January 26, 2016

7 Criteria for Analytics — from Salesforce and Me... to You

Somrat Niyogi

Somrat Niyogi

Somrat Niyogi
Somrat Niyogi

Former Employee

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In my last post, I raved about and expanded on what Salesforce’s Vala Afshar wrote in his HuffPost Tech piece, 2015 State of Analytics. It’s a great read with a stunning take away that 90% of high performers depend on analytics — 3x more than underperformers. 

This post continues with Afshar’s recommendation on criteria for analytics.

When we started Clari, we talked to sales leaders at hundreds of customers. Literally.  What they told us matches the Salesforce survey results. So I’m going to take the same approach as in my last post: remind you of Afshar’s “top 5 factors that go into the decision about analytics tools” (in bold) while getting more concrete on what you can demand of any analytics provider who thinks they’re worthy of a place in your organization:

  1. Speed and ease of deployment. No one can wait six months for value. Or six weeks. What’s reasonable? 2 minutes to new value and insight. A 30-minute phone call for training. A friendly, sales-savvy support person at your beck and call. That’s it.
  2. Ease of use for business users. You can’t wait months (or forever) for IT to deliver views or charts supporting better decisions. For sales leaders, the time from new question to answer to action is typically a few minutes. If business users can’t tap and drag their way to an answer — without help — they move on. And every time they move on, their opportunity to join those high performing organizations who are 4.6x more likely than underperformers to use data to make decisions... is lost.
  3. Self-service and data discovery tools. One of the worst (maybe the worst) word in tool deployment? “Integration.” Millions have been spent, years have been lost, and consultants have gotten rich pursuing it. No more. Sales and forecasting analytics platforms need to automatically reach out to relevant systems with no IT involvement. What’s your sales reporting structure? Derive it from your CRM. What sales processes deliver the most wins? Discover it from historicals. What’s the level of sales activity in top accounts? Extract it (and along with relationship strength) from email and calendar systems. What deals are putting the forecast at risk and what action will put them back on track? Know instantly by applying data science to historicals and real-time activity. The days of data integration are largely over. Some providers just haven’t heard that yet.
  4. Mobile capabilities to explore and share data. Does anyone work on just a desktop or laptop only anymore? We didn’t think so. As a bonus, new, useful views a user creates with a quick tap or drag in their web browser should also be instantly available on their phone or tablet. In the mobile world, they call this “continuity.” We call it obvious.
  5. Cloud deployment. Afshar had to add this, but for most of us, the train has left the station. I haven’t spoken to a sales leader in years who wanted to start a software project by buying new servers. Have you?

In addition to Afshar’s five factors, I’ll add two more: 

  1. Use all the data that matters. Too many sales analytics and forecasting initiatives focus only on the CRM. Aside from the glaring problem that CRM data is often out of date, it’s just not a complete picture even under the best conditions. Email and calendar are the most obvious gaps — reps live in their email and calendar and our data science proves that rep activity and pacing is the best predictor of deal wins and loses. Even those “big three” are just the start. For example, we already reach out to the public web for company and industry updates to help reps prepare for sales calls. Other sources like call center logs will give sales leaders even greater visibility into their pipeline and team.
  2. Sharpen gut instinct with Data Science. Andy Byrne, our CEO, refers to this as Gut 2.0 — embracing your veterans’ years of experience while making them even better versions of themselves. To deliver advanced data science, we need amazing data scientists. But using the results should be as natural as breathing. You don’t think about the turbocharger in your car (or the Insane Mode in your new Tesla) — you just step on it and go. Data science is no longer only for your analysts. It’s practical insight integrated seamlessly into the tools you use to run your sales organization.

So now you have the analytics decision factors, Afshar’s five plus two more — all with concrete details you can build into evaluation criteria.

Sales and forecasting analytics is no longer a future. Top sales teams run on tools like Clari today. They forecast with confidence, coach reps more efficiently, and close more business. Aren’t you just a little bit curious? We can demonstrate all seven factors in one phone call. Including the promise (in #1) of giving you new value and visibility in 2 minutes. Make us prove it.

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