Legal Service Design Jam - Report 3: Technology

Following on from our introductory article ‘Legal Design Jams – 9x more productive than Jack Bauer’, Panicos Iordanou reviews the output from the ‘People’ team at the Legal Service Design Jam hosted by LexisNexis, in collaboration with The BIO Agency, at Janders Dean’s ‘Horizons’ conference in May 2016.

Reviews of the output from the other two teams can be found here:

Technology: key considerations

Over the course of the day – and in particular the technology team’s initial brainstorming session, a handful of themes emerged which would inform the approach and focus of the team. These can be summarized as follows:

  • The rate of change – not just in technology terms but across the industry generally – has been rapid in recent years. Against that background, the role of technology is sometimes considered in a silo.
  • Law firms need to stop viewing technology as a series of tools to achieve certain defined tasks and look at it more as a lever that can be applied to almost any other process or objective in order to achieve step changes in efficiency and ROI.
  • Some of the biggest wins are likely to come through using technology to improve or re-invent frequent and/or business-critical processes.

Technology and pitching: the perfect match

Having identified process improvement as a key way to leverage technology the team looked for a specific challenge to work on. After settling on ‘business growth’ as a general theme, it was a relatively short jump to identifying the pitching process as a suitable area to focus on.

Reviewing the status quo and planning a new approach

Feedback from team members with varied backgrounds and job titles, including lawyers, 'techies' and a Business Development Lead from a top flight international law firm - suggested that, in many cases, the approach to pitching can be far from uniform across a firm. There can be significant variations depending on which department – or individuals – are involved.

With that in mind, the team identified that the starting point would be to put a standard process in place.

They started by imagining what a perfect pitch would look like and identified two key requirements:

  • it should be efficient – the client/prospect must find it clear; smooth; and user-friendly
  • it should be as quick as possible (without compromising the above).

With those in mind, the team mapped out the key steps in a pitching process and the order in which they needed to occur.

Pitching process

 

It was then necessary to consider how to improve each step to meet our twin objectives of improving efficiency and speed.

Knowledge (or data) is power

It quickly became apparent that the key to meeting these objectives was data.

During the planning and pitching phases, data would be needed to prepare an informed and compelling pitch and to enable firms to make quick and sensible decisions at each stage.

Win or lose, firms would then need to identify and capture as much of the (useful) data generated during the process as possible and use it to learn how to improve their chances of winning the next pitch.

Here is a summary of some of the key steps that were identified for finding/using this data:

tabledatatech

The rapid improvement phase: Machine Learning

A key part of any law firm’s strategy for winning more pitches would be to ensure that they learned as much as possible from every pitch. It wasn’t going to be possible for a single person - or even a team of people – to analyze every aspect of every pitch in enough detail to build up a truly comprehensive picture of what separated a winning pitch from a losing one.

In essence, firms would need a quick and efficient way to review and spot patterns in huge amounts of data. Framed that way, the problem was actually one that applies across a number of very familiar tasks for any lawyer e.g. disclosure.

Enter the machines.

Just as machine learning (or ‘predictive coding’ can be used to analyze and learn from millions of documents in the context of a disclosure exercise so could it be used to analyze and learn from thousands or millions of bits of data generated during the course of a pitch for new business.

An important feature of this would be the ability to extract important client data points from 'free text' historical data: because 'machine learning' systems are able to look for patterns in documents (and so learn where to look in the future) data can come from so-called 'unstructured' data, rather than structured databases (including, for example, Word documents, emails and PDFs).

Translating from ‘machine’ into ‘human’: data visualization

The final piece of the puzzle was working out how to make the huge amount of data needed for (and generated by) pitches as useful and accessible as possible.

Since the human brain processes imagery 60,000 times faster than text, finding a way to present the data in a visual format was key.

With that in mind, the team set about creating a ‘dashboard’ which would provide:

  • a quick visual snapshot any given pitch process and what’s coming up next, together with details of whether progress is on/ahead of schedule by comparison to previous successful pitches; and
  • dynamic and graphical visual representations of key decision-critical data e.g. % of pitches won for this client and industry and for similar clients/industries; stats on which team members are most likely to succeed in pitching to this client or for the matter at hand; and cost predictions (what would quote need to be to win, what would our expected recovery level be at that quote etc)
 More jam here...

Don't forget to read our reports on the output from the other two teams:

Report 1: People

Report 2: Process

Or, for a brief overview of the legal service design jam and to find out why legal service design jams are 9x more productive than Jack Bauer read this post.

Finally, to read more about the actual process of running a design jam, read this article by Alex Smith (Senior Product Lead - Platform Innovation at LexisNexis).

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