A earlier article supplied a of conceptual frameworks – analytical constructions for representing summary ideas and organizing information. 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 stream, 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 sorts, will place the highlight on framework dimensions. With the assistance of a real-life case examine, 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.
Word: 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 signify, the framework dimensions decide the content material. The size typically fall into three lessons: categorical, ordinal, and steady. The next sections study this classification of framework dimensions in additional element and go over some features that it is best to take into account 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 identify suggests, the dimension consists of a finite set of discrete classes that needn’t be in any specific order. As an illustration, if the dimension represents an organization’s markets, it might be divided into geographic classes reminiscent of “USA,” “Germany,” and “China.” Equally, you would have a categorical dimension that breaks down the corporate’s merchandise into totally different product segments (e.g., by components, relevance to clients, and so forth). It’s also a good suggestion to maintain the MECE precept (mutually unique and cumulatively exhaustive) in thoughts at any time when 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 in a roundabout way. The ordering permits you to 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. Nonetheless, 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 might be totally different from the hole between second- and third-ranked international locations. 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 members to be analyzed when it comes to the place they lie on the size for every query.
Lastly, a steady dimension offers a quantitative measure of one thing. In contrast to categorical and ordinal dimensions (which include discrete classes or values), steady dimensions can probably tackle any worth (nonetheless tiny) inside a given vary. For instance, the chance, in proportion phrases, of some occasion occurring can lie wherever 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
You will need to take into account the strengths and limitations of every dimension class earlier than making use of them to your framework. As an illustration, you would have a look at the data content material of every dimension class. The presence of an ordering and the power to tackle more and more fine-grained values inside a given vary contribute to the depth of the data 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 at any time when they are often measured in a granular, quantitative method. Nonetheless, the data richness comes at the price of the sources wanted to obtain and analyze the information underlying the scale. Additionally, presenting and explaining information-rich dimensions to an viewers could be arduous, since there may be numerous content material that must be unpacked and digested. As such, even in the event you use steady dimensions to carry out the evaluation, it could make sense to “bucket” the continual information into ordinal and even categorical information to simplify what’s proven to an viewers.
Moreover, since frameworks can contain a number of dimensions, you will need to obtain an optimum interaction between the scale. There are at the least two primary choices that you’ll want to make on this regard – what number of dimensions, and what sorts, to incorporate within the framework. Particularly within the early levels of analyzing an issue, the tendency is to be beneficiant with the variety of dimensions thought-about, for the reason that drawback 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 often is the ones that specify the answer most fully and succinctly, or those that unlock novel insights. The variety of dimensions may additionally depend upon the framework sort that you just need 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 lessons. Every class comes with a singular mind-set in regards to the underlying information. Utilizing dimensions of the identical class has the benefit of letting you switch one mind-set throughout the scale within the framework. As an illustration, if you already know 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 (assume “apples to apples” versus “apples to oranges”). Nonetheless, utilizing dimensions of various lessons additionally has its deserves. In a hierarchical framework, utilizing totally different dimension lessons for every degree within the hierarchy might 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 scale 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 almost definitely be a part of an iterative course of. The size that you just begin off with at first of the framework-building course of could not essentially be those you find yourself together with within the remaining framework. Additionally, as with most issues, there may be doubtless no “good” dimension, simply dimensions which can be roughly appropriate on your framework goal. Being conscious of the strengths and limitations of the scale and seeing framework-building as an iterative course of ought to assist take the stress off on the outset and permit you to concentrate on constructing a helpful conceptual framework.
Case Research: Gross sales Efficiency at SoftCo
The sheer number of framework dimensions, and their sturdy coupling with the framework goal, implies that hand-picking “crucial” dimensions (or deciding on based mostly on another standards) could be tough. But, altering the scale whereas sustaining the identical framework sort can result in very totally different interpretations of the framework. Within the following anonymized case examine, we’ll see how even slight modifications to the scale could make a giant distinction and yield new insights.
SoftCo is a mid-sized expertise firm that gives marketing-related software program services 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 the whole lot from figuring out potential clients to interacting with them and shutting the sale. On the finish of each month, Sally, SoftCo’s veteran Head of Gross sales, critiques the efficiency throughout all territories and experiences her findings to the CEO. She additionally offers suggestions to the gross sales reps to acknowledge achievements and recommend 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 per 30 days, whereas buyer interplay is measured by the variety of gross sales calls made per 30 days. After all, these measures alone are most likely not adequate to completely seize the 2 framework dimensions. As an illustration, the variety of calls doesn’t inform us something in regards to the high quality and distribution of the calls throughout clients, 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 present clients, or “door openers” for a brand new stream of enterprise with new clients). However, by trying on the scatterplot in Determine 1, we are able to derive a number of fascinating 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 price 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 may 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 extra 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 sturdy.
Determine 1 thus results in a spread of insights which can be derived by trying on the efficiency of particular person gross sales reps and the efficiency of the whole group. A few of the insights are pretty easy (e.g., the variety of gross sales reps, common gross sales efficiency), giving us a basic understanding of the size of SoftCo’s gross sales operation and the character of the enterprise. Different insights, such because the hole between essentially 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; moreover highlighting potential gaps between the skills of various gross sales reps and diminishing returns from too many calls, the insights additionally recommend that different components past buyer interplay may additionally be good predictors of gross sales efficiency. The scatterplot illustration additionally makes it straightforward to determine 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 scale can result in a distinct perspective, Determine 2 presents a two-by-two matrix that’s based mostly 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 necessary and must be based mostly on affordable argument. For instance, the gross sales efficiency threshold could also be based mostly on a minimal gross sales goal that every gross sales rep is required to hit, and the client interplay threshold might be associated to the purpose at which the curve in Determine 1 begins to flatten (indicating a shift within the marginal worth of extra gross sales calls).
Whereas the scatterplot in Determine 1 drew our consideration to the performances of particular person gross sales reps and the general pattern within the relationship between gross sales efficiency and buyer interplay, the two-by-two matrix in Determine 2 permits a extra simplified view that lends itself to a segmentation of gross sales reps into totally different teams. Consistent 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 accommodates “star performers” that evidently appear to work together extensively with clients and ensure 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 other dynamic, making numerous calls that don’t appear to repay – if these reps are primarily working as arduous because the star performers, why are they not attaining 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 handle the distinctive traits of every phase. For these within the bottom-left of the matrix, you will need to discover out why each buyer interplay and gross sales efficiency are comparatively low. Do these gross sales reps should cope with tough clients, 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 extra 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 will likely 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, attaining sustainability might be the important thing goal. It’s price 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 chance 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 number of the concerns mentioned above when selecting dimensions for two-by-two matrices:
Reflection Questions
This part consists of three units of reflection questions that may immediate you to assume extra deeply in regards to the materials coated above. The intention is that can assist you shortly perceive the fundamental rules and get you eager about how you need to use them in your personal work.
Set 1: Take an present framework (e.g., one that you’ve got used or in-built an actual information science mission) and analyze the framework dimensions in additional element. What number of dimensions does the framework have and which of the three lessons we’ve got mentioned do they belong to? Does altering the category of any of the scale have an effect on your interpretation of the framework and the insights which can be produced?
Set 2: You probably have seen and/or produced a number of frameworks thus far, it could be an excellent time to take inventory of the scale you are inclined to see most frequently. What lessons do these dimensions fall below? To what extent is the recognition of those dimensions an excellent factor or a nasty factor when it comes to attaining every framework’s goal?
Set 3: Are you able to consider another lessons of dimensions past the three we’ve got checked out on this article? To what extent are these various lessons totally different from those we’ve got mentioned?
The Wrap
Whereas the framework sort determines how the framework will say one thing (the construction), the framework dimensions outline what particularly will probably be stated (the content material). Three lessons of framework dimensions are particularly widespread in follow: 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 data content material (e.g., bucketing steady information to yield an ordinal dimension). You will need to take into account the amount and variety of dimensions a framework ought to have to attain the overarching goal. Embody 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 lessons additionally has its deserves.
