How would perfect data change your job?
This article was originally published on CMO.com on January 15, 2016
B2B marketers, close your eyes and imagine that all of your sales and marketing data is clean, normalized, segmented, and correlated.
In the real world, data will never be 100% perfect, but it is entirely achievable to have data that is 70% to 80% perfect. If you no longer had to struggle with poor quality data and its negative impact on sales and marketing operations, how would your short-term priorities and long-term strategy change? How would (nearly) perfect data change your job and improve your effectiveness?
Here are five ways that high-quality marketing data benefits B2B marketers:
1. Leverage Demographic Scoring For Better Lead Qualification
Most B2B marketers use a scoring scheme that is predominantly activity-based. This is because activity-based scoring is easy to implement, while demographic scoring not only requires high-quality data, but also the development of buyer personas and target customer profiles.
The problem is, using activity-based scoring without demographic scoring produces many false positive marketing qualified leads. When you get down to it, unless the lead is from the correct company with the right buyer persona, it has little chance of leading to a sale, regardless how “engaged” the person is. This is especially true for B2B companies selling to Fortune 5000 enterprises and using targeted account lists. The proper scoring scheme should be heavily weighted with demographic scoring and supplemented by activity-based scoring.
If you have lead and account databases that are cleaned, normalized, and segmented, it is simple to further segment by people and target account dimensions. All of this valuable data can power a robust demographic scoring scheme.
2. Personalize Prospect Engagements For Higher Conversions
When you have a segmented database with clear persona and company profiles, the next step is to personalize all demand generation campaigns.
It should come as no surprise that personalized engagement improves the conversion rate and reduces attrition throughout every stage of the lead funnel. In fact, campaigns precisely targeted to personas, companies, and industries create more relevant engagements across all digital and field marketing channels.
3. Target Accounts, Not Leads, For More Effective Selling
B2B marketing and selling are intrinsically account-centric exercises, but today’s marketing automation platforms tend to focus on individuals. You can easily address this issue with normalized and segmented account and lead databases.
You can also use a data automation tool to add account-based scoring using profile and activity data. A reporting tool can produce target account heat maps and dashboards to measure account health in areas around penetration, coverage, and engagement levels.
4. Optimize And Accelerate Lead Qualification
All leads are not created equal. For starters, the origin of the lead is critical.
To expedite lead qualification, consider if a lead is from an existing account or a net new lead. Is it from a targeted account or a targeted buyer persona? You should answer these questions as early as possible, as they can help to optimize the lead qualification process for better speed and conversion.
Once you have determined the origin of the lead, you can decide how to effectively handle it. For example, sales development representatives could focus their efforts and give priority to net new leads, targeted accounts, and key buyer personas.
Existing account leads could be routed to the customer success team for more effective upselling. To accelerate the process, leads should ideally be routed to the correct team from the get-go. This approach means there’s no waiting around for the lead to traverse the time-consuming lead qualification process as it bounces back and forth among teams while delaying follow-up and sales.
5. Simplify The Marketing Technology Stack
In addition to core customer relationship management (CRM) and marketing automation platforms (MAP), B2B marketers are choosing various niche marketing technologies to enhance their core marketing technology stacks.
These technologies perform specific tasks for particular systems. Whether it is predictive lead identification, Web form enrichment, or social selling, each solution comes with embedded data cleansing and normalization algorithms to service those specific needs. That is because none of these solutions would work on the dirty, raw data from the CRM and MAP.
Alternatively, if the sales and marketing data has already been cleaned at the source, the CRM and MAP core systems can do more, and there is less need for bolt-on solutions.
Marketers who have invested in data quality can focus their technology investment in a core set of analytics, workflow, and data solutions to round out the marketing technology stack. Furthermore, additional solutions can be added quickly and cheaply because each solution no longer has to repeat the data cleansing process.
Data-Driven Marketing Starts With Data
If we take a step back and think about how much of our demand generation and marketing operation teams’ daily effort is spent on struggling with poor quality data, it’s clear that high-quality sales and marketing data can fundamentally change how teams operate and improve the return on all our marketing initiatives. Clean data saves time and money as it enables:
- Higher engagement rates
- Closed-loop feedback on campaign performance
- The ability to run more experiments
- Quicker lead qualification
- Concrete guidance on budget priorities
Data is fundamental to every marketing activity. A team grappling with poor data spends time, effort, and money manually bridging the data gaps, making guesses, and fighting lead attrition.
In contrast, a team with clean and segmented data has more time to spend on strategy, improving engagement models, experimenting with new ideas, and measuring their effectiveness. So B2B Marketers, unless your data is already perfect, you need to make data quality a top priority. After all, data is the foundation of data-driven marketing.