How Your Data Hygiene Impacts the Customer Experience

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As companies increasingly rely on data to inform business decisions, data hygiene is essential to truly offer an advantage in today’s landscape.

The use of data has become so interwoven with the customer experience process, and consumers often don’t even notice how skillfully they’re being pitched. Shoppers looking for cargo shorts on the web see that peers have bought a complimentary brand of sandals. Fans who purchased an indie band’s album conveniently get notified when the act’s lead singer books a solo show in their market. With such a variety of data sources, data hygiene becomes ever-so more important.

Marketers rely on these kinds of solid data connections to land sales and nurture interested prospects. But what if the data isn’t solid at all? What if it’s incomplete and taken in the wrong context? What if it’s outdated – or even wrong? How much damage is being done to the brand’s attempt to cultivate a positive experience?

This is the double-edged sword data wields. As companies increasingly rely on data to inform business decisions, the hygiene of that data needs to be pristine to truly offer an advantage in today’s landscape. Considering how rapidly customer behavior is changing, it’s becoming more crucial than ever to pull in real-time insights and ensure that the right data is doing the right job.

Shooting in the dark

The problem of bad data creating bad experiences isn’t brand new. Before CRM and marketing automation, marketers who pulled consumer data from paper records out of the backs of file cabinets were essentially shooting in the dark. They could track patterns and target populations, but they often missed when they tried to personalize offers.

Today, marketers have more resources they can tap, which makes it far easier to assemble holistic, far-reaching campaigns and microtargeted touchpoints personalized for each consumer. They can collect data nuggets from a wide variety of sources, including the cloud, on-premises sites, and IoT devices, giving them a huge edge over their predecessors.  

At the same time, the stakes are higher. With so much data coming at them, marketers can easily misfire with wrong data or wrong contexts. Consumers usually react positively when services auto-populate purchase forms or present offers that magically hit their sweet spot. But they have little to no patience if an offer bungles their name or pitches a product that’s insensitive to them.

To improve the quality of their data and raise their percentage of hits, marketers need to do three things. They need to ensure that they collect the right data, parse and clean it to the right places and analyze the data for relevant insights in the “business moment.”

The data management challenge

First, they have to conquer the data management challenge. Consumer data comes from so many sources and ends up in so many data stores, often siloed. Organizations need to make sense of it all. They need to tie cross-platform data together, ideally without disruptive data movement, to create a single customer record – in essence, a single source of truth. The platform needs to integrate with other systems, aggregate and segment audience data, and feed that data back to paid media, email, and content management platforms for accurate real-time reporting, targeting, and personalization.

This integrated approach gives marketers the ability to access relevant information and target it correctly to deliver the right touchpoints. Advanced data modeling techniques can de-duplicate information, identify common characteristics and create customer clusters that depict the complete picture of customers’ purchasing habits and preferences.

Once the data is collected and managed, it needs to be cleaned – repeatedly and systematically. Failure to exercise proper data hygiene can negatively impact the customer experience in several ways.

It can create a frustrating customer experience for a caller who is asked to provide information more than once. If a call center worker trying to personalize a conversation makes erroneous references to personal or prior purchase information, the customer will walk away with a negative perception of the brand.

Outbound marketers that reach out with insights based on bad data will end up with irrelevant – and, in some cases, negative – brand interactions that erode the consumer-brand relationship. Personalization, based on accurate data delivered in a timely manner, can boost sales, engagement, and customer retention.

For marketers to deliver the personalized journeys customers want, brands have to rely on high-quality, “fresh” data. Incorrect, missing, or old data in customer records can lead to irrelevant retargeting. This can dissuade consumers from buying from you – and it can drive them right over to your competitors. Bad data can also derail analytics and business intelligence initiatives, causing marketers to make poor decisions.

Finally, the data needs to available and analyzed to derive actionable insights. Monthly reporting approaches of the past may be useful in identifying overall performance and trends but have limited value in addressing specific needs in the business moment, such as during a call center engagement. This requires operational analytics systems that can effortlessly scale up and down to handle dynamic query workloads to meet a diverse set of “data users” within an organization. A recent breakthrough in hybrid data warehouse systems that span between existing on-premises applications and cloud-based have proven to provide breakthrough performance at economic levels that makes this possible for both organizations, large and small.

Data’s upside

Accurate, well-maintained, fresh data and next-generation hybrid data analytics can help make segmentation and personalization possible. It can help you target the right person with the right content at the right time, engaging audiences with digital experiences that turn consumers into long-time fans. AI-powered data enrichment platforms can evaluate your data for accuracy, completeness, conformity, and integrity and fill in gaps along the way.

Here are some steps organizations can take to improve their data hygiene.

  • Assess your data quality problem: The first step is to admit that you don’t know what you don’t know. Determine the size of the data quality problem at every step of the business process by undertaking a data quality assessment.
  • Reconcile conflicts among data feeds: Remove duplications and erroneous data to improve targeting.
  • Organize your resources: Ensure data is properly categorized and tagged so users can find it quickly.    
  • Sync your clean contact data across systems: Use data integration, data quality, master data management, or simple API synchronization to ensure you’re operating from a single source of truth.
  • Refine your data model: Cleaning up databases isn’t always enough to solve problems caused by dirty data. Look at your model, determine how you’re collecting and ingesting data, and take steps to catch waves of mistakes before they derail your customer outreach.
  • Do regular contact data health checks: They should be a routine part of your marketing operations. This is easily automated using cloud services or on-premises software tools.

Conclusion

Marketers work too hard to have bad sets of data get in the way of their efforts to engage with prospects. By focusing on data hygiene moves to rid their operations of inconsistent, outdated, or wrong information and employing the latest generation of cloud-based operational analytics solutions, marketers can give customers what they want – personalized service that speaks directly to them.   

Jeff Veis

About Jeff Veis

Jeff Veis is the CMO at Actian and is responsible for product, solution, partner, brand and digital marketing initiatives on a global basis. He has over 20 years of enterprise software marketing experience at high-growth companies. He conceived and founded the Liberty Alliance Project creating a global standard for federated digital identity. Prior to Actian, Veis held senior level positions at Hewlett Packard Enterprise Software’s Big Data Analytics and Information Management Group, SAP BusinessObjects, BEA Systems, Sun Microsystems, ActiveGrid and Booz-Allen. He holds a BS in Computer Science from Northwestern University and a MBA from the Kellogg School of Management at Northwestern University.

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