How Predictive Analytics for Sales has Changed from 2011 to 2019
Today it’s March 18, 2019. We’re optimizing posts for SEO, and going into a time warp again.
I’m leaving the original post below the line, as it was written and published on August 24, 2011. Take a peek. Note a few things:
- There’s no mention of Salesforce. How can you talk about CRM nowadays without talking about Salesforce? The two are pretty much synonymous.
- Sit down before you read this sentence: “CRM helps overcome the technology hurdle of accessing information over disparate systems.” ???!!! You can stop laughing now.
One point we do still believe in is the potency and potential of predictive analytics for sales and marketing. Delivered through CRM so all users of Salesforce can benefit from it. And now, everyone else is seeing the potential.
SaaS is moving so fast, it’s surprising to see that the year 2011 was in the same decade we’re still in today. Also surprisingly, we were advised in the early days of this decade to not talk about predictive analytics for sales. Not by one person. By numerous people.
We did get the sense then that our vision was a bit ahead of its time. We were stubborn, and stuck to it. Because the potential of predictive analytics is the reason why Valgen was founded.
And look what happened since then. These are from Google Trends. Here’s searches for “Salesforce.” It doesn’t look all that steep visually but it is significant growth.
Next, searches for “predictive analytics.” Same thing, not a big look visually, but significant growing search interest.
There is much conversation now about AI and artificial intelligence. Let’s look at that. The volume makes “predictive analytics” flatline. The peak was January 2018:
Now, talk of algorithms, modeling, scoring, prediction … it’s all mainstream for sales and marketing, and becoming more mainstream for other areas of life like transportation.
What would you have done in 2011? What would the crystal ball have told you to do? Drop “predictive analytics” and run?
We admit sometimes we weren’t sure. But now, predictive analytics has grown to the point where we have Salesforce Einstein. Let’s look at the Google Search Trends for that:
Late-breaking as we write, CB Insights compiled the largest equity funding investments in artificial intelligence startups in the last 5 years:
Our predictive senses told us to not drop the crystal ball and run. We’re glad we didn’t.
In a blog full of such serious posts (we need to do something about that), this was a fun peek back in time! It also makes us wonder, what will we be doing and looking back upon a decade from now?
August 24, 2011
Customer Relationship Management (CRM) systems are the currency of customer-sales interactions. Effective, simple CRM software helps sales reps to focus on content of conversations rather than the mechanics of conversations, resulting in sales empowerment and productivity gains.
A CRM system can be a boon to sales people. CRM helps overcome the technology hurdle of accessing information over disparate systems. CRM systems help improve collaboration within, above and across the entire organization, allowing the company to speak with one voice. And from a governance perspective, these systems help elevate the customer relationship from individual dependencies to an enterprise-wide strategic asset.
When you add the potency of predictive analytics, a CRM system can be even more valuable. Leaders in analytics, sales operations and technology can fulfill their obligation towards sales empowerment by creating a cohesive approach that brings these disciplines together.
How well we achieve this determines if a CRM system just gains basic acceptance, or whether it is fully adopted and even embraced by sales people who realize its benefits for themselves as well as for their customers.
Here are guidelines to help make that happen:
Consider a multi-stage deployment
In the first stage of CRM implementation, deliver base functionality to the users so that their immediate, tactical pain points are addressed. This often involves getting the system up and running and available without interruption – the kind of stuff that builds rapport with the sales team. It can include consolidating contact hierarchy and transaction history, integrating with hardware (i.e., computer, phones) and software (procurement, shipping), and even interacting with social media.
In subsequent deployment stages, add features – often not present in out-of-the-box CRM systems – that build credibility with sales, extend the functionality and improve the outcomes of customer interaction. This is where predictive analytics can lead the charge. In order to identify hidden opportunities and capitalize on customer interactions, predictive analytics requires three components:
- Synthesizing extensive amounts of data, including cleansing and reduction base on insights
- Applying data mining and robust statistical methods
- Integrating relevant and distilled intelligence back into CRM
These stages need not run in sequence. You can put the basic team in place while initiating the gathering of data relevant for analytics. Then insights gained from analytics can help you prioritize implementation decisions.