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Summary:

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.

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When I joined Salesforce.com in 2002, the question was, why isn’t enterprise software as 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.

  1. 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.
  2. 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.

  1. This post is quite similar in concept to a TechCrunch piece from April ’12, “The Rise of Full-Box CRM.” http://techcrunch.com/2012/04/07/the-rise-of-full-box-crm/

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  2. Very interesting read. There’s a bigger trend than brought up here, which is a blurring of the line between CRM and Marketing. Marketing provides a great deal of data that allows CRM to start non-empty and the CRM systems of the future will allow for end-to-end analytics that make the sales organization far smarter than today.

    Jeanne Roue-Taylor (full disclosure, my spouse) wrote about the blurring here: http://successfulworkplace.com/2013/03/01/blurring-of-the-marketingcrm-line/

    The CRM systems of the not-too-distant future will be real-time and data rich, sure, but more than that, they’ll be ‘first contact to next contact’.

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  3. Worth it reading and indeed agreed to many of the points such as usability of client’s end data for prospecting.

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  4. I’ll take it a step further and say that with the advent of technologies such as in-memory computing that gives rise to the merging of OLAP and OLTP databases into one database on appliances that are tuned specific to a function. All the lines will be blurred. The great divide of structured and unstructured data will also be less so. Alas, in the end what will truly bring all this to real value is a human issue. How to ask the right questions. The intelligence does not come from machines or software. They are created by human beings that instruct the machine. And somewhere along the way, as with human to human communication, some of it gets lost in translation (or in context). And solving business problems will really become more like corporate therapy but instead of probing questions and image therapy, it’ll be correlated SQL queries distributed over multiple thread across multiple cores sitting in multiple CPUs in CPUs stacked in racks and racks of climate controlled iron and wire. Dashboards and 3-D moving dashboards provide the imagery of a changing business. It’ll be a world partly predicted by Isaac Asimov as Marketeers add the element of social psychology and collective cognitive behavior to the mix of tradition Data Warehouses and data marts. But as in coaching and therapy, it’s the art of the question (along with a clever framing) that will prove most effective.

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    1. Tat — you are confusing technology with solutions. Yes, many things will be possible with better processing and data analysis capabilities, but the focus should be on solving business problems not just being able to slice-and-dice data.

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      1. I don’t think he is. Coz he is clearly delineating the capability that technology will offer from the human intelligence that is required in exploring it. Asking the right question is exactly the same as solving the business problem

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  5. Harold Templeton, CFP®, EA, RTRP Tuesday, September 3, 2013

    Doesn’t “CRM” stand for “Client (or customer) Relationship Management?” Why would I want thousands or millions of people in my CRM if they’re not a client? Yes, it would be nice to have access to a vast database of people that already includes my clients so I don’t have to do manual entry but this article sounds more like a change from Client Relationship Management to Client Research Management.

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    1. CRM is not only about managing your existing customer relationships, but also your future ones.

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  6. Big challenge created by Cloud is the Big Data it generates. Marketing and Sales will need tools to trawl this and turn it into meaningful information to support and perhaps to automatically make decisions. There are some good tools that provide point solutions out there – Hubspot Signals, Salesloft, Nimble CRM to name just 3.

    Whichever company can dominate a market niche or sector in the sales/marketing app area will have a huge marketing advantage, as Microsoft did for personal productivity, SAGE did for accountancy, Salesforce.com for enterprise Sales and Apple (maybe).

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  7. Great article! You are definitely capturing the problem statement.

    That said, I agree with Mr. Templeton and Mr. Taylor who are both conveying different aspects of the thought. CRM, should be about demand management (ie Pipeline and prospect). Marketing Automation is about demand capture. The challenge is how dow we qualify all the data points (information), translate this into demand, and then capture that demand in the form of a qualified prospect in our CRM tool.

    As a VP of sales and marketing this is my “holy grail” and as a seller of integration services the topic of many a conversation with clients.

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  8. I believe the ability to use automation to self populate your CRM will hands down be the game changer over the next few years. Import your current client data and let the system go to work building your sales arsenal. It ‘s a matter of the one with the most intel wins. It will come down to of course on how you use that data and your ability to act on it but overall it will only stand to improve your chances to convert.

    Jim Williams
    http://www.salesprocrm.com

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  9. Umberto Milletti Tuesday, September 3, 2013

    Excellent piece, and I’m in complete agreement:
    Automation needs Intelligence

    With CRM Intelligence, companies increase their CRM ROI very substantially (we see this over and over again in our customer base, just one example is Rosetta Stone http://www.insideview.com/resources/CaseStudy-Rosetta-Stone.pdf)

    We are just at the beginning of the next wave of enterprise applications: intelligence applications.

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  10. I have a few comments on this.

    First, CRM is becoming Customer Relationship Marketing. Using a CRM as a giant Roladex is worthless. Unless you are uncovering ways to market (read sell) to clients it’s just data.

    Second, the system should be used to automate those things that strengthen relationships. It should know important sales and soft info about clients and send them relevant info automatically.

    And finally, I constantly hear that “we have lots of data but I can’t trust it”. So what good is mechanics if the data stinks? Companies need to constantly update their data and have true governance in place to make the data worth using.

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    1. Yes! I agree completely with your comment about the importance of data quality. As for what CRM is (or is becoming), it’s really about managing the entire “customer” (today’s and tomorrow’s) lifecycle: marketing, selling, and servicing.

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