Case Study: Lob

Improved Forecast Accuracy by 10%

Used probabilistic modelling to improve revenue projection accuracy by 10%

Weeks turned into hours

Turned seven week planning processes into weekly forecasts

More time for business partnering

Executives now see projections on demand and are confident in the outcomes themselves

Industry

B2B SaaS

Employees

350

Funding

$82.3M

Lob provides direct mail and address verification APIs which allows businesses to send personalized mail to their customers.

Technologies

The problem

The Lob team relies on a complex corporate model comprised of 100s of drivers and inputs to accurately inform a number of financial KPIs including cash flow and gross margin, and to inform pricing strategy.

With their intricate requirements combined with the need to be agile, they constantly ran into the limitations of spreadsheets:

Lack of flexibility and scalability — Being a fast-growing company with not only SaaS, but variable usage revenue and costs, it was notoriously difficult to project accurately and quickly in spreadsheets as they couldn’t confidently drag assumptions across time. With manual planning cycles taking weeks in spreadsheets and a need to forecast at multiple time frames, spreadsheets were unable to keep up.
Inability to present scenarios in a distribution model — The world is not a construct of linear assumptions. With a statistical modelling approach and a strong belief that scenarios should not be equally distributed (downside, base case, upside), the Lob team couldn’t benefit from objective confidence intervals in their spreadsheet models to drive better business decisions.
Inability to effectively communicate outputs — With so many variables driving the business, the executive team must understand how tweaking select inputs would change business outcomes. With Lob's spreadsheet model, it was practically impossible to give executives both the views and editing power that they needed to be able to understand what was worth executing on.

The solution

JS joined Lob as Head of FP&A, and saw how automation could level-up the finance team and free up time to focus on strategic initiatives. JS achieved this using Causal:

Dynamic and accurate forecasting and scenario planning

Moving from over versions for each planning cycle to now one corporate model in Causal that simply needs tweaks when changes in assumptions are made has allowed the Lob finance team to forecast on a weekly basis while having the utmost confidence in their numbers. The team can now spend 85% of their time looking forward, whereas before Causal, they spent 85% of their time looking back.

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Self-serve dashboards & reports for critical stakeholders

In lieu of creating a customized workbook for each stakeholder (executives, the board), in Causal, the Lob Finance team has created customized ‘views’ for each team member so that they can access the data that’s important to them ‘on demand’.

With up-to-date actuals and accurate projections at the right level of detail, each team member is now able to make better decisions without having to rely on finance to manually create a new workbook each time a request is made.

Ability to calculate a range of possible outcomes in forecasts

Lob is now able to effortlessly change inputs and drivers and have the outputs show a range of confidence rather than having equally distributed scenarios. Instead of communicating absolute outcomes to the executive team, Lob’s finance team approaches scenarios by saying that there is an x% chance of this outcome, resulting in better and more accurate decision making in the short and long-term.

The outcome

Software that scales the human behind it

With Causal being user friendly and intuitive, JS can leverage its capabilities whilst maintaining a lean team at scale. By cutting modelling time from weeks in spreadsheets to hours in Causal, JS is now spending his time focusing on accuracy, velocity of insights, and business partnerships.

Business partnering and better business decisions

Combining streamlined probabilistic models with personalized dashboards and views for critical stakeholders has empowered the Lob finance team to act as valuable business partners as executives aren’t only seeing the right projections at the right time, but they are more confident in the outcomes themselves.

This has put JS and the Lob finance team in a position of impact as they can truly support the executive team as they work to make better business decisions.

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350 Employees, $82.3 Million

“Causal has allowed our finance team to perform at a new level — we now spend 85% of our time looking forward instead of backwards and are able to empower better decision making across the business."

JS pelletier

Head of FP&A

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How Lob Uses Causal to Power Weekly Forecasting