Q&A: Growing your Development Business with Data Quality
:: Enable Recurring Revenue While Extending Long-Term Value to Etailers ::
The influx of constantly changing customer data challenges etailers to maintain customer satisfaction, manage returned shipments and prevent fraud. By optimizing ecommerce infrastructures with integrated data verification tools, Web developers can solve these fundamental issues and also create opportunities for recurring revenue. Packaging data quality as a service not only ensures a better performing website, but also appeals to etailers in terms of costs, results and convenience. How can this be done?
Greg Brown, vice president of Melissa Data, a provider of global data quality solutions, looks to answer that question and others in the following Q&A with Website Magazine.
What role should developers play in educating their clients about data quality?
Greg Brown: Your etailer clients may not be aware of the poor state of data quality in the marketplace. They may also assume that handling it with occasional cleansing is enough to combat the issues. In reality, up to 25 percent of good data goes bad over the course of a single year, complicated by the ongoing challenges of collecting clean data from multiple entry points, avoiding duplicates, inconsistencies, and incomplete data. Educate your clients on the concept of active data quality, the optimal approach to managing customer data that is alive and active itself. Be an advocate for ongoing, consistent data quality processes that stop bad data before it even enters their system, before it can perpetuate fraud, higher delivery costs and shoddy customer service.
How does this equate to long-term revenue for the Web developer?
Brown: Once etailers better understand the scope of potential loss and disadvantage from poor customer data, partnering with a data quality resource may hold strategic appeal. This opens a door for Web developers to monetize high-value services, solving the challenge for their clients and creating subscription type data quality operations. Ideal data quality operations are based on multisourced data, routinely updated and focused on the reality that data quality requires constant vigilance.
How can developers define return on investment?
Brown: Data quality provides a guaranteed ROI to etailers, and the investment in data quality can be proven to pay off quickly. Data quality can be credited with reductions in shipping issues such as misdelivered packages and will also enable a cleaner, more accurate database to fuel customer marketing; operations such as real-time email verification critically protect an etailer's sender reputation and prevent blacklisting. To set a foundation, assist your clients in determining the existing state of their data by profiling it. Knowing how good or how bad things are helps you recommend and package data quality operations that can create improvement, which you can then report over time to demonstrate data quality value.
How do you package data quality as a service?
Brown: Web developers can in turn partner with a data quality provider, assembling smart, sharp tools based entirely on client needs. A flexible, consultative data quality relationship is the ideal, with support available as needed or in a more formal partner agreement scaled to fit larger requirements. With conversation and guidance, your data quality resource can help increase functionality of your clients' sites, and provide technical insight and support to ensure processes are running smoothly.
What are some the data quality tools that can be integrated as an ongoing data quality service?
Brown: By offloading point-of-entry customer verification to site developers, etailers can be assured consistently correct, verified and standardized customer information. The right data enters the system from the start, so great contact data is maintained whether or not an order is fulfilled. Customers themselves also have greater convenience at the time of order, based on data quality tools that auto-complete address data as it is entered. Keystrokes are reduced and errors are eliminated, reducing the cost of shipping mistakes and product returns.
What are some other examples of data quality operations?
Brown: Operations such as address auto-completion, and global address, name, phone, and email verification assist in reducing cart abandonment; this complements tangible ROI from more accurate fulfillment and better customer satisfaction. A broad range of data quality functions can be handled as ongoing services, such as fraud prevention by cross-validating key pieces of data in real-time, or eliminating duplicate data to achieve a 360 degree view of the customer. Predictive shipping algorithms further streamline and accelerate the shopper's experience, distinguishing etailers by offering shipping costs and options from the moment they start shopping on the site. All of these tools help close the sale - demonstrating your understanding that getting a customer to say 'yes' is a marketer's goal.
Why are subscription services necessary?
Brown: All data quality tools must be driven by reference data, multisourced and constantly changing. Part of your data quality as a service is to assure this data is collated and automatically managed to an etailer's advantage. Point to the 1-10-100 rule, which demonstrates the ever-increasing cost of bad data. It is estimated to cost $1 to verify customer contact information at the point-of-entry, $10 to implement a batch solution to cleanse and deduplicate data after it is submitted, and $100 per record to do nothing - based on cost of misplaced shipments, returned mail and lost marketing opportunities.
What are the initial steps to add data quality to my repertoire of Web development services?
Brown: Given the range of data quality tools available, most important is to define the need and work with an expert to easily integrate the right slate of tools. There are any number of data quality processes that can be integrated to cleanse and verify data at the point of entry, improving the online experience for end-users and protecting data as a long-term business asset. The opportunity is there - be a data quality advocate and win with recurring revenue and happy clients.
