A previous article supplied a of conceptual frameworks – analytical buildings for representing summary ideas and organizing knowledge. Knowledge scientists use such frameworks in all kinds of contexts, from use case ideation and validation of machine studying fashions to productization and operation of user-facing options. The framework sort (e.g., hierarchy, matrix, course of circulate, relational map) and framework dimensions (e.g., categorical, ordinal, steady) largely decide the feel and appear of a conceptual framework. Whereas the earlier article devoted more room to a dialogue of framework varieties, will place the highlight on framework dimensions. With the assistance of a real-life case research, we’ll see how modifying the framework dimensions can yield a perceptual shift that may unlock new insights. This deep dive goals to raised equip readers to make use of and construct conceptual frameworks extra successfully.
Be aware: All figures within the following sections have been created by the creator of this text.
A Primer on Framework Dimensions
Whereas the framework sort defines the construction of what you are attempting to symbolize, the framework dimensions decide the content material. The scale usually fall into three courses: categorical, ordinal, and steady. The next sections study this classification of framework dimensions in additional element and go over some elements that it’s best to contemplate when together with a number of dimensions in a framework.
The Massive Three
Allow us to begin with the category of categorical dimensions, which is presumably the only class of dimensions. Because the title suggests, the dimension consists of a finite set of discrete classes that needn’t be in any explicit order. As an example, if the dimension represents an organization’s markets, it could possibly be divided into geographic classes reminiscent of “USA,” “Germany,” and “China.” Equally, you may have a categorical dimension that breaks down the corporate’s merchandise into totally different product segments (e.g., by components, relevance to prospects, and so forth). It’s also a good suggestion to maintain the MECE precept (mutually unique and cumulatively exhaustive) in thoughts each time you’re breaking down a dimension into smaller classes; in any case, you need the classes to completely cowl the scope of the dimension and keep away from redundant classes.
Ordinal dimensions are much like categorical ones, with the extra function that the classes making up the dimension are additionally ordered ultimately. The ordering means that you can say that one class is “better than,” “lower than,” “equal,” or “unequal” to a different. Suppose you took an organization’s set of markets and ranked them by a criterion like profitability. The rating would impose an ordering on the set of markets, thereby producing an ordinal dimension representing the profit-based (ascending or descending) ordering of markets. Nevertheless, the rankings needn’t suggest that the profitability values of nations are evenly spaced; the profitability hole between the top-ranked and second-ranked nation could possibly be totally different from the hole between second- and third-ranked nations. Ordinal dimensions are additionally typically used to assemble survey questions, taking the type of a Likert scale (e.g., “disagree,” “impartial,” “agree”). The ordering permits responses throughout the survey contributors to be analyzed when it comes to the place they lie on the dimensions for every query.
Lastly, a steady dimension provides a quantitative measure of one thing. In contrast to categorical and ordinal dimensions (which encompass discrete classes or values), steady dimensions can probably tackle any worth (nevertheless tiny) inside a given vary. For instance, the likelihood, in share phrases, of some occasion occurring can lie anyplace between 0% and 100%; values reminiscent of 5%, 10% and 10.00123% would all be permissible. The values of a steady dimension are additionally inherently ordered.
Selecting Dimensions Properly
It is very important contemplate the strengths and limitations of every dimension class earlier than making use of them to your framework. As an example, you may take a look at the knowledge content material of every dimension class. The presence of an ordering and the flexibility to tackle more and more fine-grained values inside a given vary contribute to the depth of the knowledge content material. Based mostly on info content material, ordinal dimensions must be favored over categorical ones, and steady dimensions must be favored over the opposite two each time they are often measured in a granular, quantitative method. Nevertheless, the knowledge richness comes at the price of the sources wanted to acquire and analyze the information underlying the size. Additionally, presenting and explaining information-rich dimensions to an viewers could be arduous, since there’s loads of content material that must be unpacked and digested. As such, even when you use steady dimensions to carry out the evaluation, it might make sense to “bucket” the continual knowledge into ordinal and even categorical knowledge to simplify what’s proven to an viewers.
Moreover, since frameworks can contain a number of dimensions, it is very important obtain an optimum interaction between the size. There are at the very least two primary choices that you’ll want to make on this regard – what number of dimensions, and what varieties, to incorporate within the framework. Particularly within the early phases of analyzing an issue, the tendency is to be beneficiant with the variety of dimensions thought-about, because the downside is probably not well-understood at this level and there’s a threat of eliminating probably worthwhile dimensions prematurely. However as your evaluation progresses, a handful of dimensions will usually stand out from the remainder as being particularly key; these dimensions stands out as the ones that designate the answer most fully and succinctly, or those that unlock novel insights. The variety of dimensions may additionally rely upon the framework sort that you simply wish to use. For instance, whereas a two-by-two matrix can solely deal with two dimensions, a hierarchy can probably deal with many extra.
When deciding on the sorts of dimensions to incorporate within the framework, you possibly can select both dimensions of the identical class or of various courses. Every class comes with a singular mind-set in regards to the underlying knowledge. Utilizing dimensions of the identical class has the benefit of letting you switch one mind-set throughout the size within the framework. As an example, if that the framework solely makes use of steady dimensions, then you possibly can probably apply the identical quantitative mind-set – and the related equipment, reminiscent of arithmetic operators and statistics – to all of them. You may thus additionally examine dimensions of the identical class extra simply (suppose “apples to apples” versus “apples to oranges”). Nevertheless, utilizing dimensions of various courses additionally has its deserves. In a hierarchical framework, utilizing totally different dimension courses for every degree within the hierarchy may also help distinguish the degrees from each other extra clearly. For instance, the top-level ideas in a given hierarchy could also be categorical, whereas the sub-concepts could also be ordinal or steady; on this case, going deeper into the hierarchical construction would even be paralleled by a rise within the information-richness of the size concerned, which can assist your analytical thought course of.
In the end, the selection of framework dimensions when it comes to amount and variety will probably be a part of an iterative course of. The scale that you simply begin off with initially of the framework-building course of might not essentially be those you find yourself together with within the closing framework. Additionally, as with most issues, there’s doubtless no “good” dimension, simply dimensions which might be kind of appropriate to your framework goal. Being conscious of the strengths and limitations of the size and seeing framework-building as an iterative course of ought to assist take the strain off on the outset and can help you concentrate on constructing a helpful conceptual framework.
Case Research: Gross sales Efficiency at SoftCo
The sheer number of framework dimensions, and their robust coupling with the framework goal, implies that hand-picking “crucial” dimensions (or choosing primarily based on another standards) could be tough. But, altering the size whereas sustaining the identical framework sort can result in very totally different interpretations of the framework. Within the following anonymized case research, we’ll see how even slight modifications to the size could make a giant distinction and yield new insights.
SoftCo is a mid-sized expertise firm that gives marketing-related software program services and products to companies. The corporate operates within the US and has about two dozen gross sales reps unfold out nationally throughout totally different territories. The gross sales reps are answerable for rising the enterprise of their territory, which incorporates every little thing from figuring out potential prospects to interacting with them and shutting the sale. On the finish of each month, Sally, SoftCo’s veteran Head of Gross sales, opinions the efficiency throughout all territories and reviews her findings to the CEO. She additionally provides suggestions to the gross sales reps to acknowledge achievements and counsel methods to enhance. Through the years, Sally has recognized a number of components that may affect the efficiency of particular person gross sales reps, together with the quantity of buyer interplay (usually cellphone calls, with a couple of subject visits). Determine 1 reveals a easy scatter plot (a matrix framework with two steady dimensions) that compares gross sales efficiency to buyer interactions for particular person gross sales reps.
The selection of dimensions in Determine 1 guides the interpretation of the framework in some ways, past the truth that Sally has chosen particularly to look at buyer interplay as a key predictor of gross sales efficiency. Using steady dimensions lends itself naturally to quantitative measurement. Gross sales efficiency is thus measured by the amount of cash every rep generates monthly, whereas buyer interplay is measured by the variety of gross sales calls made monthly. In fact, these measures alone are in all probability not ample to completely seize the 2 framework dimensions. As an example, the variety of calls doesn’t inform us something in regards to the high quality and distribution of the calls throughout prospects, and the greenback worth of the offers a gross sales rep generates in a month doesn’t inform us a lot in regards to the strategic nature of the offers (e.g., whether or not the offers have been about rising the enterprise with current prospects, or “door openers” for a brand new stream of enterprise with new prospects). Nonetheless, by wanting on the scatterplot in Determine 1, we will derive a number of attention-grabbing insights:
- There have been 23 gross sales reps working for SoftCo through the noticed month. In complete, the gross sales staff made about $858,000 on this time interval.
- On common, every gross sales rep made about $37,300 value of gross sales in the noticed month. The best and lowest particular person gross sales have been about $50,000 and $14,000, respectively.
- Essentially the most environment friendly and least environment friendly gross sales reps (when it comes to $/calls) made about $2,000/name and $160/name, respectively; that could be a roughly 12x distinction in effectivity.
- There appears to be a non-linear relationship between buyer interplay and gross sales efficiency. As much as about 75 calls, every further name appears to be correlated with a giant increase in gross sales efficiency. However past 75 calls the hyperlink with gross sales efficiency is much less robust.
Determine 1 thus results in a spread of insights which might be derived by wanting on the efficiency of particular person gross sales reps and the efficiency of the whole group. A few of the insights are pretty simple (e.g., the variety of gross sales reps, common gross sales efficiency), giving us a common understanding of the dimensions of SoftCo’s gross sales operation and the character of the enterprise. Different insights, such because the hole between probably the most and least environment friendly gross sales reps, and the non-linear relationship between gross sales efficiency and buyer interplay, are probably extra thought-provoking; in addition to highlighting potential gaps between the talents of various gross sales reps and diminishing returns from too many calls, the insights additionally counsel that different components past buyer interplay may additionally be good predictors of gross sales efficiency. The scatterplot illustration additionally makes it straightforward to establish the outliers among the many gross sales reps, which could be helpful for additional evaluation of what units these outliers aside from the remainder of the gross sales reps.
Now, to point out how altering the category of the size can result in a unique perspective, Determine 2 presents a two-by-two matrix that’s primarily based on the identical info because the earlier scatterplot. The 2 steady dimensions of the scatterplot have been reworked into ordinal dimensions by splitting them alongside sure threshold values. Gross sales efficiency figures beneath $25,000/month are thought-about “low,” whereas these above are “excessive.” Equally, buyer interplay figures beneath 75 calls/month are “low,” and people above are “excessive.” The selection of the brink worth is clearly essential and must be primarily based on cheap argument. For instance, the gross sales efficiency threshold could also be primarily based on a minimal gross sales goal that every gross sales rep is required to hit, and the shopper interplay threshold could possibly be associated to the purpose at which the curve in Determine 1 begins to flatten (indicating a shift within the marginal worth of further gross sales calls).
Whereas the scatterplot in Determine 1 drew our consideration to the performances of particular person gross sales reps and the general development within the relationship between gross sales efficiency and buyer interplay, the two-by-two matrix in Determine 2 allows a extra simplified view that lends itself to a segmentation of gross sales reps into totally different teams. In step with conventions, the bottom-left quadrant of the two-by-two matrix reveals the group of gross sales reps that could be in an undesirable place; these reps are making comparatively few calls and producing few gross sales. The highest-right quadrant incorporates “star performers” that evidently appear to work together extensively with prospects and guarantee that this difficult work interprets into precise gross sales. The dynamics within the different two quadrants appear much less clear. The reps within the top-left quadrant appear to attain excessive gross sales regardless of making comparatively few calls – what’s the secret behind their effectivity and is it sustainable? The reps within the bottom-right quadrant have the alternative dynamic, making loads of calls that don’t appear to repay – if these reps are basically working as arduous because the star performers, why are they not reaching equally excessive gross sales figures?
By drawing consideration to totally different segments of the gross sales staff, the two-by-two matrix can be utilized to develop tailor-made methods that tackle the distinctive traits of every section. For these within the bottom-left of the matrix, it is very important discover out why each buyer interplay and gross sales efficiency are comparatively low. Do these gross sales reps need to take care of tough prospects, do the reps want extra coaching, or are the reps allocating a few of their time to different worthwhile actions that aren’t captured by this month’s gross sales efficiency (e.g., coaching different employees, strategic planning, and private growth)? Armed with these further insights, Sally can develop measures that higher seize the true worth that the gross sales reps within the bottom-left quadrant of Determine 2 create for SoftCo.
Equally, for the bottom-right quadrant, a brand new technique could also be wanted to extend effectivity by translating the comparatively excessive degree of buyer interplay into precise gross sales; this may occasionally contain prioritizing sure leads over others, coaching the gross sales reps to be extra tenacious in closing every sale, and motivating them to proceed hustling. For the remaining two quadrants, reaching sustainability could possibly be the important thing goal. It’s value understanding what makes the gross sales reps within the top-left quadrant so environment friendly and what the opposite gross sales reps can study from them. On the similar time the reps within the top-left additionally want a method for lowering the danger of slipping down if their buyer interplay doesn’t persistently pan out. Lastly, a method is required to maintain the reps within the top-right quadrant motivated (e.g., by social recognition, financial rewards, alternatives for promotion) to maintain them performing persistently at a excessive degree.
To shut off, here’s a useful video by Mike Gastin that expands on a few of the issues mentioned above when selecting dimensions for two-by-two matrices:
Reflection Questions
This part consists of three units of reflection questions that can immediate you to suppose extra deeply in regards to the materials lined above. The intention is that can assist you rapidly perceive the fundamental rules and get you interested by how you should utilize them in your personal work.
Set 1: Take an current framework (e.g., one that you’ve got used or in-built an actual knowledge science mission) and analyze the framework dimensions in additional element. What number of dimensions does the framework have and which of the three courses we’ve mentioned do they belong to? Does altering the category of any of the size have an effect on your interpretation of the framework and the insights which might be produced?
Set 2: In case you have seen and/or produced a number of frameworks up to now, it might be time to take inventory of the size you are likely to see most frequently. What courses do these dimensions fall beneath? To what extent is the recognition of those dimensions factor or a nasty factor when it comes to reaching every framework’s goal?
Set 3: Are you able to consider another courses of dimensions past the three we’ve checked out on this article? To what extent are these various courses totally different from those we’ve mentioned?
The Wrap
Whereas the framework sort determines how the framework will say one thing (the construction), the framework dimensions outline what particularly can be stated (the content material). Three courses of framework dimensions are particularly frequent in observe: categorical (unordered, discrete classes), ordinal (ordered, discrete classes), and steady (a quantity line inside a given vary). It’s potential to remodel a dimension from one class to a different by altering the depth of the knowledge content material (e.g., bucketing steady knowledge to yield an ordinal dimension). It is very important contemplate the amount and variety of dimensions a framework ought to have to attain the overarching goal. Embrace solely as many dimensions as are really wanted, particularly when presenting the framework. Limiting dimensions to a single class can have some advantages, though the interplay of dimensions from totally different courses additionally has its deserves.
