights reserved. Contains Axiom Consulting Partners proprietary and confidential information.

Modern Data StrategyHow Leading Firms Turn Analytics into an Advantage

Axiom Consulting Partners | Modern Data Strategy | October 2021 3

“Can someone please tell me how to get value out of all

this data?”


The World We Live In

We’ve moved from a world of:

▪ Not enough information or data difficult to access

▪ Data science capabilities and tools rare / expensive / hard to use

▪ Relatively low interest in using data to inform decisions

▪ Ability to secure clients primarily through personal relationships

To a World Of:

▪ Hyper awareness of evolving client needs

▪ Information and data of all types at our fingertips

▪ Lots of data science capabilities and toolkits available

▪ Seemingly insatiable craving for more analyses and reporting


The World of Data Has Exploded for Legal FirmsFINANCE

ECONOMIC TRENDS & CLIENT DEMOGRAPHICS

CLIENT RELATIONSHIP MANAGEMENT

PRACTICE MANAGEMENT

TEAM MANAGEMENT

ELECTRONIC COMMUNICATIONS

MARKETING CONTENT MANAGEMENT

CLIENT FEEDBACK

BUSINESS DEVELOPMENT LEGAL RESEARCH


But not enough new insight that truly informs decisions

differently… WHY?


Traditional ideas of client needs and buying patterns

Complex and overlapping competitive landscape

Insatiable craving for more analyses and reporting

Information and data overload…without system connectivity

Seemingly poor data quality (partly true)

Propensity for a broad portfolio of legal tech

Analysis of historical/lagging indicators vs. forward-looking predictive indicators


Traditional ideas of client needs and buying patterns

Complex and overlapping competitive landscape

Insatiable craving for more analyses and reporting

Information and data overload…without system connectivity

Seemingly poor data quality (partly true)

Propensity for a broad portfolio of legal tech

Analysis of historical/lagging indicators vs. forward-looking predictive indicators

But none of these are really THE

problem…or even one of the most

significant problems. The modern

data strategy must start with:

Sharp business questions, strongly linked to the strategy

Explicit demonstration of ROI

Budgets governed in the right place to force execution but allow innovation

Diligent change adoption approach


Art of the Possible (AoP)

What focused analytics look like


Axiom’s approach delivers ROI to high-impact areas

Business Impact quantified and confirmed from actual client projects and proposals for Top 10-25 AM Law Firms

Growth Financial Management Client Service Talent

USE CASEClient Account Growth

IdentificationProfitable Pricing &

Collections Management

Team Dynamics & Effectiveness

Employee Retention & Development

BUSINESS IMPACT $70M+ $40M+ $30M+ $20M+

ANALYTICS SOLUTION

Orgaimi Client Growth Interactive Platform

Client Collections Risk Management Dashboard

Team Network Analysis & Effectiveness Signatures

Lateral Hire Predictive Success Model

Page 44

The model can predict 18% of the variance YoY changes in same Store sales

Store Factors .429a 0.184 0.171 48875 0.18 14.60 12 778 0.00

Personality Clusters .435b 0.190 0.175 48763 0.01 2.79 2 776 0.06

Network Signatures .439c 0.192 0.176 48744 0.00 1.30 2 774 0.27

Audits .442d 0.195 0.178 48687 0.00 2.80 1 773 0.09

df2

Sig. F

Change

F

Change df1RModel

Model Summary - Change in Sales 18 to 19

R Square

Adjusted

R Square

Std. Error

of the

Estimate

R Square

Change

The model is most effective at predicting YoY sales change with Store, Personality and Audits as key drivers

Store Factors .399a 0.159 0.146 0.06 0.16 12.25 12 778 0.00

Personality .400b 0.160 0.144 0.06 0.00 0.35 2 776 0.70

Network Signatures .400c 0.160 0.143 0.06 0.00 0.25 2 774 0.78

Audits .415d 0.173 0.154 0.06 0.01 11.61 1 773 0.00

R Square

Change

F

Change df1 df2

Sig. F

ChangeRModel

Model Summary - Avg Monthly Margin 19

R Square

Adjusted

R Square

Std. Error

of the

Estimate

The model is effective at predicting 2019 Margin with Store and Audit Factors as key drivers – a non-linear relationship is also present

Page 29

DISCONNECTED SIGNATURE EFFICIENCY SIGNATURE INFLUENCER SIGNATURE

DescriptionDMs who are generally not well

connected and have less developed networks.

DMs who are closely connected to others in their personal network, but distant or dislocated from

others across network.

DMs who act as brokers between networks through their connectedness with other high

influencers, often serving as a bridge between others who do not frequently communicate.

Number of DMs in Cluster

Betweenness (brokerage score) -.35 -1.73 1.42

Closeness (connectedness) -.21 4.83 -.18

Eigenvector (influence) -.25 -1.43 1.05

Degree (comm volume) -.37 -.95 1.34

In degree (comms received) -.10 -3.20 .89

Out degree (comms sent) -.40 .63 1.14

Three network “signatures” emerged from examining email patterns for the 30-day period

Note: these values are standardized


Risk Management | Cash Collections Risk Analysis

180Clients Rated

High Risk

$45MBilled Value Not Collected

from High-Risk Clients

540Clients Rated

Moderate Risk

1,620Clients Rated

Low Risk

Average Risk Ratings Client Risk Profiles

8%

23%

69%

High Risk

Moderate Risk

Low Risk

Client Avg Time to Pay Avg Value of Invoice Avg Days Outstanding

Acme Corporation HIGH RISK MODERATE RISK HIGH RISK

Initech MODERATE RISK HIGH RISK HIGH RISK

Hooli HIGH RISK MODERATE RISK LOW RISK

Sterling Cooper MODERATE RISK LOW RISK HIGH RISK

Avg. Time to Pay Avg. Value of Invoice

Acme Corporation

Initech

Hooli

Sterling Cooper

100

90

65

62

Acme Corporation

Initech

Hooli

Sterling Cooper

$9M

$3M

$2M

$1M


Predictive Pricing Toolkit


Sourcing & Selection | Predicting Lateral Hire Success

Illustrative Deliverables

Solutions

▪ Historical analysis can identify successful lateral archetypes

Firmographic CompatibilityEducational background, previous firms, referral

sources, etc.

Demographic CompatibilityAge, gender, skills,

expertise, etc.

Psychographic CompatibilityValues, preferences, goals, etc.


Sourcing & Selection | Using Machine Learning to Select a Diverse and High Performing Workforce

Problem

▪ Hiring can be biased at any level leading to inaccurate assessments of capabilities and long-term success

Solution

▪ Validated models differentiated high performers and minimized negative bias

▪ 2nd place in the competition, out of more than 200 teams comprised of over 500 professionals from industry and academia


Staffing Optimization | Demand Forecasting & Load Balancing

Problem

▪ Workforce planning agility – staffing needs to flex with the work

Illustrative Deliverables

Solutions

▪ Predictive Demand Planning‒ Forecast Client & Service revenue growth ‒ Highlight areas of staffing concern

▪ Historical Team Archetypes‒ Determine ideal staffing model for project types‒ Predict and fulfill staffing needs on a fluid basis

Illustrative

Place screenshots

here


So how do we start developing a modern

data strategy?


Guiding Principles

▪ Think BIG, start small, scale fast

▪ Focus on salient business questions with tangible impact

▪ Strive for continual progress, not overnight success

▪ Seek feedback early and often

▪ Start with a coalition of the willing


Think BIG, start small, scale fast | Avoiding Two Extremes is Critical to Maintaining Momentum

“Re-Inventing the Wheel” “Boiling the Ocean”

▪ One-off data experiments

▪ Continual increase in infrastructure spend

▪ Stagnant usage between projects

▪ Significant resources to maintain

▪ Capability building limited –long delays and budget challenges

▪ Large scale investment in large systems integration providers or data warehouse initiatives

▪ Significant resources without clear future needs or business impact

▪ Projects go stale (“data graveyards”) over time without insights that clearly drive strategic execution or decision-making

✓ Move in incremental stages that build on one another

✓ Ensure each step adds value and creates momentum

✓ Create a “model for change” that drives each step in a repeatable way

✓ Create and build a coalition of support along the way

Critical Success Factors


Focus on capability growth | Make strides without trying to go all the way at once


Focus on High-Impact Objectives | Metrics and reporting are only part of the journey

Focused Business

Questions

Hypotheses Data Gathering

Data Assessment

Interpret Results

Socialize & Refine

Support adoption &

change leadership

Collect Metrics

Report

CollectMetrics

Report


Starting the Journey | Start with what specifically do we want to learn

✓ Which clients need most attention?

✓ Which can add most profitable revenue?

✓ What relationship or delivery actions will work best?

✓ What marketing and business development signals will show us the path?

✓ What actions will grow the relationship?

What Can We Measure? What Should We Test?

✓ Observe Client Decline: Historical analysis of which clients declined by more than $250k

✓ Predict Client Decline: Supervised Learning Approach using behavioral metrics to predict which clients will decline and which clients will not

✓ Validate Behaviors: Using the findings from the model, test the behaviors with business users

What Do We Want to Learn?

INPUTS

✓ Hourly Billing Records

✓ Projects & Engagements

✓ Invoices & Payments

✓ Employee Demographic

✓ Client Development

✓ Organization Structure

✓ Electronic Communication

✓ Matter Team Feedback

✓ CRM Activity

✓ Trends & Industry Research


Use exactly what you need | Many sources, but start small if you must

People & OrganizationData about individuals,

roles, teams, and organization structures

Emails & ChatsThe flow of information between individuals and

teams

MeetingsVirtual or in person

gatherings identified in digital calendars

Finance & BillingData about timekeepers,

matters, billings and collections

CRMData about clients,

contacts, opportunities and campaigns

CXSurveys, client listening, NPS, and other ways to

measure client experiences

PsychometricsPersonality assessments, values and preferences of

individuals

Market DataLabor market, industry projections, client news

alerts, etc.


Data Assessment | Assess, enhance, repeat

KEY: High: Extremely actionable in current form for data science and predictive modelingModerate: Partially actionable but requires some enhancements for data science and predictive modelingLow: Has actionable potential, but significant challenges or limitations for data science and predictive modeling

Metric: ACCESSIBILITY COMPLETENESS CONSISTENCY HISTORY RELEVANCE

Data Category: Data Type:

“How easily can we gather and aggregate the data for data science purposes?”

“Of the data we receive, how complete are the data fields for providing information?”

“Of the data we receive, how consistent is the information within the data set and across datasets?”

“Of the data we receive, how many years of data can we reliably use?”

“How relevant is this data source for predictive analytics application?”

Finance Invoices & Payments High High High High High

Hourly Billing Records Low Low Low Low High

Matters & Engagements Moderate Moderate Moderate Moderate High

People Compensation High Moderate Low High Low

Organization Structure Low High High Low Moderate

Personal Demographics Moderate Low Low Moderate Low

Client Engagement

Pitches & Pursuits Moderate Moderate Moderate High High

CRM Activity High Low Low Low High

Client Feedback Low Moderate High Moderate Low

Team Dynamics Client Communication High High High High High

Employee Feedback Low Low Low Low Low

Internal Communication Moderate Moderate Moderate Moderate Moderate


ACP’s Data Strategy Hierarchy of Needs | Start with good collection practices in order to get more out of data

AI, Deep Learning

A/B Testing, Experimentation,

Simple ML Algorithms

Business Intelligence, Analytics, Metrics, Segments, Aggregates, Features

Reliable Data Flows, Infrastructure, Pipelines, ETLs, Structured & Unstructured Data Storage

Instrumentation, Logging, Sensors, External Data, User Generated Content

Innovate

Learn & Optimize

Transform & Explore

Move & Store

Collect


Changing Minds (If not also hearts)

Change Leadership and Adoption


Messy or Inaccurate Data Sources

▪ “Our data lacks historical consistency, and our systems aren’t fully integrated”

▪ “End consumers of analytics don’t have faith that the underlying data is accurate”

▪ “Our data relies on manual data entry and we can’t be sure of its quality”

Emerging AnalyticsCapability

▪ “Our analytics are reactive to business needs, resulting in redundant reports”

▪ “We don’t have the data science capabilities to build advanced models”

▪ “Our models lack business relevance and fail to take practical constraints into account”

Challenges with User Adoption

▪ “Our business users won’t adopt a new solution – they are stuck in legacy processes and workflows”

▪ “We have so many reports and dashboards, but none of them are taking off”

▪ “We don’t have a centralized body to connect analytics to evolving business strategy”

Listen for (and Seek) Watch Outs from End Users!


Journey to transformative analytics is not just about data…

▪ Missing data that is critical to strategic alignment

▪ Critical gaps in software, infrastructure, or data capture

▪ Changes to data governance

▪ Who to hire versus outsource▪ Critical skills gaps to be filled, for

both analyst and business users▪ Staffing size, arrangement

and/or organization

▪ Process development▪ Process optimization: right data in

the right hands at the right time▪ Barriers/gaps between data and

decisions

▪ Drivers of trust in the firm’s analytics capabilities

▪ “Rituals” that elevate analytics▪ Sequentially introducing analytics

to new areas of the firm

▪ Critical gaps to close in the content and reporting of analytics

▪ Cultivate an analytical agenda with input from leaders

▪ Allocation of resources between different analytic applications

People

Feasibility

Processes

Culture

Alignment


Socialize and Refine | Start with a small group of “willing” stakeholders to socialize the insights and refine positioning for wider distribution


Funding of Innovation and Analytics | Fund from the right spot for best results

▪ Innovation often fails many times before it succeeds

▪ It’s about the process by which you fail it fast…

▪ … and scale the successes

▪ Risk is inherent, but ROI must be demonstrated

▪ Funding from parts of the organization that are focused on traditional execution, averting risk, and hyper efficiency does not work


About Axiom


Growth, execution and transformation

experts

Firm Background and Profile

▪ Founded in 2005 to help leaders improve results by aligning strategy, innovation, organizational capabilities and design

▪ Our partners have an average of 20 years’ experience as consultants and corporate executives

▪ Our consultants come from top MBA programs, leading consulting firms and Fortune 500 companies

▪ Over 350 clients served across numerous industries

▪ Three locations: Chicago, NY, Brussels (EU)

Focus on long-term,

sustained impact and results

Insight and customized solutions that are designed for

implementation

About Axiom Consulting Partners


Our work focuses on Revenue Growth & Business TransformationWho We Are

Go-To-MarketCustomer EngagementSolution BundlingStrategic Pricing

Sales EffectivenessSales StrategySales OrganizationSales Performance

Growth StrategyBusiness Model DesignBusiness StrategyMergers & Acquisitions

Organization EffectivenessOperating Model & Organization Design

Strategic Cost TransformationOperational Excellence

Merger Integration Support

Leadership & TalentTalent ManagementTeam Effectiveness

People AnalyticsDiversity & Inclusion

Change AccelerationBusiness Alignment & Focus

Change ManagementCulture Shaping

RevenueGrowth

RevenueGrowth

BusinessTransformation

BusinessTransformation

Founded in 2005, served 350 clients

across sectors

Highly experienced professionals with global capability

Deep business, AI and Behavioral

Science capabilities


Our Firm has supported strategic initiatives at firms of different types and sizes including:


Mark J. Masson, Partner

Expertise

Mark is a highly-experienced advisor and partner to senior leaders, primarily in people intensive businesses. He serves senior leaders of large law, accounting, consulting and financial services firms on a range of topics to create value by: discovering new pathways to growth, increasing go-to-market effectiveness, maximizing return on the service and talent portfolio, and enabling effective operational execution. Mark’s work blends the nuance and experience of firm and change leadership with cutting-edge business analytics.

Professional Background

Mark has worked with senior leaders across a spectrum of industries to improve execution of strategy. Areas of focus are strategy development and execution, governance and leadership, organizational design, partner performance and compensation, and strategic workforce planning.

Prior to joining Axiom Consulting Partners, Mr. Masson spent five years as a pilot plant operations leader and chemical engineering R&D scientist for Merck & Co., Inc. While at Merck, he developed chemistry for groundbreaking pharmaceuticals in a variety of disease classes, led the startup effort for a critical phase of the construction of a $500MM pilot plant facility, and managed plant operations for a portfolio of over 50 preclinical drug candidates, overseeing engineers and union personnel.

Publications & Affiliations

Mark frequently writes and speaks on growth, execution, and technologies and tools that are revolutionizing firm management and leadership, with his work being published most recently in Accounting Today and The Legal Marketing Association publications. He is also the author of “The Lost Art of Governance,” co-authored with Grant Thornton and “AI is the Future of Everything, Right?” for Strategies – The Journal of Legal Marketing from the Legal Marketing Association (LMA). Additionally, Mark presented the groundbreaking work with DLA Piper – “Harnessing Predictive Analytics to Drive Client Growth and Retention” – at the LMA National Conference in 2017.

Education

Mr. Masson holds a Master of Business Administration from The J.L. Kellogg School of Management (Northwestern) and a Master of Science in Chemical Engineering from The Pennsylvania State University.

Notable Achievements

▪ Designed structure, process, analytics and tools for new workforce planning function of a major public utility.

▪ Designed and led predictive analytics modeling to develop client growth strategies for AmLaw 20 firm.

▪ Developed strategy and implementation plan for one of the world’s largest law firms.

▪ Developed go-to-market strategy and change management plan for the U.S. firm of one of the world’s largest professional services firms.

▪ Led Board’s review of governance practices, leading to a vote of the Partnership for Partnership Agreement modification for a major professional services firm.

▪ Redesigned the partner compensation system and led a change management process for a large public accounting firm.

▪ Developed a customer intimacy strategy and company-wide change management plan for a large regional bank, including system, people and analysis needs assessment and customer interface approach.

▪ Implemented equity transition plan for a company that provided a path to ownership transition and extraction of value created for founders.

▪ Developed external partner admit process for a major U.S. management consultancy.

CONTACT MARK

312.202.3006

[email protected]

mailto:[email protected]

partner talk #2: modern data strategy: how leading firms

Documents