[et_pb_section fb_built=”1″ _builder_version=”4.16″ da_disable_devices=”off|off|off” global_colors_info=”{}” da_is_popup=”off” da_exit_intent=”off” da_has_close=”on” da_alt_close=”off” da_dark_close=”off” da_not_modal=”on” da_is_singular=”off” da_with_loader=”off” da_has_shadow=”on”][et_pb_row column_structure=”1_5,3_5,1_5″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_column type=”1_5″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”3_5″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_text _builder_version=”4.27.4″ global_colors_info=”{}”]<\/p>\n
If you want to set your SaaS prices correctly, you need to know exactly who you\u2019re targeting. Well-executed saas customer segmentation will allow you to tailor your prices to customer needs and capabilities. Without effective customer segmentation, it\u2019s easy to make a mistake: you might scare off potential customers with prices that are too high, or fail to maximise your profit potential by offering rates that are too low to the wrong customer segment.<\/strong><\/p>\n You\u2019ve probably asked yourself why some saas companies conquer the market in no time, while others (perhaps even yours) struggle with persistent unprofitability. The truth is, their success isn\u2019t just about product innovation. What really matters is the ability to understand who your saas customer is and what they actually need.<\/strong><\/p>\n Customer segmentation lets you break away from generic approaches and create offers that address specific motivations, situations, and purchase barriers. This way, you can better match your prices and build long-term customer value for every user segment.<\/p>\n A few words of explanation for those who may be encountering this term and topic for the first time.<\/p>\n Saas customer segmentation is about dividing your customers into groups that differ in:<\/p>\n By adopting this approach, you stop treating all your customers the same and start to see that each one is in a different situation, has different customer needs, and expects different benefits. It seems obvious-after all, we all identify with some group, but if you look at the marketing strategies of many saas businesses, you\u2019ll often notice that their messaging is aimed at the \u201cmasses\u201d, in other words, at everyone, without any segmentation strategy.<\/p>\n With saas customer segmentation, you can create offers that genuinely address the real pain points and expectations of specific user segments, rather than trying to satisfy everyone with a single, \u201cuniversal\u201d proposition. In practice, this means better matching your communication, product features, and pricing to your actual customer segment. Over time, this will naturally translate into greater effectiveness in your marketing strategies and sales efforts.<\/p>\n It\u2019s rightly said that effective customer segmentation is the foundation of modern marketing in SaaS. It allows you to understand your customers better and manage their diverse customer needs in a highly effective way. Instead of falling for the illusion of the \u201caverage customer\u201d, you can respond precisely to what truly drives purchase decisions in different segments. That\u2019s how you build a real competitive advantage!<\/p>\n Segmentation by customer lifecycle stages (e.g. new users, active customers, at-risk customers).<\/strong><\/p>\n Demographic segmentation and firmographic segmentation (e.g. industry, company size, location).<\/strong><\/p>\n Value based segmentation (e.g. customers generating the highest revenue, customers requiring the most support).<\/strong><\/p>\n Segmentation by customer behavior and usage patterns (e.g. usage intensity, preferred features).<\/strong><\/p>\n Most personas are eye-rollingly useless. Product Managers usually create these personas to satisfy Agile\u2019s user story framework, adding nothing (or even causing harm) to the process of creating breakthrough products and offers.<\/p>\n The managers who create these useless personas take their first misstep in selecting the variables that form the base of the persona.<\/p>\n The most commonly chosen variables don\u2019t create personas with meaningful differences in purchasing or usage behavior or help categorize customers\u2019 suitability for a company to target and serve\u2014most managers base personas on demographic (B2C) or firmographic (B2B) variables.<\/p>\n Companies have realized that personas created from these variables do not isolate differentiated customer behavior or aid pricing decisions, causing personas to be underutilized or outright ignored. The process of developing personas via proper customer segmentation desperately needs to improve.<\/p>\n [\/et_pb_text][et_pb_code disabled_on=”on|on|on” _builder_version=”4.16″ disabled=”on” global_colors_info=”{}”][\/et_pb_code][\/et_pb_column][et_pb_column type=”1_5″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_5,3_5,1_5″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_column type=”1_5″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”3_5″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_text _builder_version=”4.27.4″ global_colors_info=”{}”]<\/p>\n Let\u2019s do a quick summary for you. Here\u2019s why saas customer segmentation is so important-in fact, we can safely say it\u2019s essential.<\/p>\n You can clearly identify which customer segments are the most profitable for you and focus your sales and marketing strategies on them.<\/p>\n It allows you to respond to market changes and scale your business effectively.<\/p>\n Remember!<\/strong><\/p>\n There\u2019s no such thing as an average customer, and trying to please everyone leads to failure. Competitors often treat the market as a whole; you can act with precision!<\/strong><\/p>\n Segmentation, Targeting, and Positioning (STP) are the pillars of modern marketing.\u00a0Unfortunately, segmentation is one of the most talked-about and yet misunderstood and misapplied concepts in all of marketing. The core truth is that your customers are heterogeneous.<\/p>\n Customers are in different situations leading to different needs and levels of perceived and realized value from your SaaS product. If you try to build a single product<\/a> for everyone, you are very unlikely to succeed.<\/p>\n If you\u2019re not thinking segments, you\u2019re not thinking.<\/em><\/p>\n Theodore Levitt, Marketing Imagination<\/a><\/cite><\/p>\n<\/blockquote>\n The essence of customer segmentation is to create marketing offers and communications relevant to each customer situation while still allowing the company to manage these diverse needs in a scalable way. There is no individual company that looks like your \u201caverage customer\u201d across all dimensions. Do not be seduced by your averages.<\/p>\n This post will not be covering Targeting or Positioning, as those happen later in the pricing strategy process and merit extensive discussions on their own.<\/p>\n There are already too many clickbaity posts on \u201cHow to Create your Ideal Customer Profile\u201d trying to jam this nuanced topic unsuccessfully into a single post. But stick with me; we\u2019ll get there. Moving on.<\/p>\n [\/et_pb_text][\/et_pb_column][et_pb_column type=”1_5″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_5,3_5,1_5″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_column type=”1_5″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”3_5″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_text _builder_version=”4.16″ global_colors_info=”{}”]<\/p>\n Segmentation is the cornerstone of SaaS pricing and is the starting point for devising a solid SaaS pricing strategy. However, most companies focus on segmentation approaches that do not effectively differentiate customers by purchase drivers or classify them in a helpful way for informing pricing decisions.<\/p>\n When considering pricing, failure to segment properly leaves you at risk of losing profits by failing to price according to value<\/a>, thereby undercharging some segments and losing others entirely by outpricing them.<\/p>\n [\/et_pb_text][\/et_pb_column][et_pb_column type=”1_5″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_5,3_5,1_5″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_column type=”1_5″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”3_5″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_image src=”https:\/\/explainvisually.co\/wp-content\/uploads\/2021\/10\/2_2.jpg” alt=”different shapes shown as methaphors for bad personas and good customer segmentation, saas customer segmentation ” title_text=”bad personas good customer segmentation” _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][et_pb_column type=”1_5″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_5,3_5,1_5″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_column type=”1_5″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”3_5″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_text _builder_version=”4.16″ global_colors_info=”{}”]<\/p>\n To give a better sense of why segmentation matters to pricing, let\u2019s use a simple example. Most of my clients start thinking about pricing by considering the right price level<\/em> (i.e., price point). So what is the right price for a simple, beloved consumer good \u2013 a can of beer? $1? $2? It turns out, without thinking about customer segments (and their different contexts) first, you end up without a defendable answer.<\/p>\n At the grocery store, you can purchase a can of beer for $1. But at a bar, that same can of beer is going to be $5. And at a music festival, that beer will be $10 or more (although they will sell you the \u201ctall boy\u201d so you feel better about your big purchase)!<\/p>\n So what is the cause of this pricing differential? You\u2019ve got several factors to consider \u2013 customer context, customer willingness to pay (driven by customer need), and relevant competitive alternatives. Notice that you are the same in every one of those contexts, a successful, 35-year-old Silicon Valley CEO with 2.5 children and a Stanford CS degree. (I forgot devilishly handsome, but you knew that already.)<\/p>\n However, those personal details were irrelevant to the price you would pay in each of those situations.<\/p>\n I\u2019m sure your SaaS product is much more complicated than beer. With this simple example, you can see how confusing this situation can become if you haven\u2019t thought about your customers\u2019 different contexts, willingness to pay, and relevant competitive alternatives well before discussing price levels.<\/p>\n Most SaaS executives think that what <\/em>you charge will determine your success. In fact, who <\/em>and how <\/em>you charge determines your success.<\/strong> Segmentation is the first step to SaaS pricing success.<\/p>\n [\/et_pb_text][\/et_pb_column][et_pb_column type=”1_5″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.16″ locked=”off” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_image src=”https:\/\/explainvisually.co\/wp-content\/uploads\/2021\/09\/3_2-scaled.jpg” alt=”man drinking beer, supermarket, bar, concert, saas customer segmentation” title_text=”weak personas customer segmentation” align=”center” disabled_on=”on|off|off” _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][et_pb_image src=”https:\/\/explainvisually.co\/wp-content\/uploads\/2021\/09\/31_3.jpg” alt=”man buying beer, saas customer segmentation” title_text=”creating personas” align=”center” disabled_on=”off|on|on” _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][et_pb_image src=”https:\/\/explainvisually.co\/wp-content\/uploads\/2021\/09\/32_1.jpg” alt=”two man drinking bear ain bar, saas customer segmentation” title_text=”weak personas customer segmentation” align=”center” disabled_on=”off|on|on” admin_label=”Obraz” _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][et_pb_image src=”https:\/\/explainvisually.co\/wp-content\/uploads\/2021\/09\/33-1.jpg” alt=”beer prize baner, concert, people jumping” title_text=”weak personas customer segmentation” align=”center” disabled_on=”off|on|on” _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_5,3_5,1_5″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_column type=”1_5″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”3_5″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_text _builder_version=”4.16″ global_colors_info=”{}”]<\/p>\n So now that you understand why segmentation is so important, you may have some questions before proceeding. Naturally, some common questions arise when segmenting customers:\u00a0<\/p>\n The answers to these questions are highly dependent on both the base variables<\/em> chosen for the segmentation and the method<\/em> used to derive the customer groups. This post will focus on the base variables, and my next post will dive into segmentation methods.\u00a0<\/p>\n [\/et_pb_text][\/et_pb_column][et_pb_column type=”1_5″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_5,3_5,1_5″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_column type=”1_5″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”3_5″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_text _builder_version=”4.16″ global_colors_info=”{}”]<\/p>\n Ultimately, the goal of effective segmentation is to divide your market into groups that share common criteria within a group<\/em> but have differentiated buying behaviors between groups<\/em>. When determining if derived segments are \u201cgood,\u201d use the following criteria:<\/p>\n <\/p>\n 1. Identifiable:<\/strong> Segments are recognizable from available, observable data.<\/p>\n 2. Differentiable: <\/strong>There are apparent differences in responsiveness (i.e., heterogeneity) to the marketing mix (including price) between segments, but homogenous responsiveness within a segment.<\/p>\n 3. Viable:<\/strong> A segment is large enough to warrant the company\u2019s attention (e.g., current # of prospects, economic benefits, future growth potential), and the company expects the segment to be stable over time.<\/p>\n 4. Understandable:<\/strong> A segment makes intuitive sense to the business stakeholders outside of mathematical models. Pro Tip: A good test for this is asking your sales team to identify their customers by your segmentation scheme.<\/p>\n 5. Actionable:<\/strong> Segments attached to profiling data are reachable and permit differential actions. Using the beer example earlier, the order of magnitude price differential between the grocery store and the music festival is because there is an enforceable price fence<\/em> (and usually a physical one at that) preventing the music festival participants from running to the grocery store between acts.<\/p>\n [\/et_pb_text][\/et_pb_column][et_pb_column type=”1_5″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.16″ locked=”off” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_image src=”https:\/\/explainvisually.co\/wp-content\/uploads\/2021\/09\/1_5.jpg” alt=”good customer segmentation” title_text=”good customer segmentation” align=”center” disabled_on=”on|off|off” _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][et_pb_image src=”https:\/\/explainvisually.co\/wp-content\/uploads\/2021\/09\/11-1.jpg” alt=”wrong and good shapes” title_text=”identigiable customer segmentation” align=”center” disabled_on=”off|on|on” _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][et_pb_image src=”https:\/\/explainvisually.co\/wp-content\/uploads\/2021\/09\/12.jpg” alt=”triangles, circles, squares” title_text=”differentiable customer segmentation” align=”center” disabled_on=”off|on|on” _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][et_pb_image src=”https:\/\/explainvisually.co\/wp-content\/uploads\/2021\/09\/13.jpg” alt=”circles representing copmany and client size” title_text=”viable customer segmentation” align=”center” disabled_on=”off|on|on” _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][et_pb_image src=”https:\/\/explainvisually.co\/wp-content\/uploads\/2021\/09\/14.jpg” alt=”different shapes, thumbs up” title_text=”customer segmentation understandable arguments” align=”center” disabled_on=”off|on|on” _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][et_pb_image src=”https:\/\/explainvisually.co\/wp-content\/uploads\/2021\/10\/15_1.jpg” alt=”three circles touching” title_text=”actionable feature” align=”center” disabled_on=”off|on|on” _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_5,3_5,1_5″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_column type=”1_5″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”3_5″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_text _builder_version=”4.16″ global_colors_info=”{}”]<\/p>\n It\u2019s essential to choose response variables that are stable over time and drive differences in purchasing behavior. However, managers face many attributes that they can use for segmentation, the most common of which are poor predictors of willingness-to-pay and likelihood-to-buy.<\/p>\n To reduce the complexity of this decision, we want to create a framework to understand possible variables and how to choose among them.<\/p>\n Wedel and Kamakura<\/a> outlined a handy data categorization method that I will use here to help make sense of the variable choices available:<\/p>\n [\/et_pb_text][\/et_pb_column][et_pb_column type=”1_5″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_5,3_5,1_5″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_column type=”1_5″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”3_5″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_text _builder_version=”4.16″ global_colors_info=”{}”]<\/p>\n 1. General vs. Product-Specific Variables: <\/strong>This categorization distinguishes between customer variables that are stable across all products versus variables that change by product category.<\/p>\n Customers are not blindly consistent in purchasing behavior across product categories.<\/p>\n This varied behavior is exacerbated in a B2B context when different departments (and thus different individuals) may be responsible for buying various products. Also, the diffusion of purchase authority has become more relevant with the move to SaaS, as purchasing or implementation decisions no longer involve a centralized CIO\/IT decision-maker.<\/p>\n 2. Observable vs. Unobservable Variables: <\/strong>These are customer variables that you can determine externally via third-party sources (e.g., Crunchbase, Pitchbook, CIA Factbook) versus those requiring primary research or direct validation.<\/p>\n The table below outlines the most commonly used variables for B2B customer segmentation sorted into these categories. I\u2019ve provided a glossary of definitions at the end of the post for some of the less well-known variables. Some data may span or change categories depending on product or situation, but this will generally hold for this discussion.<\/p>\n [\/et_pb_text][\/et_pb_column][et_pb_column type=”1_5″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_5,3_5,1_5″ _builder_version=”4.16″ locked=”off” global_colors_info=”{}”][et_pb_column type=”1_5″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”3_5″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_image src=”https:\/\/explainvisually.co\/wp-content\/uploads\/2021\/09\/product-variables.jpg” title_text=”product variables” url=”https:\/\/explainvisually.co\/dlaczego-branza-technologiczna-coraz-czesciej-siega-po-animacje-z-konkretnymi-przykladami\/?et_fb=1&PageSpeed=off” url_new_window=”on” _builder_version=”4.16″ _module_preset=”default” border_width_all=”2px” border_color_all=”rgba(0,0,0,0)” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n Possible segmentation variables are broken out across categories.<\/em><\/p>\n [\/et_pb_text][\/et_pb_column][et_pb_column type=”1_5″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_5,3_5,1_5″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_column type=”1_5″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”3_5″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_text _builder_version=”4.16″ global_colors_info=”{}”]<\/p>\n To weigh our choices\u00a0 of variables against each other, managers should consider the following five criteria:<\/p>\n <\/p>\n [\/et_pb_text][\/et_pb_column][et_pb_column type=”1_5″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_5,3_5,1_5″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_column type=”1_5″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”3_5″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_text _builder_version=”4.16″ global_colors_info=”{}”]<\/p>\n Many firms opt for variables that don\u2019t meet the above criteria, which will result in poor segmentation (and those useless personas). These variables include firmographics (geographic, company size, and vertical), psychographics, values, media habits, technology adoption attitudes, and information sources.<\/p>\n Multiple studies have shown that these variables perform poorly in creating segments that meet our criteria for effective segments and, therefore, will not give you valuable results.<\/p>\n [\/et_pb_text][\/et_pb_column][et_pb_column type=”1_5″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_5,3_5,1_5″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_column type=”1_5″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”3_5″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_text _builder_version=”4.16″ global_colors_info=”{}”]<\/p>\n The most critical and valuable variables for segmentation in the context of pricing are:<\/p>\n For those of you familiar with Jobs-to-be-done (JTBD), you may recognize the above list. Jobs-to-be-Done is a perfect tool to use for pricing segmentation.<\/p>\n At some point, I\u2019ll do in-depth posts on two foundational JTBD books (Competing Against Luck<\/em><\/a> and What Customers Want<\/em><\/a>), but for now, I\u2019ll just recommend those books to you if you want to understand JTBD in more detail.<\/p>\n I have found Tony Ulwick\u2019s focus on customer outcomes most useful for a segmentation exercise for pricing, as multiple studies have found customer value to be one of the most robust dimensions by which to segment customers.<\/p>\n I encourage you to focus on outcomes vs. customer needs or benefits because, as Ulwick points out in What Customers Want<\/em>, \u201cneeds,\u201d \u201crequirements,\u201d and \u201csolutions\u201d are generally too ill-defined to be used as inputs to the process.<\/p>\n Benefit statements like \u201cfaster,\u201d \u201ceasier,\u201d \u201cbetter,\u201d or \u201ccheaper\u201d can mean drastically different things depending on the customer and context. Finally, customer outcomes tend to be stable over time, satisfying one of our key criteria for effective segments.<\/p>\n [\/et_pb_text][\/et_pb_column][et_pb_column type=”1_5″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_5,3_5,1_5″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_column type=”1_5″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”3_5″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_text _builder_version=”4.16″ global_colors_info=”{}”]<\/p>\n I\u2019ve purposely omitted several variables you might have expected to see. Specifically, behavioral data (e.g., product usage data), feature preference, and willingness-to-pay. These variables are problematic, at least in initial customer segmentation, for different reasons.<\/p>\n There are several challenges with trying to apply usage data for segmentation in a pricing context. For one, if you do pricing research before building your SaaS product (as you should<\/a>), you won\u2019t have any usage data to use. If you are evaluating pricing for an existing product, your current pricing model may bias product usage in unclear ways.<\/p>\n This bias is a symptom of a bigger problem \u2014 your usage data will tell you, \u201cWhat happened?\u201d instead of \u201cWhat should I do?\u201d<\/p>\n Let\u2019s use another simple example to illustrate: My power company, Austin Energy, currently charges me via standard utility billing. I pay more in a progressively tiered system per kilowatt-hour (kWh) of energy I use.<\/p>\n If Austin Energy is contemplating changing its pricing model to a flat-rate model, they may look at my energy consumption in the past to figure out how much energy I may use in the future.<\/p>\n However, a switch to a flat-rate model vs. a usage model will change my behavior. I will set my thermostat at 68 degrees in the Texas summer because it doesn\u2019t matter how much electricity I use. In short, Austin Energy will not get a helpful predictor of my future behavior based upon past usage.<\/p>\n With that warning given, you should only use behavioral data to help validate results found through primary, outcome-based segmentation. Usage data is only a proxy for desired outcomes, and you\u2019re unlikely to find a bright line in your product between disparate customer needs and the usage of one or more features.<\/p>\n Features, like usage, are only a proxy for a customer\u2019s anticipated benefits. You run into a significant problem when you ask participants to rank order features. The individual participant needs to understand your product and their usage context well enough to translate the feature into their benefit on the fly.<\/p>\n This translation step is bound to muddy your research results as you depend on folks who have no context for your product to do this translation perfectly. Asking participants to translate like this has a low probability of success.<\/p>\n Asking for preference of features may work for highly commoditized products and features your respondents are familiar with. However, for most SaaS businesses building innovative features, it\u2019s unlikely the data you get back will be reliable.<\/p>\n As an example, imagine you are buying a digital camera. You start comparing models and see that the better cameras have higher megapixels. This is a feature, right? Isn\u2019t this a useful comparison of features? Yes, in this case, but that is only because the digital camera industry has spent massive time and money educating customers that megapixels translate into higher image quality.<\/p>\n The \u201cbetter image quality\u201d is the true benefit here. Customers can only perform this translation step in their heads when comparing models because of this pre-existing knowledge.<\/p>\n How about a counterexample? Your new product is going to be best-in-class in a growing, niche market. One of the defining features of your product is more dibelkrakens<\/em> (don\u2019t Google this, I made it up) than any of your competitors.<\/p>\n If you ask your prospective customers, \u201cHow important are dibelkrakens compared to other features?\u201d you will get meaningless data. Your customers have no pre-existing knowledge of dibelkrakens and cannot translate this feature to an anticipated benefit.<\/p>\n At a later stage, willingness to pay can be a helpful tool for additional segmentation. However, you have a bit of a chicken-and-egg problem here: You cannot get a clear read on willingness to pay until you clearly understand and communicate the solution\u2019s benefits to your audience.<\/p>\n When you\u2019re still trying to figure out what different segments care about via outcomes, you are in a position where it is challenging to present a coherent definition of the value your product provides. Willingness to pay as a primary segmentation variable has similar issues to asking directly about feature preferences.<\/p>\n [\/et_pb_text][\/et_pb_column][et_pb_column type=”1_5″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_5,3_5,1_5″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_column type=”1_5″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”3_5″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_text _builder_version=”4.16″ global_colors_info=”{}”]<\/p>\n OK, that was a lot of information, I know. I\u2019ve created the following table to visually represent variable categories\u2019 suitability by the tests outlined previously.\u00a0<\/p>\n [\/et_pb_text][\/et_pb_column][et_pb_column type=”1_5″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_5,3_5,1_5″ _builder_version=”4.16″ locked=”off” global_colors_info=”{}”][et_pb_column type=”1_5″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”3_5″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_image src=”https:\/\/explainvisually.co\/wp-content\/uploads\/2021\/09\/product-tranquility.jpg” title_text=”product tranquility” url=”https:\/\/explainvisually.co\/dlaczego-branza-technologiczna-coraz-czesciej-siega-po-animacje-z-konkretnymi-przykladami\/?et_fb=1&PageSpeed=off” url_new_window=”on” _builder_version=”4.16″ _module_preset=”default” border_width_all=”2px” border_color_all=”rgba(0,0,0,0)” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n Green = high suitability; yellow = moderate suitability; red = low suitability. Note that the Cost column is inverted, so \u201clow cost\u201d is Green & \u201chigh cost\u201d is Red.<\/em><\/p>\n [\/et_pb_text][\/et_pb_column][et_pb_column type=”1_5″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_5,3_5,1_5″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_column type=”1_5″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”3_5″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_text _builder_version=”4.27.4″ global_colors_info=”{}”]<\/p>\n 1. Product-specific variables are better than General variables.<\/p>\n 2. The cost of acquiring data is higher for Unobservable characteristics than for Observable characteristics (as one might expect).<\/p>\n 3. \u201cGeneral, Unobservable\u201d variables are undesirable for our purposes. I do not recommend using them for segmentation.<\/p>\n 4. \u201cGeneral, Observable\u201d variables are not helpful for primary segmentation. However,\u00a0 these variables can be beneficial for creating those customer personas later and should be included in your research process so you can use them at the end of the process (covered more in the next post).<\/p>\n 5. The best data for segmentation lay in the \u201cProduct-specific, Unobservable\u201d category \u2014 but naturally, this will cost the most to obtain.<\/p>\n You already know that saas customer segmentation is a powerful tool-but is there a catch? Of course there is, because there\u2019s always a \u201cbut\u201d\ud83d\ude06. The truth is, not every customer segmentation analysis delivers results; only the kind that\u2019s done thoughtfully.<\/p>\n In practice, saas companies often make the same mistakes, rendering the whole exercise pointless-or even harmful. Here\u2019s a short list of the most common pitfalls and a few tips on how you can avoid them.<\/p>\n If you lump all your customers into one broad group, you\u2019ll miss their real differences and fail to hit the mark with your offer. User segments need to be detailed enough to capture the most important motivations and customer needs, but not so narrow that each group contains just a handful of individual users.<\/p>\n On the other hand, dividing customers into too many micro-segments leads to chaos. Managing them becomes impractical and dilutes the whole segmentation strategy. Our advice? Find the golden mean! Segments should be clear, easy to manage, and genuinely differentiated by customer behavior.<\/p>\n In B2B and SaaS, this approach rarely works. Companies of the same company size or in the same industry can have completely different customer needs and expectations of your product. Focus on motivations, situations, barriers, and expected outcomes.<\/p>\n If you base your segmentation on outdated data or guesswork, your segments will quickly lose relevance and stop supporting business decisions.<\/p>\n Remember, customer segmentation isn\u2019t just about numbers. You need to regularly collect data and feedback from users and those who interact with them every day.<\/p>\n The SaaS market changes rapidly, and so do customer needs. Segments that worked a year ago may already be outdated. Review and update your segmentation from time to time to stay on track.<\/p>\n Customer segmentation is about dividing customers into groups, not taking an individual approach to each one. Personalisation is the next step you can implement after defining your user segmentation.<\/p>\n In my next post, I\u2019ll focus on segmentation methods now that I\u2019ve spent some time breaking down the variables that feed into them. We\u2019ll continue to explore what to do with the data you collect from them in future discussions.<\/p>\n [\/et_pb_text][\/et_pb_column][et_pb_column type=”1_5″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_5,3_5,1_5″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_column type=”1_5″ _builder_version=”4.16″ global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”3_5″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_text _builder_version=”4.27.4″ global_colors_info=”{}”]<\/p>\n Behavioral Intentions: <\/strong>The degree to which a person has formulated deliberate plans to perform or not perform a behavior.<\/p>\n Behavioral Expectation:<\/strong> A measure of an individual\u2019s perceived likelihood of performing an act (e.g., purchase\/use a product).<\/p>\n Benefits:<\/strong> The benefits people seek in products.<\/p>\n Elasticities:<\/strong> The relative change in demand in response to a relative unit change in a marketing instrument (mainly price). These are difficult to measure in the abstract and are not consistent across price categories for a single buyer.<\/p>\n Psychographics:<\/strong> A qualitative breakdown of customer\u2019s attitudes, opinions, beliefs, interests, and lifestyles.<\/p>\n Technology Adoption Attitudes:<\/strong> Associated with theories about how innovation diffuses through a market, adoption happens in sequential stages across different customer types usually labeled as 1) Innovators, 2) Early Adopters, 3) Early Majority, 4) Late Majority, and finally, 5) Laggards.<\/p>\n To conclude, a few words about the authors of this article. Why would a company specialising in 2D animation<\/a>, graphic recording<\/a>, infographics<\/a>, and promotional videos<\/a> write about saas customer segmentation?<\/p>\n At Explain Visually, we help companies transform complex ideas into clear, engaging visual stories. Do you need to present your customer segmentation analysis to stakeholders, onboard new users, or perhaps explain advanced features to different customer segments? A well-crafted illustration or animation can do all the heavy lifting for you!<\/p>\n Animated videos and infographics break down complex processes, highlight key benefits, and ensure your message reaches every user segment.<\/p>\n With visual storytelling, you:<\/p>\nWhat is SaaS Customer Segmentation?<\/h2>\n
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The Most Popular Approaches to SaaS Customer Segmentation<\/h3>\n
SaaS Customer Segmentation – Why Are We Writing About It?<\/h2>\n
Why is SaaS Customer Segmentation Important?<\/h2>\n
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Customer Segmentation is the Foundation of Modern Marketing<\/h3>\n
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Customer Segmentation and SaaS Pricing<\/h3>\n
But First, Beer<\/h4>\n
Challenges and Goals of Customer Segmentation<\/h3>\n
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The Five Criteria for Effective Segments<\/h3>\n
Categories of Variables Used for Customer Segmentation<\/h3>\n
Deciding Which Data to Use: A Five Part Test<\/h3>\n
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The Bad<\/h3>\n
The Good<\/h3>\n
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The Problematic<\/h3>\n
Behavioral Data<\/h4>\n
Feature Preference<\/h4>\n
Willingness to Pay<\/h4>\n
Summarizing Usefulness of Variable Categories\u00a0\u00a0<\/h3>\n
Takeaways:<\/h3>\n
The Most Common Mistakes in Segmentation and How to Avoid Them<\/h2>\n
Too broad segments<\/h4>\n
Excessive granularity<\/h4>\n
Basing segmentation solely on demographic data or firmographics<\/h4>\n
Using outdated or incomplete customer data<\/h4>\n
Ignoring customer feedback and the sales team<\/h4>\n
Lack of regular segment updates<\/h4>\n
Confusing segmentation with personalisation<\/h4>\n
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Appendix: Definitions\u00a0for Select Variables<\/h3>\n
How Can Explain Visually Support SaaS Customer Segmentation?<\/h2>\n