Slow, inefficient processes for data analysis
As a senior research analyst for Directions, Rachel Cummins has over twenty years of experience diving deep into data to find answers for her clients. She knows that sometimes, finding the answers they need takes time.
“Our motto as a company is that the client is king,” Rachel explains. “We don’t stop looking at the data until the client does, so we spend a lot of time mining in order to create strategic insights for our clients.”
Directions provides lots of different types of analyses for clients in a variety of sectors, from finance to hospitality to CPG.
“It runs the gamut of just about every type of analysis you could think of,” Rachel says. “We do correspondence analysis, Shapley, MaxDiff, and TURF.”
But the process Directions had in place to conduct these analyses was slow and inefficient and often required a lot of back and forth between their data specialists, analysts, and marketing sciences department.
“Our team of data specialists would set up our tables and provide our analysts with a set of Excel tabs that we would then work through,” she explains. “If there are any additional requests or things we wanted to dig into, we would then have to go back to our data specialist for them to run it.”
As the business world evolved, clients needed access to insights more quickly: Directions knew they couldn’t spend hours on every project every time their clients had a new question they needed answers to.
“It became a necessity,” Rachel remembers.