How to Leverage Your Data in an Economic Downturn

How to Leverage Your Data in an Economic Downturn

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If data is the new gold, then controlling your organization’s data is invaluable, especially in the face of economic uncertainty. For startups, that time is now. Capital is much harder to come by, and founders who were receiving unsolicited terms of reference just a few months ago are suddenly investigating how to widen the runway. Growing an audience is also more challenging now, thanks to new data privacy legislation and restrictions on Apple devices.

So what’s a founder to do: curl up in the fetal position and fire half his staff? Decelerate. Get off Twitter. Recessions and downturns leave their battle scars on everyone, but truly spectacular companies can and do emerge during economic downturns, and your company can be one of them with the right data strategy.

Your data can be the superpower of your organization. When harnessed properly, data can help marketing teams do more with less, such as:

  • Personalize onboarding and product experiences to increase conversion rates
  • Understand where users are struggling and proactively help
  • Apply sales pressure at the right time, generating expansion income that may have occurred naturally a few months later.

But for many organizations, user data is most often stored within product and engineering teams, outside of marketing and sales, and is often not tied to monetization results. This does not have to be your company. Good hygiene and sensible, efficient data setup can help your team ensure data is accessible and available to everyone who should be using it.

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product measurement

A major problem organizations face when trying to democratize data is translating actual product usage into business value. When a user takes advantage of a key feature of your app, that’s good, but if he does it 50 times in his first week, that’s great. Simply measuring usage and storing it somewhere reduces the value of these key activities.

That’s why it’s helpful to have a cross-functional team meeting as you set up your data structures to consider facts and measures.

Defining Facts vs. Measures

The facts are simple: they are actions that are taken on your product. For example, the use of functions, together with the identification of the user and the identification of an organization are all facts. Product engineers and managers are usually pretty good at identifying and capturing facts in a data warehouse.

Measures, on the other hand, are calculations that arise from the data. Measures can tell the story of the value of the facts on which they are based, or they can illustrate the importance of that particular step in the user’s journey.

An example of a measure might be simple, such as a person qualifier, ie “They selected that they are looking for a business use case in onboarding” in a column labeled “business or personal.”

Measurements can be more complicated, such as a running count of how many times a user has visited a pricing page, or a threshold of whether or not they were triggered.

I always recommend that organizations leave the engineering and fact tracking to the product creators: engineering and product, and then team up around measurement. The best teams treat metrics as a product in and of themselves, interviewing users within support, marketing, and sales on how those customer-facing and merchandising teams view and use that data, and a roadmap for building metrics that matter

Implementation of data collection and distribution

Once your team has mapped out what it wants to track, the next key question to ask is “How can we store this?” It seems like every day a new data solution hits the market, and less technical audiences and founders can be blown away by options for storing, ingesting, and visualizing their data.

Start with these basics:

  • The data (the facts) lives in a data warehouse
  • The data is then transformed into measures with an extract, transform, and load (ETL) tool, and those measures are also stored in the data warehouse.
  • If necessary, measures and facts can be moved to employee-facing tools to democratize them with a reverse ETL tool.

There are tons of options on the market for data storage, ETL, and reverse ETL for moving data, so I won’t mention the providers here. It’s important to involve not only your engineering team here, but the product and roundtable teams you’ve set up to produce your measurements as well. That way, no one is missing actionable data in the tools they use.

Take action with your data

The final and most complicated step after storing your data and identifying and creating your team’s ideal metrics is making that data available where your team works every day. This is where I normally see the biggest drop. It’s not easy getting sales, support, and success teams to log into a dashboard and take action with data every day. Getting the data into the tools they already use is key.

This is where data democratization becomes more of an art than a science. Being creative with what you do with your own data will help you own the destiny of your organization. You need to use reverse ETL to get those measurements 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 start to find value with the tool or introduce highly active users to the sales team for direct outreach.

In a downturn, it’s extremely valuable for support and success teams to understand if an account is using their product tool less than usual, or if a key player is no longer with the customer organization.

Remember:

  • Look outside of product and engineering to think about critical use cases for your data
  • Bring players together from across the organization by setting up a reporting structure
  • Data democratization dies when data is siled on a dashboard

We as an industry are obsessed with those companies that do 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 data-enabled, but they can only happen when you have the right data structures and hygiene, and you’re putting information in the hands of the right people at the right time.

Sam Richard is the Vice President of Growth for OpenView.

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