How To Ensure Success In SaaS And Analytics

In SaaS

S peak to any SaaS application vendor and you will hear the same message: customer adoption is critical to the application’s long term success. Unlike in traditional on premise software sales models, where the customer tends to purchase software in large suites and pays the bulk of the cost upfront, SaaS applications are purchased in bite-sized chunks and unless the users are seeing value, subscription agreements are simply not renewed, and/or vendors can forget about discussing expansion of the solution’s footprint.

When it comes to analytics, focus on user adoption is actually nothing new. Because analytics or business intelligence solutions are not needed to automate operational processes or to “keep the lights on”, there has always been a certain amount of culture change needed to truly get the most out of analytics investments. People often do not have the time to learn BI tools; as a result, fires are fought only when they grow out of control instead of proactively prevented, and strategic decisions are made on gut feel rather than supported by hard data.

For Numerify, being a SaaS and an analytics vendor, this means customer onboarding, ease of use and overall user adoption of our solution are doubly critical and will always remain an area where we need to drive continuous improvement. In future blog posts I intend to revisit this topic, to share some of what we have found to work well for our customers, and of course to highlight what pitfalls to avoid. For now, here are some general areas of focus:

  1. Monitoring Usage. By understanding login and usage patterns, SaaS application vendors can identify bottlenecks where more training or simpler UIs may be needed, or even do A:B testing to see which approach to surfacing information is more effective.
  2. Integration with Systems of Record. Insights gained from analytics need to be provided in context and be actionable. To accomplish this, we will focus on key capabilities such as providing drill down capabilities from our dashboards into operational systems and the ability to embed our analytics directly in those systems’ user interface.
  3. Provide Self-Service Learning. Instead of lengthy manuals and training classes, provide easy to find and consume help topics and training videos. Additionally, ensure there is a rich knowledge base, vibrant user community to exchange ideas, and regular “tips & tricks” or best practice content made available via blogs, webcasts or email newsletters.
  4. Flexible Content Delivery Options. Every user is different. For casual users, a regularly scheduled email delivery of key dashboards works wonders. For users on the go, a full-featured mobile application is key. For Excel jockeys, there is an Office add-in. For a broader stakeholder community, surfacing content via third party portals makes sense.
  5. User Experience. Today’s workforce expects a beautiful, well-designed interface and will not tolerate slow response times. Customization and personalization is important, including simple things like aligning names of fields with their organization’s terminology. Additionally, single sign-on with other enterprise systems is table stakes to minimize the barrier of entry.
  6. Onboarding, Change and Engagement Management. SaaS vendors have invested in building teams of “trusted advisors” whose main purpose is to ensure their customers are getting the most out of the solution, creating a “win-win” partnership. Numerify is no different – our customer success team is here to help!
  7. Providing Differentiating Capabilities. Finally, adoption of analytics is made a lot easier if killer insights or KPIs can be provided which (i) have a clear, immediate business impact and (ii) cannot be achieved easily in other ways. This is a broad definition but frequently this category includes forecasts, predictive alerts, or analysis of unstructured text to spot common issues not visible otherwise.
    Do you struggle with this in your organization or do you have other suggestions for getting more out of your analytics projects? Please let us know in the comments.
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