Defining target markets using data
Customer analysis may be the most important market segmentation analysis you can perform for your business. Without it, trying to find new customers, or selling more services to existing customers, will likely have limited success. Unless you really get to know your customers, your business won’t be a in position to meet their needs.
The traditional approach to customer analysis is often driven by marketing because this team needs to understand the customer to target them directly. But in performing customer segmentation analysis, marketing teams have too often limited themselves to tools that access only a small fraction of the information available for customer analysis—like surveys or digital search records.
Marketing teams, like all other groups, do much better with more customer information. Much of this insight is buried in other departments, like operations, customer support, finance, and sales.
Businesses need to mine information from everywhere in their organization to make truly smart, strategic, data-driven decisions. Doing so can enable companies to develop much richer and more accurate customer segmentation analysis from which to build multi segment marketing. Employing advanced data segmentation techniques in all types of marketing research and customer segmentation analysis can mean the difference between sure success and stagnation.
What is customer analysis? Customer analysis is a process where companies try to infer customer behavior based on data they have collected for the purpose of informing product, marketing and sales decisions. It is closely tied to market segmentation analysis.
What is segmentation analysis? Segmentation analysis
is a process of splitting a market into sub-segments based on similar characteristics, with a goal of finding high potential opportunities for growth.
What is benefit segmentation? Benefit segmentation uses the specific benefits or outcomes associated with a company's product in order to divide customers for a target market.
So, what precisely should analytics be used for? The short answer is, everything. There’s not much that can’t be better understood, more intelligently used, or made more efficient by integrating good customer segmentation analysis.
Most businesses should first and foremost use analytics for reporting. It’s crucial that your organization have access to consistent data everyone agrees is correct and up-to-date. An ad hoc or inconsistent approach to data reporting will inevitably create data silos, which will, in turn, lead to different versions of the truth, some of which will be treated as gospel within your organization.
Such multiple versions of the truth can only increase friction between teams, which in turn creates confusion, delays, and/or questionable decision-making. Further, data siloes often lead to, or worsen, a culture of corporate isolation and collaborative breakdown—all of which will make ad hoc reporting and data silos more prevalent. It’s an ugly, dangerous, and wholly avoidable cycle of data misuse and misunderstanding.
The second is to report only with specific aims in mind. Running reports just to have some numbers to pass around at that next big meeting is a temptation that should be avoided. Indeed, since reporting is so crucial to business success, it should only be performed when you’re looking for detailed information on how well—or how poorly—your campaigns and other initiatives are performing. Reporting should answer specific questions, in other words.
And the only way to be certain your company is accessing accurate, usable data is through using a system enabling automated record linkage between lower-level data sources and top-level reporting. This will help you understand exactly why you’re seeing certain high-level results. If your reporting lacks automated connections between source data and reporting, you will experience extra friction whenever you try to perform root cause analysis. Being able to automatically dig deeper to simplify your customer segmentation analysis will not only gain you more complete information now, but it will also help prevent some problems from arising at all.
Finally, you should use the insights gathered from smart, selective, and accurate data analytics reports and data segmentation to create measurable improvement—through new initiatives, making changes to programs currently running, and by abandoning duds. Your ultimate task should be as specific as the data you use to fuel it and the insights you draw from it; you should know exactly what you’re doing and why.
Data is at the core of all truly successful businesses now. And all data-based organizations have this in common: Without happy, engaged customers, they will fail. To keep current customers, attract new ones, and increase customer lifetime value (CLV) means directing most of your company’s data segmentation towards customer analysis (or consumer analysis).
Customer analysis is about creating your ideal customer profile or profiles; your company almost certainly has all the evidence required to do this, having gathered it from all its myriad interactions with its customer base. Thorough and sophisticated types of customer segmentation analysis can provide deep understanding of which categories each customer belongs to. You may then connect such behavioral insights with identifiable business results, incorporate components like customer profitability analysis, and plan your business activities accordingly through multi segment marketing.
Further, a holistic approach to customer analysis (or consumer analysis) can help companies identify what new customers might look like, based on their current customer relationships. Such a method can also—and this is critical—help organizations much more effectively retain their customer base, as well as more profitably cross-sell and upsell to them.
A holistic approach to customer analysis offers:
. You should be able to measure precisely how every marketing and sales initiative affects each unique customer segment, so you can respond with more precisely focused initiatives. Propensity modeling is particularly important if you aim to increase profitability through customer value analysis. If you have enough information, you can develop recommendations, based on customer profitability analysis, to guide the development of sales and marketing tactics that encourage customers to purchase more products or services.
Such a savvy approach to building and maintaining customer relationships relies on different types of market segmentation analysis, which divides a company’s current and potential customers into smaller groups displaying shared interests, buying tendencies, or demographic indicators.
This is not to take away from collecting customer feedback, far from it. The problem is that most types of market segmentation analysis come from basic segmentation analysis and customer feedback alone isn’t enough to do this properly; rudimentary types of customer segmentation analysis are only really effective when used as part of a larger, more complete analysis based on customer behavior.
Think about all the different ways you interact with your customers: through sales calls, billing, customer support, website visits, tech support, social media, and yes, even customer surveys—to name just a few. By applying customer segmentation analysis to all these sources, not just one or two, you will gain a much better view of who your ideal customer segments are. With such a complete customer picture, you can:
The nirvana for many marketing and sales teams is to support marketing initiatives customized for each customer segment. But whether you achieve this ideal or just radically improve your types of marketing research by integrating effective customer analysis, your market segmentation analysis results ought to:
All the information gathered from performing consumer analysis to extract market segmentation analysis details should form a good, solid basis from which to support your target marketing strategies.
Types of marketing research and data segmentation variables
Target marketing strategies have traditionally been built on single data segmentation variables such as: geography, willingness to pay, interests, values, behavior, demographics (age, gender, race, etc.), purchase cycle, age, income level, education level, marital or family status, occupation, or psychographics.
Adding as many data segmentation variables as you can to the information mix will help you much more effectively communicate with customers. We’ve been discussing in general terms the problem with not using as much corporate data as possible. Let’s get a little more specific and consider how more traditional, less sophisticated approaches to data have affected marketing initiatives—and why they don’t work in a data-driven, highly competitive market.
Also known as niche marketing, concentrated marketing usually involves a company devoting most or all of its resources—intellectual, monetary, and time—to just one particular segment. There are some business advantages to this approach; concentrated marketing:
The problem with concentrated marketing is that very few companies, once they reach a certain size, can afford to engage with only one customer segment. Either the company has to stop growing, or they have to find a way to make their one audience artificially grow with them. Eventually, concentrated marketing must be abandoned for something more multi-faceted.
Defining target marketing strategies using multiple correlated data segmentation variables that fit your company’s strategies and resources is the ideal way to maximize both customer profitability and customer lifetime value. Using data segmentation variables to increase revenues can only effectively be done if you have the required mechanisms in place, first to perform complete and accurate consumer analysis and customer profitability analysis; then, to be able to update data reporting as needed without starting from scratch each time.
A more profitable and sustainable way than niche marketing to run your business is to filter your consumer and customer profitability analysis through the lens of benefit segmentation, to develop target marketing strategies.
Benefit segmentation divides customers into groups based on what they believe they’ll gain from a product or service. (A company offering high-quality infant car seats, for example, is really selling peace of mind.) Analyzing customer interaction transcriptions can provide companies with a deep understanding of exactly what benefits their customers believe they’ll receive by purchasing their products and services.
Crucially, benefit segmentation derived from a robust set of company-appropriate segmentation variables can also reveal deep insight into what benefits your customers see in your competitors’ offerings. This will open new opportunities for growing your base much more effectively through multi segment marketing than a simple, old-school approach like focusing only on price.
Ultimately, all of the previous approaches lead to the development of a differentiated marketing strategy. This approach involves multi segment marketing, with different messages and channels appropriate to each type of customer. (Differentiated marketing’s evil twin, undifferentiated marketing, tries to attract and keep customers using only one product, which is often promoted in only one way. Markets are becoming less homogenous every day and in every possible way; success in business is no longer found by behaving as though there is only one type of customer that wants only one type of product or service.)
The key to a successful differentiated marketing strategy is in gaining a complete picture of each of your company’s target markets. Companies that can speak in a unique way to their customers win. And the only companies that can do this know how to use their own data intelligently: by continually revisiting what data segmentation variables they use and how they conduct customer profitability analysis. They know people change and segments grow and shrink, so they match their data reporting practices to these facts to bolster profit margins and CLV for long-term, sustainable growth.
Now, let’s put this all together and apply it: Based on a customer survey, your company learns customers are saying they want to purchase your products or services because they solve key problems for them. They list their three main criteria for purchase as price, quality, and support. But…
Transcripts from your customer support calls show customers rarely mention cost. Qualitative language, such as “indispensable” and “absolutely necessary,” is commonly used with reference to your product or service, however. Logs also show customer support is critical to your base and they’re happy overall with how your company provides it. Great! Next step:
Performing an account-based customer profitability analysis, you learn 60% are moderately profitable, 30% are not profitable, and 10% are extremely profitable. The highly profitable accounts purchase your high-end products or services, as well as add higher-tier customer support options where possible. Using only survey results, you would likely segment customers by price and highlight the company’s exceptional customer support.
Data gathered from those customer support logs, however, minimizes the importance of price, while financial data and customer profitability analysis shows your most lucrative customers consistently purchase extra support packages.
Knowing all this, you should ask yourself:
Since truly holistic customer analysis (or consumer analysis) uses data from across an organization and applies it throughout, too, consider this other hypothetical: Let’s say your company has two sales channels, inside and field; a bottom-up customer analysis example shows 90% of your customers are happy being served by inside sales. You could adjust your sales structure accordingly—both serving your base better and using your resources more effectively.
This hypothetical company wouldn’t have gotten anywhere near these kinds of results relying only on customer surveys. This is why you need proper data analytics tools to move beyond the guesswork and gut instinct of the past for good.
Past approaches to customer analysis and some types of market segmentation analysis were limited by available technologies. From the 1960s to the mid-2000s, marketing teams or the third parties they hired conducted different types of marketing research, usually by interviewing people. Some of these habits linger, in spite of the advent of customer analytics, CRMs, and data analytics.
Digital analytics have made it possible for companies to get better insight into their customers’ behaviors, as well as how well or poorly their campaigns fare. By integrating many different data sources, companies can develop rich models that look at an incredibly large number of variables, including CLV, customer profitability analysis, customer pain points, and customer desires, to name just a few. And by integrating this data with other systems, companies can better plan their strategies to target different customer segments based on their available resources and human capital.
Data drawn from customers you already have can and should be used to attract new customers, but also, of course, to cross-sell and upsell to existing customers. This approach to customer data analysis should form an ever-evolving relationship between your data, how you reach consumers, and how you run your business.
Most companies are sitting on goldmines of customer and market information they’re not using and may not even be aware of having. We at 3AG Systems can help you mine that untouched vein—so you can transform it into increasingly profitable business strategies and choices.
Originally published on the 3AG blog: What is customer analysis and segmentation analysis?
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