As we become accustomed to a real-time, data rich world the existing enterprise applications have to adapt — including customer relationship management software. Here are some thoughts on making it better.
When I joined Salesforce.com in 2002, the question was, why isn’t enterprise softwareas easy to use as Amazon.com? That simple idea transformed an industry and gave rise to a $24 billion dollar business. With enterprise cloud computing, the technical barriers to CRM adoption were overcome, providing a clear path to CRM success.
A little more than ten years later, I believe we are on the precipice of another disruptive shift. The question we are asking today is, why aren’t companies able to operate with the same data-driven intelligence as an Amazon? As I see it, there are at least two major obstacles holding us back.
- The first obstacle is that CRM databases start empty. When you start your trial there are a couple records of demo data, but really it is up to you to fill it up. Sure there are add-on products where you can pay-per-record to import data. But that is a very different experience from joining LinkedIn, where the database starts full. If you’re a first time user of LinkedIn and you do a search for “VP of Sales, Bay Area” it returns over 15,000 results! Not only that, but data quality is way better than anything you could maintain on your own.
- The second obstacle is that CRM isn’t very intelligent. It does a terrific job of surfacing data through filter views, but that approach starts to break down after you reach five or more criteria. Picture a filtered view that uses “greater than,” “less than,” “starts with,” “includes,” “does not contain,” etc. It gets messy. And think about all the new data that’s being collected — social interactions, web analytics, connected devices, and so on. There is just too much data to make sense of with our existing tools.
Every business has data. Let’s use it.
It’s time to re-imagine how businesses operate based on data. I’d argue that this is the next disruptive shift for CRM. Think about it. How much energy do you spend on prospects who don’t convert? Half of your energy? Maybe more? In a world where we have tons of data that seems crazy.
We should be able to accurately predict winning outcomes. Up until recently the excuse has been “I’m not like Amazon.com or Google, I don’t have a team of computer scientists to tackle these kinds of projects.” But we’re entering an arms race powered by data. If your competitor finds a way to increase win rates or conversion by 100 percent, you’ve simply got to keep up.
Here’s how to shift
The next generation of CRM begins with the premise that the database starts full. It should constantly uncover everything it can about your prospects and customers. It should monitor website updates, news, public filings, posts to social networks, technology vendors, job openings and new hires. This is data you could piece together on your own, but it is far more efficient to tap into a provider that solves the problem end-to-end — everything from crawling the web, to striking data deals, to molding the huge corpus of insight into something actionable.
Just adding hundreds of additional fields to a contact record isn’t of much value. Instead it should extract insight using machine learning. Just as Waze routes the fastest path through traffic, companies need apps to tell their employees where to focus their energy. At any given time, don’t you want your CRM system to help you understand which prospects are most likely to convert? And which are going to have the biggest revenue impact?
Intelligence vs. Automation?
It remains to be seen who will be the winners and losers in this next chapter. Sales and marketing automation have done a terrific job of structuring the funnel (from leads to prospects to customers) and capturing transactional data along the way. But, when it comes to turning that information into insight, there is a strong case to be made that intelligence will be a parallel track to automation. As David Rabb says in his white paper, it will be “a separate integration layer that connects to the execution system without replacing it.”
This will allow companies to move quickly, without disrupting existing workflows. It also lets you choose the “smartest” intelligence provider for your business. Depending on your business model and target customers, you may find that one vendor has better data coverage or better algorithms to meet your needs. Or it might be like credit scores where certain providers can uncover the right answer for a very specific problem in your industry.
Any way you look at it though, business will boom. With the democratization of predictive intelligence, we’ll see huge productivity improvements. And just like the cycle of the past decade, those businesses that are first to leverage these services will emerge as victors.
Jamie Grenney recently joined Infer as the VP of Marketing and can be found at@jamiegrenney on Twitter.