27% of consumers have experienced online personalization
(Source: Infosys: Rethinking Retail)
Personalization can improve your bottom line and consumers are increasingly asking for it…
What are you waiting for?!?
The first step in this process — before you can start serving up personalized offers, content, product recommendations, etc. — is identifying who each customer is, and then being able to share that information across your entire organization. And at the root of this is having clean, accurate data.
Contrary to what you may have heard or even experienced firsthand, there are cost-effective ways to identify a customer across multiple data channels. It involves data being cleansed, updated and tagged with a persistent ID number to keep the customer linked to one profile across multiple databases.
Finding the right provider requires doing your research to identify a partner that has the data necessary to link a large variety of consumers, has the technology in place for advanced consumer matching (i.e., advanced matching algorithms), and can provide ongoing updates to the data once captured.
Lost Opportunities and Revenue
Bad data, no matter how it’s acquired, costs companies billions every year in wasted resources and productivity.
- Bad data costs the U.S. economy over $3 trillion a year.
- The average company wastes $180,000 per year on direct mail that doesn’t reach the intended recipient because of incorrect data.
- Estimated business cost of inaccurate data may be as high as 10% to 25% of a company’s revenues.
(Source: Software AG)
The bottom line: there’s a lot of money at stake.
This waste manifests itself in many different ways for retailers, including easily quantifiable costs such as mailing duplicate records within a CRM database (physical mail and email) and returned mail, as well as more qualitative data such as the opportunity cost of not reaching the correct consumers, conversions lost from not being able to follow consumers through multiple channels, and squandered upsell and cross-sell opportunities from not knowing a customer’s purchase history.
In addition to the cost of mailing duplicate records and lost potential revenues from bad data, retailers without accurate data are also more susceptible to fraudulent orders. Access to accurate historical data for a customer enables retailers to quickly identify potentially fraudulent behavior (e.g., shipping to known fraudulent addresses, shipping to a prison address, not being able to answer knowledge-based authentication questions), saving them money from chargebacks.
To help prevent this loss of revenue — and retain more customers — retailers can invest in automated data processing solutions such as identity validation and verification, enterprise data linking, master data management, and data cleansing processes.
It’s well worth it.
Clean Data + Persistent ID = Improved Results
Here are a few examples of the impact that a data management solution, including data cleansing and persistent data identification can have on your business.
Case Study: National Nonprofit Organization
A leading national nonprofit organization sought a master data management solution that could help it maintain a donor database critical to its operation. The database needed to be accessible to several different departments — marketing, IT, finance, data governance, analytics, management — and contain accurate data, as a significant percentage of the nonprofit’s donors are repeat contributors.
A single entry point, master data management solution was implemented, enabling all departments the ability to access, query and download segments of the database. In addition, a database cleansing process was implemented to update data elements and apply a persistent ID to each donor, reducing record duplication and linking disparate records within the database.
Result: the nonprofit has seen significant improvement in overall data quality, reducing marketing costs, and improving overall campaign ROI.
Case Study: Database Management Agency
A national database technology company needed a partner that could support its forward thinking database management technology.
The client was approached by a national health insurance carrier to reduce marketing costs through improvements to its database quality. The master enterprise database consisted of over 200 million individuals, and was experiencing two primary challenges: one, postal mail return rates were averaging 20 percent using standard data cleansing and merge/purge processing and, two, ROI rates were steadily declining.
To solve these problems, a database cleansing process was used to identify and quantify areas of data quality improvement. The multistep process standardized the data, applied multiple NCOA and Proprietary Change of Address (PCOA) updates, corrected address elements, appended date of birth and date of death, and assigned a persistent ID.
Result: These actions enabled the health insurance carrier to significantly reduce costs and improve mail response rates, saving the company $3.5 million annually.
Case Study: National Marketing Agency
A national marketing agency that specializes in improving call-center conversions on inbound marketing programs was looking for a solution that would allow agents to optimize their messaging to inbound consumer calls based on a predictive model of the consumer’s propensity to purchase.
Based on historical purchasing and behavioral information, the agency created a proprietary data model to categorize consumers, giving them a predictive score based on propensity to purchase. This proprietary data model was overlaid on a vast telephone database. Each individual was assigned a modeled score based on his or her propensity to purchase. The telephone database included over 510 million telephone numbers, names and addresses, giving the client a high probability of identifying the incoming caller.
As a consumer placed a call to the call center, an API immediately identified the name and address of the caller, assigned a modeled score to him or her, and instantly routed the call to a specific agent with the appropriate script based on the modeled score’s prediction of the consumer’s propensity to purchase.
Result: The agency was able to better identify and segment its callers, producing a 7 percent increase in purchases.
Breaking down data silos and uniting all enterprise data through persistent ID assignment will give all departments in your organization significant insight into each consumer your company engages with. Marketing, analytics, fraud, risk mitigation, and compliance all benefit from looking at your consumers through a single, unified view. And by selecting the right partner, this process can prove ROI almost immediately, making it both cost-effective and operationally efficient.
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