B2B Sales Analytics – Build Them to Benefit Your Sales Team!

B2B sales is a difficult, brutal job. Leading and managing a B2B sales team can be equally challenging, but good analytics—built to benefit your sales team—can help your team become more effective.

Does your sales team have the tools they need to prioritize and organize their sales efforts? Are leads falling through the cracks? Is your team working on the right accounts? Can they monitor on a daily basis how close they are to hitting their quota?

While most companies have an abundance of sales data, many are not able to answer these types of questions because they are not embracing sales analytics as the foundation for sales planning and performance management.

When implemented well, sales analytics will make your team stronger, improve forecasting, and speed up decision-making.

Customer Spotlight: Anheuser-Busch InBev leaders are working smarter with new sales analytics solution

Getting Started with Sales Analytics:

1. Put your sales team in the center of your analytics efforts
   Most importantly, your sales analytics should be used to help your sales team—not just as a hammer during pipeline reviews. To help your sales team, get into the shoes of a B2B salesperson and really understand their pain points and needs before you build a solution.

2. Ensure your CRM system is the right tool for your sales team
   You need a CRM system implemented with the attitude that it is a tool for the sales team. It needs to be easy to use and helpful with their daily work. It should be instilled that the CRM be used religiously—that all opportunities, contacts, and accounts are entered correctly and consistently. (CRM data is notoriously dirty, but that’s a story for a different day.)
3. Enable sales reporting with a modern analytics platform
   CRM data is the core of B2B sales analytics, and while CRM systems contain built-in reporting, they lack the kinds of analytics you need to make meaningful decisions. CRM data needs to be extracted, cleaned, and put into an optimal format for reporting. A modern analytics platform, at a minimum, should include a relational database, business intelligence (BI) front-end, and an extract-transform-load (ETL) tool. While most CRM platforms have rudimentary BI and ETL functionality that can be part of a solution, they are not up to the task of real sales analytics.
4. Enhance your core customer CRM data
   Once you have created a core set of useful interactive sales dashboard and reports, you can enrich the data with additional sources—data from marketing automation systems, financial/ERP systems, and external data—to give you a real customer/prospect 360 view.
5. Integrate your sales analytics with your CRM system
   To increase user adoption, embed your sales analytics into your CRM system so your sales reps and management have a single experience whether managing their daily sales activities or doing sales analysis.

Once you have a BI tool in place that is driven by your sales teams’ needs, you can start digging into your sales data. There are a million ways to slice and dice this data, but we recommend focusing on the following five kinds of B2B sales analytics:

5 Kinds of B2B Sales Analytics to Focus On 

1. Opportunity Tracking
   The focus of B2B sales are the opportunities, or potential deals, that your reps are working. A page or tab on your analytics dashboard that focuses on opportunities will be one of the most used areas of a B2B sales analytics system and will become the focus of pipeline reviews. Build a straightforward sortable list of opportunities (won, lost, and open) with the most important details about those opportunities easily visible. Allow filtering by rep, time, status, or any other relevant category. And each opportunity should link directly to the opportunity in your CRM system.

2. Account Summary & Lifecycles
   For sales teams, the goal of “Always Be Closing,” is a great one, but in reality, sales reps need to “Always be Qualifying” and figuring out if what they are working on is likely to lead to a sale. Picking accounts is where that starts. You need an easy way of looking at all your accounts and determining which need attention. This is nearly impossible if you rely only on your CRM software and the built-in reporting that comes with it. There are other factors and data outside of your CRM that should be considered, such as where leads and accounts are engaging with you online, offline, or within marketing campaigns, their location, their industry, and your own history with those accounts. Sales analytics should also be used to expose potential clients. The key is doing the analysis at the account-level, and not the contact- or opportunity-levels. Visualization or machine learning techniques can help you separate the wheat from the chaff when looking at which of the thousands of potential accounts you reps should spend time on. For those reps that “farm” existing accounts, additional data is needed for analysis, such as your financial relationship and support interactions with them. The more information you can make available to your reps or account managers, the better equipped they will be to serve those clients and enrich the relationship.

3. Campaign Analysis
   Whether a call campaign, a marketing campaign like a trade show or webinar, or even a passive campaign like an ad campaign, these more targeted efforts should be tracked and analyzed. Though this crosses heavily into marketing, the analysis you do on your campaigns can be very valuable to your sales team. Expose this information to the sales team so they can 1) focus sales efforts on successful campaign members and 2) be part of the conversation on what campaigns are working. In order to get as much value as possible from campaign analysis, you’ll need data from your CRM system plus your marketing automation systems (like Marketo, Hubspot, or Pardot), your website, Google Analytics, LinkedIn, and others.

4. Sales Rep Performance & Quota Tracking
   Whether you pay on monthly, quarterly, or annual targets, your reps will want to know on a daily basis how much more they need until they hit their numbers or get into “accelerator” territory. Giving them this information will encourage them to sell more and provide transparency that will build trust between your management and your sales staff. If you pay on bookings, you may only need data from your CRM system. If you pay on invoice or on actual cash receivables, you’ll need to hook into your financial system to provide the necessary data. In addition to tracking sales, you should be tracking call/meeting/email activity. Tracking activity can give an early indicator of actual sales. It can also help understand the lifecycle of your clients so your reps (especially new ones) can get a feel for how much effort it takes to get a sale.

5. Pipeline Accuracy
   One critically important piece that is often missing from sales analytics solutions is an analysis on pipeline accuracy. Three components make up pipeline accuracy: projected deal size, projected close date, and the likelihood of closing. This sounds easy, but when you realize that these variables change constantly, you realize that today’s pipeline does not tell you what the pipeline was yesterday, a week, or month ago. You need a daily snapshot of your pipeline, but CRM tools don’t do this for you. Sales organizations feel the pains of “sandbagging” where closed deals show up out of nowhere, “grandbagging” where projected magnificent sales never materialize, and inaccurate projected close dates. When this is exposed through data, reps can work to improve. Pipeline accuracy allows sales leaders to better project sales, which enables better financial forecasting. Accurate financial forecasting is critical for organizations to make big decisions and to secure capital to grow.

Sales Analytics Must Be Backed by Leadership

Great analytics can make a huge impact on your sales effectiveness, but it requires support by leadership. Your organization needs buy-in from leadership to make data-driven decisions, and leaders must instill discipline among sales teams to use and track data. They must also be committed to investing in a strong data infrastructure and the tools needed for better sales analysis.