Lextech spreadsheet

Lextech spreadsheet


  • How to Measure Your Organization’s Data Maturity
  • Diligen Review: Tackling Due Diligence and Contract Review Pain Points with Machine Learning
  • He and partner Dino Colombo created software to manage and monetize the flow of business into Colombo Law, a personal injury law firm. The software was then named Lead Docket and released to the public with a welcome reception and rapid growth. It was brought to my attention by multiple Personal Injury attorneys, including my own! This is a rare occasion in the legal technology space — to have law firms promoting your platform raving about the interface and its impact on their business.

    This is an impactful metric for their client base who are dominantly Personal Injury and Mass Tort firms running paid ads. From the moment you log in, it is clear what the platform is intended for — lead management and follow-up. When your law firm is paying for advertising from various sources, excessive CRM functionality can work against you. One of the apparent differences Lead Docket displays is that the system was built to drive action by specific users. Namely, the front of the house staff; receptionists, secretaries, intake personnel and the attorneys who vet and assign new cases.

    The system is organized to see upcoming appointments and leads that need to be worked by using automated tasks and messages to make the processes as efficient as possible. Complete with thoughtful nuances added to lead details such as appointment information, contact information and more. This eliminates the step of checking a calendar to see which attorney they are meeting with that day.

    To that point, every action in Lead Docket shaves time off processes. Here are some examples, to name a few: Address verification when creating a lead speeds up data entry and improves accuracy.

    The level of detail in the identification of where a lead came from demonstrates the first-hand knowledge the Lead Docket team poured into the application. Not only do you identify the source of the lead, but the vehicle by which it was originated. For example, someone who contacted you via Web Chat, may have heard about you from a TV ad and someone who saw your Billboard, may have filled out a web form to contact you.

    Assessing the Value of Case Customizable intake forms give your intake staff the ability to quickly qualify and categorize a case with fields specific to each practice area. Once complete, the attorneys can choose to take on the client or refer them out. Referral partners can receive an email with the complete lead details your staff collected.

    Follow up emails and tasks ensure that all leads that have been referred out are automatically tracked. The system is practically chasing down your referral fees for you! Never again will you need to copy and paste lead details from your email. Tasks and Automation Managers that have had to enforce the use of a CRM will say that the hardest part is getting employees to log their activity regularly. Lead Docket makes these processes painless with tasks assigned to initiate processes that keep the team focused on working the funnel.

    After an unsuccessful attempt at contacting a potential client, Lead Docket will prompt you to make another call attempt after a few hours. After sending a lead documents for electronic signature, Lead Docket will prompt to follow up if the documents have not been signed. Messaging Tools Multiply Success Rates Any successful business person knows that it is our responsibility to pursue potential clients. This is common practice with car dealerships, dentist offices, and even barber shops.

    Not only can you send and receive text messages through Lead Docket, but picture messages can also be exchanged and saved in their case file. Document Automation and eSignature Fee agreements, letters and other documents can all be automated in Lead Docket.

    Seamless eSignature is possible thanks to an integration with DocuSign that sends out the documents and automatically notifies you when they are completed. Custom reports are easy to set up and reflect real-time results of campaigns, geographic location, case conversion rates, cost of acquisition and more. Case Management Software Integrations After a client retains the firm, the traditional process of casework begins.

    Current integrations include: TrialWorks.

    Stage 1: Manual data processing We consistently hear stories from sales management about their frustrations with manual data processing. These sales people are also crunching performance numbers to share with their field sales manager. The field managers pull data and analyze sales rep performance. Regional managers are doing exact same thing. And so on… all the way up the organization. Since every individual is creating a unique document, this manual approach lacks consistency and results in large amounts of time wasted digging into how specific numbers were reached and verifying accuracy, rather than understanding what the data is trying to tell us.

    Stage 2: Death by dashboards The data sharing challenge is often addressed by flooding each individual in the chain with even more data. BI Business Intelligence is one of the most mis-used tools in the enterprise. This often encourages the creation of shadow IT and data teams local team members creating tools to answer the many specific questions their team has, outside the overall data and IT accountability chain. Once again, large amounts of time are wasted in creating and navigating these tools in an attempt to reach answers.

    Both manual data processing and magical dashboards mask one of the core problems at this stage of maturity: multiple, inconsistent sources of truth.

    The same answers reside in different systems and each part of the organization looks in different places for their answers. Copies of data are spread around as it is crunched and report writers typically grab whatever is easiest to access as only key individuals typically know where and how to get to the source of truth.

    Stage 3: Data tells a story As organizations evolve digitally, the data starts to tell stories to their teams. This typically starts when efforts focus around understanding what information employees in various roles actually need to do their jobs successfully.

    This focus simplifies the data discussion and brings business and IT into partnership to serve those employees. Focusing at the employee role level also uncovers that answering the most valuable questions what is our sales performance — and why? To prevent this from leading to yet more information overload for the team, aggregated data must be stripped down to what is specifically required to answer the questions at hand.

    Successfully achieving role focused data requires close coordination between IT and the business to understand the needs of the role and deliver the right answers at the right time. For example, it is important to understand where various roles intersect i. Stage 4: Intelligence emerges As intelligence from data begins to emerge, organizations start to see real business results.

    The speed to produce value starts to increase as employees learn the tools, improve their understanding of the data, and grow more comfortable. As you become more sophisticated setting alerts and exceptions, employees should feel more supported with proactive information. However, even as your data provides more relevant information, good cross functional development teams are still crucial. At this level of maturity, personalized customer experience can become a reality.

    Your data has matured enough to identify individual needs better and provide your employees with the necessary tools to raise your customer service levels. Counterintuitively, your teams may begin sharing more data with customers and vendor employees because your information is now more reliable. In our experience, people in your organization who never talked to customers will begin to do so and naturally want to share valuable and relevant information. If you are indeed finding that your employees are sharing more information, make sure your data governance processes are working efficiently.

    For example, have workable guidelines for sharing data with customers and vendors. This includes what data to share, easily understood restrictions, and a process for how to share data. At this stage, restrictive, traditional data access limited to a few key users described in Stage 2 should be gone and employees should generally feel comfortable accessing data as needed. Employees free to pursue investigations within the data can derive novel solutions. Lastly, improved business outcomes should be more tangible and measurable as your data maturity progresses.

    Although you may be just starting machine learning and other AI applications, your employees should already be deriving significant value from your data. Stage 5: Transformed organization No matter what you read online, there are few organizations operating at this stage.

    At this stage, Machine Learning is mature, your data is clean, and your teams have broad data science skills. Your employees use data to work on behalf of each other and customers. In general, the organization is operating with a flatter structure and governance is more cross functional than department led.

    Machine Learning is automating low value, repetitive work, and employees are able to focus on high value activities, especially exception handling. They are using algorithms for deeper analysis on competitive pricing, service, product profitability, forecasts and so on.

    Indeed, your new data driven view of the market is game changing. We believe the most difficult management issue for this stage is the pressure to use algorithms to create new business models that can impact employee roles. While this will threaten employees, change is never easy.

    Teams of people may be redundant, not just from low value work that is eliminated, but from changes in how you price, develop products, and interact with each other, vendors and customers.

    Identifying where your organization fits in the Data to AI Maturity curve is critical to creating a plan for moving your data use forward. Once you know where you stand, you can take action. He is passionate about refining the future of work, helping organizations transition from a set workplace to work-any-place. Alex is the author of Billion Dollar Apps and an adjunct professor of computer science at Northern Illinois University.

    One of the apparent differences Lead Docket displays is that the system was built to drive action by specific users. Namely, the front of the house staff; receptionists, secretaries, intake personnel and the attorneys who vet and assign new cases. The system is organized to see upcoming appointments and leads that need to be worked by using automated tasks and messages to make the processes as efficient as possible.

    Complete with thoughtful nuances added to lead details such as appointment information, contact information and more. This eliminates the step of checking a calendar to see which attorney they are meeting with that day.

    To that point, every action in Lead Docket shaves time off processes.

    Here are some examples, to name a few: Address verification when creating a lead speeds up data entry and improves accuracy. The level of detail in the identification of where a lead came from demonstrates the first-hand knowledge the Lead Docket team poured into the application.

    Not only do you identify the source of the lead, but the vehicle by which it was originated. For example, someone who contacted you via Web Chat, may have heard about you from a TV ad and someone who saw your Billboard, may have filled out a web form to contact you. Assessing the Value of Case Customizable intake forms give your intake staff the ability to quickly qualify and categorize a case with fields specific to each practice area.

    Once complete, the attorneys can choose to take on the client or refer them out. Referral partners can receive an email with the complete lead details your staff collected. Follow up emails and tasks ensure that all leads that have been referred out are automatically tracked. The system is practically chasing down your referral fees for you!

    Never again will you need to copy and paste lead details from your email. Tasks and Automation Managers that have had to enforce the use of a CRM will say that the hardest part is getting employees to log their activity regularly. For example, it is important to understand where various roles intersect i.

    Stage 4: Intelligence emerges As intelligence from data begins to emerge, organizations start to see real business results. The speed to produce value starts to increase as employees learn the tools, improve their understanding of the data, and grow more comfortable. As you become more sophisticated setting alerts and exceptions, employees should feel more supported with proactive information. However, even as your data provides more relevant information, good cross functional development teams are still crucial.

    At this level of maturity, personalized customer experience can become a reality.

    How to Measure Your Organization’s Data Maturity

    Your data has matured enough to identify individual needs better and provide your employees with the necessary tools to raise your customer service levels. Counterintuitively, your teams may begin sharing more data with customers and vendor employees because your information is now more reliable. In our experience, people in your organization who never talked to customers will begin to do so and naturally want to share valuable and relevant information.

    If you are indeed finding that your employees are sharing more information, make sure your data governance processes are working efficiently. For example, have workable guidelines for sharing data with customers and vendors. This includes what data to share, easily understood restrictions, and a process for how to share data. At this stage, restrictive, traditional data access limited to a few key users described in Stage 2 should be gone and employees should generally feel comfortable accessing data as needed.

    Employees free to pursue investigations within the data can derive novel solutions. Lastly, improved business outcomes should be more tangible and measurable as your data maturity progresses. Although you may be just starting machine learning and other AI applications, your employees should already be deriving significant value from your data.

    Stage 5: Transformed organization No matter what you read online, there are few organizations operating at this stage. At this stage, Machine Learning is mature, your data is clean, and your teams have broad data science skills.

    Diligen Review: Tackling Due Diligence and Contract Review Pain Points with Machine Learning

    Your employees use data to work on behalf of each other and customers. In general, the organization is operating with a flatter structure and governance is more cross functional than department led. Machine Learning is automating low value, repetitive work, and employees are able to focus on high value activities, especially exception handling.

    They are using algorithms for deeper analysis on competitive pricing, service, product profitability, forecasts and so on. Indeed, your new data driven view of the market is game changing. We believe the most difficult management issue for this stage is the pressure to use algorithms to create new business models that can impact employee roles.


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