Growing Revenue

MAB's business is focused on a high-end niche segment with few of the price pressures typical of mass fashion markets. Delivering high-quality products with excellent service is table-stakes if the business is to survive. Clients of custom clothing place a high value on quality, design, and fashion and are willing to invest when they find a provider meeting their exacting needs. With a business model that is highly personal and differentiated, profitability is not the primary challenge as in most of fashion retail. Rather MAB's challenge is to attract prospects who value custom clothing, convert them into a full-package wardrobe experience, and retain them in a long-term client relationship.

Client Acquisition

MAB's acquisition strategy involves attracting the attention of discerning clients through various channels such as search optimization, website, email campaigns, and advertising in targeted magazines. The best channel for attracting new clients is referrals from satisfied clients.

Client Engagement

When a client new to custom clothing makes their first visit to MAB's well-appointed studio, a MAB stylist introduces the client to the custom clothing experience of hand-crafted and personally styled menswear. More than simply selling high-end men's clothing, MAB's stylists pride themselves on expert and honest style advice, offering opinions on everything from the best cut to the most flattering patterns and colors. Through this process, clients with means and appreciation for perfectly fitted clothing become committed to an investment in personal style and a quality wardrobe.

Client Retention

MAB retains valuable clients through quality production and timely delivery of their products. MAB offers free alterations on garments for six months after delivery to ensure their clients are satisfied. From this level of personal interaction, clients view MAB as their partner in their clothing experience. Clients satisfied with the quality of MAB's service and products engage in a long-term relationship with MAB over years, maintaining their wardrobe, adding new styles, and replacing worn clothing.

In sum, MAB's business is driven by acquiring discerning clients, committing these clients to an investment in a personally styled wardrobe, and retaining them in a long-term partnership.

Client Types

The heartbeat of MAB's business is its client base. MAB thrives on meeting the professional and personal needs of clients who value custom clothing and are seeking a partner to bring their style to life. Custom clothing clients are passionate about their wardrobe, viewing it as an essential tool of their livelihood and an expression of their personal self.

In thinking about the client base, MAB finds it helpful to evaluate the client types based on their wardrobe desires and needs. After discussion with Michael Andrews, the founder and namesake of MAB, we learn of two core needs leading a client to invest in a custom wardrobe. Based on these core needs, clients can be broken down into two primary persona types:

1. The Wardrobe as a Professional Asset

   New York City is a rich market for clients who are expected to wear high-end suits every day to work, such as investment bankers and lawyers. While they appreciate quality and good service, these clients view their clothing as an extension of their work and consider the investment in a custom wardrobe as a cost of doing business. They trust MAB to deliver the right style that will set them apart within the norms of their industry.

2. The Wardrobe as an Expression of Personal Style

   This client type loves clothes, can afford them, and wears suits to work even when not required because he wants to. He views his clothing as an extension of himself. This persona is seeking to build a complete custom wardrobe to speak to his personality. He buys slowly, concerned about fit and design. He likes being part of the design process and can be more discerning and value conscious. This type of client will budget specifically for his wardrobe investment.

Other, secondary client personas that MAB serves include:

1. Purchasing for Special Events

   Another client type that MAB services are guys shopping for a special event, such as a wedding. They are willing to spend extra to ensure they look their best for the special day. Tuxedos are the primary clothing item these clients will purchase.

2. Purchasing to Enhance Their Style

   MAB also attracts the guy who loves clothes and collects clothing experiences, following styles and fashions, changing with the times. This client seeks out MAB to purchase a custom suit to add to his ensemble, often with exacting requirements because he is seeking to fill a specific niche in his wardrobe.

3. Hard to Fit

   The final client type that MAB services is the hard-to-fit guy. Falling outside the norm of physical dimensions, unable to find ready-to-wear options that fit, often his only option is custom tailoring for the times when he needs to look his best.

Client Life Cycle

The typical life cycle of a client's journey in the custom clothing experience follows three stages:

1. Exploratory Stage

   At this stage, clients try out the custom clothing experience and/or the stylist they engage to service them. They may begin with a suit or jacket and pant order to see what works for them. If they have the financial means and are well-served, they engage in a longer-term relationship with MAB. Clients facing a special event or challenge or seeking to enhance their wardrobe may not move beyond the exploratory stage, but nonetheless are a valuable source of business for MAB.

2. Commitment Stage

   If MAB is successful in providing a client the style, value, and service they require, they will engage in a relationship that can span years. Clients in the commitment stage investing in a custom wardrobe for the first time or overhauling an existing wardrobe will typically spend a considerable sum of money on a wardrobe comprising suits, sport coats, pants, and shirts.

3. Maintenance Stage

   Having established their core wardrobe, a client will maintain it, replacing items over time as they wear out or styles change. The professional wardrobe client, viewing his wardrobe as an extension of himself, is more likely to maintain his wardrobe over an extended period of time due to the utility nature of his clothing needs. The personal wardrobe client's motivation for his wardrobe investment is personal and intrinsic; once satisfied, his volume of purchase activity may decrease.

Business Analysis

Michael Andrews began the business in 2006 based on his personal passion for custom clothing, growing it to be a successful operation by 2009. Reaching his personal capacity to market and service the business, Michael brought on two stylists who helped him quadruple the business in just four years. As the client base evolved from the exploratory stage to the commitment stage, MAB needed to make a greater investment in stylist time and teamwork in order to service the more demanding needs of the client. With expansion of the stylist force to service the growing business, the original stylists, strong individual contributors, struggled and eventually left the business in 2013. Even with the departure of his two most experienced, top-grossing stylists, Michael was able to face the challenge of building his stylist team while maintaining strong revenue growth.

Three years later, in 2016, with the stylist team on solid footing, MAB annual revenue growth has continued on a strong trajectory, between 7 and 8 percent for the past three years. Michael's business problem is one many managers would love to have. He wants to develop a strategy to take a healthy company with good growth and make it better.

To understand key drivers of the business, we perform initial discovery to identify metrics we can use to evaluate MAB's business growth opportunity. When engaging in discovery, it is helpful to work from the top down, first examining overall performance and then successively drilling into underlying components.

In many of the charts we present, absolute values with respect to revenue have been removed to preserve the confidentiality of the data; however, the patterns and trends are not obscured.

Business Performance

The first observation we can make is that while MAB has healthy revenue growth, total client orders have been declining since 2011.

Spend and Order Volume

In 2010 Michael expanded his stylist force to service his growing client base. In 2011 and 2012, MAB's business focused on higher volume, lower spend clients. However, whether a client buys one or several suits, time spent servicing the client is approximately the same. When they repeat purchase, less time may be needed, but many clients purchase only once or twice a year, so time efficiencies are not easily gained.

Figure CS.1 is a graph representing the growth in client spend versus the decline in client orders. The company is able to devote more of the stylist's time to servicing clients at a higher spend level, thereby resulting in larger purchase orders and growing revenue.

Business Capacity

We know from a capacity analysis that the order servicing capacity is approximately 190 orders per month, considering normal hours of operation and preserving ample studio space for each client to have a comfortable experience. During peak season, MAB may work extended hours to accommodate the bursts in business. However, we observe that from 2011 through 2013, MAB frequently encountered capacity constraints managed by a combination of stylist overtime, extended studio hours, and perhaps a more crowded studio and less time per order.

Following Michael's change in strategy to focus on clients seeking the full custom wardrobe experience, order volume has decreased to a manageable range and hovers right at order capacity for the current studio (Figure CS.2).

Client Performance

MAB has served over 5,000 clients since it began business. Clients returning after their initial purchase account for about 50 percent of its business and on average order about 2.5 times per year, spending approximately $5,000 per year. The remaining 50 percent of clients explore a relationship with MAB, spending about $2,500 in their initial order.

From 2012 to 2013, spend per client plateaued, threatening overall revenue growth as MAB was operating at full capacity with respect to studio space. Due to the high cost of studio space in New York City and minimum time to fit a client, Michael shifted his acquisition strategy to focus on clients purchasing wardrobe packages versus individual items. In this way, he could improve resource efficiency and continue to grow revenue without having to make expensive investments in additional studio space. Furthermore, MAB's target clients are discerning customers, placing a high value on service and product quality given the investment they are making. Continuing to pursue a high-transaction strategy versus an annuity business would likely impact repeat sales.

Acquisition

Following retooling of its marketing strategy and stylist force in 2013, MAB has been successful in attracting and retaining clients focused on investing in a quality wardrobe and seeking a trusted partner to service their needs. Average spend per existing client continues to increase (Figure CS.3).

Retention

As MAB's client base ages in tenure with the company, it has been successful at engaging clients in a long-term relationship, with returning clients accounting for 50 percent of its client base by 2016. Figure CS.4 shows the retention rate, year-over-year, from returning clients. To elaborate, in 2012, 29 percent of the sales came from clients from prior years and 71 percent of orders came from new clients. In 2015, 50 percent of orders came from clients from prior years and 50 percent of orders came from new clients.

Figure CS.5 shows the client retention by year of first order, which tells us the percentage of clients by retention category: multiyear client, one-year client, or onetime client. Each year is a cohort, that is, 2015 represents clients who first ordered with MAB in that year.

To explain further, from the 2010 class, 48 percent are still clients today, 15 percent were clients for just one year, and 37 percent were clients just once.

For the 2014 class, 21 percent of the clients are still customers today, 20 percent were customers for just one year, and 63 percent of them were customers just once. The ending report period for this dataset is July 2016, so the 2015 class of customers have not yet had a full additional year to reorder; hence, the low multiyear client percentage.

One insight we draw from this analysis is that in 2010, MAB had a great crop of clients that have remained loyal and are still clients. In addition, with each passing year the number of returning clients is increasing, building a solid base for MAB to expand upon.

Business Levers for Monetization

Next we identify the monetization business levers available to Michael to achieve his revenue growth objective. We have learned that Michael's business is a top-line business so we focus on the Revenue branch of our Business Lever framework. We have also learned that clients typically order about twice a year, much of which is driven by spring and fall seasons. Custom clothing ordering is an involved process requiring a significant commitment of time for fitting, refitting, and waiting for production and delivery, a process that is not easy to fit into clients' busy schedule on a more frequent basis.

While price is a potent lever in every business, the market for custom clothing is not as price sensitive as other retail clothing segments, and as we learned in discovery, Michael has capacity constraints that are expensive to expand. Driving more volume through lower price will likely have a negative impact on profitability. Channel expansion is an action to be considered as well, but we are drawn first to the opportunity to grow revenue organically. We have learned that approximately 50 percent of MAB's first-time clients only purchase one time and 15 to 20 percent of clients do not return after the first year of ordering. We believe there may be an opportunity to drive revenue growth organically through proactive targeted marketing focused on retaining and engaging more clients in a long-term relationship. Figure CS.6 calls out the Business Levers we believe Michael can consider in developing revenue-driving actions. Further analysis will determine which of these levers will deliver the most promising results.

Hypothesis

With these insights, we are ready to articulate our hypothesis to guide development of our Decision Architecture.

Next Steps

Upon the completion of the Discovery phase we have achieved a good understanding of client types and life-cycle stages. Looking forward, we can apply data science to MAB's data to estimate purchase patterns to understand the degree to which MAB has been successful in acquiring, committing, and retaining clients.

We can use insights to seek out untapped opportunities for organic revenue growth through increased engagement and retention of its existing client base, the most cost-effective sales channel there is. This analysis can help MAB develop effective strategies to retain strong clients and grow high-potential clients.

Lastly, we can identify KPIs and success metrics that MAB can use to monitor the performance of its monetization strategies and act quickly on opportunities to improve through Guided Analytics. In sum, we can help MAB monetize its data.

Question Analysis

Our Question Analysis helps us determine how Michael thinks of the business when solving a problem or trying to find an opportunity. Through our Question Analysis, we align to the base hypothesis that MAB's strongest growth engine is its client base, through which it can grow business organically through retention, engagement, and referrals. Figure CS.7 summarizes the key questions that arose from the working session.

The first question Michael asks is, how are they performing? To gauge the health of the business Michael wants to know if business sales are up or down over this time last year and how sales are trending overall. This performance-based questioning is part of the Inform level of questions in our analytical cycle.

In continuation with this line of questioning, Michael wants to know the number of appointments, as this is a key indicator of the health of the organization. For example, if Michael were to only look at sales for a given weekend, a large order resulting in a big sale may mask the fact that not many clients made appointments, hinting at a bigger issue.

The next set of questions can be grouped into several categories: Orders, Client Segmentation, Client Engagement, and Client Retention. All four of these categories are at the Diagnose level in our analytical cycle as Michael is trying to problem-solve for issues or generate opportunities.

Category Tree

From our Question Analysis, we develop our Category Tree. Figure CS.8 shows the categories and hierarchy of the question groupings.

We also capture the types of analysis Michael would like to perform in the Inform stage.

1. Performance—The first analysis is centered on helping Michael answer the set of questions on how the store is performing today and over time. Is MAB improving performance over same time last year? Is customer spend per order healthy? Are we encountering capacity constraints, and if so, how often?
2. Client Profile—This analysis is focused on client profiles to understand the mix of business and types of clients against several segment attributes.
3. Client Segmentation—For the Client Segmentation analysis, we would like to know if MAB's client base can be segmented into groups of clients based on similar attributes and behavioral characteristics that can be used to identify opportunities for improvement. By comparing segment order history, we would like to know whether MAB's share of clothing spend can be improved through a greater scope of purchases, such as sport jackets and pants, in addition to suits and shirts. What indicators can MAB use to be more targeted in identifying prospects who have the potential to develop into a fuller partnership for their garment needs with respect to variety, value, and quantity of garment purchases?

In the Diagnostic stage of the analytical cycle, we would like to perform three diagnostics:

1. Client Engagement—The first diagnostic is centered on opportunities for various marketing activities. As MAB expands its client base it will increasingly encounter constraints with showroom capacity and stylist availability. Located in the heart of New York City, expanding studio space and the stylist force represents a significant investment in space and overhead costs. Michael is anxious to invest in the growth of his business but at the same time he would like to optimize his existing investment and ensure the business grows profitably. With better targeting of client engagement through unique marketing activities, Michael would like expansion needs to be driven by clients who have a sustaining value for the MAB experience.
2. Client Retention—About 50 percent of MAB's business in a year is new clients. Michael would like a diagnostic centered on the high-potential clients who are not likely to return the following year based on data science efforts of a threshold time period in order to target them for engagement marketing. In addition, he would like to know if signs of possible attrition of his best clients can be identified in order to implement preventative measures.
3. Order Fulfillment—The final diagnostic is based on the process to fulfill orders. In this diagnostic, Michael would like to know the status of orders to better determine scheduling and optimization of the appointment calendar.

In working with Michael, we decide to focus on the following analytics: Performance, Client Segmentation, Client Engagement, and Client Retention.

Key Decisions

After framing Michael's questions and building our Category Tree, we develop key decisions that came from our working sessions. As we learned in our discussion on Decision Analysis, decisions are developed through diagnostic analysis and usually come at the end of the root-cause analysis set of questions. Following is a list of questions with the resolving decision. (Please note that decisions are denoted by D.)

1. Q1 How are sales performing over last month and this time last year?
2. Q2 Who are our high-value clients and are we getting the most from our current business?
3. Q3 Which clients do we believe are undervalued?
4. Q4 Which clients offer the best opportunity to improve retention and engagement?
   1. D1 Which clients among my high-value clients show a risk of leaving?
   2. D2 Which exploratory clients should I work to engage in a long-term relationship?

Let's see how the decisions we have identified map back to our Business Levers (Figure CS.9).

Action Levers

Next we want to understand the possible actions we can take from our decisions. These should be actions that Michael can execute to drive a decision. From our two decisions, the team developed the following actions. (Please note that actions are denoted by A.)

1. D1 Which clients among my high-value clients show a risk of leaving?

   1. A1 For multiyear clients who have not been to the studio in over 6 months, offer a free shirt to come back in as part of a “test a new style” campaign.

2. D2 Which exploratory clients should I work to engage in a long-term relationship?

   1. A2 For multiyear clients not in the high-value spend tier, target a marketing event to drive to a more engaged relationship.

   2. A3 For newer clients who have placed large orders, conduct a group dinner to incent a multiyear relationship.

We return to our Business Lever framework once again and map the actions we will execute through our Monetization Strategy to the levers we selected (Figure CS.10).

Success Metrics

Now that we have our decisions and actions, we need to understand the major metrics that drive these decisions. Let's see what metrics we have for each of these decisions. (Please note that Success Metrics are denoted by SM.)

1. D1 Which clients among my high-value clients show a risk of leaving?
   1. A1 For multiyear clients who have not been to the showroom in over 6 months, offer a free shirt to come back in as part of a “test a new feature” campaign.
      1. SM Clients not ordering in 6 months or greater as a percentage of total
2. D2 Which exploratory or one-year clients should I work to engage in a long-term wardrobe relationship?
   1. A2 For high-value clients, target a marketing event to drive to a more committed relationship.
   2. A3 For newer clients who have placed large orders, conduct a group dinner to incent a multiyear relationship.
      1. SM Distribution of high-potential clients by Engagement Score

Decision Architecture

At this point, we have completed the Decision Analysis section of our Decision Architecture template. To recap:

Data Analysis

Having identified the success metrics that measure our strategy, we turn our attention to the following metrics to enable diagnostic analysis and performance monitoring.

• Operating metrics
• Diagnostic metrics
• Performance metrics

Data Development

Data produced by operational systems is rarely found in the form and quality needed for analytics. The MAB data is not a complex dataset and comes from a well-organized CRM system, but it still requires some cleansing and transformations in order to prepare it for our analytic purposes. The following are some issues typical of many analytic projects.

Analytic Structure

While sorting out data issues, we also study the inherent structure of the data and the transformations we need to uncover insightful patterns.

The MAB dataset is extracted from a CRM system that we import into our analytic database to produce the data layer. We apply transformations to create a set of views in the analytic layer to develop the metrics we need for analysis. We then bring the views together in the reporting layer to create the analytic dataset we need for analysis, diagnostics, and reporting. The completed analytic data mart structure is shown in Figure CS.11.

We next connect our visual analytic tool to the Client Order analytic dataset and develop the metadata to organize the data as shown in Figure CS.12.

Transformations

As we explore the data for structure, we also develop the transformations needed to develop the diagnostic and Success Metrics for analysis and reporting.

Creating fields to measure client tenure is a good example of typical transformations needed to support analytics. From the dataset we have a field that captures the date of the order. Many clients order two or three times a year but in different calendar months on different days. As custom clothing is not an everyday purchase and requires six to eight weeks to complete the process, tracking order by month will be sufficient for our purposes. Client tenure is a measure of the number of months from the initial order for subsequent orders. For example, the year and month of a first order date in January 2016 is tenure month 0. A subsequent order by the same client in June 2016 is tenure month 5.

This is important as we want to look for patterns when we contrast clients in comparable periods in their order experience, for example, comparing a client's order metrics, such as quantity of items, number of item types, and value of order, for clients in their initial tenure month and their second tenure month.

First we create a metric to calculate client tenure month:

Next we transform the metric to tenure years:

As time progresses, we keep track of the maximum value of the client tenure as of the latest date of reporting:

Finally, we identify tenure segment to use for analysis:

We perform a similar transformation for the diagnostic and Success Metrics we identified earlier.

Data Science

To help provide more insights into the data, we employ several data science techniques. These include building attribute-level segmentations that provide flexibility to group like customers based on various client descriptive and behavioral characteristics. In addition, we perform a statistical box-plot analysis to gain insights into natural cohorts to help create the segmentation groupings.

Purchase Scope

The first attribute we examine is purchase scope, a measure of the variety of garment types the client purchases. Suits, shirts, jackets, and pants are foundational garment types for a complete wardrobe, with tuxedos a frequent add-on for more formal occasions.

Using a box-plot analysis, we determine there is a meaningful difference in lifetime value of client spend between two and three garment types purchased. While clients purchasing two garment types have a wider range of lifetime spend, the median spend is half that of clients purchasing three garment types. This translates to clients spending, on average, 50 percent more than those who only purchase two garment types. These individuals have selected MAB as their partner to create a fuller wardrobe than just suits and shirts. This is a big epiphany for us and can drive many of our actions.

While clients purchasing four garment types spend more than those purchasing three, the difference in median spend is only 30 percent. We decide to group clients with purchases of three or more garment types into a scope segment labeled Wardrobe and group those with two or less as Ad Hoc.

We also identify a segment labeled Wedding to track clients who purchase for special events and are less likely to return for other items. These are clients with a onetime order that includes a tuxedo, although there may be other garment types on the order.

The box-plot diagram in Figure CS.13 is segmented based on how many garment types clients ordered. When reading the box-plot, the darker-shaded dots represent individual customers and the line between the light and dark-gray boxes is the median lifetime spend for that group.

MAB Client Segmentation

Having completed our segment analysis, we summarize our segmentation model in Figure CS.14.

We validate our segmentation model by evaluating the distribution of clients in the various segments, seen in the next three figures. In these charts, the bars represent the number of clients and the circles represent the median lifetime spend.

In Figure CS.15, segmenting by order scope, we see that our Wardrobe clients, those who purchase more than three different types of garments, have a lifetime spend on average more than 50 percent larger than clients who only order two garment types. This tells us that when clients purchase three or more types of garments from MAB, they begin to see MAB as a solution for their wardrobe, not just for individual articles of clothing. An opportunity presents itself to target clients in the Ad Hoc segment, encouraging them to expand the scope of their garment purchases with MAB.

Figure CS.16 shows clients segmented by tenure. In the first cohort, the clients only ordered once and their median lifetime spend was $3,000. In the second cohort, the client ordered only during their first year with MAB with a median lifetime spend of $7,000. In the last grouping, those clients who have been with MAB for multiple years have a median lifetime spend of $12,000.

The last of our charts to validate our segmentation is Figure CS.17. In this analysis we group clients into occupations segmented as Personal, Finance, and Legal. The three categories have distinctive attributes for targeting marketing and sales initiatives. The Finance segment has the highest lifetime spend of the three segments (considering the overall average for the Personal segment), which validates our initial assumption.

One-time Clients

Examining our Client Segmentation Matrix (Figure CS.18) for clients active with MAB during the past two years, we observe that clients ordering from MAB only one time, not surprisingly, tend to fall in the lower third of lifetime spend. One-time–Wardrobe clients have a higher spend but there are not many of them. Clients in the one-time order segment represent 36 percent of clients but only 12 percent of spend. There are a number of reasons that clients order only one time. Some clients value the quality and fit of custom clothing but have limited budgets. Some clients give MAB a try but opt for another preferred partner, or other reasons. While MAB welcomes all clients passionate about custom clothing, the probable return on investment for clients in this segment does not justify the expense of a proactive marketing strategy.

One-Year Clients

The one-year cohort provides some interesting opportunities. Clients in this group, representing 17 percent of the total clients and 15 percent of spend, present a value opportunity. One-year clients in the wardrobe segment are in the top two-thirds lifetime spend tiers and are good candidates for a retention strategy. Selective marketing actions targeted at this segment are likely to produce increases in client value and conversion to multiyear clients. One-year clients in the Ad Hoc segment fall in the lower third of lifetime spend and may present some selective opportunities.

Multiyear Clients

Multiyear clients are MAB's best clients, representing 47 percent of clients but 73 percent of spend. Multiyear–Wardrobe clients represent 33 percent of clients and 63 percent of revenue. MAB offers discounts for high-spend orders and various marketing activities targeted at this high-value segment. However, it would be helpful to MAB if clients showing early signs of possible attrition could be proactively addressed to prevent attrition.

Clients in the Multiyear–Ad Hoc segment fall into the top two-thirds of client spend and represent a rich opportunity for marketing programs to encourage increased engagement and expanded scope.

Figure CS.21 summarizes our recommended marketing strategies based on the opportunities by segmentation attributes that we have uncovered. The areas highlighted represent segments where MAB should target monetization strategies for retention and engagement to drive revenue.

Monetization Strategy Requirements

With our segments for targeted marketing strategies identified, we are ready to complete our Monetization Strategy requirements.

Decision Matrix

We move on to develop two decision matrixes to help us implement the actions identified in our Decision Analysis and Agile Analytics phases.

Engagement Monetization Decision Strategy Matrix

To enable Michael to make decisions on the first set of actions around Engagement, we develop a Decision Matrix to support an Engagement Monetization Strategy (Figure CS.22). This strategy will focus on moving clients from two segments, Multiyear–Ad Hoc and One-year–Wardrobe, to the Multiyear–Wardrobe segment.

Next, we focus on clients who have ordered within the past 24 months. As shown in Figure CS.23, MAB has 421 clients in these two segments with median lifetime spend ranging from $6,000 to $9,000. The median lifetime spend for Multiyear–Wardrobe segment clients is about $17,000, which we use for our target goal.

We would like to develop engagement actions to migrate “likely to move” clients from these two segments and capture a part of the $8,000 to $11,000 lift per client in lifetime spend from the segment shift.

However, not all clients are likely to move segments for a variety of reasons. In order to implement a more effective strategy, we develop an Engagement Score based on recency of the client's last order and difference in lifetime spend from the $17,000 target. We reason that clients with more recent order activity and already close in lifetime spend to the $17,000 target are more likely to respond to marketing incentives. Our scoring rubric is shown in Table CS.1.

Percent Spend Diff	Time Since Last Order	Score Assigned	Likely to Migrate Probability
> 0 and < 30%	0 to 6 months	High	80%
> = 30% and < 60%	0 to 6 months	High	80%
> = 60%	0 to 6 months	Low	20%
> 0 and < 30%	7 to 12 months	High	80%
> = 30% and < 60%	7 to 12 months	Med	50%
> = 60%	7 to 12 months	Low	20%
> 0 and < 30%	13 to 18 months	Med	50%
> = 30% and < 60%	13 to 18 months	Low	20%
> = 60%	13 to 18 months	Low	20%

Our goal is to develop two strategies for moving clients to the Multiyear–Wardrobe segment. In order to develop our strategy, we will develop a Decision Matrix as shown in Table CS.2. As a reminder, acts, events, outcomes, and payoffs are the four building blocks of decision theory. Acts are the actions or decisions that a person may take. Events are the occurrences taking place, usually with a level of uncertainty. Outcomes are the results of the occurrences, and payoffs are the values the decision maker is placing on the occurrences.

Client Tenure	Client Order Scope	Engagement Score	Clients	Lifetime Spend	Target Lifetime Spend	Potential Lift	Opportunity—Migrate to Champaign Client	Events—Full Engagement	Outcomes—Clients	Outcome Opportunity
Scotch Event—Invite Only
Multiyear Client	Wardrobe	High	97	16,463	20,000	3,370	326,850	50%	49	163,425
Multiyear Client	Wardrobe	Med	21	16,346	20,000	3,717	78,055	50%	11	39,028
Multiyear Client	Wardrobe	Low	182	8,933	20,000	11,210	2,040,183	20%	36	408,037
Multiyear Client	Ad hoc	High	14	16,125	20,000	3,398	47,569	80%	11	38,055
Multiyear Client	Ad hoc	Med	5	13,926	20,000	5,131	25,656	50%	3	12,828
Multiyear Client	Ad hoc	Low	189	5,835	20,000	13,647	2,579,333	20%	38	515,867
TOTAL			508	9,248	20,000	10,035	5,097,646	29%	147	1,177,239
Exclusive Group Dinner with Michael
One-Year Client	Wardrobe	High	10	15,761	20,000	4,580	45,798	80%	8	36,638
One-Year Client	Wardrobe	Med	4	15,069	20,000	4,121	16,484	50%	2	8,242
One-Year Client	Wardrobe	Low	104	7,365	20,000	12,085	1,256,840	20%	21	251,368
TOTAL			118	8,386	20,000	11,179	1,319,122	26%	31	296,248
TOTAL			626	9,094	20,000	10,250	6,416,768	28%	178	1,473,487

We outlined two actions in our requirements:

• A2 For multiyear clients not achieving the spend target, conduct a marketing event to drive to a more engaged relationship.
• A3 For newer clients who have placed large orders, conduct a group dinner to incent a multiyear relationship.

Let's build a potential monetization strategy targeting the entire segment. When we get to the final solution, we will decide on whom we target and the actual spend. For our strategy, the Act is two events, a Scotch Event for invited guests only, and an Exclusive Dinner with Michael Andrews. Both events are geared to promote continued engagement with MAB. The Events are the percentage of clients that convert to full engagement. The Outcome is the number of clients who migrate to the full engagement level. The Payoff is the opportunity value from the actions.

From our payoff matrix we see that we can generate $1.5 million in additional revenue, assuming full participation. We do not expect to be able to generate the full amount but believe we can execute strategies to capture some of this opportunity.

Performance Dashboard

The Performance dashboard helps Michael monitor progress of the company, providing a snapshot of the current sales and orders as well as monthly and annual comparisons.

On the Performance dashboard there are also Success Metrics providing a window into opportunities and issues. Success Metrics connecting performance to diagnosis are displayed on Inform dashboards as well as Diagnostic dashboards. These metrics alert a user that an opportunity or issue exists and they need to do further investigation in a Diagnostic dashboard to make a decision.

Success Metrics we implement include:

• Clients not ordering in 6 to 24 months as a percentage of total—This metric measures the number of clients who have not ordered within the past 6 to 24 months and therefore serves as an indicator of attrition. We apply this metric to high-value clients in the Multiyear–Wardrobe segment.

  An alert is signaled when the percentage of clients in an attrition risk exceeds 10 percent of total clients. The alert signals Michael that attrition risk is building and countermeasures should be considered. In our example dashboard in Figure CS.25, we see the metrics have turned a darker shade, alerting Michael to take further action.

• Distribution of high-potential clients by Engagement Score—This metric assesses the ability to increase a client's degree of engagement with MAB. This metric scores individual clients on a scale of High, Medium, or Low. Clients scored high are expected to be more likely to respond to targeted marketing activities.

  Clients in the Multiyear–Ad Hoc segment with a better than low potential and One-Year–Wardrobe clients with high potential are highlighted in Figure CS.25. This tells Michael that these clients are good candidates for increased engagement.

Client Profile Dashboard

Once Michael has viewed the Performance dashboard, he may go directly to a particular diagnostic or navigate to the Client Profile dashboard (Figure CS.26). This dashboard is informational and provides a view in the various profile mixes of client base.

The profiles include clients by Occupation, Tenure, and Product Scope, allowing Michael to understand the composition of the client base.

From here, Michael will go to the Client Segmentation dashboard to see if there are groupings of like customers where he might have an engagement or retention opportunity.

Client Segmentation Dashboard

The Client Segmentation dashboard (Figure CS.27) provides a quick view of the value of each of the client segments. On this dashboard, Michael can visually see if there are any opportunities of underserved segments that need additional engagement.

We know that we have a Monetarization Strategy that buckets various segments into Engage and Retain. The dashboard provides a quick visual into the health of this overall strategy as well.

At this point, Michael wants to build an engagement strategy and will navigate to the Client Engagement dashboard to determine which of the clients in the Engage segments would fit a particular strategy.

Client Engagement Dashboard

There are two Client Engagement dashboards: Diagnose and Action. From our monetization strategy and our client segmentation, we are going to focus on moving clients to the Multiyear–Wardrobe segment, our highest-value segment.

We developed two actions that we would like to execute on:

1. A2 For high-value clients, target a marketing event to drive to a more committed relationship. MAB has determined a Scotch tasting, invite-only event at a cost of $100 per attendee.
2. A3 For newer clients who have placed large orders, conduct a group dinner to incent a multiyear relationship. MAB has determined a host group dinner with Michael Andrews at a cost of $200 per attendee.

From here, we need to determine who and how many clients we want to target for both events. We start with our Diagnose dashboard (Figure CS.28), which points us to the two segments. Michael decides that he will only take one action, the Scotch tasting event. He navigates to the Action dashboard to decide on the individual clients to invite.

The Action dashboard (Figure CS.29) provides specific information to help Michael take action. On the dashboard we see Client Number, Average Lifetime Spend, Potential Lift, and Potential Lift Percentage. This information provides Michael with the most attractive clients to pursue based on the likelihood of the client generating revenue for MAB.

The highest-potential clients identified on the Performance dashboard are sorted to the top and highlighted.

Michael decides to invite everyone in the Multiyear–Wardrobe segment and Ad Hoc segments with high engagement scores. This is a total list of 111 clients. If everyone attends, the cost of the event will be $11,100. From the event, Michael expects that 49 of the clients will increase their engagement with MAB, delivering an estimated revenue lift of $201,480.

Client Retention Dashboard

The next set of diagnostics Michael wants to see is focused on helping him execute a retention campaign of the clients that are at risk of leaving. Michael goes to the Client Retention diagnostic (Figure CS.30).

On this dashboard, Michael is able to view the clients by segment to determine groups at risk by the number of clients in the segment who have not purchased within a particular period of time. Michael decides to focus on the clients in the 7 to 12 months since last order window to attract them back into the store and refresh the relationship (Figure CS.31).

The MAB staff develops a Try a New Style Shirt campaign to entice these high-value clients to come back into the studio. The campaign will cost $100 per client, for a total of $11,600 in costs if all 116 clients take them up on their offer. The team expects to generate $6,000 in orders from 20 percent of the clients, yielding $63,600 in additional revenue.