There are several benefits organizations see from a single customer view. A consolidated view helps with business intelligence, staff efficiency, marketing campaigns and effectiveness, collections in billing and the overall customer experience.

There are several reasons why organizations struggle to create a single view. First, many organizations house several databases, which collect unique data and are used by different departments. This siloed approach leads to duplicate database records, inconsistent data entry and less tailored customer interactions.

All of these scenarios lead to inconsistent data spread out across the organization. To leverage the information, organizations need to ensure consistent data entry across the organization, identify patterns of data errors and leverage matching technology.

Regular database maintenance needs to take place to ensure information is accurate and duplicate records are merged. This regular cleaning will help businesses achieve a single customer view and improve a variety of business processes.

While there are many components and challenges to big data, data quality needs to be a part of any strategy. With the proliferation of data via a greater variety of channels, organizations are working with more information than ever before, but it can be hard to keep all of that information accurate.

The concept of big data can be divided into 5 categories and data quality fits within each.

1. Volume is the amount of information stored and aggregated, which often lead to duplicate information and incorrect data entry.
2. Variety focuses on the assortment of information, including structured and unstructured varieties. Most organizations struggle to even format data, thereby affecting analysis.
3. Velocity is the speed at which data is acquired and accessed. Businesses need accurate data and a holistic view of the database to properly access data at the speeds required.
4. Veracity is the accuracy of information, which is certainly related to data quality.
5. Value is related to the business benefit for better data practices and aggregated analytics.

To gain the benefits of big data, businesses need to make sure the information feeding those analytics is accurate. Get as much structured information right within your system and put automated processes in place. These tools will allow you to ensure data quality and let staff spend more time on other aspects of your big data initiative.

To achieve a customer-centric strategy, many businesses are looking to tailored marketing, one-to-one customer service, brand loyalty and insight-led collaboration. All of these customer-centric strategies rely on accurate customer information and strong analytical abilities. Without accurate data, an organization may not be able to generate actionable intelligence.

There are several common road blocks to accessing the data required for customer-centric initiatives. First, information is often siloed. There are different databases and records based on individual channels or departments. It is difficult for organizations to aggregate and manage information in one central place.

Next, there may be poor data quality. Overall in the industry, there is a lack of verification techniques and a large number of individuals who can change information. In fact, a recent Experian QAS study found that 94 percent of businesses believe some of their customer and prospect information might be inaccurate.

Finally, businesses struggle to match new data to an existing record. Often, information is placed in a non-standard format that is difficult to consolidate and analyze.

To correct these road blocks, organizations need to create a plan around accurate data for their customer-centric strategy.

To correct these road blocks, organizations need to create a plan around accurate data for their customer-centric strategy. This should include standardization rules, verification techniques at the point of data capture and enhanced searching capabilities.

Customer-centric strategies are extensive and require many elements, just don’t forget the data quality in your planning.

Today, many businesses operate channels within siloed environments, segmenting information and interactions. Unfortunately, the consumer judges a business based on their overall experience and expects the same level of engagement regardless of the channel. If a consumer has a bad experience at one touch point, it will affect buying patterns across the organization.

As part of an omni-channel experience, businesses must create a consistent dialogue that extends across all channels and points of contact to produce a stronger brand identity. To achieve a consistent approach, businesses need to have parallel messaging, personalized call center experiences and better business intelligence. This will allow customers to interact with the organization in a positive way, central to their wants and needs.

However, to gain that business knowledge, stakeholders need to ensure the accuracy of consumer information. Here are two ways businesses can achieve this business knowledge.

1. Create a centralized, accurate data source – Often organizations segment information across departments. By having one database, staff can understand preferred points of interaction, the consumer purchase history and other preferences.
2. Ensure contact data quality – Contact information is consistent across records and can serve as a set of unique identifiers to help in consolidating duplicates. Ensure the accuracy of contact information to help create one record for each customer.

Sit down with your team to determine how accurate customer information can improve your omni-channel experience.

There are several trends in data quality to look for over the year ahead. For today’s post, I’ll focus on the trend of software-as-a-service. To get everyone on the same page, SaaS refers to a software distribution model in which applications and any associated data are hosted by a service provider and stored centrally, away from the client’s network.

This deployment model is becoming more prevalent. According to a recent Experian QAS study, 60% of respondents cited that some or all software is hosted in the cloud. The majority of these organizations house both on demand and on premise solutions.

SaaS deployments are particularly popular for data quality. These tools require less deployment resources and little maintenance. With data quality, there is also referential data to consider, which can take up a fair amount of space on local servers. With hosted solutions, there is no need to worry about that space.

Before deciding on a deployment model there are several questions stakeholders should ask themselves:

1. What is the company’s existing IT strategy and experience with SaaS?
2. What is the immediate and long-term budget for installation and maintenance?
3. What kind of response time does the company require?
4. Are there regulatory or compliance restrictions to consider?

Check back tomorrow for the next data quality trend of 2013!

All of this online ordering means one thing – shipping. Retailers are now playing Santa, needing to make sure the sleigh arrives before Christmas, to the right location, with the right gift. As many retailers know, shipping can be expensive and time-consuming, especially during the holiday season.

With margins low and free shipping offers everywhere, retailers need to keep fulfillment costs low. One way to do that is through successful package delivery. Retailers can do this by ensuring their consumer information is as accurate as possible.

Data quality can seem boring when we think about the overall holiday season strategy. However, if incorrect or incomplete, this basic piece of information can cost retailers thousands of dollars in return shipping fees and lost employee efficiency, not to mention customer dissatisfaction.

Even with this knowledge, retailers are struggling to keep data accurate. According to a recent Experian QAS study, 86 percent of retailers suspect that their customer and prospect data might have errors.

Even with this knowledge, retailers are struggling to keep data accurate. According to a recent Experian QAS study, 86 percent of retailers suspect that their customer and prospect data might have errors. On average, respondents believe that as much as a quarter of all contact data might be inaccurate.

eRetailers are up against a unique challenge when collecting consumer information online. While you would think self-entered contact details would generally be accurate, information entered online is often the dirtiest in a database. This is because details are frequently entered quickly by untrained individuals – the consumers themselves. During the holiday season, information can be more at risk when consumers are entering addresses other than their own, like those of friends and family.

While many retailers already have verification processes in place, some use back-end techniques that validate details after a consumer has left the site. In this scenario, if an error is found, the retailer has to call the consumer to correct the address. Phone calls can waste valuable employee time and delay the shipment.

Retailers should check to see if current verification practices are providing a worthwhile ROI. Any percentage point of error can be exacerbated by increased volume at peak times in the holiday shopping calendar.

Stakeholders can check accuracy levels by reviewing data. Businesses should ask themselves:

• Are addresses complete?
• Are shippers charging extra fees because a suite or apartment number is missing?
• How many packages are returned each month?

From there, retailers should optimize the verification process. Make certain that information entered during the checkout process is as accurate as possible. A small change can make a big difference.

This holiday season, make sure you are helping Santa by giving him good directions and accurate addresses to direct his sleigh!

Here is a quick and easy reference guide by Experian CheetahMail to help you follow point of sale email acquisition best practices. Remember, best practices start before point of sale and continue after the transaction.

For more information on managing compliance, privacy and deliverability with in-store email acquisition, download our latest Point of sale email white paper.

With the proliferation of the Internet and mobile technology, today’s consumer operates differently and expects more from their favorite brands. To adapt to that new American consumer, marketers are using highly targeted strategies to drive interest. These can be messages that are relevant to a few hundred consumers or detailed one-on-one communications that target individuals at the point of sale or online.

But some marketers struggle to execute these tactics effectively. Most segmentation is currently done prior to a campaign, meaning that marketers determine which message a consumer will receive before ever interacting with that individual. Unfortunately, with the rapid nature of purchasing decisions and buying transactions, businesses often miss opportunities because it takes too long to get the right message to the right consumer.

To keep up, marketers need to collect intelligence at the point of contact so they can understand each individual consumer’s habits and preferences during that connection. This intelligence can then feed modeling algorithms that enable automatic offers based on an individual’s preferences.

To develop a strategy around real-time marketing intelligence, marketers should take the following steps:

1. Clean existing data – at the root of any intelligence strategy is data. Information determines a company’s ability to reach target individuals – and understand who they are and what they’re interested in. Unfortunately, if the data that feeds intelligence efforts is inaccurate, marketers are simply unable to communicate with or understand consumers. Ensuring the validity of contact information, internal records and third-party data elements helps organizations target consumers and ensures that sophisticated analysis is as precise as possible.
2. Identify strategies – organizations should analyze their target markets and determine which communication channels could benefit from a more personalized customer experience. Marketers should decide how they want to change each communication to help drive the desired action from each consumer. Consider personalizing website displays based on geographic regions, customizing an introductory message or revamping loyalty campaigns based on purchase history and consumer interests.
3. Real-time intelligence – marketers should build models to help predict the best offers for each target audience. These models can be designed to take into account demographic and behavioral information, as well as purchase history and internal data. Marketers can feed these models with intelligence gained at the point of contact to prompt consumers in real time with specific, relevant offers.

As marketers continue to enhance and refine targeting efforts, it’s important to gain customer insight. Those who leverage these advanced technologies and strategies will create stronger customer engagement.

Segmenting customers and taking measurable action in real time are advanced techniques that appeal to many marketers today. Achieving this level of interaction allows organizations to optimize marketing efforts and provide the right offer at the right time to the right consumer.

Customers are the lifeblood of any business, yet businesses lose anywhere from 10-30% of them per year.

Today’s “always on” shopper makes decisions in every channel – direct, online and in-store. In looking for better ways to sell more products to current and new customers, marketers seek out ways to expand their prospect universe to improve customer acquisition levels. Why? Customers are the lifeblood of any business, yet businesses lose anywhere from 10-30% of them per year. This makes new customer acquisition extremely important to both protect and grow business year over year.

The acquisition challenge is that traditional prospect universe expansion is becoming increasingly difficult. Many marketers have exhausted list and co-op rental names, and optimization strategies. They’ve been using the same predictive prospecting models against compiled prospect data or co-ops for a long time. But these tools, which are based on traditional demographics, are increasingly leading to a decline in good prospect identification and acquisition. We know that in prospecting, there is no customer behavioral data to drive higher performing analytics – no plastic surgery or proverbial fountain of youth.

In today’s multi-channel shopping world, the fresh face of online behavior can’t be overlooked. While direct marketers in self-preservation mode have leveraged traditional acquisition models for years, the reality is that if they do not consider fresh new data and modeling solutions, they will be looking for their next, new job.

So, what can marketers do to overcome prospect universe expansion hurdles with online insight to increase acquisition and ROI?

Meet direct marketing’s fresh new face: online behavioral data and modeling.

Online behavioral data addresses a key challenge of traditional direct marketers: profitable prospect universe expansion. By incorporating online behavioral data (website traffic and behavior, search, demographics, lifestyle data) into offline marketing strategies, marketers can:

• Identify previously untapped prospect universes
• Suppress likely non-responders
• Enhance offline prospecting

Where else do consumers so actively demonstrate their purchase interest and behavior? Where else do we go when we want to learn about something? Anything? We go online. But most direct marketers have not enhanced their offline marketing efforts with online data, missing the opportunity to incorporate online behavior into targeting efforts.

With this fresh new data source, direct marketers are able to:

• Better understand customer behavior across channels
• Refresh their current models
• Develop better models, built from true, multi-channel data

Ultimately, direct marketers will be able to identify new pockets of high-value prospects that traditional response models miss, resulting in an increase in revenues and profit.

Fashion models and trends change, and sometimes that’s for the best, otherwise we might still be wearing parachute pants and slap bracelets. Luckily, you now know what the new face of marketing is and can begin integrating online behavior into your existing customer data today to leverage new marketing opportunities. Discover the new fresh face of direct marketing; learn more about online behavioral profiling and modeling.

What is the best course of action for the highest ROI?

There are a number of services providers that have proprietary processes to correct approximately 20% to 50% of records that are not +4 coded or have a missing/invalid secondary (apartment) number. However, even after this hygiene processing, there will most likely still be 1% to 3% of records with deliverability issues. Now the questions become: Should all of these be mailed? Should none of these be mailed? What is the best course of action for the highest ROI?

Even though a record has deliverability issues, it doesn’t mean it won’t get delivered. Here are two examples:

Jane Jetson
124 Main St
Saint Cloud, MN  56301

Jane’s address is not +4 coded because 124 Main St is not a valid delivery point. However, 123 Main St. is valid and is, in fact, where Jane Jetson actually lives. Because of this, there is a strong likelihood this mail piece will be delivered.

Fred Flintstone
456 Oak River Run Apt C3
Saint Cloud MN  56301

Fred’s deliverability issue is an invalid apartment number.  Instead of C3 it is 3C. In this case, there is also a very strong likelihood this mail piece will be delivered.

The goal for marketers is to identify and drop records with deliverability issues performing below breakeven to ensure they only mail to the most effective set of records. Over the past year I’ve worked with a top 200, top 100 and a top 50 retailer whose breakeven deliverability analysis saved them each between $900,000 and $1.7M annually.

How would it affect your budget if you had similar results? If direct mail is a critical part of your marketing ecosystem today, read more about breakeven deliverability analysis.