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Analytics Sub-Contracting: Yes It's a Good Idea

This is a post that’s geared a bit more towards my fellow research and analytics suppliers. But if you’re mainly a research buyer, you might find this interesting as well, when thinking about how you purchase research.


I launched a LinkedIn poll last week, looking for people’s thoughts on subcontracting analytics work, and the results were a bit surprising. It was nearly unanimous that sub-contracting out analytics work should be reserved for 'special projects' only.


Now I was surprised by this because I am a big proponent of sub-contracting analytics work. I think everyone should be doing it on a pretty routine basis. The reason is one that I think goes underappreciated, which is, it is often in the best interest of clients.


I can actually show this through simulation, and we'll see that in more detail below. But first what do we mean by sub-contracting?


What is subcontracting?


In short, what I mean by sub-contracting is recruiting a person or firm with specialized skills to complete a part or a portion of a larger job. In the market research industry, sub-contracting analytics work may involve getting special help with crunching the numbers to support a segmentation project, conjoint exercise, or other modeling work. It's not the same as outsourcing, as you're usually seeking out specialized skills or capabilities in a sub-contractor, and rarely would you ever find a sub-contractor with a lower rate than you'd find internally. Generally, the goal is to fill in any potential gaps on your existing team.


The problem of 'gaps' on your team


And this is where I think a lot of the hesitation comes from: the idea of "gap". I don't know any consultant who doesn't speak about "gaps" on their team without some degree of regret or frustration. I've never been involved in commissioning projects where the idea of a "gap" wasn't intuitively seen as a major red flag.


But is that really rational? Do we really expect our research teams to have every capability under the sun? And do we risk our ability to build long term trusted partnerships if we do? Isn't there an advantage to having well developed partnerships with consultants, who know your business, and can recruit necessary talent as needed to fit your needs?


A thought experiment


Let's try a thought experiment to understand the implications of this aversion to "gaps". Consider the following propositions:


  1. You are a research and analytics consulting firm with a talented client service team that can drum up 100 jobs a year (picking an arbitrary number for illustration purposes)

  2. The average size of each job is $30K, you’re currently doing a brisk $3 million in business per year

  3. You notice that jobs that include a data science component command a 50% premium and up to 20% of jobs could incorporate data science.

  4. A data scientist will cost you $150K

  5. Contracting out the data science work would eat up 60% of the premium you can charge

On the surface this seems like a no-brainer. 20 jobs a year pulling an extra $15K each means an additional $300K in revenue. Back out the $150K salary and you’re making an extra $150K per year profit by hiring the data scientist. Contrast this to the $180K cost of contracting out ($300K*0.6), and you would only be left with $120K profit. Hiring seems like the better option: you make more money and you've filled in your "gaps".


The trouble, however, is these are averages. There’s a range of possibilities to account for. Your average job size might vary, the average data science premium might vary, and the number of actual data science jobs your team could sell might vary. So we need to simulate this variability. The parameters I've picked for this are summarized in the below table.



Hiring marginally beats sub-contracting but with greater risk


Generally, hiring a data scientist produces a much greater range of outcomes. The mean outcome is higher than subcontracting ($150K vs $120K by definition), as is the 90th percentile outcome ($310K vs $208K). But importantly, the lows are much lower too. 8% of the time hiring the data scientists loses you money. In fact, due to the lower uncertainty in outcomes, subcontracting outperforms hiring in 42% of scenarios. Hiring continues to have a slight edge, but it's not nearly as large as it may seem, and you have to be tolerant of possible negative returns.




Still, the differences go further. Because outside of profitability the different strategies impact what levers you have to pull to improve profitability.


Changes to sales and pricing strategy


From these simulation, we can use regression to understand which factors drive profitability in each case. Looking at the hiring scenario, we see that profitability is driven primarily by increases in the data science premium and mean contract value. That is, there is a strong incentive to increase prices for clients. This extends to both the data science premium, as well as the base cost for non-data science work, because non-data science work anchors price expectations. In terms of considering client welfare, an across-the-board price increase is probably not in their interest.




In contrast, profitability in the subcontracting scenario is driven mainly by the ability to manage subcontractor costs, with the revenue drivers playing comparable but smaller roles. Fortunately, managing subcontractor costs is thoroughly in the control of managers, mainly through efficient project management and minimizing waste or duplication in the job.


This, of course, isn't to say that revenue isn't important. Of course it is important. But we want to avoid scenarios where pricing and business development decisions are driven by operational concerns instead of by client needs. Client needs should dictate how services are sold and priced, and therefore staffed, not the other way around.


Applying this to your business


Now, you might object: “Ken, you just pulled these numbers out of your ass! How are we supposed to believe you?” And you’d be right, I did. Change the specific assumptions, and the specific outcomes will change, and the recommendations will change (did I mention I’m open for business?). But the overall trend, will likely hold:


By leaning towards hiring to avoid ‘gaps’ on the team, there is an increased incentive to charge higher prices across all projects. By sub-contracting, you focus more on efficient project management, and ensure pricing decisions are driven by client demands instead of operational concerns.


And yes, there are times when it makes sense to fill in those ‘gaps’. Generally, this is when average contract value increases or the available 'data science premium' available to capture increases. But even if you have the business, you will likely need to make hiring decisions at the margins, which suggests subcontracting should be a critical part of any responsible growth strategy, until volumes are secure enough to justify long term hires.



Client-focused consultants should embrace sub-contracting


In short, subcontracting helps de-risk a business and avoids perverse incentives that can motivate suppliers to make decisions counter to the client’s welfare. This is really important, and one of the reasons I’m happy to provide subcontracting support as part of Rapport Research’s services. It may not be as attention grabbing, given NDA’s prevent me from sharing the details, but it provides important value in the industry.


And for research buyers who may be reading this, I hope I’m making the case, you should not look poorly upon any consultant who may confess to there being some ‘gaps’ on their core team. As long as they can cobble together the talent they need for your project, staying nimble, staying lean, is how they keep costs down and will reliably deliver value for your money.

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