Objectives

The main objectives of the Lifetime Customers stage are:

• Create a customer experience so good that customers are retained for life.
• Optimize the customer experiences across all online and offline channels, using real-time predictive automated personalization to offer the most relevant content.
• Maintain competitive advantage by being the fastest and most agile in testing new initiatives.

Benefits to Your Customers

Anyone who shops at Amazon.com or Zappos.com can attest to the benefits consumers get when working with a Stage 7 organization. The organization knows who you are when you contact it. It “knows you” as an individual and has the remarkable ability to predict what you want.

Stage 7 organizations make the customer decision journey frictionless. Customers find it easier to make decisions because predictive algorithms present the customer's best or most probable decision paths in front of them. Customers feel like they are being helped to make decisions rather than being sold.

The organization appears to be a single entity with a single image and brand no matter whether the customer approaches it offline, online, or through multiple channels. Although analysts used to say there is no loyalty on the Internet, that isn't true. Customers of Stage 7 organizations have built a commitment and trust with the organization. The customers of Amazon, Nike, Lego, and Zappos would rather deal with them than a substitute. Customers are advocates for the organization and feel uncomfortable when forced to work with someone else.

Benefits to Your Organization

Major benefits come to organizations that reach Stage 7. Lifetime customers add significantly to the profitability of an organization (or to the achievement of organizational objectives for nonprofits). These benefits come because lifetime customers:

• Repeat purchases
• Require minimal marketing costs
• Do not require recapture
• Advocate for you and bring in new customers

In a longitudinal study, Bain & Company found that, among the most profitable customers, having greater customer loyalty was an important differentiator.

Small changes in loyalty alone, especially among the most profitable customers, can account for the long-term divergence of initially comparable online companies, with some rising to exceptional returns and others sinking to lasting unprofitability.³

Other studies by major consultancies and analysts produced similar findings. Another study by Bain & Company found that a 5 percent increase in customer retention increased profits by 125 percent.⁴

In their book Leading on the Edge of Chaos (Prentice Hall Press, 2002), authors Emmett C. Murphy and Mark A. Murphy found that a commitment to customer experience results in up to 25 percent more customer retention and revenue than sales and marketing initiatives produce.

Commitment to lifetime customers creates a more stable business environment. With the connected single view of the customer that comes in Stage 6 and the ability to predict customer sales and support, you can make more accurate predictions about marketing results, customer support costs, budgets, and more. It's easier to see what's coming.

Amazon, in its SEC filings, has alluded to being able to calculate which marketing channels, content, and products produce the greatest impact on business. The company uses that information to make major and minor changes to business. In fact, Amazon does real-time price adjustments on books to get the maximum return. By making minor adjustments on a book's price, it can see the effect on purchase rate. That allows Amazon to adjust pricing constantly to get maximum profit volume.

Many organizations now have product or service calculators online that are doing more than making calculations. Organizations we've consulted with have very cool online calculators that help online customers select:

• A car model, color, suspension, transmission, and trim package
• Insurance options for auto and life insurance
• Payroll changes that calculate tax and cost effects
• Airline and hotel packages that match price, time, and experience

Although these calculators look like they are just helping the online customer make a better decision, which they are doing, they are also capturing data about individual and customer segments. Marketing and product development in each of these examples can examine which selection combinations were tried and which were finally selected. With that type of information in your customer data hub, you have an incredibly rich amount of data with which to design new products tailored for specific customer segments.

Another advantage to having a customer data hub with this rich customer preference data is that your sharp analysts will set alerts to identify changes in trends in customer segments: “We need more ruby-crimson paint for the sedan models,” or “We need a low-cost apartment insurance package for newlyweds.” In the old world, identifying these new product opportunities or changes in demand took months and sometimes years. At Stage 7 you should be able to identify these changes as they happen and automate insights based on predictive algorithms.

How to Approach Marketing for Lifetime Customers

Marketing at Stage 7 should involve all customer touch points. Every touch point should have access to the same single view of the customer so customers can have the same great experience at all touch points.

At Stage 7 your organization's culture should be data driven and learning oriented. Marketing especially should be run using the scientific method:

Using the word scientific here doesn't take away the fun and creativity in marketing. It means that after the creative idea or observation, marketing must create a hypothesis (guess) about what will happen. A small test can then be done to see whether the idea works. If the marketing idea isn't effective, it can be modified and repeated until it works better than existing methods. When you identify your vendors, you need to select systems that let marketers be marketers again. The experience platform should do the testing. Information technology (IT) should not be a barrier to this continuous testing and improvement cycle.

In his 2014 letter to shareholders, Jeff Bezos directly addressed this concept of failing fast, failing often, and failing small:

Failure comes part and parcel with invention. It's not optional. We understand that and believe in failing early and iterating until we get it right. When this process works, it means our failures are relatively small in size (most experiments can start small), and when we hit on something that is really working for customers, we double-down on it with hopes to turn it into an even bigger success.⁵

Identifying Processes Critical to Engaging Lifetime Customers

Even though Amazon had a considerable head start against its online retail competitors, it never slowed its rate of innovation. It continued using the scientific method of creating ideas, testing them, and then adopting the ideas that tested well.

Amazon's testing was not just on the website or in marketing content. The Amazon 2014 letter to shareholders comments on the company's use of the kaizen approach. (Kaizen is the practice of gradual and continuous improvement in manufacturing, management, and processes.) That approach produced 280 software updates in one year.

Some organizations adopt kaizen as a way of gradually introducing change and ignoring the need for quantum change. That is counterproductive and will kill bureaucratic companies that look only to incremental improvement and refuse to innovate. Notice that kaizen in Amazon did not stop massive innovation. Rather it was used to make gradual improvement in working systems. Quantum and massive change continued, such as with the Kindle, as a way of leapfrogging the competition.

Cross-Channel Marketing Portfolios

Cross-channel marketing is a natural part of Stage 7. Stage 7 organizations should not have silo marketing programs that operate autonomously. Surveys show that 77 percent of marketers agree they can drive more sales and profits using a balanced cross-channel marketing strategy.⁶ A balance should be maintained across all channels to achieve the desired impact at the lowest cost. It's like making a fine meal. Adding more wine to a sauce just because it is an expensive wine does not guarantee the meal will be better, but it could waste the wine.

By the time you approach Stage 7 your organization should be balancing cross-channel marketing using Value per Visit as a metric to compare marketing efficiency in channels. The use of Value per Visit from Stage 3, Align, along with Return on Marketing Investment (ROMI), gives you the information you need to manage cross-channel marketing as a single holistic entity. Without Value per Visit and ROMI you can only manage the individual channels as independent silos and cannot see how they compare in marketing impact or efficiency.

As a marketer you should first estimate what percentage of your marketing should go to each channel. Your marketing channel mix, how you allocate resources across channels, will depend on the preferred channel for the audience, the competitor's strategy (do they already dominate a channel?), and your marketing strategy. For example, if you are a B2B selling industrial chemicals, most of your marketing will go to emails describing new information and informative content by thought leaders. However, if you are targeting millennials (those born during the period from the early 1980s to the early 2000s) with a trendy product, you will need to focus on social channels, word of mouth, and trendsetters related to that trend.

At this stage you have more than Value per Visit as a metric to balance resources and efforts between channels. Although Value per Visit measures the efficiency of your marketing, and you can compare it between channels, it doesn't tell you anything about costs or Return on Marketing Investment (ROMI). With the integrated expense and revenue data you are able to calculate with the integrated data available in Stage 6, you can calculate an accurate ROMI for each channel. With that as a guide, you know where to put your next dollar. Value per Visit and ROMI are great metrics for balancing market resources across channels and predicting future revenue.

Mature multi-channel marketers claim to have significantly better tactical results. Forrester Research reports that 40 percent enjoyed a significant increase in marketing-attributed revenues and 69 percent gained more than a 10 percent increase in customer satisfaction scores.⁷

Data-Driven Marketing and Big Data

Stage 7 solutions involve massive amounts of customer contact data at all touch points and stages in the customer's decision journey. Stage 7 solutions can be on premises, but the massive size and speed requirements usually make a cloud-based solution better. On the front end, putting your Stage 7 solution in the cloud reduces requirements planning, IT development cycles, integration problems, and so on. This not only reduces marketing's dependency on IT, but it also enables IT to move out of maintenance mode and work on continuous innovation to advance marketing and improve the customer experience.

There are many advantages to running your customer experience platform in the cloud. Three big advantages are scale, speed, and reduced complexity. First, the data hub you started creating in Stage 6 requires massive amounts of data and integration. Integrating, storing, maintaining, and serving massive amounts of complex data takes a lot of horsepower and scalability. Second, supporting real-time personalization for thousands of customers across multiple channels requires fast data access to massive data sets and extremely fast computational power. And finally, for some vendors and implementations these systems are actually multiple integrated systems, which build out to be very complex.

The huge growth in capturing customer data comes from customer data being captured at almost every customer touch point. Currently B2B and retail are capturing customer data across, among others:

• Web
• Email
• Social media
• In-store (using Wi-Fi or iBeacons)
• Radio-frequency identification (RFID)
• Point of sale (POS)
• Consumer panel data
• Data-sharing cooperatives

Big data and big analytics must go together to give you the ability to find the unknown unknowns. Unknown unknowns are the events and patterns you can't predict or hypothesize. They are the highly profitable customer segments that no one foresaw.

Eric Siegel in his book Predictive Analytics (John Wiley & Sons, 2013) tells stories of how big analytics combines with big data for marketers. He describes the now-famous story of how marketers for Target stores began sending ads and coupons for baby items to a 16-year-old girl. Target's big analytics had detected that her purchases indicated she might be pregnant. Her irate father complained to the store manager. The following week the father apologized to the store manager, saying that he had not been aware that his high-school-aged daughter was pregnant. (Target had been aware, however, because of its analytics.)

Another case is that of Tesco, one of Europe's largest store chains. Tesco sends personalized coupons to both online and offline customers based on their predicted needs. One of our European executives was a target of Tesco's analytics when he received a passel of coupons and discounts for all the items he normally purchased. He had been traveling for a few weeks. The store detected his absence and sent him packets of coupons and discounts for the things he purchased most at Tesco. It was casting lures to bring him back into his local store.

Multiple vendors are developing analytics to work with big data. The analytics you select need to meet three requirements:

1. Publish management and executive reports accessible at any time and in any place. These reports access the known knowns (predictables) and the known unknowns (alerts about anomalies). Managers and executives don't need a great deal of drill-down or ad hoc capability, but they do need access from their mobile devices.
2. Create and publish operational marketing reports about known knowns and known unknowns that enable marketers to optimize their marketing. These operational reports and dashboards need to work at two levels. The basic level makes it easy to create predefined dashboards used by marketers and channel managers. A more advanced level allows operational analysts to drill in and discover solutions to operational issues. It is critical that any analytics system be able to group and correlate data so that you can identify your persona used in personalization and test the results of marketing that targets that persona.
3. Allow in-depth data science. At this level, experts in statistics and predictive analytics use big analytics to find unknown unknowns and discover hidden patterns that could be new fields of gold.

Personalize All Touch Points

At Stage 7, Lifetime Customers, the customer is not only expecting a response that is personalized and appropriate to the context, but in many cases she is also expecting a prediction or recommendation that will help them. It's your marketing and prediction engine's ability to remove some of the friction from life that will keep your customers coming back.

Personalization at this point is a big job, with customer touch points ranging from web to mobile to point of sale to social connection from friends to who knows what in the future. Again, the requirement here is to have a single view of the customer that any touch point can access in real time, find the message for the individual's persona and context, and then respond—all within a second. Systems like this are not science fiction.

In Stage 7 the predictive personalization system you select should go beyond just publishing personalized content through a preferred channel. At this stage the system looks at the historical data for a customer, calculates which content and channel will produce optimal results, and then delivers optimized personal content through the optimum channel for the customer.

Optimizing content and channel is accomplished through a combination of examining historical data and iterative comparison testing, much like multivariate testing of items on a landing page. However, this is like multivariate testing on steroids at the macro level. Developing a system like this in-house is difficult. Your solutions vendor must have this on its development road map.

Making recommendations about your customer's next steps or next purchase requires analysis of many patterns and large data sets. You have two choices:

1. If you are not using predictive systems, you can narrow the choices available to your customers and then use A/B testing to determine which choices give the results you want. In smaller or niche businesses where you have less complexity and high customer knowledge, this is an acceptable choice. You can make and test a few hypotheses and serve your customers well. The downside to this solution is that if you expand your products or customer persona, the system complexity increases geometrically.
2. If you are using predictive systems that analyze big data, then the system can automatically analyze and recommend products or solutions. This is necessary for organizations that have many products or services, many customer personas, or many lines of business.

As Amazon.com has demonstrated, to our chagrin and delight, making great recommendations increases the number of purchases and increases customer loyalty. That big wish list we share with Amazon is not only an offload of memory from our brains, but a data dump into Amazon of where our interests lie and what we are most likely to purchase in the future. Amazon's use of predictive analytics has extended this “future service” even farther. In 2014 they filed patents for anticipatory shipping, shipping goods to customers before the customer orders them.

Automatic Recommendations to Optimize Marketing and Forecasting

At the more mature levels of Stage 7 you will implement automated recommendations. This provides recommendations marketers can act on. Stage 7 analytics doesn't present primitive metrics like time on page or bounce rate. Instead, it makes actionable recommendations on search engine optimization (SEO), content preferences by different facets, social intent, campaign improvements, and more. Marketers are able to accept or reject recommendations. For example, a marketer would be given the choice to accept or reject an increase in pay-per-click (PPC) budget on an ad campaign that caused a change of 12,000 Engagement Value points.

Most solutions vendors dealing with Stage 7 companies should supply some level of simple marketing recommendations. However, you need to look for a solution supplier that is looking farther ahead. Once you reach Stage 7, your system has the data and the test results to also make predictions about budget and revenue. Using Bayesian statistics, Monte Carlo simulations, and similar applied mathematics, an advanced Stage 7 system will give your managers the ability to model the effect that changes in budget and channel allocations have on forecast revenue.

Developing these systems in-house takes a tremendous effort and specialized knowledge. Make sure that any solutions vendor you work with has an automated data analysis and recommendation system working or on its product road map.

People

Your people and organization in Stage 7 are not normal. As consultants, we've worked in many organizations with many different cultures. Within the first 30 minutes in a new organization it's usually easy to feel the culture. In some organizations everyone in the room waits for the opinion of the highest-paid person in the room before making their politically correct statements. In some the culture is by the book, always using standardized processes. In other organizations, meetings blaze with the fire of new ideas and new processes.

The culture and people in Stage 7 organizations are a mixture. They have well-proven processes that they use for standardized and consistent work. But everyone looks for ways to improve. New improvements in work flow and marketing are tested and the winning methods adapted. Stage 7 cultures and people are continually learning and adapting.

One thing that should be obvious at this point is that the culture in Stage 7 focuses on customer experience. All functional departments share the single view of the customer.

One new skill set that Stage 7 organizations will find difficult to fill is data analyst or data scientist. These are not just traditional web or business analysts. These are statisticians, data scientists, and applied mathematicians who can dive into big data and come up with previously unidentified products, services, marketing strategies, and customer segments.

Process

At Stage 7 you must continue to build an organization with a culture that values innovation, testing, and learning. Stage 7 organizations are continually innovating and testing ways to improve the organization, improve the customer experience, and build the competitive moat. Amazon.com is a great example to follow.

Amazon recognizes that it can't rest just because it is far ahead of its competitors. In this time of entrepreneurial fervor it is death to slow down. Amazon has continued its push by building a competitive moat that may be impassable. This moat is filled with customer loyalty and is as wide as Amazon's rapid pace of innovation. Following its famous (or infamous) 1-Click Shopping patent filed in 1999, Amazon continued with its culture of innovation. The following is a short list of the many innovations described in the 2013 letter to shareholders signed by Jeff Bezos.⁸

Notice that many of these innovations combine to create vertical and horizontal barriers to competitors.

Technology

At Stage 7 marketing is for marketers. The best marketing systems vendors aim not only to solve marketers' problems but to build systems that marketers can use without IT's daily intervention. This leaves IT available to create innovative new tools, not spend time assisting marketing with what should be day-to-day operational tasks. (No marketer wants to wait a week or more for IT to tag content or modify a program just to test a campaign, and any good IT person would rather be creative.)

Systems that have been developed to meet marketers' needs arise from web content management. These customer experience platforms already own the content across the different digital channels. By building on the single view of the customer and connecting to other repositories, they enable marketers to create connected experiences across all channels controlled by the customer experience platform. These new customer experience platforms put marketing in the hands of the marketers.

What this means is that costs are coming down as functionality goes up. At this time most vendors are developing on-site and cloud-based solutions. With the massive amounts of data and more advanced analytics, cloud-based solutions should be considered. Cloud computing enables marketers to focus on their marketing expertise and not on defining system requirements and working with IT. Some of the advantages of cloud-based marketing systems are:

• Systems are quickly implemented and rapidly scalable, allowing marketing to test and innovate without incurring high costs.
• Security is managed by dedicated professionals.
• Costs are less expensive because overhead and start-up costs are distributed among customers.
• Costs are flexible, being dependent on need. You can ramp up quickly.
• Data backup is more reliable with continent-wide redundant systems.

In this new environment, marketing can be left to marketers. IT can work on new innovations. According to Amazon's founder and CEO, Jeff Bezos, technology should focus on innovating solutions for marketing and the customer. In the 2010 Amazon Annual Report, Bezos said:

Look inside a current textbook on software architecture, and you'll find few patterns that we don't apply at Amazon. We use high-performance transactions systems, complex rendering and object caching, workflow and queuing systems, business intelligence and data analytics, machine learning and pattern recognition, neural networks and probabilistic decision making, and a wide variety of other techniques.

And while many of our systems are based on the latest in computer science research, this often hasn't been sufficient: our architects and engineers have had to advance research in directions that no academic had yet taken. Many of the problems we face have no textbook solutions, and so we—happily—invent new approaches.

…All the effort we put into technology might not matter that much if we kept technology off to the side in some sort of R&D department, but we don't take that approach. Technology infuses all of our teams, all of our processes, our decision-making, and our approach to innovation in each of our businesses. It is deeply integrated into everything we do.⁹

What that means is that although marketing systems may be moving to the cloud, the opportunity still lies with IT personnel to stop doing the maintenance and support they have been doing and focus their talents on extending marketing capabilities.

How Long Will It Take?

If you were developing the systems and processes in-house to try to reach Stage 7, it could take you more than a decade. (It did for Amazon.) However, with the technology from top vendors, and the proven processes and best practices that we've outlined in this book, an organization can go from Stages 1 and 2 to Stage 7 inside of two years. All of this depends on commitment, political will of the executives, and how dynamic the culture is.

The length of time to reach Stage 7 is perhaps not as important as beginning the change immediately. As outlined earlier, you should use quick wins like rules-based personalization, A/B testing, and Experience Analytics to quickly grab executive attention and buy-in. Once you have those, you can present your long-range vision and plan. Chapter 13, Selling to the Board, describes how to sell your vision upward.