Inspired by thought leader Mark Ritson, and drawing from the fundamental laws of marketing laid out by Byron Sharp and the Ehrenberg-Bass Institute for Marketing Science, we have developed machine learning algorithms tailored to emulate the science of marketing. Like an architect must understand the laws of physics, effective data science in the marketing space must adhere to the data-driven evidence that proves these laws and provides a reliable and repeatable model for success.
What We Do
We live in a world where personalized marketing is required. When it comes to mass marketing in this segmented world, reaching your less loyal customers is just as important – if not more so – than reaching your most die-hard fans. The proof is in the data, and it tells us a story that in many ways flies in the face of traditional marketing dogma.
Mysteriously, when it comes to mass marketing, the biggest opportunity for growth comes from your non-loyal customers. Time-series modeling of a customer journey gives us a better view of who your customers are, and how they got there, for a more precise implementation of mass marketing.
Media Mix Modeling
Quantify the impact of individual marketing investment to the total business outcome; simulate the maximal output for a pre-determined marketing input/effort; and determine the minimum marketing spend that achieves a desired target output (e.g. revenue). Reallocate spends for maximum effectiveness.
Leverage the power of Customer Data Platforms to get more and better data, on more customers.
- More data on more customers
- Better data on more customers
- Reach additional customers
- Add additional ‘marketing’ and ‘offer’ channels.