Enhancing customer-centric strategies. An Experian Data Quality white paper

Transcription

1 Enhancing customer-centric strategies An Experian Data Quality white paper

2 Introduction The retail industry is slowly shifting the way in which contact data is collected, used, and prioritized. With this shift, businesses are using contact data for more than just mailings. While shipment deliverability and marketing offers are still important, today, retailers are using contact data to create an enhanced customercentric strategy. Accurate and complete customer data is a prerequisite to creating a customer-centric atmosphere and ultimately, improving business results. And while data capture falls to customer facing employees, the affects of that data are felt by everyone. An enhanced customer-centric strategy improves a myriad of results, from the success of a loyalty program, to the effectiveness of targeted up-sell and cross-sell offers, to customer satisfaction, to customer profiling. If this reliance on accurate data (and other business departments) is not daunting enough, consider how the communication landscape has changed. Retailers are now expected to communicate and provide information across digital and traditional mediums. This directly impacts data capture as organizations are now collecting more data than ever before, across more databases. This report explores how retailers currently perceive contact data within a changing landscape. Readers will learn why their peers prioritize data quality and how the new digital consumer has impacted retailer priorities and created a new business theme, centered on the customer. Research methodology Experian Data Quality worked with Dynamics Markets in December 2013 to execute a global research study focused on current approaches to contact data accuracy. Respondents were surveyed across the U.S., U.K.,France, Germany, Spain and the Netherlands. This report focuses on the 9 percent of respondents within the retail and distribution sectors. Individuals are all tied to data quality in a variety of ways, across marketing, CRM, data management, customer service, IT, and other departments. Respondent seniority varies, from senior leadership to data analyst or administrator levels. Page 1 Enhancing customer-centric strategies

3 The current state The statistics 77 percent of survey respondents believe that their department funds have been wasted within the last 12 months as a result of inaccurate contact data. What s more outstanding is that, on average, respondents found that 12 percent of the department budget was wasted. These dollar estimates were given in relation to suspected contact data inaccuracies. On average, respondents suspect as much as a quarter might be inaccurate. Among those with data inaccuracy issues, human error was identified as the root cause. When asked why their organizations attempt to maintain data quality, respondents selected multiple drivers. Surprisingly, cost was not a chief concern. The main drivers for ensuring accurate contact data were improving efficiency, customer satisfaction, and informed decision making. What this means Respondents across departments were surveyed, highlighting that all groups suffer from the same problem. Contact data impacts more than just deliverability. Challenges most often occur within departments that are not charged with data capture. With this in mind, organizations need to work across departments to improve contact data quality. Customer-facing departments must consider the impact that their data has on downstream departments. What may be irrelevant or small scale for them may easily magnify when considering affects across IT, marketing, analytics, loyalty, and other essential business stakeholders. These research results highlight a growing trend, the customer-centric approach. Retailers no longer focus on customer data for simple cost reductions. Each new piece of customer insight, from a new address, to a mobile phone number, to a new shopping trend, or a change in address, affects the overall impression or knowledge of that customer. Therefore, departments need to work together to ensure data accuracy. Often times, this means implementing contact data validation tools at the point-ofcapture. While the purchase may appear only suited to an ecommerce or store operations group, in actuality, every team will benefit. To enhance a customer-centric strategy, retailers are prioritizing three new initiatives, all of which involve accurate customer data. First, retailers continue to discuss a single customer view. Second, retailers are reprioritizing communication channels to better align with the new digital consumer. And third, retailers are using customer data for more coordinated profiling and target marketing efforts. Software solutions While the digital landscape may look grim to the customer insight or analytics group, there are two ways to improve data captured through these channels. 1. Leverage point-of-capture validation tools. Real-time address, , and telephone validation tools are utilized by many retailers. This method ensures that customer data is validated immediately as any capture form is submitted. Validation at the point-of-capture ensures that invalid data may be correct by the customer while he or she is still engaged. 2. Audit all new customer data. For retailers who may not be ready to roll out point-of-capture software tools, it is important to at least validate customer data on a regular basis. Some retailers run new customer records through list processing each day. Once the information is validated, it is officially transmitted into the database. In this instance, invalid data is flagged for employee resolution or often updated automatically if data appends are available. An Experian Data Quality white paper Page 2

4 Customer spotlight Situation Experian Data Quality works with a retail and manufacturing company that sells multiple products to end-consumers and small businesses. That company maintains several customer databases and had two concerns: 1. Sales needed to represent all products (instead of having multiple people calling into the same account). 2. Over the phone orders were placed across multiple systems. The company worried that customer satisfaction would decrease, call center volumes would be difficult to manage, and fulfillment would become disorganized. Solution The company standardized address records across all data sources using Experian Data Quality software and services. The standardized address acts as a simple and indisputable data reference point to begin matching records. The company found address matches for 91 percent of records, with 64 percent categorized as high matches. High match results were reviewed and verified first, providing quick wins for the data integration project. Most of the project, however, was spent reviewing the low match results (27 percent of the database) and the no match results (9 percent of the database). These records were not complete and therefore could not be standardized for matching across databases. Results The company saved time on what would have been a very manual identification and matching process. Moreover, a single customer view, achieved by creating a one-to-one match across multiple customer data sources, allows the company to improve customer interactions and to streamline business processes. Quick wins 1. Experian Data Quality normalized 91 percent of address records automatically and refined the search for bad records, reducing manual rework. 2. So far, distribution changes have been smooth. Customers are easily identified and orders are taken and processed, all without impact to the customer. An Experian Data Quality white paper Page 3

5 A single customer view The statistics This initiative continues to pick up steam amongst retailers. 37 percent of companies maintain a data quality strategy to support a single customer view. What this means While retailers recognize the need, it appears that disparate databases and incomplete or inaccurate customer data are impeding these efforts. Organizations who manage data quality with automated methods, such as real-time data validation, are more likely to make progress on this initiative. The new digital consumer The statistics With the growth of online and mobile channels, businesses are trying to entice the digital consumer. 36 percent of businesses cite as the most important piece of contact data in But social media and mobile phone are right behind and are gaining ground. Mobile is an especially popular channel in the retail and manufacturing sectors. What this means While these digital channels are less expensive than traditional direct communications, they create headaches for businesses if data is inaccurate. Some businesses take less care of contact data collected from digital channels. As businesses leverage more digital channels, data accuracy needs to be considered. This ensures message deliverability, but also allows retailers to consider more robust target marketing efforts. For example, some retailers are now optimizing their websites for Smartphone users and developing strategies around social media. Unfortunately, data collected through online applications is generally the dirtiest. Human error occurs frequently, as users mistype information or enter data into the wrong field. The use of touch screen technologies also plays a large role in the deterioration of data quality. Coordinated profiling and target marketing The statistics Marketers are increasingly challenged with understanding, attracting, and engaging with their changing target audiences. To better engage audiences, 84 percent of companies have a loyalty or customer engagement program. Unfortunately, 74 percent of businesses have encountered problems with loyalty programs, mainly due to inaccurate information on customers. With a growing need An Experian Data Quality white paper Page 4

6 to create relevant marketing offers and loyalty programs, almost every retailer is now faced with a new profiling and targeting challenge. What this means Contact data accuracy and data appends enhance customer insight and improve profiling and targeting efforts. Currently, almost half of the retailers surveyed use customer data to capitalize on profiling efforts. These appends provide additional intelligence, with data ranging from household income to number of family members at an address. And while better customer data allows for more coordinated profiling and marketing efforts across departments, many of these efforts fall short of expectations. If customer data is captured incorrectly up front, correcting the error and finding other relevant customer information becomes almost impossible. With this in mind, retailers must implement address, , and perhaps even telephone validation tools in order to have more actionable data for target marketing and to even consider appending additional customer data. The ideal contact validation tools reside at the point-of-capture, which requires input from other business stakeholders. Retailers who focus on data analytics must encourage interest amongst other stakeholders, including ecommerce, call center, store operations, and other customer facing groups. Page 5 Enhancing customer-centric strategies

7 Encouraging business stakeholders Customer intelligence is invaluable, but only if analysis is based on accurate data. Unfortunately, it is difficult to show a concrete ROI for customer analytics. Therefore, in order to justify budget for contact data validation, retailers need to encourage all business stakeholders to work together (and strategize behind departments with hard costs/savings). Consider tangible costs when building an ROI for contact data validation. This typically involves reviewing the number of packages and/or marketing pieces sent per year. The chart on this page highlights how retailers justify contact data validation tools by leveraging operations or shipment data. In many instances, the justification relates to immediate business savings, but the end goal relates to enhancing customer data and achieving a stronger customercentric business approach. Conclusion Surprisingly, retailers are using marketing campaign response rates as the main way that they measure contact data quality. With this approach, customer insight, data analytics, and other marketing stakeholders quickly see how profiling and targeting attempts play out. Any deviation from the norm or change in response will ignite interest. From here, retailers must remember to work across departments. While results may be recognizable to one group, it is very likely that similar data issues are also affecting other departments. Additionally, as the previous section describes, retailers need to unite to justify budget for overall business benefits. About Experian Data Quality Experian Data Quality is a global leader in providing data quality software and services to organizations of all sizes. We help our clients to proactively manage the quality of their data through world-class validation, matching, enrichment and profiling capabilities. With flexible software-as-a-service and on-premise deployment models, Experian Data Quality software allows organizations around the world to truly connect with their customers by delivering intelligent interactions, every time. Established in 1990 with offices throughout the United States, Europe and Asia Pacific, Experian Data Quality has more than 13,500 clients worldwide in retail, finance, education, insurance, government, healthcare and other sectors. For more information, visit Page 6 Enhancing customer-centric strategies An Experian Data Quality white paper Page 6

8 Experian Data Quality 125 Summer St Ste 1910 Boston, MA T dataquality.info@experian.com Intelligent interactions. Every time Experian Information Solutions, Inc. All rights reserved. Experian and the Experian marks used herein are service marks or registered trademarks of Experian Information Solutions, Inc. Other product and company names mentioned herein are the property of their respective owners. 09/2013