The Ultimate 4 Step Guide to Operationalize Your Customer Data
Operationalizing data is at the heart of how revenue operations empower your business. But, to fully operationalize your data to get actionable insights, you must first understand your data as well as what you want to get out of it. Start by exploring the data you have, look for errors and inconsistencies, and identify gaps in your data.
Understand Your Data and Business Needs
What insights does your business need?
Sales operations teams are often chartered with pulling data from various sources to draw out insights that help optimize the business. However, they are often overwhelmed with too much data and underwhelmed by not enough insights.
Start by outlining your strategic objectives, the vital sales questions, and the sales outcomes you’re trying to achieve. What are the critical decisions that your sales reps need to work through during a customer conversation? What information do they need to achieve their quota? Identify your most important sales metrics that reflect your strategic priorities as a sales team, and then break down what data you need to measure those metrics.
What data do you have?
Next, determine where your data lives to measure the metrics important to your sales team. Your customer data may be stored across several sales, marketing, customer success, or CRM applications. Or, it may be housed in a cloud database, a data warehouse like Snowflake, Redshift, or Google Big Query. Often, for earlier stage companies, the data sits in Google Sheets or Excel. You may also be accessing 3rd party data sources like Zoominfo, LinkedIn Sales Navigator, Clearbit, or Bombora to maintain up-to-date contact and account information. Knowing where to find your customer data will allow you to better handle what you already have and how to best leverage it.
How good is your data?
Next, look for connections and duplication in data across the different data sources. Do various sources have data relevant to the same account? Is there duplicate information across sources? If so, is the information consistent? Fixing data quality and defining how to connect multiple data sources to form a coherent view of the customer will determine what insights you can expect to get out of your data.
What is the source of truth for your data?
Identify your “source of truth” for customer data. Often, customer data sits in different spreadsheets, applications, databases, and data warehouses, lacking clarity on which one is the source of truth of customer data. Which source is considered to be accurate? For most companies, especially with a B2B sales motion, this is their CRM application, such as Salesforce Sales Cloud, FreshSales, or LeadSquared. It is where the frontline sales representatives update customer information daily. It is also where sales operations first turn to pull data and generate reports or forecasts.
Build Sales Teams’ Trust in Customer Data
According to a recent Gartner’s State of Sales Operations Survey, only 47% of respondents believe their organizations had high-quality data, and 13% report their organizations’ overall data quality to be poor. Six key metrics assess the quality of data: accuracy, completeness, uniformity, uniqueness, timeliness, and security. Low-quality data is lacking in one or most of these areas. According to Gartner, here are the three top issues at the root of why companies suffer from low-quality data.
CRM Adoption and Discipline. Even the best tools are useless if they are not adopted and used correctly. Leadership needs to foster a culture of process and compliance. To succeed in this, companies need to create buy-in across all users. Users need to understand the tool’s benefits and how it helps them be successful in their roles.
Data Governance. Companies need to have a definition of data quality standards, and many do not. Data governance works to ensure that data is usable, accessible, and protected. Leaders need to understand why they use a CRM and what standards users must follow to meet these objectives.
Data Quality Inspection. Even with the best standards in place, data quality needs to be regularly measured and inspected. Data needs to be built into recurring conversations.
How do you Measure Data Quality?
To have high-quality data that your users can access and trust, it must meet six key attributes:
Accurate. Data needs to be precise and correct. If it is not, you cannot use it, or worse, it leads you to the wrong conclusions. If even just a portion of your data is inaccurate, it undermines all of your data. Once trust is lost in the accuracy of your data, it is difficult to regain.
Complete. It is common to have incomplete data. It can often occur when prospecting or after accounts move or make organizational changes. When a company experiences chronically incomplete data, it is frustrating for the user and becomes a huge problem. It is essential to identify what information is relevant and create a process for getting that information.
Uniform. Data that isn’t uniform can be confusing, frustrating, and unusable. Uniform data creates organizational and informational rules that allow it to be processed and used. It needs to have consistent formatting, scale, and correct fields. Being thoughtful before adding fields is critical as it can quickly get you into trouble.
Unique. Data needs to be unique. Having duplicate data is confusing, misleading, and makes it difficult to find correct data. When cleaning up duplicates, it’s critical to be careful to prevent being left with incomplete data. If duplicate data is being created, ignoring this problem will only lead to a much larger data cleanup project in the future.
Timely. Data needs to be timely to be accurate and actionable. Out-of-date information needs to be routinely updated for it to be useful. It is essential to be careful when doing bulk imports not to replace records with outdated data.
Secure. Security is of immense importance to any organization, and procedures must be in place to ensure that the right people have the proper access and privileges. As many companies work to democratize and increase their ability to operationalize data across the entire organization, security needs to be top of mind.
Build Confidence and Improve Data Quality
If you can’t access your data, you can’t use it. If you can’t trust your data, you shouldn’t use it. When there is a high level of skepticism, actions become based on opinion instead of evidence, resulting in missed opportunities and less revenue. To build confidence in your data, you need to improve your processes to prevent data quality issues and clean (or potentially purge) the data you have. Here are six practices that will help your organization improve the quality of data and restore user confidence.
Data Governance Policy. Data governance is a set of principles and practices that manage the availability, usability, integrity, and security of the data in enterprise systems to ensure high quality through the complete lifecycle of your data. It safeguards the trustworthiness and consistency of your data. These uniform practices are necessary, given that not everyone’s intuitive “right” way is the same. Creating a data governance policy promotes a company culture of data and understanding the importance of quality data.
Data Profiling. Data profiling is the process of reviewing source data, understanding structure, content, and interrelationships, and identifying potential for data projects. It is used to measure the integrity of your data. Automating data profiling and data quality alerts ensures consistency in data control and management.
Manage Incoming Data. Your data often comes from external sources, whether from other organizations, other departments, or third-party software. When receiving data from external sources, you cannot control the quality you are receiving. It is critical never to assume that incoming data is good and always profile and check it.
Data Matching. Data matching involves finding records that refer to the same entity. While this seems simple enough, your database does not understand that it means the same thing when you write something two different ways. For example, a person’s name may be spelled differently due to misunderstandings, typos, and nicknames, but your code reads them as unique entries. This is a common issue, but data matching techniques can identify duplicate records and match records across multiple data sources.
Data Quality Reporting. Implementing data quality KPIs will allow you to better track and measure the health of your data. Maintaining an issue log will document and highlight trends to guide follow-up preventative and data cleaning undertakings.
Master Data Management. An MDM framework ensures the uniformity, accuracy, stewardship, semantic consistency, and accountability of a company’s official shared master data. Master data establishes “the source of truth” for a company’s data. It is defined as the consistent and uniform set of identifiers that describe the core entities of the company. Data is often shared and used by multiple applications, opening the door for many opportunities to compromise the quality of your data. An MDM framework will create alignment and prevent the need for future data cleaning initiatives.
The 9 Most Important Tools to Operationalize Your Customer Data
Now that you understand the data you have and improve its quality, you are ready to operationalize your data! Updating systems and assessing the best tools for your team is an ongoing process. Having the right tools and using them to their total capacity is key to operationalizing your data and creating actionable insights. There is an abundance of sales tools that can feel overwhelming when building out your tech stack. Tech tools are often unsupported, underused, and provide a poor return on investment but have immense potential to increase performance and grow a business.
Customer Relationship Management. CRMs are arguably the essential tool in sales, and it is the core software that drives a company’s revenue. For most companies, it is or should be, the source of truth of customer data where sales teams and sales operations rely on staying connected with customers, streamlining processes, and increasing profitability. Salesforce and Microsoft Dynamics are considered leaders in sales automation, Hubspot and Marketo for marketing, Zendesk for customer support, and Gainsight for customer success.
Sales Forecasting. Sales forecasting software analyzes historical data and creates an expected sales report based on trends. It calculates the revenue a business can expect, often organized by account, territory, or salesperson. Tools like Clari compare actual sales to expected sales to determine if they match, and if not, why. It helps identify the method that will produce the most accurate forecast and directly integrate it with a CRM.
Product Pricing – CPQ (configure, price, quote). Product pricing software solutions define, manage, and analyze pricing strategies to configure optimal pricing strategies for a product or service. It tracks the impact of pricing strategies on profitability, improving win rates and margins and allowing for customized pricing. CPQs reduce pricing errors, speed up quote generation, reduce quote approval time, reduce the time from quote to close, and increase forecasting accuracy. Product pricing software integrates with CRM, ERP programs, and other business software.
Sales Enablement and Training. Sales enablement software provides a storehouse for sales and marketing content that allows sales reps to demonstrate product value to prospective customers, engage potential leads, and boost sales team performance. Tools like Seismic and Highspot track prospect and customer engagement with content and sales pitches. They ensure that marketing initiatives and sales are aligned. Sales enablement software is typically used in conjunction with a CRM.
Sales Compensation Management. Sales compensation software offers customization and administration of sales compensation plans, produces reports for sales performance analysis, and integrates with other sales, administration, and accounting software. Tools like Anaplan, Capterra, and recent entrant Spiff.com use customizable criteria such as role, tenure, and sale type to automate the accounting and administration of incentive and commission plans. Additionally, they analyze past earnings, forecasted revenue, and compensation calculations to plan for different compensation scenarios.
Sales Territory Planning and Management. Optimizing sales territory leads to increased sales, productivity, and performance. Sales territory mapping software identifies where customers and prospects are located, maps how customers are distributed across domains, and shows how parts are distributed across individuals in a sales team. It ensures equitable territories between sales reps, aligns territories to customer data, and allows rollout scale changes. Sales territory mapping software often integrates with a CRM.
Sales Engagement. Sales engagement software like Outreach and Salesloft streamline the sales process by integrating communication channels into a single platform; managing standardized sales content, automating sequences of tasks, communications, and workflows; and providing analytics and actionable insights into sales performance. It typically integrates with CRM, communication software, sales intelligence software, and sales enablement software.
Lead Management. Lead Management software such as Salesforce, MS Dynamics, and Zoho manage the sales pipeline by enabling the collection, tracking, and forecasting of sales leads. It helps organize leads, schedule follow-up contacts, and increase closure rates. Lead Management software often overlaps and integrates with CRM.
Business Intelligence (BI) Tools. Sales and revenue operations is a data-driven role at its heart. Sales operations tools like Domo, Tableau, Looker, and PowerBI commonly use BI analytics and dashboards to generate reports for executives and discover trends. Since these tools work with aggregated data, they are great for visualizing, planning, reporting, and forecasting. However, they are not great at driving actionability (aka revenue) and a new category of tools is now emerging that operationalize the analytics to drive actionability.
How do you Operationalize Customer Data?
Business intelligence tools are designed to aggregate customer data for visualization, planning, and forecasting. However, they are not designed to drive an operational cadence for every user. Sales and revenue operations teams have an amazing opportunity ahead to provide actionable insights to all users to increase productivity, conversion rates, upsell, cross-sell and renewals. Immersa is building a data automation platform to democratize data that drives revenue for sales teams. Designed as a no-code platform, it connects spreadsheets, sales applications, and data warehouses to automate the flow of data intelligence to your sales and service teams in their CRM of choice.
Meet our team today and find out how we can operationalize your customer data to accelerate revenue.
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