Evolve Into a Data-Driven Organization, Part 2

Until an organization actually acts on data, a Web analytics initiative’s return on investment (ROI) is zero. Web analytics’s most important element, acting on the data, is also the part organizations struggle with the most. In part one of this series, we reviewed five common stumbling points in becoming a data-driven organization and some solutions that frequently help overcome them. Today, five more roadblocks that hinder evolution into a data-driven company.

Limited Access to Data

Far too often, the people who decide what and how to change their organization’s Web site don’t have access to data that could provide potential solutions to site issues. When decision makers get data based on key performance indicators (KPIs), their recommendations can be better informed, more targeted, and usually much more successful.

With some of the tools larger Web analytics providers offer, including dashboards and Excel functionality (Omniture and WebTrends), this has become significantly easier. In many cases, decision makers may not even have to log in to the tool interface to obtain the necessary data.

Also, different data types are frequently siloed within an organization. Often, these data sets can drive insight to many other people in the organization and greatly complement data they already have. We’ve found success creating a diverse team to identify the following:

  • Different data types

  • How data types are used, or could be used, by different people
  • What types of insight each can provide

In some cases, data can be integrated into one system. More commonly, it’s simply a matter of making the data available to all the stakeholders who can benefit from it.

Goals Not Tied to KPIs

When the correct KPIs are set, they can frequently linger without much focus. People lose sight of the KPIs and return to the old way of doing business. By tying group and individual goals to specific KPIs’ performance, you ensure everyone focuses on the right destination. Performance reviews, bonuses, and so forth can all be tied to KPIs. When our clients tie KPIs to individuals’ performance goals, we find they have more success accomplishing goals and improving site performance.

Starting Too Big

Trying to solve all problems at once can lead to a long, slow process in which focus is lost and the most important elements don’t receive enough attention. Our most successful clients start small:

  • Pick one page, form, or promo that isn’t performing well. Try to improve it based on the monetization and protestation discussed in part one.

  • Start with a basic A/B or multivariable test.
  • Go through the steps, evaluate the outcome, then continue from there.

After the first successful optimization steps, the others are much easier. You start to understand the process and upside of such work.

Overly Data Driven

Though data help drive a lot of insight, you can go too far. The Web is about persuading visitors to make a series of decisions. Like advertising, it’s an art, not a science. Overanalyzing can stifle the creativity that fosters persuasiveness. It’s imperative to consider attitudinal data, customer feedback, research, competitive information, and the like when identifying opportunities and making site changes.

Lack of Committed Individual and Executive Support

Arguably, two of the most important factors in successfully becoming a data-driven organization are executive support and committed resources. Without that support, Web analytics easily moves to the back burner when other pots start to boil.

Dedicate a person to be responsible for Web analytics, whether she’s an internal or external resource. This person can champion data use and provide insight to other team members on how to use the data. Ongoing site optimization is a paradigm shift for a lot of organizations, so executive support is important to help get (and keep) the ball rolling.

Like many others, you may find your organization impeded by many of these roadblocks. That’s why so many people must struggle to close the loop on Web analytics/data-driven ROI. Let me know how you overcame some of these issues or what struggles you’ve had in overcoming them.

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