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Restore Old Customers

Traditional customer re-activation strategies are struggling to deliver the results they once did. This has been fueled by cuts in consumer spending and communication channel fragmentation, forcing marketers to develop new approaches. A Target Marketing Magazine article told the stories of innovators who are leveraging customer data, analytical tools and new customer touchpoints to fuel their remarketing efforts with results.

Start With the Basics
The fundamentals haven’t changed. Identify your best customers and the attributes that make them the best. Analyze purchasing trends, patronage patterns and channel usage to bring to light key behavioral characteristics of the ideal candidates.

Don’t stop there. Demographics, wealth data, transactional information and other lists can be used to enrich the customer profile. This information is useful for assessing the value of former customers who had sparse purchase histories but may still be good candidates.

Last, match these reactivation profiles against dormant customer files to “pop” the segments most likely to yield a profitable level of response.

Reactivation efforts most often are targeted at customers who have not shopped or purchased in the last year or more. While these consumers may not be shopping with you, they are buying from someone.

Reactivation Rundown
Reactivation is a form of advanced prospecting. By applying predictive scores to dormant customer files before fielding a reactivation campaign, resources can be prioritized toward those households with the greatest likelihood of response.

A good reactivation strategy encompasses not only who to target, but how to target them. In today’s multichannel environment, opportunities to blend print and other media into an optimum delivery stream for each target segment exist. For example, leads might be generated via a print mail campaign. These leads might then be further qualified using lead scoring and either prioritized for rapid follow-up by phone for high potentials or routed to another channel for less qualified candidates. This blended approach can yield more profitable results. Marketers should choose the medium that optimizes reach and response, according to budget.

Using Predictive Scoring
Aim for a clear view of your best customers. While it is possible, and sometimes economical, to target all former customers, it’s more often the case that a campaign targeting high-value or niche segments produces the best financial results. Focus on predicting who will respond, and then determine the best channel and sequence for the message.

Build New Relationships
A reactivation strategy should include follow-up plans and next steps as well as an outline with how often customers would like to receive communication. Lastly, update files with new customer information and data to ensure future campaigns maximize the information available.

Do You Need All That Data?

Just in case we get lost in over-analyzing everything, including customer data, Ron Ashkenas suggested that we step back and think about what is really useful in a post for the Harvard Business Review.

Organizations love data: numbers, reports, trend lines, graphs, spreadsheets — the more the better. And, as a result, many organizations have a substantial internal factory that churns out data on a regular basis, as well as external resources on call that produce data for onetime studies and questions. But what’s the evidence that all of this data is worth the cost and indeed leads to better business decisions? Is some amount of data collection unnecessary, perhaps even damaging by creating complexity and confusion?

For many years the CEO of a premier consumer products company insisted on a monthly business review process that was highly data-intensive. At its core was a “book” that contained cost and sales data for every product sold in the company, broken down by business unit, channel, geography, and consumer segment. This book (available electronically but always printed by the executive team) was several inches thick. It was produced each month by many hundreds of finance, product management, and information technology people who spent thousands of hours collecting, assessing, analyzing, reconciling, and sorting the data.

Since this was the CEO’s way of running the business, no one really questioned whether all of this activity really was worth it, although many complained about the time required. When a new CEO came on the scene a he decided that the business would do just fine with quarterly reviews and exception-only reporting. Suddenly the entire data-production industry of this company was reduced substantially — and the company didn’t miss a beat.

Obviously different CEO’s have different needs for data. Some want their decisions to be based on as much hard data as possible; others want just enough data to either reinforce or challenge their intuition; and still others may prefer a combination of hard, analytical data with anecdotal and qualitative input. These preferences at the top of the company often influence the “data culture” that is created. In all cases, though, managers would do well to ask themselves four questions about their data process as a way of improving the return on what is often a substantial (but not always visible) investment:

  1. Are we asking the right questions? Many companies collect the data that is available, rather than the data that is needed to help make decisions and run the business. So the starting point for simplifying and improving data processes is to be clear about a limited number of key questions that you want the data to help you answer — and then focus the data collection around those rather than everything else that is possible.
  2. Does our data tell a story? Most data comes in fragments. To be useful, these individual bits of information need to be put together into a coherent explanation of the business situation, which means integrating data into a “story”. While “enterprise data systems” have been useful in driving consistent data definitions so that things can be added and compared, they don’t automatically create the story. Instead, managers should consider in advance what data is needed to convey the story that they will be required to tell.
  3. Does our data help us look ahead rather than behind? Most of the data that is collected in companies tells managers how they performed in a past period — but is less effective in predicting future performance. Therefore it is important to ask what data, at what time frames, will help us get ahead of the curve instead of just reacting.
  4. Do we have a good mix of quantitative and qualitative data? Neither quantitative nor qualitative data tells the whole story. For example, to make good product and pricing decisions, we need to know not only what is being sold to whom, but also why some products are selling more than others.

Clearly business data and its analysis are critical for organizations to succeed — which is underscored by the fact that companies like IBM are investing billions of dollars in acquisitions in the business intelligence and analytics space. But even the best automated tools won’t be effective unless managers are clear about the questions raised above.

What’s your assessment of data in your company? Is there anything we can do to help you make sense of what you have?

Scrubbing Data

Deliver Magazine reported that despite the ROI potential from data hygiene, many companies still haven’t cleaned up their lists.

Data management is still a hot topic for many companies these days. Marketers too often focus on how best to use the data rather than spending enough time wondering whether the data itself is clean. Good data hygiene can have a significant impact on your company’s ROI, minimizing waste and building trust with consumers by contacting them at the correct address.

Rod Ford, founder and chief executive officer of CognitiveDATA, a data-quality management company discusses a few of the misconceptions about data hygiene with Deliver.

Deliver: Why aren’t companies today putting enough resources into data quality?

Ford: Data hygiene is typically grossly under-budgeted. Many direct marketers spend less than 1 percent of their overall direct marketing budget on data quality.

Deliver: Why do companies say that they value data quality but not fund it properly?

Ford: Many organizations live under the misconception that their data is already highly accurate. This is because they are passing this data through vintage tools a few times a year and not finding incorrect addresses or other problems with the data.

Deliver: What should marketers be doing to improve data quality?

Ford: Several issues are forcing marketers to be more efficient in their mailings. The green movement and the push toward less waste in the mail stream is one. Then there’s the fact that response rates have declined because, during the recession, the consumer has less discretionary income than in the past. Finally, direct mailers are facing rising costs in almost every area of mail production. These issues already were forcing marketers to take a closer look at data hygiene before the recession hit. What the macroeconomic environment has done is accelerate the adoption of data-hygiene technology.

Deliver: Do you think that marketers will go back to ignoring data hygiene once the economy recovers?

Ford: Right now, direct mailers are learning important lessons about the impact of more accurate data. When you reduce the number of undeliverable pieces of mail in a campaign, this increases the overall response rate, for example. These lessons will transcend whatever is happening in the economy.

Another Reason for List Hygiene

Deliver Magazine told of a valid reason to regularly scrub mailing lists. Sure, mailings sent to the deceased get responses — but they’re usually from distressed family members commenting on how disrespectful and downright rude the company is for sending it in the first place.

“Not only is this a waste of a company’s time and money, it also can be extremely damaging to a brand, resulting in customers lost rather than gained,” says Kirk Schuh, vice president of marketing delivery services at ARGI, a database marketing company. “Regularly cleansing files must become part of a marketer’s regular list hygiene routine”, Schuh says. “Deceased suppression is a delicate issue,” he adds. “No matter how vigilant marketers are, their lists always can benefit from routine maintenance and enhancement.”

Customer Data Best Practices

The Aberdeen Group published a report in December, 2009 that explored customer database practices to reveal how organizations are capturing, storing, analyzing and acting on customer data.

The Best Performing Organizations:

  • Currently achieve 163% mean class Return on Marketing Investment (ROMI); 9% average year over year growth in ROMI
  • 51% year over year mean class growth in revenue

The best performing firms shared several common characteristics, including:

  • 46% access a full view of customers across all departments and functions in the organization (versus 14% among laggards)
  • 52% improve or enhance customer data through regular marketing and IT collaboration (versus 21% of laggards)

The study recommended that companies wishing to become more like a best performing organization:

  • Develop a formal data hygiene (cleansing, enrichment, de-duplication and regular updates) strategy. A lack of formal data hygiene can prevent organizations from using customer data in more personalized engagement.
  • Enhance existing records with periodic augmentation and enrichment. Only 32% of all respondents actively augmented customer data for accuracy. But, 48% plan to incorporate database enrichment services in their 2010 budget for improving the customer database.

What can you do to maintain and enrich your customer information?

  1. Even if you don’t want to send mail to your customers right now, process your list through the National Change of Address database. This is a simple service that will provide move information for individuals, families, and businesses. Learn more about your current and previous customers by obtaining their current addresses. Our reports will identify undeliverable and incomplete addresses. You will know that some of your valuable customers have had changes in their lives and organizations.
  2. Think about enriching your customer list with more information. For consumers we can add income, demographic and home characteristic information to your existing data file. For businesses we can add “firmographic” data including number of years in business, number of employees, industry classification and estimated annual revenue.

If you are not sure what information would be best for your business, we are great at asking the right questions to get you started.

Go Beyond Customer Segmentation and Explore Predictive Analytics Part 2

Direct Marketing Magazine shared some ideas about predictive analytics.

Predictive Analytics Road Map

To get the most out of customer segmentation analysis, organizations could create road maps incorporating the following steps:

  1. Determine the Overall Business Objective. Get everyone on the same path and in agreement with what you want to accomplish, such as improving the yield on lead-generation efforts, identifying cross-sell opportunities or identifying customers most likely to go to a competitor.
  2. Capture All Potential Customer Data. Segmentation begins with gathering customer data from a wide variety of resources, including data warehouses, point-of-sale systems and loyalty programs. A database of static customer information is valuable, but until key active knowledge is applied—like preferences or motivations—there’s an incomplete picture of the customer. Capturing feedback from any touchpoint—in any language—provides a clearer understanding of customers’ needs, preferences and attitudes, and improves the segmentation process.
  3. Perform Recency, Frequency and Monetary (RFM) Analysis. To obtain the most accurate picture of customer lifetime value, organizations first should perform RFM analysis to classify customers according to: those who have spent the most—the most often and most recently; those who have spent the most—the most monetarily, but may not have purchased in a long time; those who spend the most in the fewest number of transactions; and those who spend the least, or rarely, and have not purchased in a long time.
  4. Outline the Segmentation Process. Once customers have been identified based on purchasing patterns, then segmentation analysis can begin to get to the core of the audience you want to target. The key to a successful segmentation program is to first define the many ways the results can be used. An approach might take the following path:
    • Create customer segments to enable differential marketing programs.
    • Use past purchase data and demographics to construct customer subgroups.
    • Isolate key performance factors linked to long-term customer value as major data drivers for the segmentation.
    • Use cluster analysis to form homogenous groups of differently valued customers.
    • Use techniques such as rule induction to automatically extract the profile of each cluster.
    • Align the marketing spending priorities against each subgroup.
    • Link product line or category affinity to each subgroup.
    • Develop marketing plans incorporating value-based budgeting and category affinity to make programs more relevant and efficient.
  5. Auto-Segmentation. With a customer base more clearly defined through effective segmentation, organizations then can add predictive modeling functionality within each segment to produce greater insight that’s required to more effectively and efficiently acquire, grow and retain the right customers, and also identify fraud and minimize risk. The modeling functionality in predictive analytics technology helps organizations accurately determine which customers best match specific offers or campaigns. By eliminating the guesswork when targeting customer groups, organizations quickly increase ROI through more efficient use of resources and reduced spending.
  6. Deploy and Share Results Throughout the Business. The final step is to create an environment in which an organization can manage and automate its analytical processes and easily deploy the results across the enterprise—thus improving productivity and collaboration and increasing ROI. This includes the ability to automate the database scoring process, publish and distribute output and reports, and integrate the analytical process into other business applications. For example, when a customer calls a call center, that agent should be able to pull up information on that specific customer and know what type of offer should be made at that particular time.

Hit the Target

With predictive analytics technology, organizations can move toward a one-to-one conversation with customers. Insight gained from even the most elementary analysis of customer characteristics can have profound implications on the business and result in marketing success.

Go Beyond Customer Segmentation and Explore Predictive Analytics Part 1

Direct Marketing Magazine shared some ideas about predictive analytics.

Personalize Customer Relationships

Segmentation is a way of grouping people or organizations with similar demographic profiles, attitudes, purchasing patterns, buying behaviors or other attributes. This helps to understand customers more thoroughly and thus market to them more effectively.

Many businesses use segmentation to recognize that customers have some unique characteristics. But they stop when going further may be possible, for this reason, segmentation can be a “blunt instrument,” leading to “one-to-some” marketing. It can perpetuate “accepted wisdom” about customers and the market that are not necessarily accurate.

Marketers can add predictive analytics to the segmentation process to generate insight needed to more effectively and efficiently acquire, grow and retain the right customers. The result could be a better understanding of what products and services customers are likely to want next. Predictive analytics can be thought of as auto-segmentation. This technology can discover groupings in customer data and find relevant patterns that are likely to be more subtle, extracting greater predictive insight than traditional segmentation. This would ensure that insight obtained into what customers want and how they behave, and marketing decisions made would be evidence-based and result in more profitable outcomes from one-to-one customer interactions.

Predictive analytics incorporates data collection, statistics, modeling and deployment capabilities. This drives the entire segmentation process, from gathering customer information at every interaction to analyzing the data and providing specific, real-time recommendations on the best action to take at a particular time, with a particular customer. The result is more effective customer relationship management strategies, including advertising and marketing campaigns; upsell and cross-sell initiatives; and long-term customer loyalty, retention and rewards programs.

In the next post we will look at a predictive analytics “road map”.

Define Your Customers Part 2

This is the second part from Target Marketing Magazine’s invitation to take a look at who your customers really are.

What Customers Believe

Understanding customer beliefs leads to building a powerful brand and can pay dividends in creative presentations and communications. Psychographics, or attitudinal data, are generally established by evaluating the thoughts and feelings of various cohort groups—groups of consumers in similar age ranges or stages of life.

Overlaying your customer data with psychographic information allows you to classify customers in one of a number of predefined segments. These segments are named and grouped based on common beliefs held among members. Psychographic data goes beyond demographics by exploring how people feel about everything from finances to shopping to technology to family and friends to religion, and more. Often, “messaging briefs” or tips for communicating with each group better and more specifically to their own beliefs also accompany these overlays.

If you conduct demographic and psychographic overlays you produce a picture of what the customer looks like and what she believes. To continue building a better picture of the customer, though, we have to know what she says.

What Customers Will Tell You

A good survey provides actionable answers to questions that discern the respondents in one group from those in another. By building a customer survey—one that can be executed online as well as over the phone—you can get the customer’s opinions of your brand as well as your competitors’ brands. The essence of brand differentiation is defining what you stand for and knowing what you don’t stand for in the mind of the customer.

Asking customers a variety of questions about themselves, your company and the competition, you begin to develop an understanding of what’s important to them in making purchasing decisions and how you rate on those issues versus the competition. In a perfect scenario, you find that you excel in the areas that are most important to your customers and that you have a significant advantage over the competition in those same areas.

Surveys don’t have to be long; seven minutes is an eternity on the phone. What surveys must do is ask the right questions. And to highlight your strengths even more, put non-buyers and lapsed buyers into the survey mix along with “better” customers. This way you see how your best buyers perceive you compared to your older buyers and nonbuyers.

What Customers Buy

Square-inch analysis, or squinch analysis, sheds light on product performance. In an effort to understand customer behavior completely, you should enhance your profiles with merchandise analysis. Asking questions like, “Do my best customers buy differently from my worst customers?” helps you address merchandise mix; price point; and creative issues in the catalog, on the Web site and in direct mail. It is at this stage that you’ve not only developed but implemented a better, more meaningful view of your customers.

Demographics are great for modeling, but if you want to paint the best picture possible of who your customers are, you can  move beyond age and gender to an understanding of what they believe and what they think about your company. Putting all of the pieces of the customer information puzzle together allows you to build more targeted and relevant creative, a more appealing merchandise mix and a more profitable contact strategy by contributing to an ironclad brand positioning. To define yourself, you first must define your customers.

Define Your Customers Part 1

Target Marketing Magazine printed an invitation to take a look at who your customers really are.

Many marketers think of customers in straight-forward terms: females, 45 to 60 years old, $75,000-plus household incomes, for example. These broad-sweeping descriptors have a place in customer definitions but aren’t the end in defining who does business with you.

Many of the data points necessary to understand your customer are available in your database. Purchasing data, for example, provides the foundation of analysis in marketing, merchandising and price points. But some of the more meaningful data—the information that allows you to complete the puzzle—is often available through knowledgeable providers. These professionals can offer data appends that allow you a more complete and robust view of who the customer really is.

Let’s look at several techniques you can use to learn more about your customers (and even your noncustomers) and how, put together, they give you the intelligence and insights to tighten your brand, improve your marketing and boost profits.

What Customers Are

The first step in building a comprehensive customer profile is the application of demographic data. There are more than a thousand different demographic variables that can be appended to the typical consumer’s name and address record. These demographics, or descriptive characteristics, can range from age and income to gender and ethnicity to home ownership and consumer credit availability. By compiling data from a variety of outside sources, a professional list provider can overlay demographics data onto any consumer data file and provide back either a series of descriptive reports or, better yet, appended data for additional analysis.

The demographics most commonly employed in developing customer profiles are generally age, income and gender. Additionally though, it can be powerful to know more about the customer, like how much she paid for her home and how long she’s lived there; how wealthy she is, beyond just annual income estimates; etc. By applying these data points, you start to paint a picture of the person “materially”—essentially the 45- to 60-year-old, $75,000-plus household income female mentioned earlier. The additional demos also allow for more understanding about the kind of home she lives in, how thin she spreads her income, how settled she is and more. And from a marketing standpoint, demographics can enhance RFM (recency, frequency, monetary value) selection as well.

In the next post we will look at customer beliefs (psychographics) and the meaning of purchasing patterns.

Expand Markets And Optimize Costs With Segmentation Strategy Part 3

This is a continuation of Target Marketing Magazine’s article about customer segmentation.

Modeling for Performance

Behavioral models can help you refine campaigns and follow-up marketing by allowing you to predict the likelihood of profit-driving behaviors such as payment, multiple orders and renewal. Model-based, predictive segmentations can be applied in the following ways:

  • Refine descriptive segments to increase campaign impact.
  • Manage campaign costs by eliminating lower-performing groups.
  • Expand market reach by targeting the top-performing groups.
  • Segment incoming orders for fulfillment and upsell by profiling for profitable behaviors.

As an example, imagine that your descriptive Segment Y is statistically more likely to respond to a given offer, but the payment rate for the group is only 50 percent. Let’s say your business requires a 60 percent payment rate on new orders to be profitable. A net payment model can help you identify the individuals (or households) who are more likely to respond and pay within the segment. By subsegmenting the group, you can see that targeting only the top 80 percent of the file is likely to achieve your 60 percent cutoff.

Building a solid model requires that you have results from previous campaigns to that segment and that the modeler has access to a large source of relevant data. The resulting model can dramatically expand the reach of your marketing campaigns and significantly increase ROI by helping you identify and target only the most profitable groups. The result? Your overall mail costs go down because you don’t promote to the entire file, but your net response stays more or less the same and payment goes up. You deliver higher ROI.

Models also can help you expand list populations by letting you mine segments that you haven’t been able to work with before. If you’ve been using on a 60-day selection, the model should let you expand to a 90- or 120-day selection, providing larger universes.

Finally, your models also can work for you in untargeted media or other channels. Models can be applied to responders—or in real time on your site—to help you make decisions about fulfillment, upsell targeting and future marketing contact.