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How exactly to leverage your computer data in an economic depression

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If data may be the new gold, then controlling your organizations data is invaluable, especially when confronted with economic uncertainty. For startups, that point is currently. Capital is a lot more difficult ahead by, and founders who have been receiving unsolicited term sheets only a few months ago are suddenly investigating how exactly to extend the runway. Growing an audience can be more difficult now, because of new data privacy legislation and restrictions from Apple devices.

So, whats a founder to accomplish relax in the fetal position and lay off half their employees? Decelerate. Step from Twitter. Recessions and downturns leave their battle scars on everyone, but truly spectacular businesses can and do emerge during economic downturns as well as your business could be one of these with the proper data strategy.

Your computer data will probably be your organizations superpower. When leveraged properly, data might help go-to-market teams do more with less, like:

  • Customize onboarding and product experiences to improve conversions
  • Understand where users are struggling and proactively help
  • Apply sales pressure at the proper time, yielding expansion revenue that could have occurred naturally a couple of months later

But, for most organizations, user data is most regularly siloed within product and engineering teams, locked from marketing and sales, rather than often linked with monetization outcomes. This doesnt need to be your organization. Good hygiene and a competent, sensible data setup might help your team make sure that data is obtainable and open to all who ought to be deploying it.


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Product measurement

One major issue that organizations face with regards to democratizing data is translating actual product usage to business value. Whenever a user leverages an integral feature in your app, thats good, but should they take action 50 times within their first week, thats excellent. Simply measuring usage and storing it somewhere dampens the worthiness of the key activities.

Thats why its beneficial to have a cross-functional team meeting while establishing your computer data structures to take into account facts and measures.

Defining facts vs. measures

Fact is simple: Theyre actions which are used your product. For instance, feature use, alongside the users ID, and an organizations ID are facts. Engineers and product managers are often pretty great at identifying and capturing facts in a data warehouse.

Measures, however, are calculations that emerge from the info. Measures can tell the story of the worthiness of the reality that theyre built upon, or can illustrate how important that one step is in the users journey.

A good example of a measure could be simple, such as a qualifier of an individual, i.e., They selected that theyre searching for a business use case in onboarding in a column named business or personal.

Measures could be more complicated, such as a running count of the changing times a user visited a pricing page, or perhaps a threshold of if theyve activated.

I usually advise that organizations leave the engineering and tracking of the reality around the builders of the productengineering and product, and come up with a team round the measures. The very best teams treat measures just like a product themselves, with user interviews occurring within support, marketing, and sales concerning how those customer-facing and go-to-market teams view and use that data, and a roadmap to generate measures that matter.

Implementing data collection and distribution

Once your team has mapped out what they would like to track, another key question to ask is How do we store this? It feels as though every day a fresh data solution is arriving at the marketplace, and less technical audiences and founders will dsicover their head spinning with options to store, ingest, and visualize their data.

Focus on these basics:

  • Data (the reality) lives in a data warehouse
  • Data is then transformed into measures having an extract, transform, load (ETL) tool, and the ones measures may also be stored in the info warehouse
  • If needed, measures and facts may then be moved into employee-facing tools to democratize them with a reverse ETL tool

A great deal of options are available on the market for data warehousing, ETL, and reverse ETL to go the data, therefore i wont mention vendors here. Its vital that you involve not merely your engineering team here, but additionally product teams and the roundtable youve create to productize your measures aswell. This way, no ones missing actionable data in the various tools they use.

Taking action together with your data

The ultimate & most complicated step after storing your facts, and identifying and creating your teams ideal measures, is making that data available where your team works on a day-to-day basis. That’s where I typically start to see the most fall-off. Its challenging to obtain sales, support, and success teams to log right into a dashboard and do something with the info every day. It really is key to obtain the info in the various tools they already use.

That’s where data democratization becomes more of a skill when compared to a science. Your creativity using what you do with your personal data can help you own your organizations destiny. You should employ reverse ETL to obtain those measures right into a CRM, a person success platform, or perhaps a marketing automation tool, but everything you do with it really is your decision. You can create dynamic campaigns for accounts that begin to find value with the tool, or offer highly active users to the sales force for direct outreach.

In a downturn, its extremely valuable for support and success teams to comprehend if a merchant account is making use of your product tool significantly less than usual, or in case a key player is not any longer at the client organization.


  • Look beyond product and engineering to think about critical use cases for the data
  • Generate players from over the organization when establishing a reporting structure
  • Data democratization dies when data is siloed in a dashboard

We being an industry are fixated on those businesses that fantastic things making use of their data, but we dont speak frequently enough concerning the underlying structures and frameworks that got them compared to that point. Most of these playbooks are enabled by data, but can only just happen if you have proper data hygiene, structures, and so are getting information in to the hands of the proper people at the proper time.

Sam Richard may be the VP of growth at OpenView.


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