Why SaaS Needs To Get Smarter: No One Has Time For Your Clunky SoftwareThis is a guest post by David Skok, a partner at Matrix Partners. He blogs at For Entrepreneurs.
December 16, 2013
by David Skok
Three years ago I spent a lot of time looking at SaaS business intelligence companies. I loved what I saw in the demos: easy data connections, slick looking graphs, powerful drill down tools and custom dashboards made the tools look like no-brainers. And then I began my diligence calls. All of these bells and whistles were useful for data analysts I learned, but mostly worthless for regular users. Customers didn’t want to become data analysts, they wanted the software to do the work of the data analyst.
It then dawned on me that there’s a massive mismatch between the areas where vendors focus—namely graphics, dashboards, query and reporting tools—and the reality of customers’ needs. No one has time to dig through dashboards, graphs and reports. In fact, customers don’t want to spend any time in your application unless they absolutely have to.
It turns out, this mismatch doesn’t just apply to business intelligence tools but to any software that manages data. Take Salesforce’s Sales Cloud for instance. It collects and manages a ton of data, but does very little to pro-actively analyze that information and provide insights. Wouldn’t it be better if Salesforce emailed you when it detected interesting insights instead?
This is exactly what I heard from customers. They want an email or SMS alerting them to an abnormal condition, stating the insight along with as much information as possible about the issue’s root cause. Here’s an example in a sales context:
We are projecting that you will miss your plan for Bookings this quarter.
Your Plan is $2.5m; Based on the data we have, we project Bookings will be $400k below plan.
This is because you have too few Opportunities in the pipeline given historical conversion rate from Opportunities to Closed Deals (55%). The total of all Opportunities in the funnel that are projected to close this quarter is $3.8m.
Western Region appears to be the problem. More specifically, reps John XX and Kate XX are below targets.
To some, this may sound like science fiction, but in reality it’s not that difficult to pull off. Without realizing it, we interact with smart software all the time. Amazon’s automatically recommends products we might like. Nest optimizes thermostat settings. VideoIQ even figures out when someone is about to commit a crime. The key is that all of these products anticipate what a user wants, and then do it automatically.
As all software developers will tell you, making things easy for the end user usually requires some very hard work. So what is required to build smart data analytics software that can automatically and proactively deliver insights to users?
1. Start With Focus
Vendors need to focus on a specific application and move beyond creating broad horizontal platforms. That way, the information in the system becomes understandable. I.e.“This data represents Bookings,” instead of just a bunch of numbers.
BI vendors typically get this and have created a set of applications that are built on top of their platforms. While some of these are getting quite good, they are still short of what customers are hoping for.
2. Figure Out Important Moments
BI Tools usually provide alerting functions. But the ones I have seen are far too simplistic, and require the user to define the rules for what is an exception. Once again, the software is expecting the user to do the work instead of figuring out how to do that step for them.
Here are some initial thoughts for how to detect unusual events that require human attention:
Baseline the data
Look at budgets or forecasts
Use application knowledge to determine what is abnormal
Send regular updates when nothing is abnormal
3. Determine Root Causes
If your software determines that bookings are about to miss plan, that is somewhat useful. But it immediately raises the question: Why?
Normally to find the answer to that question, you would need to “drill down” into the bookings chart to figure out the root cause. Was it because one of your regions is underperforming? Because a certain product didn’t meet the expected sales target? Or because the overall sales productivity is lower than expected?
What we see from this is that most data is hierarchical in nature. For each application, there will be a small number of really important high-level metrics. But behind each of these there will typically be a hierarchy of supporting metrics that will shed more light on the root cause of a problem.
Let’s take Profit as an example. If we missed our profit target, we would start looking at the following components to see where the problem had come from:
Then, if we dive deeper in to Bookings we might look at the following set of components:
Knowing this hierarchy makes it possible for smart analytics software to drill down instead of making the customer do the work.
4. Work With A Domain Expert
In addition to referring to data hierarchy to figure out what is important, it also makes sense to ask an executive in the application domain to walk you through the key insights they are after, and how they would go about diagnosing common problems. Once you know what they care about, work on setting up the metrics that will allow you to provide them with those Insights.
If you are building software that generates, collects, or manages data in any way, ask yourself: Can customers easily gather insights from my data? There is a remarkable opportunity for us to build smarter software that gives customers what they want, it just takes a little more work.