Accelerate
your event

Bear Analytics software suite enriches and reorders historical attendee data so your marketing team can send the right message to the right person at exactly the right time—when they’re most likely to register.

Before coming to us, Bear Anatylics’ processes were basically manual or involved a lot of different external software. Many years of experience helped them invent there own ideal work flow, but there was no perfect tool available to do that. And our challenge was to create the platform that will cover their flow and even better.

User interviews

We started with the most important and vulnerable phase like interviews. Our team hold up several interviews with stakeholders and actual future platform users. This stage helped collect a lot of useful data. We understood how the work flow is structured and what role user has. In order to understand the whole working process we conducted several user interviews asking a couple of questions, however the main ones were:

• Please explain your work routine
• What are you pet peeves?
• What functionality do you wish for?

This practice helped us gather a lot of critical and essential data and emphasize the major problems shown below.

Information architecture

The idea of the system was so unique that there were no actual competitor. We needed to come up with a solution for something new and never been built before. That is why after the interview we prepared the information architecture. It helped structure all the data we collected and see the general logic of the system.

Dashboard

Every user will start their work with the dashboard. On the dashboard one can see all the projects, clients and events currently worked on with actual statuses.

Data cleaning steps

Main idea was to create a system which will cover all the data cleaning process in one place, step by step. Following screens represent three main steps: mapping, enrichment, composing.

Step 1: mapping data

Step 2: data enrichment

Step 3: composing data

As a result, our team delivered an MVP and continued working on the next phase. It was a big challenge and we gained a lot of experience, especially in the field of data and in communication in general. It is also worth mentioning the volume of work done on creating big and complex data tables, all consistent, clean and functional. By now the project grew into a big system and we started the development of the third phase.