For sales teams, the data value chain has never been more promising than it is right now. As your prospects go about their day, interacting with websites, smartphones and the social web, the behavioral data profile about them continues to grow. It’s possible to know more than ever about their product preferences, usage, interactions, peer group, opinions, budget and pain points than ever. Personas are still helpful to the extent that sales managers are able to create playbooks for specific types and buying stages. However, contextual sales data about prospects gives us the opportunity to go far beyond personas and truly personalize each interaction.
The question is, how do sales teams do this without sacrificing productivity? And for that matter, can tailored pieces of data be delivered in context so that workers make smarter decisions during the course of their workflow?
The means with which to do this has been evolving right under our noses. With the right tools and strategy, sales and marketing teams can work together to build a data value chain that can deliver the data you need when you need it most.
Traditionally, business data has been something that is analyzed periodically – weekly, quarterly or monthly, to help companies make important decisions. Frankly, some data is still more useful this way. For example, Wall Street and the film industry are just two examples where referencing long-term data spanning years or decades is used as a reference point for today’s investments. Knowledge is gained by looking at long-term trends in response to specific market forces and buyers.
Contextual data is that which is delivered to the right person, at the right time, within an actionable context. For example, the date of your cousin’s birthday is posted on her Facebook page all year long, but that doesn’t mean you’ll remember or even see it. Think how much more useful that data becomes when Facebook automatically alerts you to the fact that it’s her birthday.
This is even truer for inside sales agents, who now spend, by some estimates, up to 24% of their time researching to make outbound phone calls. That may seem like a lot of time, but consider the laboriousness of the process. First, assuming that a sales rep isn’t simply working down a call sheet, the sales rep has to decide who to contact. That requires a priority analysis of leads, contacts and opportunities that may or may not be assisted by a lead scoring solution. The sales agent may dig into CRM to see past communications history, including communications – calls, emails or meeting logs – with other agents. Often, that history is not complete. The sales agent may reach out to colleagues to ensure they did not engage in phone calls or emails that were not logged in the CRM.
If using marketing automation software, the agent may check to see whether the prospect has recently interacted in a formal way with the company, such as a webinar, free trial, demo request or whitepaper download.
Now satisfied with what is known internally, agents practicing social sales techniques will scan company news. After all, bad news that is public may highlight pain points that play right into the sales agent’s hands, while good news – such as closing big customers or a big round of funding – may mean that the prospect has ample budget.
Finally, there’s social media. According to Nielsen, approximately 46% of online users count on social media when making a purchase. Customers use social channels to communicate needs for products, recommend products to colleagues and vent when they’re dissatisfied with a brand. This is all data that can be collected by companies and, with the right tools, turned into actionable insight. A Hubspot Study showed that social media lead conversion rates are 13% higher than average lead conversion rates. Sales agents who can leverage social data can therefore gain an advantage over those who cannot. However, a lot of sales reps haven’t integrated social data into their sales process yet because they’re not quite sure how.
For these reasons and others, the most successful sales agents will look up the prospect on LinkedIn, Twitter and other social media sites. What are they interested in? What’s on their mind? Are they discussing any relevant pain points right now?
Obviously, all these different data points serve to create a rich profile of each prospect, and possibly some sense of their buying stage (if any). However, it takes a great deal of time to amass, impacting productivity. More importantly, every single one of these items is data that is best viewed in the context of the sales agent’s workflow.
This type of behavioral data needs to be made available to agents within the context of their workflow. Currently, it falls into three broad categories. The first category, prioritization, is primarily achieved through lead scoring. The second category, urgency, is most often handled through marketing automation solutions that use, in part, lead scores based on specific behaviors to help determine when sales agents are ready for specific buying stages. The newest and most powerful type of behavioral data can be described as opportunity data, and encompasses social, collaborative team feedback, real-time marketing campaign data and other data types. This is potentially the most powerful, since it can show sales agents what prospects want (desire) and what to sell to them (action).
That’s an introduction to contextual data. In my next post, we’ll cover these three categories in detail and discuss tools that can make them useful in context.
Howard Brown is a three-time entrepreneur with a proven track record of success and innovation in marketing, sales, and cloud computing. Thanks to his study and practice of clinical psychology as a marriage and family therapist, he brings a unique perspective to the technology companies he has created. With his newest venture, RingDNA, Howard has combined his passion for the science of conversation with his expertise in revenue performance optimization. RingDNA is poised to transform the sales industry through integrated communications, data science, user-centric design, and optimized workflow.