Best Practices for Data-Driven Executive Decision Making

We decided to make this week's post about executive data driven decision making. Empowering data-driven decision making for our clients is part of our company's core business value. In addition recent executive opinion interviews we are conducting to elicit leadership insights 2024 (to be published this coming December) indicates that data-driven decision making is a key priority for both Chief Revenue Officers and Chief Product Officer for the coming year.

What are some of the best practices we have learned in our 20 years of working with the world's most innovative technology companies? Here are some practices that will get you on your way to ensuring leadership team members are best equipped to make the most effective business decisions for 2024: 

1. Defining the scope and parameters of the data required for the specific decisions. In our market research practice, we have determined that at least 40% of the data needs to be external data, ideally a combination of primary (survey and qualitative in-depth research) and third party/syndicated data in a 70/30 split.
2. These parameters and the inclusion of primary data are important because frequently internally generated data has a bias towards favorability. In other words, when relying solely on internal data, there is a likelihood of interpreting customer feedback and other variables as more positive than market reality. This is because of an inherent selection bias-customers that are willing to voluntarily share data and opinions with their vendors tend to be those who feel close affinity and are therefore among your best champions and fans.
3. Whenever we conduct an A/B style matching between a client's internal data and our external findings, we often discover significant disparities. The external primary data we generate for our clients is much less positive, also indicating favorable predispositions towards competitors and product substitutes. In addition because your customers can be candid with us in their true impressions of your company's products support leadership and general brand connotations, we pick up a lot of market realities that are not found in internally generated data.
4. Another aspect of externally gathered and validated data (which as we stated above should be at least 40% of the data relied upon to drive the decision is that we frequently pick up several competitive uplifts that are not generally found when only analyzing a company's internal customer data and support escalations. One recent use case was that a product offering from one of our client's most significant competitors was trending significantly in the target customer base. When we reported this to our client, they indicated they had not seen it trending in their escalation data. They did not therefore regard this data point we uncovered as significant enough for immediate proactive action, until four months later, when our reported trend also began to show up in their internal customer data. Those four months could have been crucial in enabling our client to make counter offensive moves in response to their competitors encroachment into their customer base. The external data we provided should have served as an early warning detection system to the client.
5. Externally generated primary data can also provide significant insights into future anticipated moves by a company's customers. In our Future Scape offering for our clients, we are able to provide anticipatory insights and data into how a client's customers are likely to proceed in the coming 12 to 24 months. Some of these findings include whether technology vendors are likely to be consolidated as well as risk intelligence on how ta company's contracts customers may be at risk. Pre-emptive insights such as these can have significant impact on revenues and profits by enabling company's to proactively counter risks to customer churn or scope downsizing at contract renewal.

In short, executive decision making on important customer-facing strategies and tactics are best facilitated by providing these leaders with access to a complete data set consisting of both internal and external data market intelligence.

Next week's post will be titled Brand Singularity. Quick synopsis: We introduce the concept of "brand singularity" as a best practice for brand and messaging development in an even more fragmented and crowded space for mindshare. Many brands end up creating brand diffusion by attempting to stand for too many attributes. We will share highlights from our brand development research.
 

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Alan Nazarelli is Founder & CEO of Silicon Valley Research Group. Based in San Jose, CA with offices in Seattle and New York, the company works with the world’s most innovative brands to provide timely and actionable market intelligence and strategic guidance to enable them to make well-informed decisions to positively impact revenues and profits and to achieve their growth targets. Connect with Al on Linked in