Concurrent Triangulation
How does one deal with the limitations & keep up with the timing challenges over those Research & Design phases?
In case you’re interested, here’s a LINK to the post.
There’s absolutely nothing in the world that is straightforward & simple & one could so easily, nearly drop the phrase “LINEAR” when one is talking in the context of research, design & phases of product development.
Think of how products in general & software in particular got built decades ago. Each stage right from conception to deployment was thought to be mutually exclusive of the other, with the subsequent phase mandatorily facing a deferred start until such time that the prior phase doesn’t culminate & results in a complete handover.
One may argue that although one’s still adhering to Agile product development there’s still some bit of linearity prevalent as some phases ought to be mutually exclusive of each other even to date. When there’s no denying that altogether, the penchant to club phases & run them as inclusive events in parallel always seems to get the better of some teams, helping them get their noses in front of the competition, which is what it all comes down to effectively at the end of the day.
TRUTH be told, there seems to be a daylight of gap between the metrics that stood to represent team’ health between 2015-16 & 2019. And if the trendlines in the graphic were to be any testimony following-on one could just imagine the state in as of today in 2025.
“If concurrency was the one-stop solution to all problems that pertain to the optimization alongside channelizing effort, then the world would have been a much better place today, but from what we see that clearly isn’t happening”
But having said that there are situations, places where concurrency could act like a godsend. Take the example of research. Most times, there could be near zero to very minimal overlap between the individual chunks of work that aggregate towards forming what is hardcore research – the fact finding or even landing a few handsome discoveries.
Like for instance: someone could first pin on & collect all the qualitative data which could then be followed by a collection of the quant bit, ultimately getting analyzed & aggregated to qualify as the all-important insight. [SEQUENTIAL RESEARCH METHODS]
Given how most research undertaken in the early product phases is either exploratory (towards deeply exploring a given phenomenon) & transformative (to serve as supportive method towards serving a theory / hypothesis), concurrent research methods could suit most teams better.
Here are few basic differences between sequential & concurrent methods of research:
NOTE: Although pertaining to research alone, this understanding could practically be extended & extrapolated to any other phase in product, like say Design / Development as well.
Good research often does employs triangulation which intern hinge on the clubbing of multiple research legs, datasets, methods, theories towards addressing a question given a situation. It could be employed across both QUAL & QUANT research types.
And when one essentially employs Concurrent Triangulation when one mixes those individual legs, datasets, methods & factors them in over one single iteration towards sense making / getting to an inference.
At a basic level, triangulation could be of four different types:
Lets’ dive into a couple of case studies to understand how concurrent triangulation fits in over a practical scenario.
CASE STUDY 1: A Fintech Startup launching an App
Background:
An org. is looking to build an understanding of the markets, the users, their pain points (Major / Minor) prior to commissioning the building an app whilst gleaning enough insight into whether or not the strategic direction happens to be good fit for the org. looking to pass the desirability, feasibility & viability tests
Conventional route:
Conducts a short survey to gauge the attitude of the user groups towards investments in the first place whilst ensuring a wider sample space coverage as it could be quite a viable proposition across age groups
Tracks survey results to understand if a basic demand for the App is established
Takes additional 1:1s with the one’s who are willing to participate towards gauging their behavior towards why they think investments into certain classes are good & why they think the others are risky
Concurrent Triangulation:
Floats a survey to arrive at how many salaried employees in the age groups 25-50 would be willing to make investments whilst understanding their perception of RISK
(Investments are a risk alright but I am willing to invest if it is proven to be safe)
Resorts to a few additional user interviews in parallel splitting the sample space on age group along with the introduction of various asset classes RISK
(Equities are riskier, yes. But I think one could land a handsome reward if one puts their foot right, when the latter part seems to be the most difficult)
Triangulate the results of the above 2 steps to determine the likelihood of a given demographic to take the risk pitted against their perception of rewards
The data could then show a rather skewed distribution towards a certain asset class validating the decision to proceed with the App targeting the given market segment
(Equity investments are attractive but if there is a way to manage the risk element, it could be a welcoming route for most)
CASE STUDY 2: A big-ticket Event Management Org.
Background:
An org. that’s into event management organizes mega events flowing in international artists in to perform for an audience & now they are looking to replicate the M.O. over an entirely new geographic region, so understanding what fits the bill as for the audience is something of a first step they plan to take
Conventional route:
Floats a banner advert targeting the given geography placing it strategically at high-traffic sites / social media to measure the interest by intern pinning on the CTAs
Takes the survey route to gauge the no. of people who have been / would like to go to a rock concert if in case it was accessible in their city
Tracks survey results to establish a satiable demand pitting it against the size of the sample space so as to further the idea
Concurrent Triangulation:
Floats a survey over a sample space of 5K trying to establish interest in music concerts
(Yes, people seem interested, indeed starved for concerts in the geography)
Hits up 18 – 40 age groups to understand the preferred genre of music & get a fair idea of the willingness to pay given the price range of the tickets
(Excitement is at its peak as for rock & pop concerts, when most are ok the price point seems to be a concern for a select few)
Uses Triangulation to combine the results of the above 2 steps to arrive at a workable result
(given the sample space in question, a rock concert seems to the most preferred)
In Conclusion:
There are many ways & means to conduct good research. When concurrent triangulation could prove to be effective over conventional methods that happen take a stepwise approach to slicing up the data-pie, the one important piece of wisdom that ought to prevail above all else is NEVER & ABSOLUTELY NEVER to proceed with ideas sans clearly establishing data to support that hypothesis, that conviction, that “gut feel”. Test out your ideas thoroughly.