4 things to keep in mind when starting an analytics project

Analytics projects are typically focused on a few key outcomes; increasing revenue, identifying operational inefficiencies or creating new revenue streams. For this post, lets look at it from the perspective of creating executive level dashboards for a B2B software company.

Who will be consuming the data and what do they care about?

Before we jump into the data itself, lets take a step back and understand who the analytics will be surfaced to and what their challenges are. Make personas with their top priorities, pain points and the typical questions they will be asking. One way to get started is to make a persona priority matrix listing the top three to five challenges for each (ex. below.)

Once the matrix is laid out, we can begin mapping specific questions to each priority. What answers might help a VP of Sales increase the effectiveness of the sales team and ultimately revenue?

  • What do our highest velocity deals look like (vertical, company size, who’s involved)?
  • What do our largest deals look like?
  • Where do our deals typically get stuck in the sales process?
  • What activities and actions are our best reps performing?

What data do we need to include and where can we find it?

After getting a fairly firm grasp on what answers we’re looking for, we can determine which data sources it make sense to include. As our executive dashboard project involves multiple personas and a very broad set of priorities, each area will have their relevant data sets and specific focus.

Let’s say our company uses Salesforce CRM which will be the primary data source for our VP of Sales dashboard. We will also need to ensure that our CRM is enriched with firmographic data from somewhere like NetProspex so we can identify company sizes, verticals, etc. It would also be helpful to include Marketo data which will include each individual customer’s browsing and search activity on our site.

via Optymyze

How often do we need to update the data and at what level of detail?

After determining where we are going to be pulling data from, we need to identify what frequency and level of detail we need to capture. Does it it need to “real-time” and if so what does real time mean for this project? While something like stock trading might need data to be as close to real time as possible, a B2B sales use case will probably not suffer too much if we only update the data once a day. Based on the questions we are going to ask, which fields do we need to include? Keep in mind each specific data source has different limitations regarding what you can extract and how often. For example, one API may only allow you to pull out certain pieces / amounts of data once daily while another may let you do so every 15 minutes.

What are we trying to measure?

Once we’ve outlined data and sources, we can develop specific reports and metrics to answer our business critical questions. It’s a good idea to whiteboard out your dashboard tabs and group reports appropriately. As a best practice, start at the highest level and work into more granular insights as you move through the analytics experience. Solicit feedback from the primary consumers to better understand what makes the most sense. Keep a definition list for all metrics created , what they contain and what they are supposed to measure.

Thanks for checking out my post, if you found some value in it you might also like Adding context to analytics with different types of data.