The accuracy of your go-to-market planning, forecasting, segmentation and ABM strategies depends on your organisation’s data management and validity.
Businesses use data to guide, assess and optimise their decision-making; consequently, revenue operators must establish a data-driven decision framework throughout the customer life cycle.
As Jeff Ignacio, Head of GTM Operations and Growth at Regrow Agriculture, shared:
“RevOps serves as a single source of truth; it should govern your GTM motion, and your GTM notion needs to impact your revenue. Data-driven insights and decision-making underpin these three notions.”
However, data is often managed independently across teams, and siloed data trapped in different tech stacks may damage its quality and accuracy.
In fact, 70% of revenue leaders aren’t confident in the accuracy of their CRM data.
This leaves the accuracy of your strategies to chance. Reps may waste valuable time wondering where to find the correct information about potential customers and try to navigate buying committees they know little about.
That’s why data hygiene is essential. It is the key to creating a unified data strategy for go-to-market teams to grow revenue.
Data hygiene is the process that refines and strengthens the value of the data in your CRM, ensuring your data is clean and reliable.
It continuously removes errors, inaccuracies and duplications. It helps to identify and update any missing information from multiple internal and external sources.
This includes implementing standardisation practices, validating data against established quality rules and handling any inefficiencies that come with manual data entry.
Data hygiene ensures data is clean, complete and consistent. Clean data is critical to any data management strategy.
Businesses make data-driven decisions every day, but poor data can result in up to a 12% loss of revenue.
Data becomes stale at a fast rate. Failing to regularly update and cleanse your data damages your ROI, as revenue operators risk building strategies and making decisions around stale, inaccurate or outdated data.
Limited visibility and gaps in your data can prevent teams from pulling the necessary insights, leading to incorrect reporting and blurring reliable predictions and strategic forecasting.
Bad or incomplete data can damage your understanding of your customer segments and lead personas and can skew your idea of your ICP.
This is crucial, as Jeff said:
“You have to start by looking at what segments matter.”
“Generally, how you segment could be based on demographic, firmographic or psychographic data, but data can be faulty or hard to come by. When you don’t have third-party enrichment tools that could provide you with the best in data for those segments, this can lead to some pitfalls.”
This can cause organisations to incorrectly segment their audiences and shift their focus to the wrong aspects of business, neglecting the ones that matter and are most likely to convert.
Poor data prevents teams from running customised and personalised customer experiences, damaging your ROI, conversion rates and customer satisfaction.
Data inaccuracies surrounding contact information, such as names, job titles and emails, can also harm outreach efforts and waste time.
Reps might research and contact prospects with the wrong information. With 75% of respondents losing company customers due to inadequate outreach, this is critical.
Before making changes to your database, you need to understand which data points are needed and which areas need improvement. Start by auditing your existing records and identifying all B2B data sources in your organisation.
Evaluate the internal systems which provide your company with customer information. This allows you to identify any inaccuracies, discrepancies or other problems.
It’s important to cross-reference with other sources or use automation tools to identify any issues quickly and accurately.
Not all data is created equal, and instead of being overwhelmed by vast amounts of data, focus on understanding what data truly matters to your business.
Take it from Sid Kumar, SVP of Revenue Operations, who said:
“It’s easy to get caught up in data for data’s sake, and more is not necessarily better in this context.”
“I think being targeted and focused about what you’re using your data for and how it’s helping you connect your go-to-market strategy with your customers is essential.”
Data often has a shelf life, and knowing when data is 70-80% accurate is usually enough to base healthy and conscious decisions on.
As Sid explained:
“You sometimes have to accept it’s never going to be perfect. There’s no such thing as ‘perfect’ data, and as soon as it becomes perfect, normally it’s outdated and stale.”
Your organisation must strike the correct balance of internal and external data. Internal data highlights your company’s customers, transactions and potential partners.
However, when it comes to prospecting, segmentation, and forecasting, these might not provide a complete and comprehensive enough view when defining your market. They can also include inaccuracies.
As Sid Kumar said:
“You tend to get more data as a company starts to become larger, but you’ve really got to find a balance between first-party and third-party data.”
“I think you’re naturally going to have a bigger mix of first-party data down in the lower end of the market, and then you’re going to have to compliment that with third-party data when you start to go upmarket.”
Refreshing and enriching your data with third-party providers is crucial. Third-party data is more accurate and less error-prone, making it more clean and current.
As contact information and the marketplace change, data decays quickly. This means that sales, marketing and CS reps risk sending messages to the wrong contacts.
Tools like Cognism can provide accurate phone numbers and enrich contact data instantly to clean up historical contact records.
You can automate data integration through API connectors, which link data sources to the business applications that need them.
This streamlines data provision and improves the timeliness of data delivery.
Human input error is inevitable and a primary cause of bad data. You can use tools and systems to automatically enrich, clean and validate data to ensure It is actionable and complete.
Cleansing systems can also help to remove any duplications.
In many organisations, it’s common for sales and marketing teams to operate in different systems and with their own data standards and formats.
That’s why creating a data hygiene culture across your teams is crucial.
Integrating databases and encouraging collaboration between GTM teams can ensure alignment on data accuracy and remove silos.
With clean data, teams can create more complete customer profiles and better comprehend their audiences and customer behaviour.
This can inform your GTM strategy as marketing and sales teams can generate relevant and refined customer segments and optimise the customer journey.
As Sid shared:
“You need to focus on how data is helping you connect your go-to-market strategy with your customers.
“You should start top-down and get a real perspective of your total addressable market. Then you can understand what segment of that total addressable market you feel you have the real right to win as a company.”
“You have to go and look at what data you need to understand the firmographics, demographics, technographics and everything about your prospects in that segment.”
“That way, you can understand where you’re going to orient your go-to-market motions around and get smarter about your prospects and existing customers.”
Bad data or a lack of data fields can damage your understanding of your ICP and your GTM effectiveness.
Clean and enriched data can give you a more accurate view and understanding of your audience, helping to define your ICP.
This can uncover insights that help understand your ideal customer’s unique needs, pain points, and buying behaviour.
It also enables a highly targeted and precise outbound strategy; teams can create personalised customer experiences at various stages of the selling process and execute successful ABM campaigns.
This improves engagement and customer satisfaction, amplifies lead generation and boosts conversion rates.
Data hygiene can help make prospecting more efficient for your sales team and increase win rates.
Bad intent data can lead to poor lead scoring decisions.
CRM enrichment and improved data accuracy can provide a more well-rounded and holistic view of each lead and their behavioural patterns.
This can help your RevOps team identify which prospects are most likely to close through predictive lead grading.
It helps prioritise efforts towards the most promising customers and ensures teams focus on the highest quality leads, optimising resources and improving conversion rates.
With access to the most correct and updated data on their prospects, organisations can assign leads to the best-suited representatives.
Different leads require different lead routing approaches and could be routed based on company size, location, or industry. Accurate data allows this to be done more efficiently and accurately.
This also improves your speed to lead and, ensuring that your potential customers get timely responses and your sales reps can capitalise on initial interest.
Data hygiene can help identify and target customers who are high-risk or have an opportunity for expansion.
This allows you to encourage your customer base to adopt more of your product, expanding their revenue.
It can also help companies provide more accurate responses to customer requests. Companies can respond more swiftly and accurately by having up-to-date information surrounding purchase history and contact details.
This reduces churn rates and increases customer lifetime value.
Data hygiene is also essential regarding B2B compliance and data privacy regulations. It ensures organisations remain compliant.
It helps protect companies from fraud and securely stores all personal information.
Looking for more insights? Our RevOps guide will help you establish a Single Source of Truth in your RevOps function!
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