Too Much Data, Too Little Time
Today’s CPGs are fighting an unwinnable battle: too much data, not enough people and resources to get the analysis done. The complexity and innovation of consumer goods has made growth harder to gain than in the past. Traditional analysis models can’t deliver the growth they once did, and the CPGs that want to win will need to be in lockstep with the right data and analytics strategy that will drive growth.
The massive amounts of sales data available through syndicated providers and retail portals is becoming a herculean task to mine it all without help. Analyst or not, without a business intelligence tool to help extract meaningful insights and capitalize on the data, sales teams are left scrambling to combine multiple data sets to measure what stakeholders ultimately demand — a cohesive view of performance and insight into category growth.
The Gap in How Data is Supplied
The organizations that collect, curate and sell CPG market data offer increasingly distinct syndicated data. The truth is, each of these syndicators have untimely gaps in their coverage. To overcome these gaps, many companies get data from multiple sources, but that creates another challenge: comparing different datasets with categories, labels and data that don't line up. Companies can either ignore the differences or invest in unifying the datasets — a manual process that requires time and resources.
Harmonizing product and market data across sources will become essential for CPGs in the near future. With the help of artificial intelligence and machine learning, CPGs will be able to not only harmonize data for analysis, but present the findings and highlight the deeper insights that will resonate with sales teams and buyers, in a fraction of the time.
Access for All
Too often, the people who access these platforms aren't the ones presenting the insights. If the CPG brand is lucky, it’s their analyst going into somewhat of a data dumping ground, trying to interpret what the sales team will need for their presentation. There’s a disconnect between the analyst and salesperson, siloed from one another, without clear direction on what they’re evaluating and extracting.
Sales teams have unique requirements and goals to deliver results. Data analysis and custom presentations through product and category insights help sales teams accomplish their goals. Analysts tend to follow a mechanical approach and get started with analyzing data sets without clearly defining the business problem and objective first. They don’t always know the nuances that the sales team does.
Bridging the Gap: Analyst Findings and Why They Matter
It’s easy for analysts to spend too much time downloading records, creating pivot tables, and producing reports that fail to generate actionable insights. Actually, they spend roughly 80 percent of their time on generalized reporting tasks, extracting the necessary metrics and translating them into a presentable format.
It can feel like grunt work because they don’t have visibility or control with what happens to the data.
The analyst’s true goal beyond reporting is to find insights that drive the most effective business strategies, creating a real impact for the company.
Data is an Asset: Adopt Technology That Knows How to Use it
The surplus of data shouldn’t be intimidating, but the only way to contend with the data sources is to adopt new tools and technologies that reveal an up-to-date snapshot of category and competitive intelligence.
Even analysts don’t want to be caught in data drudgery. They want to work smarter and be part of strategic results. The tools exist and are ready to be leveraged. If you think there's too much data today, wait six months, two years, five years from now. If you’re not struggling to keep your chin above the data lakes, you soon will be.