Sales performance in the omnichannel era: data quality is no longer optional
In today’s omnichannel ecosystem, turning scattered data into real business opportunities is a major challenge. CRM, e-commerce, sales, marketing, data, and IT leaders all stand to gain from properly qualifying their data to maximise its value. In fact, effective data governance that is built on accurate and reliable information proves to be a critical driver of commercial success. Let’s take a closer look at data quality best practices that can turn omnichannel data into sustainable growth.
The more diverse the data sources, the more complex it becomes to harness scattered information. That’s why it’s essential to qualify data as early as possible in its lifecycle. This simplifies governance and, downstream, maximises its impact on business development.
1. Clean data, effective governance .
Omnichannel collection inevitably leads to non-homogeneous, non-unified data of variable quality. What’s more, poor-quality data can slip through the cracks, and omnichannel operations amplifies the effects. Initial input errors may be inaccurately repeated across multiple touchpoints, or data may be incomplete or outdated. In this context, managing data to support sales performance becomes a real challenge.
To succeed, qualifying data, especially identification and contact information, must become standard practice. These data elements act as reference anchors to centralise, unify, and synchronise customer information linked to each contact. Therefore, they must be reliable. That’s precisely where data quality comes in, filling in gaps in quality by controlling input, validating contact information, and removing duplicate records from the database. In doing so, data quality ensures that contacts are both identifiable and reachable – the two cornerstones of effective customer data governance.
It’s important not to underestimate the role of data quality in data governance. Data collected via multiple touchpoints and fed into the CRM system is rarely clean, consistent, or trustworthy. And the CRM system itself cannot clean or verify data, as it simply wasn’t designed for that purpose. However, using a dedicated data qualification solution that is robust enough to handle high volumes enables governance to apply to a clean, actionable data pool that can truly support sales performance.
2. Without qualified data, sales performance suffers
In marketing, sales, and CRM systems, campaign performance simply can’t reach its full potential without qualified data. This is especially true in an omnichannel environment, where there is a high risk of inaccurate data. Without data quality, sales performance faces several pitfalls:
Poor reachability: Engaging and retaining customers starts with the ability to communicate with them. When contact details such as email addresses, postal addresses and phone numbers contain errors or are no longer valid, the relationship breaks down. According to Hubspot, 30% of email addresses become invalid each year. Gartner reports that invalid or blocked numbers cause 48% of outbound B2C calls to fail. By eliminating these missed communication opportunities, qualifying contact data becomes a powerful lever for boosting engagement performance.
A poor conversion rate: whether online or in person, the customer journey needs to be frictionless. But form-filling can disrupt this flow when the process is tedious or time-consuming. Cart abandonment is common here, directly impacting the conversion rate. Lost revenue opportunities: Some DQE clients have reported conversion gains of 20% or more after implementing input help.
Duplicate records create confusion: in most cases, duplicate records account for 20% or more of entries in the CRM database. Yet once this rate exceeds just 10%, it hits a critical threshold where duplicates begin to disrupt operations and impact results. When three or four different contact records exist for the same person, this jeopardises unified customer views and the ability to know your customer becomes unreliable. Operational errors quickly multiply: customers may receive the same email several times, or long-standing customers may be mistakenly treated as first-time contacts.
Inaccurate customer insights: if customers and prospects can’t be accurately identified and recognised, marketing segmentation is bound to be flawed. And when customer data isn’t unified, loyalty programmes struggle too: tracking earned points and distributing associated benefits becomes difficult. Once again, data qualification significantly boosts KPIs across the board, particularly in terms of customer satisfaction.
3. How data quality capabilities support sales performance
DQE One encompasses all of DQE’s customer data quality management solutions. It enables real-time data quality control across all customer input forms, while also validating the integrity of customer data within your existing databases.
In an omnichannel ecosystem, data quality is the foundation of effective governance, which in turn ensures more targeted and efficient sales campaigns. Data quality delivers tangible benefits throughout this value chain, from data collection to its operational use by business lines:
- Qualifying data at entry: a winning habit
Among the best practices in data quality, real-time qualification at input stands out. While it’s possible to cleanse databases retroactively, the ideal approach is to validate and qualify data in real time on input forms. Data quality enables assisted data input, including detecting typos, verifying contact details using current postal and telecom repositories, and autocomplete. This delivers two key benefits: the user experience is optimised, and the quality of stored data is ensured – all in real time. As a result, the effectiveness of data-driven marketing, sales, and CRM actions gets an immediate boost!
- Data enrichment: a driver of sales performance
Another powerful lever for improving performance is data enrichment. Beyond validation and unification, data quality solutions can enrich data through autocomplete for input forms. This is especially valuable for complex data, such as B2B information. From a single legal business name or company registration number, a specialised solution can populate all remaining fields in the form. This provides significant time savings, and highly accurate data that directly fuels sales performance. DQE clients have reported
a fourfold reduction in form completion time for B2B entries . Data Quality’s ability to standardise data also makes it a key asset in transitioning to electronic invoicing
- Data quality: a driver of team satisfaction
Since data quality offers numerous benefits for its users, it’s worth highlighting how it also improves team satisfaction . It gives teams access to a reliable data pool, eliminates common frustrations caused by duplicates or unreachable contacts, and, most importantly, frees them to carry out their marketing, sales, and CRM activities without the setbacks that come from bad data. This sense of effectiveness and job satisfaction also plays a role in improving employee retention.
Implementing data quality means teams are equipped with data that is reliable and deduplicated, and that they can reach customers. Each team member is empowered to fully leverage their skills and turn every customer interaction into a business opportunity. The result is stronger and more sustainable sales performance.
About DQE
Because data quality is essential to customer knowledge and the construction of a lasting relationship, since 2008, DQE has provided its clients with innovative and comprehensive solutions that facilitate the collection of reliable data.
17
Years of
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