A data-driven approach to account prioritization can help you use your time effectively to hit - and exceed - your quota.
In enterprise sales a crucial – and often underrated – skill for professionals is time management. Time is our most valuable resource, but the frantic pace of everyday life means it’s easy to forget that.
I hear again and again from sales professionals how busy they are. There’s endless work to be done, pursuing new opportunities or getting the next deal across the line. There are never enough hours in the day to qualify and follow up every lead and opportunity. Our sales team at Databook have experienced this at first hand.
That’s why we see the most intelligent sales organizations take a data-driven approach towards prioritizing accounts and deals.
In this post, I’ll explain a framework for using data to prioritizing accounts and opportunities. This approach can improve sales productivity at both an individual and organizational level, by helping sales professionals optimize where they spend their time.
But first, let’s look at the approach many sales teams take today.
Salesforce’s State of Sales report found that 50% of sales reps are still relying, at least in part, on intuition, gut feeling and educated guesses when forecasting sales, and I suspect the figures are similar when prioritizing leads or opportunities.
Most account executives use a number of sources when deciding which accounts to focus on in the quarter ahead. It is mostly intuition and gut-feel:
In my view, there are a couple of big problems with this approach.
First, the companies with the biggest revenues may not be the ones who have the strongest need for your offerings. If, for example, your solutions help a company cut their sales and marketing costs, you should be looking to identify the companies who have challenges there.
Secondly, unless you’re extremely well connected, many of your contacts are likely to be in the middle of the org chart. But any decisions about a company’s strategy and major investments are taken in the C-suite, so your contacts may not be fully aware of where the company’s strategic priorities really lie.
Lastly, the knowledge passed on from a previous rep or the news articles that pop up first for your customer may not be up to date. The annual turnover of management and fast pace of digital transformation leads to quickly changing priorities at many companies.
Leading companies are already moving to a data-driven approach that focuses on pipeline quality, rather than quantity.
At Databook, we’ve developed a data-driven approach to prioritizing accounts, taking into consideration each company’s financial situation and their strategic intent.
The idea is to find a handful of high-value, high-probability opportunities to focus on for the typical six-to-nine month enterprise sales cycle. This will mean ignoring some lower-weighted-value opportunities, but should give you a better chance overall of meeting or exceeding quota.
Here’s a framework for prioritizing your accounts.
1. Understand management intent at your accounts
Large enterprise sales deals need to be signed off by the C-suite, and that will only happen if these deals align with the company’s strategic goals.
To find out what the C-suite are thinking, you need to look through investor presentations, transcripts of earnings calls with investors and annual reports, and read what executives are saying. This research takes time and not all the data and insights are easily accessible.
We’ve designed the Databook sales intelligence platform to make this analysis painless, by carrying out a quantitative analysis of these documents, to see how often companies are talking about topics that relate to your solutions. This score can be used to prioritize the accounts where management intent is most aligned to your offerings.
2. Identify whether your accounts have a financial case for change
For all your accounts, analyse where the strengths and weaknesses of their business lie. Look at key metrics, such as revenue growth, selling, general & administrative expenses, and gross margins to understand whether your accounts are under-performing or outperforming competitors.
This information can be developed by analyzing investor documents such as annual reports or 10-Ks, but it can take a few days per company if you have the time. An alternative is to subscribe to Databook, connect your CRM, and Databook will do all this analysis automatically for your accounts.
Those companies whose financial weaknesses match where your solutions have the most impact are likely to be stronger prospects, so you can use this intelligence in your outbound marketing, introductory meetings and when building the business case.
A shift to a data-driven approach can appear daunting at first, but it soon becomes a simple beginning-of-quarter routine. It’s important to continuously prospect as there are so many changes happening with your customers that it can be hard to keep track. Failing to stay up to date, means you could be leaving money on the table.
Once you have prioritized your accounts, the next step is to find the right buyer. If your solution adds value to the target organization, then aim high for the decision maker who would feel the most pain from failing to act. For example, if you sell solutions that impact pricing in the retail industry, look for weak gross margins. Retailers typically have Chief Merchandising Officers or Chief Supply Chain Officers who would be the most likely to be interested in hearing from you.
At Databook, we see this as part of an approach to sales that mixes strategic thinking with execution. We’ve also observed that having the data to back up your opinions when selling has been a valuable source of confidence for both our team and our customers.
If you’d like to discuss in more detail how a data-driven approach could help you target the right accounts – either at an individual or organizational level – please get in touch.
Anand Shah is CEO of Databook, the sales-intelligence platform that supports scalable precision selling to the line of business. Find out more at www.trydatabook.com
Photo by Erik Witsoe on Unsplash