[Editor’s note: This is a guest contribution by Jamie Grenney, VP of Marketing at Infer. The post went through Pando’s usual editorial process, and Mr. Schroeder was not paid for his work.]
When I joined Salesforce.com in 2002, the question we were trying to answer was “Why isn’t all enterprise software as easy to use as Amazon.com?” That simple idea gave rise to a billion dollar business.
The cloud-computing model was so disruptive because it dramatically reduced the risk and lowered the total cost of ownership for software. For the first time, companies of all sizes were able to successfully adopt CRM systems. Today I believe we are on the precipice of another disruptive shift. One that is going to unfold quickly and unlock huge productivity gains for companies.
The next shift in business software will inject predictive tools into every business. The question we should be asking today is, “Why can’t every business operate with the same data driven intelligence as a Google or Amazon.com?” We need applications to filter out the noise and automatically optimize the allocation of sales and marketing resources.
This is necessary now more than ever because of two big trends that are shaping sales and marketing today:
1. More Channels. The first is that we have many more channels to account for. It used to be that a 360-degree view just meant you were logging phone calls and emails. But today there is much, much more that you can know about your customer. Unfortunately, most companies don’t have a framework to collect all this data and make it useful. There is no way to cram it into custom fields and related lists. It’s just too much noise.
2. More volume. The other related challenge that is that we’ve also got way more volume to contend with. Whether they’re coming in through content marketing, freemium products, mobile apps, or social advertising, chances are you’ve got a ton of top-of-the-funnel sales leads.
It’s no wonder that the Marketing Automation space has been hot. To cope with the proliferation of channels and surge in volume, companies are looking to improve lead management and get more value out of their nurtural database. But automation alone is not enough. Many B2B companies are taking their cues from consumer products that are hitting home runs with predictive technologies.
Personal Predictive Apps are Winning Big with Consumers
We’re all seeing visible examples of consumer apps solving similar challenges. The successful ones have been able to keep up with the explosion of data and use it to their advantage. For example, without any configuration, Gmail filters out spam and flags promotional emails. It learns from your behavior and figures out which emails belong in your priority inbox. Amazon is another example. Some estimates say that 20 to 30 percent of its revenue is coming from the recommendation engine. That’s pretty amazing, but it makes sense if only 15 percent of site visitors come with clear buying intent.
And finally, there is Waze. It maps the fastest way from A to B by taking into account thousands of real-time predictive signals. It proactively spots trouble ahead and tells me how to outsmart traffic. While virtually every hot new consumer app is using predictive, most B2B companies aren’t.
Clearly the challenge holding companies back is that predictive is very, very hard. B2B companies are used to making decisions based only upon the limited data that is easily accessible – that’s how they’ve been operating for decades. And they’re daunted by the idea of hiring data scientists, setting up a Hadoop stack, crawling the Web for external signals, building machine learning models, and plugging it all back into existing production systems. That’s why very few companies have gotten it right so far.
The Time for Predictive Business is Now
I’d argue that it’s time to finally embrace a predictive approach within businesses. There is a new generation of startups emerging whose mission is to solve this problem end-to-end and deliver predictive as a service. By plugging into these third-party services, B2B companies can take advantage of their rapid time-to-value, minimal risks, and significantly lower total cost of ownership. These solutions are within reach for average companies and they’re getting better all the time.
Few companies realize how big of an impact predictive intelligence can have on their top-line, or the ease with which we can now automate basic decisions. It doesn’t have to mean building a huge data team or forcing employees to adjust to a complex new piece of software. By pinpointing specific questions to answer first, and finding the right partner to integrate into current workflows, any company can re-imagine the way it operates. Those that inject predictive into their business will have a competitive advantage over those stuck using the old paradigm.