From actionable insights to action: Decreasing value path friction in data SaaS

Your SaaS product may generate the most earthshaking data-driven insights for enterprises, but without anticipating and addressing how customers will act on them, you’re creating barriers to sales and client success.

Most enterprise-focused software companies have successfully moved their products up the maturity curve from data to information to insights, creating the potential for big impact with their customers by uncovering new ways to look at their businesses.

Maybe flashy dashboards and a suite of reports are sufficient for some applications — particularly new-to-world data with a high “wow” factor — but customers now often demand more. Action instead of merely actionable insights.

There are a variety of options how this can happen:

• Customer manual: Customer personnel manually use your product’s insights, perhaps combined with other data, to influence strategy and operations. Example: Website analytics platforms.
• Customer automated: Downstream customer systems call your product’s APIs (or receive data via downloads), picking up the value ball and taking it across the goal line. Example: Content analytics tool feeding a marketing automation system.
• Partner: Partners of your customer consume your insights and data, and in turn deliver broader solutions to your shared client. Examples: Lead generation tools feeding rep firms, application performance monitoring platforms used by outsourced tech providers.
• Managed services: Your customer success or operations teams do the some or all of the legwork in lieu of customer personnel. Example: Yield management in ad tech.
• Open loop: Your product automates insight-driven actions, but the results are only verifiable outside of your platform. Example: Content personalization for brand sites where attribution is determined by separate systems (if at all).
• Closed loop: Your product gathers and analyzes data, generates insights, automatically acts on those insights and provides the metrics quantifying efficacy. Example: Website optimization platform that tests various designs, gathers success metrics, promotes winning components, and validates lift in conversion.

There’s no right answer that covers every industry, customer and company stage.

For instance, highly disruptive solutions that attack previously intractable problems may have to be built in waves, starting with problem identification and iterating into a closed loop solution once the issue is better understood and there’s a credible analytical foundation.

On that other end, manually intensive industries that are later adopters of automation may need the human bridge — the customer’s, vendor’s or partner’s — between insight and action. Same goes for problems with highly subjective solutions that ML/AI algos haven’t yet mastered.

But nirvana is the “closed loop:” identify the problem, fix it, and prove to the customer that your product is valuable. An “action platform” instead of an analytics platform is the epitome of a turnkey solution. For the right product and problem combo, this is the proverbial “money-printing machine.”

Regardless of which outcome is most appropriate, everything starts with understanding your target customers’ internal/external processes and their perceived pain at each step along the way:

• If you had an end-to-end solution, how would your solution change your customer’s processes in terms of eliminating inefficiencies, collapsing process steps, replacing inferior incumbent solutions, and automating manual work?
• If your customer instead used an alternate model, such as in-house manual labor or a third-party provider, what are their costs to do this?
• What’s the delta between how they and you perceive the problem, and what’s the root cause for perception gap? How can you overcome it?
• How would both of you quantify the value related to fixes at each step along the way and at what point does the accumulated value become meaningful to your target? Is there a point of diminishing returns (e.g., fixing the first four steps of a defective process has high value, but the last step not so much).

And as you think about bridging that last mile between delivering insights and taking action, how do the various models above fit your targeted customers?

Sophisticated industries or verticals with savvy service providers may be able to easily pick up the ball, while there may be significant barriers within less mature customers, who lack the appropriate systems, capabilities or partners to fully act on your insights. Customers in highly regulated industries may only trust certain partners for operations while are less picky about vendors further upstream.

Finally, everything can be hacked (in a good way). While a line may be the shortest distance between two points, you can always move the points closer together to create a solution. In other words, it’s often better to solve a smaller, definable problem through a closed loop solution before tackling a larger, messier one.

This also allows you to more easily evolve through the SaaS hierarchy of making solutions first successful, then repeatable, then scalable, then profitable.