The goal of any analytics engagement should be to reveal obvious as well as hidden insights that yield actionable, granular guidance to drive marketing growth. The C-Suite wants it, and as marketers, you need roadmaps that help you to achieve it while adapting to changes in consumer behavior. That is why we frame the solutions we offer as Business Impacting, tailoring them to reflect the unique complexities of your business.
In order to be impactful, results must be delivered within a timeframe in which you CAN apply them to your objectives: Too slow, and you miss your window of opportunity. Too fast, and you can’t even react. We strive for right-time, not real-time, and measure our success, against what we do to help you, tactically today and strategically for the future.
Marketing & Media Mix Performance Measurement
The core analytic task in any company is to figure out how marketing, distribution, and other activities drive sales. Doing this core job correctly sets up a culture that is accountable for the efficiency of marketing spending and is willing to experiment to see what might work better. Marketing mix and multi-touch attribution are the main tools. Both seek to refine our understanding of what is spent on marketing activities, how much contribution was made to sustaining the business, and whether our activities drove profitable growth. When extended to include factors outside our control, like macroeconomics and weather, the analysis can become a deep, detailed model of all business drivers.
The successful implementation of a mix or attribution program requires a process involving the client, the media agencies, and the modelers. Even the simple review of data by all three groups around the same table can provide added value. The Modeling then adds the magic of explaining the relative contribution of all the activities at once. The end result should not be just a “report card” on the ROI of each media item. Instead, each member of the team should leave with a clear idea of what can be done to improve both the underlying business performance and the process of measuring it.
Unlike a lot of providers, in4mation insights provides both a modeling expert and a senior consultant in every engagement. This way, we do not miss the chance to adapt the model to your unique needs and, at the same time, extract maximum business value from the outputs.
When Speed and Cost are a Consideration
Media Mix Modeling (MMM) has been the gold standard for advertisers with exceptionally large media spend and analytics budgets to match, and the tolerance to wait months for deeply granular analytics for decision support. This has put it out of reach for most smaller advertisers, or any brand that still needs timely, straightforward, and unambiguous topline KPIs for optimizing topline media investments quickly. Even though our own full MMM solutions regularly beat the industry standard for turnaround time and value, we recognized this gap in the market. Leveraging decades of experience and the unique application of hierarchical Bayesian analytics, we have created i4iFASTMIX as a solution. The gold standard for actionable media insights and KPIs, now made simple, fast, and affordable for a broader range of advertisers.
You can read more about i4iFASTMIX here and contact us today at firstname.lastname@example.org for a detailed discussion on how we can customize i4iFASTMIX to address your most critical media planning needs.
Choice-Based Conjoint Analysis
in4mation insights is known for its industry leadership and innovation in Choice-Based Conjoint Analysis (CBCA). Partner & Co-Founder, Steve Cohen, first introduced CBCA to the marketing research community ten years prior to the introduction of Sawtooth Software’s CBC product, and he has been instrumental in the evolution of choice modeling through the introduction of MaxDiff and Menu-based Conjoint. Our firm has been at the forefront of implementing improvements to CBCA, several of which are unique to in4mation insights. We believe that these improvements significantly enhance results and their implications over competing techniques.
Our conjoint models are based on Hierarchical Bayesian statistics. Bayesian statistics seeks to understand and quantify the drivers of behavior not just as an average, but rather by quantifying differences across people. The Hierarchical model that we employ gives us an additional layer of understanding of consumer behavior and specifically quantifies sources of variability across people
The latest CBCA approach that we are pioneering is Budget-CBC (B-CBC). Budget-CBC estimates a specific budget constraint for each person and suggests pricing strategies based on each buyers’ price sensitivity. Our experience is that this approach yields more plausible price-demand relationships than does the standard approach to analysis, which typically suggests that people are willing to pay higher prices than is likely. If people are constrained by their money budget, B-CBC will always provide better results than will the standard.
You can read more about Budget-CBC here and contact us today at email@example.com for a detailed discussion on how choice-based conjoint analysis can accelerate your product or service pricing and feature decisions.