Make Money From The Long Tail Of Billing Losses

Today, I am going to tell you another customer story- of a company that leaked revenues in numerous ways. But before that a larger picture.

The Pareto principle or the 80/20 rule has been a powerful tool for managers when faced with a large number of challenges.

The Pareto principle states that for many outcomes, roughly 80% of consequences come from 20% of causes.

The principle holds good in a variety of situations, that’s why it is a classic. 

But things have changed, technology being a powerful driving force. It was not possible in the past to deal with 80% of the causes because of huge costs for small payoffs (20%). It is possible now. 

Nothing new in that. In his book, "The Long Tail", in 2006 Chris Anderson showed that in the case of online marketplaces  money can be made in the long tail of a large number of items, each selling in small quantities. 

Exploiting a long tail of possibilities, each a small payoff, has not been done in project type industries like IT services, EPC, Industrial systems, etc. Why? Companies have relied on enterprise software more suitable for black boxes and a patchwork of standalone packages.  

A confluence of big data, shrinking devices, communications, and new enterprise software technologies have opened up the long tail of problems faced by project industries as opportunities to make money.

Today, I plan to show that there is money to be made by addressing the long tail of causes of revenue losses that IT services companies face. Mature and off-the-shelf digital technology required for this has been available for several years now.

Time for another customer story.

Data Science Engineering Ltd. (DSEL)

DSEL, a fictitious company, resembles one of our customers. It delivers projects to its customers for data preparation, analytics, and related requirements. Like many other companies, its management was usually focused on large customer contracts and major billing milestones while leaving smaller projects and incremental invoicing to its operations. 

DSEL’s has a growing business and has thousands of software engineers working for it. What’s more, its growth has been accelerating. But DSEL’s CEO was acutely aware that the company was leaking money. 

Below the radar revenue losses that everyone at DSEL knew.

1. Missed billing in new projects: Every new order was marked by no or very low billing for the first month or two.  The company’s RMG couldn’t find suitable resources in time. This amounted to thousands of dollars of missed billing in nearly every new project.
2. Scope changes or amendments: Whenever customers needed extra resources on existing projects, the RMG faced the resource crunch. Monthly billing losses were significant.
3. Employees on leave: On an average, a typical team of 10 people had at least one of them going on leave. This amounted to a billing loss of 1 resource-month and added up to thousands of dollars of loss for all the DSEL’s teams combined.
4.  People serving their notice periods: Those who had resigned but still working through their notice period would eventually depart leaving a gap in resource allocation in their respective projects. DSEL suffered significant revenue losses on this account.
5. New orders: DSEL had a stream of new orders. Its accelerating growth was welcome but it compounded the problem of billing losses at project starts. It also caused customer dissatisfaction.

If you look at each instance of revenue lost due to the above it might appear to be small. Everyone is aware of them. This is a classic case of a long tail of small losses. All such losses add up to a huge amount.

Clearly, DSEL left a lot of money on the table!

Fortunately, the bigger effects of the long tail didn’t escape DSEL CEO’s attention.

There was always a big gap between DESL’s revenue forecasts and actual month-on-month revenues. Its growing orderbook aggravated the problem. Not only DSEL missed its revenues, it missed its profit and cash flow projections. Even if the missed revenues materialized in the future, its expenses continued.  A perpetual scramble for resources, lower profits, and higher working capital didn’t escape the CEO’s attention.

Worse, customer complaints about delays in projects started mounting.

The CEO needed a telescopic view into potential billing losses over the next 3 months in addition to the billing loss in the current month and the total of the last 3 months. An ability to put any of the billing losses under a scanner and zero down to their causes was also required so that preventive actions could be launched. The CEO needed it all in real time. 

The solution that was not

DSEL’s hard working people were managing almost everything in their spreadsheets. Updating the spreadsheets manually was labour intensive.  Data inconsistencies and lack of alerts ( e.g. future resource shortage, leave plans) made it nearly impossible for them to stay on top of the situation.

The solution

If you have been following PSA Insights, you would have guessed the solution to all the above problems would be a capable PSA software. 

An advanced PSA is designed ground up to fully digitize opportunity-to-cash cycles of projects. It maintains data fidelity by sourcing validated data from its source (e.g. timesheet of person for a project, approved by project manager) and sending it where it is required ( e.g. calculation of resource hours of the same project). The PSA also has a workflow engine to let managers configure their workflows and connect them with validated data.

We joined DSEL’s team to implement PSA.

After implementing PSA

1. Revenue forecasts based purchase orders and based on resource allocation: With every new purchase orders the PO based forecast now gets adjusted for the coming 90 day ( and beyond) window. As when asset allocation happens the allocation based revenue forecast gets updated. The variance between the two at any future date is clearly visible to the CEO and other managers. A specific week’s ( or month’s) variance can be drilled down to identify resource shortages. 
2. The opportunities pipeline: The above approach is also taken for all deals likely to be finalized in the rolling 90 day ( and beyond) window - deals with say > 70% confidence ( settable) would be considered for revenue forecasts. 
3. Leaves and departing employees: Resource allocation and their corresponding revenue forecasts automatically get adjusted by the PSA to account for absence of employees. Suitable alerts have been configured.

A culture of proactive behaviors

The telescopic (future) and microscopic (drilling down to detailed factors) visibility enabled actions to reduce the gaps between revenue forecasts based existing and future orders and forecasts based resource allocation that is feasible. Here are a few examples of actions that DSEL’s managers started taking using the PSA.

1. Identify resources with matching skill sets from the bench.
2. Hire short-term contractors.
3. Encourage people to plan leave in advance.
4. Give enough lead time for talent acquisition.
5. Collaborate to prevent problems

Bigger gains

The improvements made possible by a PSA platform triggered more beneficial changes beyond higher revenues and profits. Better resource allocation is now being done by avoiding skill set mismatches. This optimizes costs, avoids demoralizing people, ensures best deliverables to customers. 

Conclusion

After implementing the PSA, DSEL could reduce revenue losses by nearly 40%. The company made money from a long tail of revenue losses.  Addressing long tail problems using the PSA technology yielded 10% more revenue when compared with revenues from previous year after adjusting for the order book. 

Kytes, an advanced PSA, helps you overcome a long tail of seemingly small problems and make handsome gains. To know more or watch a demo, please contact us at sales@kytes.com