Drive conversion, engagement, and retention metrics. Focus on improvements targeted at specific cohorts of users. Build key customer cohorts by incorporating both behavioral, acquisition, and demographic/firmographic attributes.
Establish a key owner of your experimentation efforts (this is traditionally done in growth, product, or customer retention teams). Measure the impact of each experiment and combine iteration and innovation to get a competitive edge.
Leverage a data warehouse and BI tool to further exposure to data and dive deeper into your analysis. Set up clear best practices around self-serve analysis and when to involve the data team.
You’re not in Kansas anymore—you need more than just one or two people managing your data. Make sure you have a data analytics team that includes analytics experts and data engineers to help support company-wide reporting.
Now that you’re getting some robust insights, it’s time to start prioritizing your data roadmap. This probably means it’s time to unsilo your data. Plan out how you want to use data 3 months, 6 months, and 1 year from now and how to get there. Does that means more experimentation? Collaborating with other teams? Put together and plan and begin to work it. Remember data will always involve some reactive component so make sure you allocate time for long-term, highly leveraged projects and support your team’s immediate needs.
As you build out your personalization efforts, make it a goal to get a 360-degree view of your customers. This means integrating even more data sources to enable a more in-depth analysis of both customer behavior and demographics. Your 360 view can serve as the basis for attribution models, machine learning, predictive cohort generation, and more.