Whether we’re selling or buying insights, the systematic evaluation of their relative merits is a vital career skill. On the one hand, it allows us to defensibly establish which solutions will provide the most value for our money. And on the other hand, it provides us the means to best articulate the value of those solutions to our internal and external clients.
At the end of the day, any insights product or service can be evaluated along two broad axes: Applicability and Total Cost of Usage.
Applicability. The applicability of an insights product determines the degree to which it satisfies the client’s needs. It determines the extent to which it answers the client’s questions, provides actionable intelligence, and can be relied upon. The higher an insight solution’s applicability, the more value it can provide the client.
Total Cost of Usage. The total cost of using an insight determines the aggregate cost (in time and money) of acquiring, configuring, and using that solution. As any CFO (or business student) will tell us, the lower total cost of usage, the better for our bottom line.
However, these two dimensions are typically in mutual opposition, as there’s an inherent trade-off between applicability and TCU. Think of it this way: The most applicable dataset imaginable would be a census-level, passively/observationally-collected, custom-designed dataset with 100% accuracy. However, the costs of collecting such a perfect dataset would be astronomically high. The converse also holds: We can have the cheapest possible research study, and more likely than not you’ll get what little you pay for.
These are common-sense distinctions, and we can understand them fairly instinctively. So how then should we actually explore and articulate an insight solution’s applicability and estimate its total cost of usage?
Applicability: Does the Client Get What They Need?
When all is said and done, the client is generally looking for insights:
- whose breadth is wide enough to cover their subjects of interest,
- whose depth is deep enough to provide actionable conclusions,
- whose accuracy is high enough to be relied upon, and;
- whose timeliness keeps the conclusions relevant.
If we keep cost out of the equation (because we’ll be discussing it in a moment), the studies with more breadth, depth, accuracy, and timeliness will deliver more value to the client.
The good news is that the degree to which a solution satisfies these four dimensions is ultimately controlled by the solution’s creator. In the design phase, we can shape the final product’s applicability through:
- the methodology used for data collection,
- the scope of data collected,
- the techniques applied in data analysis, and;
- the frequency of data collection & processing.
The art of designing viable and sustainable insight solutions is to strike the right balance between these elements to deliver maximum value at minimal cost.
Total Cost of Usage: More than just Price
When we go to buy a new car, it is tempting to focus on the sticker price as the cost of that car. But in reality, that sticker price isn’t the end of what we’ll end up paying. Once the car’s been bought, we’ll have to pay for gas, for maintenance, and (emotionally) for the stress of having our teenage child take it out on Friday nights. Insights products are similar, in that they typically have a sticker price, and a host of further associated costs. When comparing the real cost of an insight solution, it’s important to understand its total cost of usage.
How easy is the solution to use? This matters deeply, because it determines how much effort (i.e. time and money) will need to go into both training its users and extracting actionable conclusions from its contents.
The more difficult a solution is to work with and the more effort it requires to fit it into its audience’s workflow, the less utility it will have for the audience. How many times have clients said (about a competitor’s product, of course): “It’s a great product, but no one on my team actually uses it.”
The important thing about accessibility, however, is that it is also controlled by the solution’s developer. Designing new deliverables, optimizing their UX, exposing relevant APIs, all can increase the accessibility of your insights and thus lower the client’s total cost of usage.
How much will it cost to implement the solution? The direct financial implications may include hardware costs, software costs, and training costs. In today’s integrated big data world, you may also be facing significant refactoring costs for both upstream and downstream systems.
For example, consider the costs of implementing a new web analytics platform: First, there’s the time needed to configure your new platform and design relevant dashboards. Second, there’s the (upstream) cost of replacing tags throughout your web site. Third, there’s the cost of refactoring (downstream) systems that consume the platform’s API to use your new platform’s API. And finally, there’s the cost of re-training the users of the platform.
Generally speaking, these are costs over which the solution designer has limited impact. And yet, wherever possible, we should try to optimize our UX and implementation workflows so as to minimize these switching costs.
Price: Is the Benefit Worth It?
And finally, we come to the sticker price for the solution itself. Typically, this price will include the cost of data collection, data processing, and data delivery – basically, everything that it takes to get you the final deliverable that you can then work with.
Obviously, the lower the price the better. However, there is almost always a direct trade-off between price and applicability, since a lower-priced solution will usually have to make sacrifices in either breadth, depth, accuracy, or timeliness.
Therefore, the real question about price needs to be reformulated. Rather than: “Is it as low as it can go?” the true question should be: “Is this price reasonable for the applicability of what I’m buying? Will the conclusions I apply be worth more to me than the solution’s total cost of usage?”
If you’re a solution provider, you want the answer to these questions to always be YES. And you want to provide your clients with the arguments/evidence to support that decision.
I’d love the chance to discuss how you’re approaching your insights products, either when designing them for your clients or evaluating them for your business:
- If you’re at the CASRO Digital Research conference next week, please ping me on Twitter @InsightIndustry.
- If you’re not at the conference, then feel free to reach out directly or on LinkedIn.
- And if you want to read more of my thoughts on competing in the insight industry, come visit/subscribe here.