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When data is the new gold, controlling your organization’s data is invaluable, especially in the face of economic uncertainty. For startups, that is now. Capital is much harder to come by and founders who received unsolicited termsheets just a few months ago are suddenly exploring how to extend the runway. Growing audiences is now more of a challenge, too, thanks to new data privacy laws and restrictions on Apple devices.
So, what should a founder do – curl up in the fetal position and lay off half of his staff? To slow down. Step away from Twitter. Recessions and downturns leave their battle scars on everyone, but truly spectacular businesses can and do emerge during economic downturns — and your business can be one of them with the right data strategy.
Your data can be the superpower of your organization. When data is used properly, go-to-market teams can do more with less, such as:
- Customize onboarding and product experiences to increase conversion rates
- Understand where users struggle and help proactively
- Apply sales pressure at the right time, resulting in expansion revenues that could have arisen naturally a few months later
But for many organizations, user data is usually locked in product and engineering teams, cut off from marketing and sales, and not often linked to results for monetization. This doesn’t have to be your business. Good hygiene and efficient, sensible data configuration can help your team ensure that data is accessible and available to everyone who should be using it.
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A key problem facing organizations when it comes to democratizing data is translating actual product use into business value. When a user uses an important feature in your app, that’s great, but if they do it 50 times in the first week, that’s great. Simply measuring consumption and storing it somewhere dampens the value of these core activities.
That’s why it’s helpful to have a cross-functional team meeting while setting up your data structures to consider facts and measures.
Defining Facts vs. Measures
Facts are simple: they are actions taken in your product. For example, the usage of functions, in addition to the user ID and the ID of an organization are all facts. Engineers and product managers are usually pretty good at identifying and recording facts in a data warehouse.
Measures, on the other hand, are calculations that arise from the data. Measurements can tell the story of the value of the facts on which they are based, or they can illustrate how important that particular step is in the user’s journey.
An example of a measure could be simple, such as a person’s qualification, i.e. “They selected they are looking for a business use case in onboarding” in a column labeled “business or personal”.
Measures can be more complex, such as a running count of the number of times a user has visited a pricing page, or a threshold to determine whether it has been activated or not.
I always advise organizations to leave the engineering and fact-tracking to the product builders – engineering and product, and then put together a team around the measures. The best teams treat measures themselves as a product, with user interviews across support, marketing and sales about how those customer-facing and go-to-market teams view and use that data, and a roadmap for creating measures that matter.
Implement data collection and distribution
Once your team has mapped out what they want to track, the next key question is, “How can we store this?” It feels like a new data solution is hitting the market every day, and less tech audiences and founders are spinning their heads with options to store, record, and visualize their data.
Start with these basics:
- Data (the facts) live in a data warehouse
- Data is then converted into measurements with an extract, transform, load (ETL) tool and those measurements are also stored in the data warehouse
- If necessary, measures and facts can then be moved to employee-centric tools to democratize them with a reverse ETL tool
There are numerous options in the market for data warehousing, ETL and reverse ETL to move the data, so I won’t name vendors here. It is important to involve not only your engineering team here, but also product teams and the roundtable you have set up to productize your measures. That way, no one misses useful data in the tools they use.
Take action with your data
The last and most complicated step after storing your facts and identifying and creating the ideal measures for your team is making that data available where your team works on a daily basis. This is where I usually see the most fall-off. Getting sales, support, and success teams to log into a dashboard and take action with the data every day isn’t easy. Getting the data into the tools they already use is essential.
This is where data democratization becomes more of an art than a science. Your creativity in what you do with your own data will help determine the fate of your organization. You need to use reverse ETL to get those measures into a CRM, customer success platform, or marketing automation tool, but what you do with it is up to you. You can create dynamic campaigns for accounts that are starting to find value with the tool, or submit highly active users to the sales team for immediate reach.
In times of downturn, it is extremely valuable for support and success teams to know if an account is using your product tool less than usual, or if a key player is no longer with the customer organization.
- Look beyond product and engineering to envision critical use cases for your data
- Get players from across the organization when setting up a reporting structure
- Data democratization dies when data is stored in a dashboard
We as an industry are fixated on those companies that are doing amazing things with their data, but we don’t talk often enough about the underlying structures and frameworks that got them to that point. All of these playbooks are powered by data, but can only happen if you have the right data hygiene and structures and get information into the hands of the right people at the right time.
Sam Richard is the VP of Growth at OpenView.
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