You want to achieve greater revenues for your business by building on better value propositions, increasing customer spend, cross-selling, shortening the buying cycle, leveraging customer loyalty, and increasing your overall market share. Great goals! But how do you exactly plan to achieve them?
A very good place to start with is to get to know your customers well. This article aims to present you with some insights on how you should go about to target the right customers!
Market Segmentation is the discipline that enables companies to identify revenue opportunities through knowledge of, among others, customer buying behavior, interests, and geo-demographic characteristics. It usually involves classifying customers into groups based on their unique quality, common needs, and how they respond to different marketing approaches. Ultimately market segmentation is about matching right products to the right people; the better you are at doing this, the more effective you can covert sales!
So how do I segment my customers I hear you ask! The first step you will need to decide what information you should be collecting on your potential customers. Picking the most relevant customer profiling methods is critical so thatuseful customer data can be collected and profiles can be formed for effective targeting.
Customer Profiling Techniques
Here are some profiling methods that will allow you to see your customers from different perspectives.
1. Geo-demographic profiling. Data collected through this technique typically include the customers’ age, income, gender, marital status, education level, and location. This method generally works for categories that are broadly targeted.
2. Transaction-based profiling. This technique involves tracking the customer’s past purchasing behavior to predict future purchasing behavior. Amazon is known for successfully leveraging this profiling method to offer products to customers who are very likely to buy them—because they have bought similar or related products in the past.
3. Contextual targeting. If you’re on Google’s network, you really don’t need to worry about this. “Contextual targeting technology enables marketers to find consumers based on where they are interacting with related content.” With this method, ads are presented only on pages with relevant content—as if you know what your customers are reading.
4. Behavioral targeting. In this method, customers are targeted based on their interests or needs, as exhibited by their browsing behavior. Ads are displayed based on what a customer has searched for in the past.
5. Look-alikes. This method is relatively new and has arisen partly because of the proliferation of social media, blogs, and online products and service providers. The social profile (what articles or pages do they usually “like” or recommend?) and browsing history of customers that end up buying a particular product are analyzed to identify “look-alikes” or the type of customers the brand is likely to have.
You can take advantage of all of the above techniques or pick just a few relevant ones based on your purpose and the type of business you are in. The more important question is how you make sense of the information that you gather to achieve your larger business goals.
Types of Segmentation
Now that you have collected sufficient data about your existing and prospective customers, what do you do next? How do you know which sets of data are relevant for which purpose? Or do you always have to rely on information that you actively take in from various sources? It’s important that you know the different approaches to segmenting before you make your judgment about these concerns.
1. A priori segmentation. As its name suggests, a priori segmentation relies on customer information known beforehand and “not based on any empirical research specific to the segments being created.” Segments are created using pre-established classifications, e.g., Value, Attitudes & Lifestyles (VALS) Framework, developed by social scientist and consumer futurist Arnold Mitchell.
This segmentation method could provide more immediate results and be less expensive than other approaches that require extensive research. However, it poses some risks to the business utilizing the prepared data due to the volatility of market segments.
In today’s age of digital technology, everything is changing very quickly—including how customers receive, consume, and respond to information they are bombarded with everyday. Segmentation is supposed to help businesses reach their target audience in the most effective and powerful way possible. Outdated information could be well in the way.
2. Post hoc segmentation. This approach is also called cluster analysis. It is “based on empirical research conducted specifically for outlining market segments.” Because it is based on research, and one especially carried out to establish relevant segments, post hoc segmentation could be relied on for more accurate data.
No pre-existing information is used in this method. Any and all attempts at segmentation will begin only after actual data is obtained. Consequently the risk here is spending a large portion of the marketing budget on research and ending up with non-actionable results.
3. Benefits-based segmentation. In this approach, the specific benefits of a product that appeal most to customers are given greatest importance. Segments are created based on that—customers that focus on different benefits of a given product are treated differently.
Consider this case: a product is both great-tasting and nutritious. From these two benefits, at least two groups of customers will emerge—those who are happy with a great-tasting snack and those who are critical about nutritional value in the food they eat. Marketing campaigns targeted at these two segments have different selling points in their message. Some concern may arise, though, on trying to market the product as both great tasting and healthy. Thankfully there are visual segmentation technologies out there that help marketers effectively develop campaigns targeting people with multiple attributes, and all done in a few clicks within your marketing automation or CRM tools.
Market segmentation is a great way to get to know your customers and inform your marketing strategies. In fact, most companies rely solely on segmentation for identifying customers for their campaigns. However, segmentation combined with another method of gaining insight into customer needs and behavior will bring the greatest results for the company.
Predictive modeling is “the practice of forecasting future customer behaviors and propensities and assigning a score or ranking to each customer that depicts their anticipated actions.” Similar to segmentation, it leverages information collected about customer behavior to identify characteristics that will likely lead to specific goals.
There is no ideal number of predictive models that a company should strive to achieve. This will be determined by, as in segmentation, the business goals and capability of the company to act on these goals and models. It will boil down to “the number of different profit-driving behaviors a company believes it can influence with customer data-driven campaigns.” And the higher this number goes, the more challenging, as it is rewarding, for a company to execute specialized campaigns aimed at a highly targeted audience, because every predictive model is normally dedicated to a single behavior.
Common applications of predictive modeling include identifying specific customer targets for campaigns, forecasting customer behaviors, optimizing ROI, and assessing impact of marketing various components on customer behavior.
1. Predictive modeling is very useful in identifying the target audience for highly specialized campaigns. This is done by rating each customer based on how likely they are to react to a given program.
2. Predictive modeling allows marketers to forecast customer behavior, such as customer loyalty, tendency to purchase, and expected customer spend. It also enables marketers to gain insight into the lifetime customer value of each customer. Through this you can identify your top value customer groups.
3. As companies always aim at optimizing revenue performance, predictive models are an essential element in leveraging primary market levers like pricing and value proposition. For example, in determining the optimal offer for each customer, analysis of the price elasticity curves of different segments is crucial. Such insight from predicting modeling is highly valuable in business planning tied to revenue goals.
4. Predictive modeling enables companies to evaluate the impact of individual marketing efforts on customer behavior. This is significant because with the heap of information and stimuli that compete for the customers’ attention and allegiance, so to speak, it is difficult to accurately measure the effectiveness of specific marketing initiatives. And failure to do so would keep companies in the dark as to which activities to continue because they are effective; and discontinue, because they are a waste of time and money.
Getting to really know your customers entails being clear on your business goals, having genuine interest in understanding your customers and providing them with the best product and service according to their needs, practicing intelligent planning, leveraging technology to achieve results, and keeping a strategic mind that sees the big picture of the entire buyers’ journey.
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1 Neuner, Chris, “The Future of Consumer Profiling is Here! How to Realize the Power of Prediction,” DTC Perspectives 11, no. 3 (2012): 18-22, http://www.dtcperspectives.com/wp-content/uploads/2012/10/Neuner-ConsumerProfiling.pdf.
2 “Segmentation Simplified: Basics Ensure Actionability,” MrPerspectives, accessed August 23, 2015, http://cdn2.hubspot.net/hub/58820/file-15751967-htm/newsletters/mr_best_practices/mrp_0408_segmentation_analysis.htm.
4 Customer Lifecycle, LLC, “Predictive Segmentation Enables Optimal Targeting of Your Customer Database,” Greenbook, May 16, 2013, http://www.greenbook.org/marketing-research/predictive-segmentation-enables-optimal-targeting-of-your-customer-database-34754
5 McGuirk, Mike, “Customer Segmentation and Predictive Modeling. It’s not an either/or decision” (Whitepaper, iKnowtion, Sept 2007), http://www.iknowtion.com/uploads/papers/7/customersegmentationandpredictivemodeling.pdf