Stage 3: Seven Steps to Grow Your Customer Base
Customer Lifecycle, LLC
Grow is the third in a series of four papers that will discuss how to get the best business results from each stage of the customer lifecycle. This paper discusses tools and techniques that can be used to create a practical process that focuses on improving the financial results from all customer groups.
Each Lifecycle Stage Offers New Opportunities Each stage in the customer lifecycle—acquisition, service, growth, retention—has its own unique customer needs, attitudes and behaviors. This creates the opportunity to identify and measure competitive performance requirements and metrics for both a particular stage and its relationship to the entire lifecycle.
The first paper in this Acquire, Serve, Grow, and Retain series, examines factors driving customer acquisition and outlines a systematic process to attract and build a profitable customer base. With that information an organization can develop a targeted customer acquisition and retention strategy and up-selling programs as well as leverage the desired communications channels in order to improve lifetime value.
Serve, our second paper, discusses approaches to identify how well you satisfy customer needs with service that meets or exceeds their expectations. We also discuss whether the targeted customers you have secured will remain satisfied and loyal or whether they will leave due to dissatisfaction with the products or services you are providing.
In that paper we define a service process based on the fact that not all customers have the same expectations and criteria for superior service delivery. Consequently, it is useful to segment the customer base and identify the unique requirements of various purchasers at each touch point. This makes it possible to develop service delivery standards for each function based on meeting and exceeding the needs of each segment. This also makes it possible to optimize both service delivery and service profitability by delivering neither more nor less than what will be experienced as superior service by customers in a particular segment.
Grow is the third in a series of four papers that will discuss how to get the best business results from each stage of the customer lifecycle.
Seven Steps to Grow Your Customer Base and Your Customer Revenue
Successful development of a growing and profitable customer base is a critical requirement for business survival in a highly competitive and economically challenging market environment. Unfortunately, most companies lack an integrated approach to both attract and retain profitable new customers and maximize revenue and profitability from existing ones.
This paper discusses tools and techniques that can be used to create a practical process that focuses on improving the financial results from all customer groups through:
- Reducing churn
- Identifying needs by segment
- Optimizing customer loyalty drivers
- Incorporating customer requirements into operating processes
- Creating a customer-focused value proposition
- Measuring and managing the Total Customer Experience
- Increasing share of wallet
Each of these tools can be used individually and quite often are. However, when used in combination to create an enterprise-wide, holistic view of revenue and profit opportunities in prospective and existing customers, they produce a customer-focused operating framework that improves marketplace performance and business results.
1. Don’t Churn Away your Revenue and Profits
Among the findings of a recent Chief Marketing Officer Council study, “Business Gain From How You Retain,” respondents say customer churn significantly impacts business performance through revenue loss (59.9%), reduced profitability (39.6%), and greater marketing and re-acquisition costs (36.3%).
An existing customer base contains real growth potential, but many companies fail to realize its full potential. Often, more energy goes into attracting new customers than looking after current ones.
However, it is generally recognized that the longer a customer stays with a company, the more that customer is worth. Long-term customers buy more, take less of a company's time, are less sensitive to price, and bring in new customers. Best of all, they have no acquisition or start-up cost. Good long-standing customers are worth so much that in some industries, reducing customer defections by as little as five points from, say, 15% to 10% per year-can double profits.
“Churn” relates to both customers’ defections and to the loss of value from customers who remain. So “churn rate” refers, on the one hand, to the percentage of customers who end their relationship with your company or, on the other hand, to the customers who still use your products or services, but in less volume or not as often as they used to. The difficult challenge in developing effective approaches to reducing both kinds of churn is to be able to identify predictors of each type of churn and take corrective actions to address the problem.
Predict Customer Defections and Reduced Purchasing
Most companies have large amounts of data on customer purchasing behavior, although it is frequently in several different databases. Fortunately, technology is available to merge these disparate sources of data and provide the necessary predictors of churn.
Predictive analytics is data mining technology that uses your customer data to build a predictive model specialized for your business. This process learns from your organization's collective experience by leveraging your existing logs of customer purchases, behavior, and demographics. The wisdom gained is encoded as the predictive model itself. Predictive modeling software has computer science at its core, undertaking a mixture of number crunching and trial and error.
A predictive model tells you which new customers are likely to return and which are probably one-timers. The model is created with data mining methods that "learn" from the collective experience of your company contained in your sales records. The model then applies what has been learned to produce a predictive score for each new customer in real time.
In this way, new customers you would otherwise never see again are targeted and enticed to stay. Because you don't waste the retention offer on new customers likely to return, the numbers work out very well. The growth rate and medium-term profits potentially skyrocket, and immediate-term profits are not put at risk.
Another Useful Tool is Customer Churn Trend Analysis
Customer churn trends can be analyzed with time-series analytic tools. These tools can identify trends of customer segments in various ways: geographic, demographic, psychographic factors, and others. Typical sales trend analysis includes:
- Which customer segments are having highest growth in dollar terms?
- Which customer segments are having highest revenue decline in dollar terms?
- Which customer segments are having highest growth rates in percentage terms?
- Which customer segments are having highest revenue decline rates in percentage terms?
- How solid the growth (or decline) trend is?
- Which customer segments are showing exponential growth (or decline)?
Once churn detection models are developed, they can be applied to your customer databases on a regular basis. This will let you identify customers who have potential for defection but have not been contacted for retention purposes recently. Preventive actions can be followed for customers who have been identified as potentially at risk of either defecting or reducing purchase volume.
2. Use Segmentation to Grow Your Customer Base
Importance of Customer Segmentation for Marketing Activities (Source: IBM Institute for Business Value)
Segmentation is a well-known and widely-used marketing practice used to identify prospects and customers who are similar in terms of specific criteria and different from other prospects and customers in terms of these same criteria. These similarities and differences allow the customers and prospects to be segmented into distinct groups. Unfortunately many companies take an overly simplistic a priori approach and end up with segments that are not maximally differentiated.
Limitations of A Priori Segmentation
A priori segments are the most basic way of creating market segments. In a priori segmentation the market is split according to pre-existing demographic, geographic, or product use criteria. Such segments are easy to define and easy to target with advertising and media. For some sectors, for instance technology, there are such strong relationships between age and use, that a priori segments are all that are needed. However in other markets it is more difficult to use pre-existing variables for segmentation.
A priori segmentations are also the simplest segmentation to apply and use. A database can be flagged or sorted on the pre-existing data and that data used to drive sales and marketing campaigns.
Although a priori segmentations are better than pure mass marketing, even the most sophisticated a priori systems are quite crude. For example, in geodemographics there is the assumption that you buy or think the same way as your neighbor, which is clearly not always the case.
Improve Actionability with Predictive Segmentation
Given the limitations of a priori segmentation, other models have been developed that overcome the shortcomings of this technique by segmenting respondents based on those attitudinal, demographic, and company characteristic variables that affect key customer behavioral measures, such as category usage and spending. This segmentation approach, called Predictive Segmentation, differentiates groups of customers and potential customers based on their behaviors as well as the attitudes and other characteristics that are associated with these behaviors.
Predictive Segmentation has been used successfully in segmentation research for over 15 years. The diagram below is a representation of a typical Predictive Segmentation model:
In this model, an approach can be used that will differentiate segments based on the subjective measures (brand equity, brand personality, company image) and objective measures (brand/product performance) that are associated with varying behaviors and demographics (“criterion variables”). In this way, the attitudes that are most associated with customer behavior and targetable customer characteristics will be identified as those that are most important in differentiating each segment’s particular configuration of behaviors. In addition, each segment will be profiled in terms of any other factors important in the overall analysis, but not included in the model itself.
This model is “predictive” in that it only segments the market based on those attitudes and characteristics that affect customer behavior. Factors that do not drive the business do not drive the segmentation (of course, the segments can be profiled on these “dropped” attributes once the segmentation is determined). Put another way, segments are distinctive in terms of the ways in which different attitudes drive different market-related behaviors.
The model will not only provide actionable and maximally differentiated market segments, it will provide an understanding of what factors, among those measured, actually affect the decision making and varying behaviors of different customer groups. The overall model can be fine-tuned by identifying the “worth” of each customer in terms of the customer’s segment’s expected contribution to the business. For example, some actions might lead to the retention of low-worth customers while other actions might lead to the retention of high-worth customers.
Predictive Segmentation is a two-step process in which the relationships among the predictor and criterion variables are determined in the first step. Once these relationships are determined, a straightforward cluster analysis (often K-Means but at times simple partitioning of the perceptual space) is conducted and each respondent located within each cluster. Generally, several solutions, based on varying segment counts, are generated.
The best solution is typically the solution which yields the smallest number of segments while also retaining the lion’s share of relevant information and maintains the necessary degree of segment distinctiveness. Generally, once the segmentation solution is selected, a Discriminant Function Analysis follows in order to determine the distinctiveness and predictability of the solution.
This approach produces highly differentiated segments that can be targeted with marketing communications and sales activity directed to their specific needs, attitudes, and behaviors.
As an outcome of the Predictive Segmentation, it is possible to develop a prediction formula that classifies each customer into a specific segment. Management can then target appropriate growth and retention strategies.
The formula makes use of any information that is currently available in the companies’ database. The classification formula can be validated by testing its ability to correctly classify respondents from the study whose segment classification is already known. However, the success of the formula development depends on having sufficient data base information to correctly classify respondents.
The prediction formula can then be applied to your customer and prospect databases. It is also possible to create a segment classifier tool that can be placed on sales representatives’ laptops, iPads, or PDAs. This classifier allows the sales rep to input a modest amount of information about a prospect or customer and learn the likely segment to which the prospect or customer belongs.
3. Optimize your Customer Loyalty Drivers
To attract new customers and retain and grow your existing customer base, one best practice is to identify and measure the relative importance of the specific attributes that drive customer loyalty and retention. It is necessary to understand specific customer’s needs and the impact on loyalty of satisfying those needs.
However, focusing primarily on performance quality, product features, and existing internal processes will not create a loyal customer base or present any true differentiation relative to competition. There is a significant difference between satisfaction, which is largely a passive customer condition, and loyalty, which is an active or even proactive relationship with the product or service supplier.
Loyalty is based on delivering a superior value proposition, based on a deep understanding of customer segment needs. Truly aspiring to meet customer needs, developing direct measures of loyalty, retention, and attraction based on these same needs, and linking those measures to internal processes is what finally puts companies on the path to creating shareholder value through loyal customers.
Loyalty metrics and the ability to define the drivers of customer loyalty for specific market segments can help marketing and business managers to develop their competitive strategies based on one or a combination of the loyalty drivers. Additionally, building a direct link between customer requirements supported by business strategy and targeted value propositions allows the organization to align its people and processes to better deliver value to targeted customers. An effective, comprehensive quality program is critical to operationalizing the business strategy and the marketing plan.
The conceptual model shown below is an overview of the factors contributing to customer attraction and retention.
Customer Loyalty Analysis
Typically, a survey contains multiple indicators of loyalty which are then used to develop specific loyalty measures. These indicators are weighted and then used to score each customer on loyalty to your company and its competitors, as appropriate. Customers can also be classified into loyalty groups based on their scores. For example, you might create three loyalty groups: Committed, Fence Sitters, and Vulnerable. Each group would then be profiled on its characteristics and perceptions of your company. If sufficient information is available in your database, it is also possible to create an algorithm to tag each customer in the database with a loyalty indicator.
Prioritization of Customer Requirements
The survey analysis will prioritize those factors that impact customers’ satisfaction and loyalty, in order to provide guidance and direction for allocation of company resources. Typically a derived importance approach is used to prioritizing among the drivers of customer loyalty.
Derived importance analysis examines the relationship between an overall criterion, such as a measure of loyalty, and performance on specific performance attributes. The analysis identifies the key performance issues that best explain or predict customer loyalty, using a technique that is similar to multiple regression analysis but is designed to deal with data sets that have high intercorrelations among the attributes.
In addition to measuring importance, customers are also asked to give their perceptions of current performance by your organization and your competitors. The analysis combines performance ratings with relative importance to identify clear areas of strength and weakness, as indicated below. This analysis should be performed for all relevant subgroups, as well as for the total sample (see below).
Based on this information, you will be well-equipped to provide targeted value propositions and benefits focused on the specific needs and benefits of each of your customer and prospect segments.
4. Incorporate Customer Requirements into Your Operating Processes
Failure to effectively use results of satisfaction, loyalty, and engagement research to improve day-to-day management and operations is the single most common failure in loyalty research initiatives. According to recent studies, a large majority of managers surveyed indicated that their organizations were not taking effective action on Voice of the Customer research findings. The research investments were not improving business performance.
In order to realize the full value of the investment in customer loyalty and engagement research, management needs to have a solid understanding of the results and the related process and communication actions that are warranted.
Based on the research findings, upper management and key functional managers need to develop detailed deployment and integration plans which ensure that key drivers of customer loyalty and engagement are linked to key business processes and outputs. Requirements for who, what, how, and when are agreed upon, with clear linkage to day-to-day management and operations activities. Ongoing performance metrics are also established.
This is accomplished during a series of one-or-two-day sessions with the following goals:
- Identify those processes that require improvement efforts;
- Gain commitment from process owners to utilize the customer data;
- Translate customer needs and expectations into product and/or service requirements;
- Develop priorities for action;
- Assign ownership and timing for action items;
- Develop data-based action plans;
- Establish performance metrics;
- Implement customer-focused improvements; and
- Define specific measures of progress.
Your research has identified and measured the drivers of loyalty, engagement, and retention. Don’t waste your research investment by failing to implement a systematic and structured way to translate those research findings into improved marketplace performance and better financial outcomes.
5. Create a Powerful Customer-focused Value Proposition
Value propositions are widely misunderstood. They are not about how wonderful a company’s products and services are. A powerful value proposition is a customer-focused description of value that demonstrates a company’s knowledge about the customer’s experience or challenge as well as the company’s specific offer to address it, underscored by what differentiates their offer from any other. It should be a clear and succinct statement indicating the specific value of a service or product or offer to a specific audience in order to differentiate its value.
Based on best practices, a persuasive value proposition should do each of the following:
- Center on the customer’s experience, not product or service
- Communicate customer benefits, not product or service
- Focus on uniqueness and specifics, not generalities
- Include external and internal drivers
- Cover both qualitative and quantitative factors
- Be both believable and demonstrable
To create a persuasive value proposition, a company must meet each of these objectives:
- Know which customers belong with which offers
- Understand what they care about
- Speak in their language
- Create the right message for the customer
- Link the offer to the customer objective
- Lead with the customer, not a product or service
It is also worthwhile to customize a value proposition for specific audiences:
- By market segment;
- By major account;
- By Product or Service;
- By Individual (VP or Director or Manager); and
- By remembering that one size does NOT fit all
A well thought out value proposition should be used in sales materials (such as brochures, websites, product/service sheets), sales proposals, and sales presentations.
Organizations should also consider crafting questions for sales people who use the value proposition as a base and determining how well the Value Proposition connects to the company tag line.
The process of developing a compelling value proposition, as presented here, will help you to identify and frame any discussions you need to have concerning development of a value proposition that will clearly support your company’s positioning strategy.
6. Measure and Manage the Total Customer Experience
The value of measuring the end-to-end Total Customer Experience was previously discussed in our paper on Serve. Because of the equal importance of a superior total customer experience in growing a loyal and profitable base of prospects and customers, the process is further discussed in this section.
The various techniques described above all provide actionable information to attract, retain, and grow your customer base. However, you must also integrate these practices into a holistic view of the total experience your customers have when they deal with your company.
Prospect and customer perceptions of your value and service are created by a diverse set of experiences with your organization, from brand promise and positioning to operating performance, product and service quality, and customer support.
These experiences originate from different levels of your entire organization and combine to create an end-to-end total customer experience. This drives customer beliefs about your company and your competitors at the highest level. These experiences are also framed at a second level in the context of their life cycle experiences, and are further informed at a third level by event and transaction experiences at various touch points. All of these impressions and experiences are driven by your internal processes at the foundation of your organization. To the extent that all of these impressions, touch points, and drivers are aligned to optimally meet and exceed customer requirements and expectations, the better will be the Total Customer Experience.
An integrated multi-level measurement and management system that is designed to provide a clear and consistent customer-focused line of sight through your entire organization is very useful. It represents a framework for managing each of your customer-facing touch points and processes according to metrics which balance both external customer requirements and internal performance metrics, which then enable revenue growth and profitability. This will ensure that your branding, people, products, services, and processes are working together to create a Total Customer Experience that will differentiate you from your competitors.
Shown below is a diagram of how such a multilevel measurement and management process is designed.
Desired End State: Holistic, Integrated, Modular Customer Feedback System
Depending on the size and structure of your company, such a process does not need to be unduly complex. If nothing else, it gives your management and employees a mental model of a framework for working together in a systematic and structured way to attract, retain, and grow a profitable customer base.
7. Increase Share-of-Wallet
Basically, there are two ways companies in most markets can grow their revenues: by either selling more to their existing customers or by acquiring new customers. As discussed previously, acquiring new customers is an expensive way to grow revenues, as it costs significantly more to acquire a customer than to retain one already on your books.
Selling more to existing customers can generally be done in one of two ways: either through the introduction of new products and services that will create opportunities to increase your customer’s overall spend or through the increase in spend of existing customers who are not giving your company their full share-of-wallet.
Share-of-wallet is the percentage of a customer’s spend with a given company over a given amount of time. Any given consumer has dozens of shares of wallet that businesses are after. Our daily coffee spend, our monthly telecom spend, our yearly insurance spend, or even our once-every-three-years new car spend is rarely consolidated with one company per category. It is spread out among numerous companies in many different sectors.
Your company’s objective may be to capture 100% share of wallet from each of your existing customers in your particular category. However, of necessity you may be conducting share-of-wallet initiatives by appealing to your entire customer base, with little or no targeting, since you lack the information necessary to predict which customers have potential to spend more and which ones don’t.
With such information, you could increase share on a one-to-one level with customized offers aimed at consolidating spend and capturing significantly increased share-of-wallet for your product or service. Seven steps to understanding each customer’s share-of-wallet are shown below:
- Score your customers on some key criteria such as profitability, tenure, recency and frequency of purchase, number of potential buyers and previous spend history;
- Use your scoring methodology to rank and segment your customers;
- Design a study using an interview methodology. Be sure to take a customer-centric perspective when creating the discussion guide, and be sure answers to the questions will result in your ability to create customer-focused metrics, including stated share from your company and key competitors.
- Conduct interviews with as many customers as you can from each of your segments;
- Analyze the data to determine the answers to your questions and to compare the difference in the answers between the different segments;
- Use the data to establish your performance metrics and a benchmark; and
- Develop an action plan based on the data.
Upon completing these steps, you should be able to determine your share of wallet and the average number of products typically used by a customer in each of the segments, as well as additional insights into each of the customer segments and competitors.
As a result, you should able to identify the accounts that were most likely to increase budgets and their spending and provide information to help develop strategic account plans based on real account feedback. You can also profile the ideal customer account, identify indicators of accounts at risk, and develop plans of action for recovery. This information lets you decide how to best allocate your resources and facilitate conversations with operations, sales, and customer service.
The Customer is the Key
The evidence is clear that successful growth of a profitable customer base is driven by a combination of tools and techniques that optimize the process of Acquiring, Serving and Growing the right customers in the best way. Customer Lifecycle, LLC understands the important relationship of growth to other stages of the customer lifecycle, and we work with our clients to use the voice of the customer to frame the development and successful deployment of metrics for the growth phase.
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