How Will the Sales Force of 2020 Engage Buyers?August 6, 2013 by Derek Singleton
The first wave of sales force automation technologies focused on improving efficiency. Systems such as Siebel and Salesforce helped sales teams organize prospect data, track sales and marketing interactions, and more accurately forecast sales revenue. While these efficiency gains helped sales management better manage their teams’ performance, they didn’t always help salespeople sell more effectively.
With the emergence of big data and predictive analytics, we’re on the cusp of a major shift in how salespeople connect with customers. As these technologies develop and mature, they will help salespeople surface relevant prospect insights and information to sell more effectively. Here are three predictions on how sales technology will change the way the sales force of the future operates.
1. Predictive analytics will improve buyer engagement.
According to CSO Insights, salespeople spend only 40 percent of their time actually selling—the rest is spent looking for new leads, performing administrative tasks and combing through various systems for prospect data. This data can help salespeople identify the best way to reach out to a buyer or move a sales cycle forward, but searching for it can be particularly time-consuming.
AJ Gandhi, vice president of customer solutions at Lattice Engines, says that he routinely sees “salespeople spending 20 to 25 percent of their time researching prospect information,” digging through as many as 15 different data sources, such as their ERP and CRM systems, LinkedIn, Facebook and search engines.
Anneke Seley, founder at Reality Works Group, believes that as technologies become more adept at finding actionable insights in things like social data and company news, they will “help salespeople connect with and engage with buyers in more relevant ways that are timely, as opposed to helping [salespeople] pitch in high volume like we saw with more rudimentary technologies such as email.”
Today, finding a way to make a timely connection can be a fairly manual process. For instance, Seley–who helps sales executives improve their sales teams–monitors her LinkedIn connections to see when someone in her network starts a new role or moves to a different company. Why? People transitioning sales roles are often open to considering a new way of selling, which signals to Seley that this is “a great moment in time to reach out and talk about where sales 2.0 is going” and offer her services.
In the future, predictive analytics tools could automate processes like this to uncover important pieces of information and, most importantly, recommend a plan of action to capitalize on it. For example, a sales automation system might automatically alert a salesperson to a prospect’s job change. It could also mine other data sources to see that several other individuals have recently joined the company in similar roles–an indicator of growth–and suggest things the salesperson could offer to help manage that growth.
As another example, software could scour the Internet for company news and alert a salesperson to recent acquisitions by a prospect’s company. It could then recommend products or services to the salesperson that might now be of interest to the company given the strategic direction the acquisitions suggest.
These technologies can also make it easier to understand which deals are most likely to close. With a salesperson who owns 200 accounts, for instance, Gandhi says “predictive analytics can crunch all of the data related to these accounts, and—based on previous customer buying history, customer interactions with the company’s website, etc.—predict the top 10 accounts that are most likely to close.” With this information, a salesperson would know on which accounts to focus their energy. And, according to Gandhi, “salespeople with this information at their disposal tend to sell 10 to 20 percent more [than their peers].”
2. Voice technologies will map a buyer's personality type.
Ken Krogue, president at InsideSales.com, believes that voice recognition and analysis technologies will be used by remote sales organizations to identify the personality type of their prospects. Salespeople will then be able to use this information to adapt their selling technique to match the personality type of the prospect.
Think of it as Myers-Briggs personality testing meets Siri: voice recognition technologies would “listen” to a prospect over the phone to analyze their voice data, looking at their voice print, intonation, inflection and myriad other patterns. The technology would then look for correlations between these patterns and known personality types to predict which type the prospect is. Krogue says the technology “would alert the salesperson of the personality type of their prospect with a pop up on their screen so they quickly understand the type of person they’re dealing with.”
It could then trigger a workflow screen that tells the salesperson how to cater their pitch, with personalized selling tips along the way to help conflicting personality types better connect. For example, Krogue says that “dominant personality types generally do not sell very well to an amiable prospect.” With the information provided by this technology, however, a dominant salesperson could adjust their sales pitch to focus more on building the relationship with the prospect rather than moving quickly toward closing the sale.
While this may sound like something out of 2001: A Space Odyssey, it may not be as far-fetched as it seems. Seven years ago, the first iPhone hadn’t yet made its debut. Today, Siri can tell you the nearest place to grab a latte. And Google knows that I’m looking for the movie "Sideways" when I search for “that film where those two guys drink wine.”
Of course, our personalities are much more complex than a string of queries, but search engines and natural language technologies are getting better all the time. It might not be long before they’re able to understand our personalities as well, and when they can, selling by phone will become that much more refined.
3. Big data will augment cross-selling and upselling.
Many sales departments can increase revenue simply by cross-selling or upselling to their existing customers. The challenge here is knowing how to choose the right product to cross-sell or upsell a customer on.
As Gandhi says, “Many companies have 30 to 50 products that they can sell any customer on. If you consider that the average salesperson carries 20 to 100 accounts, you suddenly have 600 to 1,000 different choices a salesperson can make on who to call and what to sell them on.”
In the future, big data will help salespeople figure out what to cross-sell or upsell by determining which offer has the highest likelihood of acceptance by a particular customer. Or, if an existing customer recently viewed a complementary product on your site, then the data might suggest that the salesperson reach out to tell the customer more about the product.
Wells Fargo is a great example of an organization that realized significant results from amplifying their cross-selling strategy. To cross-sell customers, Wells Fargo uses in-house technology to monitor how many of their products–and which specific products–each customer uses. Based on things like the customer’s account balance, life events and transaction history, they can predict which product their customer is most likely to need next. The result: the average customer now uses more Wells Fargo products than ever before.
Source: Wells Fargo
Wells Fargo also now sells more financial products to each of its retail customers–an average of 6.1 products in 2013, up from 5.9 in 2012–than any other financial institution. Their proficiency in cross-selling has helped them increase profitability even as revenue growth has slowed.
As big data technologies become more sophisticated, Wells Fargo and others could look at even more customer data sources to not only improve their choices around what to pitch, but also how they pitch it. While most sales calls are still based off some kind of script, big data technology could be used to dynamically update a salesperson’s calling script to let them know how their product can address the question they just asked on Quora or the Tweet they just shared.
As these technologies become more efficient and effective, salespeople will be able to make more genuine and meaningful connections than they could previously. The key with the evolution of sales technologies will be ensuring that these technologies build on the efficiency gains of previous sales tools to make salespeople more effective.